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Publisher: Elsevier   (Total: 3162 journals)

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Showing 1 - 200 of 3162 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 97, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 26, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 37, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 412, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 254, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 28, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 153, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 32, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 29, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 24)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 46, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 58, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 23, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 7, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 23)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 12)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 9)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 64)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 399, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 11, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 46, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 342, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 453, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 3)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 10)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 51, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 27, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 47)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 211, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 64, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 177, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 197, SJR: 1.58, CiteScore: 3)

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Journal Cover
Journal Prestige (SJR): 1.073
Citation Impact (citeScore): 4
Number of Followers: 6  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0925-2312
Published by Elsevier Homepage  [3162 journals]
  • Fast and efficient algorithm for matrix completion via closed-form
           2/3-thresholding operator
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Zhi Wang, Wendong Wang, Jianjun Wang, Siqi Chen Matrix completion is arguably one of the most studied problems in machine learning and data analysis. Inspired by the closed-form formulas for L2/3 regularization, we employ the Schatten 2/3 quasi-norm to approximate the rank of a matrix, which can provide a better approximation than traditional ways. We also establish a necessary optimal condition and propose a fixed point iterative scheme for solving L2/3 regularization problem. Through analysing the monotonicity and the accumulation point of L2/3 regularization problem, the convergence of this iteration is analysed. By discussing the optimal selection of the regularization parameter together with a fast Monte Carlo algorithm and an approximate singular value decomposition (SVD) procedure, we build a fast and efficient algorithm that solves the induced optimization problem well. Extensive experiments have been conducted and the results show that the proposed algorithm is fast, efficient and robust. Specifically, we compare the proposed algorithm with state-of-the-art matrix completion algorithms on many synthetic data and large recommendation datasets. Our proposed algorithm is able to achieve similar or better prediction performance, while being faster and more efficient than alternatives. Furthermore, we demonstrate the effectiveness of our proposed algorithm by solving image inpainting problems.
  • Accurate Ulva Prolifera Regions Extraction of UAV Images with Superpixel
           and CNNs for Ocean Environment Monitoring
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Shengke Wang, Lu Liu, Liang Qu, Changyin Yu, Yujuan Sun, Feng Gao, Junyu Dong UAV (Unmanned Aerial Vehicle) monitoring mounted with high resolution camera is a rising way to monitor the ocean environment, and it can make up the shortages of low spatial and temporal resolutions of SAR images. How to get the accurate regions of Ulva prolifera in the very high-resolution images remains a lot of challenges. Due to the limitation of GPU memory, the popular pixel-level image segmentation methods cannot deal with the raw resolution images(Up to 6000*4000). In this paper, we propose a novel framework to get the Ulva prolifera regions, which incorporates both superpixel segmentation and CNN classification and can deal with raw resolution images. We first process the raw images with superpixel algorithm to generate local multi-scale patches. And then a binary classification CNN model can be trained with the labeled patches. With the result of superpixel segmentation and the classification of CNN model, a more detailed segmentation of Ulva prolifera can be abtained. Two datasets UlvaDB-1 and UlvaDB-2 are also proposed in this paper. The experiment results show that the proposed method can achieve state-of-the-art performance compared with the recent pixel-level segmentation and instance-aware semantic segmentation methods.
  • Multi-Kernel Gaussian Process Latent Variable Regression Model for
           High-dimensional Sequential Data Modeling
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Ziqi Zhu, Jiayuan Zhang, Jixin Zou, Chunhua Deng Modeling sequential data has been a hot research field for decades. One of the most challenge problems in this field is modeling real-world high-dimensional sequential data with limited training samples. This is mainly due to the following two reasons. First, if the dimension of the data is significantly greater then the number of the data, it may result in the over-fitting problem. Second, the dynamic behavior of the real-world data is very complex and difficult to approximate. To overcome these two problems, we propose a multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling and prediction. In our model, we design a regression model based on the Gaussian process latent variable model. Furthermore, a multi-kernel learning model is designed to automatically construct suitable nonlinear kernel for various types of sequential data. We evaluate the effectiveness of our method using two types of real-world high-dimensional sequential data, including the human motion data and the motion texture video data. In addition, our method is compared with several representative sequential data modeling methods. Experimental results show that our method achieves promising modeling capability and is capable of predict human motion and texture video with higher quality.
  • Functional Networks and Applications: A Survey
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Guo Zhou, Yongquan Zhou, Huajuan Huang, Zhonghua Tang Functional networks (FNs) are extensions of neural networks (NNs). Unlike NNs, FNs considers general functional models instead of sigmoid-like models. Additionally, in FNs, there are no weights associated with the links that connect neurons. In this paper, we review the research progress and applications of FNs models in recent years. First, we introduce FNs architecture, three typical functional models and the learning process, and we explain the differences between NNs and FNs. Second, we discuss recent applications of FNs that have been introduced in many fields, such as time series prediction, differential and functional equations, pattern classification, detection and prediction, approximation computation, complex system modeling, computer-aided design (CAD), and linear and nonlinear regression. Finally, we present some remarks on future research directions for FNs.
  • Automatic Fabric Defect Detection with a Wide-And-Compact Network
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Yuyuan Li, Dong Zhang, Dah-Jye Lee Automatic detection of fabric defects is an important process for the textile industry. Besides the detection accuracy, an automatic fabric defect detection solution for a resource-limited system also requires superior performance in terms of processing time and simplicity. This paper proposes a compact convolutional neural network architecture for the detection of a few common fabric defects. The proposed architecture uses several micro architectures with multilayer perceptron to optimize network. The main component of a micro architecture is constructed using techniques of multi-scale analysis, filter factorization, multiple locations pooling, and parameters reduction, to improve detection accuracy in a compact model. Experimental results show that, compared to mainstream convolutional neural network architectures, the proposed network achieved superior performance in terms of detection accuracy with a much smaller model size. It worked well not only for fabric defects detection, but also for object recognition on a few public datasets.
  • Multi-view coupled dictionary learning for person re-identification
    • Abstract: Publication date: Available online 10 November 2018Source: NeurocomputingAuthor(s): Fei Ma, Xiaoke Zhu, Qinglong Liu, Chengfang Song, Xiao-Yuan Jing, Dengpan Ye In recent years, person re-identification is becoming an important technique, which can be applied in computer vision, pedestrian tracking and intelligent monitoring. Due to the large variations of visual appearance caused by view angle, pose changing, light changing, background clutter and occlusion, person re-identification is very challenging. In practice, there exist large differences among different types of features and among different cameras. To improve the favorable representation of different features, we propose a multi-view based coupled dictionary pair learning framework, which can learn dictionary pairs for multiple categories of features, e.g., the color features, texture features and hybrid features etc. Specifically, with the learned color feature dictionary pair, we can obtain the color feature representation coefficients of each person from different cameras. The texture feature dictionary pair seeks to learn the texture feature representation coefficients of each person from both cameras. The hybrid feature dictionary pair aims to learn the hybrid feature coefficients for each person. The learned coupled dictionary pairs can demonstrate the intrinsic relationship of different cameras and different types of features. When the resolution of image is too low, the texture information will be lost to some extent. There is few high-resolution person dataset so far, we contribute a newly collected dataset, named High-Resolution Pedestrian re-Identification Dataset (HRPID) on the campus of Wuhan University. The size of person images is normalized to 230 × 560 pixels, which is bigger than existing person re-identification datasets. Experimental results on a new dataset and two public pedestrian datasets demonstrate that our proposed approach can perform better than the other competing methods.
  • Errata to: A 3D polar-radius-moment invariant as a shape circularity
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Carlos Martinez-Ortiz, Joviša Žunić In this paper we point out the theoretical results from [2] that were established and discussed earlier. All these statements were a part of results proven in [1], and do overlap partially with the results established in [3] and [4]. Several mistakes in [2] are pointed out, as well.
  • Rough extreme learning machine: A new classification method based on
           uncertainty measure
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Lin Feng, Shuliang Xu, Feilong Wang, Shenglan Liu, Hong Qiao Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and a simpler neural network structure on most data sets; RELM cannot only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data.
  • Periodicity of Cohen–Grossberg-type fuzzy neural networks with impulses
           and time-varying delays
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Fangru Meng, Kelin Li, Qiankun Song, Yurong Liu, Fuad E. Alsaadi The periodicity problem of a class of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays is concerned in this paper. Via constructing a delay differential inequality, and applying fuzzy theory and the Lyapunov method, several criteria which ensure the existence and exponential stability of the periodic solutions for the considered systems are derived. An example is shown to illustrate the validity of the obtained results.
  • Extension of Reward-Attention Circuit Model: Alcohol’s Influence on
           Attentional Focus and Consequences on Autism Spectrum Disorder
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Karine Guimarães Attention is a key element that allows us to enhance or decrease the cognitive processing of distinct stimuli, depending on their relevance. In this work we investigate the influence that alcohol exerts on attention focusing, modeling the coupling of reward and thalamocortical circuits. Computer simulations of the reward-attention circuit reflect the spiking behavior of each neuron in the network, under the presence or absence of alcohol.Each neuron in the neural networks that replicate such circuits is described by a carefully designed coupled system of nonlinear differential equations that details essential neurophysiological properties. The computational simulations highlight aspects of clinical inattention symptoms in the autism spectrum disorder.Our results indicate that alcohol may lead to distraction or lack of attentional focus. Also, the simulations suggest why people with ASD might relaxes enhanced attentional focus when exposed to alcohol.
  • Bounded Z-type neurodynamics with limited-time convergence and noise
           tolerance for calculating time-dependent Lyapunov equation
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Bolin Liao, Qiuhong Xiang, Shuai Li Z-type neurodynamics (ZND) is an effective tool for calculating time-dependent problems and has been extensively used. However, the convergence rate and the noise tolerance of ZND models have frequently been addressed separately. In this work, a unified design formula for the ZND is proposed by combining the nonlinear activation function and the integral term. On the basis of a formula, a bounded ZND (BZND) model is proposed and used to compute a real-time-dependent Lyapunov equation in noisy environments. Notably, the proposed BZND model, which adopts the Li activation function, not only converges in a limited time but also has inherently noise-tolerant characteristics. Theoretical analyses of the convergence and robustness of the BZND model are further presented. In addition, the upper bound of convergence time is also derived theoretically. Finally, illustrative examples are conducted. Results confirm the superior performance of the proposed BZND model for calculating the real-time-dependent Lyapunov equation with various types of noise to the existing ZND models.
  • Finite-time consensus of input delayed multi-agent systems via non-fragile
           controller subject to switching topology
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): R. Sakthivel, S. Kanakalakshmi, B. Kaviarasan, Yong-Ki Ma, A. Leelamani This paper focuses on the problem of finite-time consensus of linear multi-agent systems with input time-varying delay by employing a non-fragile control scheme, where the network topology of the communication process among agents is subject to a switching directed graph. The main intention of this paper is to propose a non-fragile controller design protocol that guarantees the finite-time stability of the resulting closed-loop system even in the presence of time-varying delay in the input channel. Sufficient conditions for the solvability of such a problem are obtained by using the Lyapunov–Krasovskii stability theory, the algebraic graph theory and some integral inequalities. Based on the obtained conditions, an explicit expression of the desired non-fragile control gain matrix is then presented. Finally, an illustrative example is given to exhibit the applicability and effectiveness of the proposed finite-time consensus control design method.
  • Identification of drug-side effect association via multiple information
           integration with centered kernel alignment
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Yijie Ding, Jijun Tang, Fei Guo In medicine research, drug discovery aims to develop a drug to patients who will benefit from it and try to avoid some side effects. However, the tradition experiment is time consuming and expensive. In recent years, computational approaches provide many effective strategies to deal with this issue. In fact, the known associations between drugs and side-effects are less than unknown associations, thus it can be seen as an imbalance classification problem. Although several classification methods have been developed to predict drug-side effect associations, the performance of predictors could also be further improved. In this paper, we propose a novel predictor of drug-side effect associations. First, we construct multiple kernels from drug space and side-effect space, respectively. Then, these corresponding kernels are linear weighted by optimized Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL) algorithm in two different spaces. At last, Kronecker Regularized Least Squares (Kronecker RLS) is employed to fuse drug kernel and side-effect kernel, further identify drug-side effect associations. Compared with many existing methods, our proposed approach achieves better results on three benchmark datasets of drug side-effect associations. The values of Area Under the Precision Recall curve (AUPR) are 0.672, 0.679 and 0.675 on Pauwels’s dataset, Mizutani’s dataset and Liu’s dataset, respectively. The AUPRs are improved by at least 0.012, 0.013 and 0.014 on three different datasets. Experimental results show that our method has outstanding performance among other excellent approaches on identifying drug-side effect associations.
  • A finite frequency approach for fault detection of fuzzy singularly
           perturbed systems with regional pole assignment
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Jing Xu, Yugang Niu This paper investigates the robust fault detection problem for fuzzy singularly perturbed systems with pole assignment constraints. A finite frequency approach is proposed for the simultaneous achievement of the fault detection objectives and control objectives, which combines the computation of filter gains into a unified framework. By designing the fault detection filter, the residual signal for the detection of finite frequency faults can be produced, and the transient system behaviors can be improved to a satisfactory level based on the pole assignment. In addition, the estimated upper bound of the singular perturbation parameter is used to evaluate the performance of the proposed design method. Finally, a simulation example on an inverted pendulum controlled by a motor via a gear train is provided.
  • Multi-Modal Mention Topic Model for mentionee recommendation
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Kai Wang, Weiyi Meng, Shijun Li, Sha Yang As one of the most commonly used communication functions in Twitter-like social media systems, mention is playing an important role in users’ online interactions. With the dramatic increase in the number of social media users, the problem of mentionee recommendation, i.e., recommending mentionees (mentioned users) when mentioners (mentioning users) attempt to mention others, has received considerable attention in recent years. While an increasing line of work has studied this problem, the existing efforts focus only on the contribution of the non-visual data like post text. In fact, many social media posts contain not only textual but also visual contents like images, and these two heterogeneous data sources both describe users’ mentioning tendencies. In this work, we proposed a novel generative model, named Multi-modal Mention Topic Model (MMTM), to tackle the mentionee recommendation problem by learning users’ semantic patterns and the correlations between contents in different modalities of users’ multi-modal mentioning documents in a unified way. Extensive experiments were conducted on a real-world dataset to evaluate the performance of our method. The experiment results demonstrated the superiority of our method in terms of making more effective recommendations compared with other state-of-the-art methods.
  • A novel photovoltaic power forecasting model based on echo state network
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Xianshuang Yao, Zhanshan Wang, Huaguang Zhang In this paper, a novel photovoltaic (PV) forecasting model based on multiple reservoirs echo state network (MR-ESN) is proposed to forecast the power output of PV generation system. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine, the relative feature of input information can be extracted. According to the forecasting performance evaluation criteria of PV forecasting model, principal component analysis is used to extract the main feature, such that the inputs of MR-ESN and the number of reservoirs of MR-ESN can be determined. Secondly, in order to improve the prediction accuracy, an improved parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is given to optimize the reservoir parameters of MR-ESN. Thirdly, in order to guarantee that the MR-ESN can be stably applied for PV power forecasting, a sufficient condition of the transient stability of PV forecasting model is given. Finally, a PV power generation forecasting example shows that the proposed PV forecasting model can significantly improve the forecasting performance.
  • Independent component analysis employing exponentials of sparse
           antisymmetric matrices
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Ying Tang Independent component analysis (ICA) is a standard method for separating a multivariate signal into additive components that are non-Gaussian and independent from each other. This paper introduced a novel algorithm to perform ICA employing matrix exponentials, which performs similarly to geodesic based methods but based on a different insight. First, we showed that the ICA problem can be formulated as an optimization problem in the space of orthogonal matrices whose determinants are one, which can be further transformed into an equivalent problem in the space of antisymmetric matrices. Then, an efficient approach was presented for iteratively solving this problem using the antisymmetric matrices with one or more nonzero columns and rows. Especially, we proved that in the sense of local optimization it is sufficient to employ antisymmetric matrices with only one nonzero column and row. The analytical expressions of exponentials of such special antisymmetric matrices were also explicitly established in this paper. Compared to other competing algorithms, experimental results indicated that the proposed method can achieve separation with superior performance in term of the precision and running speed.
  • Finite-time leaderless consensus of uncertain multi-agent systems against
           time-varying actuator faults
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Rathinasamy Sakthivel, Ramalingam Sakthivel, Boomipalagan Kaviarasan, Hosoo Lee, Yongdo Lim This paper addresses the finite-time leaderless consensus problem for a class of continuous-time multi-agent systems subject to linear fractional transformation uncertain parameters using an observer-based fault-tolerant controller. Here, it is considered that the network of the system is described by an undirected graph subject to fixed topology and the aforementioned controller is impacted by time-varying actuator faults. Then, the desired consensus protocols are proposed in such a way that the effects of possible uncertainties and actuator faults are compensated efficiently within a prescribed finite-time period. More precisely, the leaderless consensus analysis is carried out in the framework of Lyapunov-Krasovskii functional and the required conditions for the existence of proposed fault-tolerant controller are derived in terms of linear matrix inequalities. Moreover, the proposed consensus design parameters can be computed by solving a set of linear matrix inequality constraints. Finally, two examples including a formation flying satellites model are provided to show the efficiency and usefulness of the proposed control scheme.
  • Reactive obstacle avoidance of monocular quadrotors with online adapted
           depth prediction network
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Xin Yang, Hongcheng Luo, Yuhao Wu, Yang Gao, Chunyuan Liao, Kwang-Ting Cheng Obstacle avoidance based on a monocular camera is a fundamental yet highly challenging task due to the lack of 3D information for a monocular quadrotor. Recent methods based on convolutional neural networks (CNNs) [1] for monocular depth estimation and obstacle detection become increasingly popular due to the considerable advances in deep learning. However, depth estimation by pre-trained CNNs usually suffers from large accuracy degradation for scenes of different types from the training data which are common for obstacle avoidance of drones in unknown environments. In this paper, we present a reactive obstacle avoidance system which employs an online adaptive CNN for progressively improving depth estimation from a monocular camera in unfamiliar environments. Pairs of motion stereo images are collected on-the-fly as training data based on a direct monocular SLAM running in parallel with the CNN. Novel approaches are introduced for selecting highly reliable training samples from noisy data provided by SLAM and efficient online CNN tuning. The depth map computed from the CNN is transformed into Ego Dynamic Space (EDS) by embedding both dynamic motion constraints of a quadrotor and depth estimation errors into the spatial depth map. Traversable waypoints with consideration of the camera’s field of view constraint are automatically computed in EDS based on which appropriate control inputs for the quadcopter are produced. Experimental results on both public datasets, simulated environments and unseen cluttered indoor environments demonstrate the effectiveness of our system.
  • A novel method for graph matching based on belief propagation
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Xue Lin, Dongmei Niu, Xiuyang Zhao, Bo Yang, Caiming Zhang Graph matching is a fundamental NP-problem in computer vision and pattern recognition. In this paper, we propose a robust approximate graph matching method. The match between two graphs is formulated as an optimization problem and a novel energy function that performs random sample consensus (RANSAC) checking on the max-pooled supports is proposed. Then a belief propagation(BP) algorithm, which can assemble the spatial supports of the local neighbors in the context of the given points, is used to minimize the energy function. To achieve the one-to-(at most)-one matching constraint, we present a method for removing bad matches based on the topological structure of the graphs. Experimental results demonstrate that the proposed method outperforms other state-of-the-art graph matching methods in matching accuracy.
  • Semi-supervised deep embedded clustering
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C.H. Hoi, Zenglin Xu Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints in the feature learning process such that data samples belonging to the same cluster are close to each other and data samples belonging to different clusters are far away from each other in the learned feature space. Extensive experiments on real benchmark data sets validate the effectiveness and robustness of the proposed method.
  • Almost sure synchronization criteria of neutral-type neural networks with
           Lévy noise and sampled-data loss via event-triggered control
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Kaiyan Cui, Junwei Lu, Chenlong Li, Zhang He, Yu-Ming Chu This paper addresses the synchronization problem for neutral-type neural networks with Lévy noise and sampled-data loss. An event-triggered control scheme is employed to overcome occasional sampled-data loss and solve the synchronization problem, which is a sampling controller with selection mechanism. Under the scheme, the sampled data is not transmitted to plant unless a predetermined threshold condition is violated. The Lyapunov method and linear matrix inequality technique are employed to analyze almost sure stability of synchronization error system. Finally, the numerical example shows the effectiveness of the derived results.
  • Computational modelling of salamander retinal ganglion cells using machine
           learning approaches
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Gautham P. Das, Philip J. Vance, Dermot Kerr, Sonya A. Coleman, Thomas M. McGinnity, Jian K. Liu Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear – non-linear cascade model, which models the cell’s response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron’s response. In this paper we present an alternative to the linear – non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear – non-linear approach in the case of temporal white noise stimuli.
  • D3-LND: A two-stream framework with discriminant deep descriptor, linear
           CMDT and nonlinear KCMDT descriptors for action recognition
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Ming Tong, Mengao Zhao, Yiran Chen, Houyi Wang In order to improve recognition accuracy, a two-stream framework which incorporates deep-learned stream and hand-crafted stream is proposed. Firstly, a discriminant nonlinear feature fusion method is proposed, which introduces the category structure information and obtains the nonlinear relationships between features. Secondly, the global features and local features are extracted from deep network, both of them are fused by the proposed fusion method to obtain a discriminant deep descriptor. Thirdly, to capture the spatio-temporal characteristics of video, the temporal derivatives of gradient, optical flow and motion boundary are extracted in the space-time cube centered at trajectory and taken as low-level features. Subsequently, the covariance and kernelized covariance of low-level features are respectively computed to obtain the Covariance Matrix based on Dense Trajectory (CMDT) and Kernelized Covariance Matrix based on Dense Trajectory (KCMDT) descriptors. Finally, a two-stream framework with discriminant deep descriptor, linear CMDT and nonlinear KCMDT descriptors (D3-LND) is presented, which shares the benefits of both deep-learned and hand-crafted features, and further improves recognition accuracy. Experiments on challenging HMDB51 and UCF101 datasets verify the effectiveness of our method.
  • Theoretical foundations of forward feature selection methods based on
           mutual information
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Francisco Macedo, M. Rosário Oliveira, António Pacheco, Rui Valadas Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on mutual information have become very popular and are widely used in practice. However, comparative evaluations of these methods have been limited by being based on specific datasets and classifiers. In this paper, we develop a theoretical framework that allows evaluating the methods based on their theoretical properties. Our framework is grounded on the properties of the target objective function that the methods try to approximate, and on a novel categorization of features, according to their contribution to the explanation of the class; we derive upper and lower bounds for the target objective function and relate these bounds with the feature types. Then, we characterize the types of approximations taken by the methods, and analyze how these approximations cope with the good properties of the target objective function. Additionally, we develop a distributional setting designed to illustrate the various deficiencies of the methods, and provide several examples of wrong feature selections. Based on our work, we identify clearly the methods that should be avoided, and the methods that currently have the best performance.
  • Vibrational resonance in a scale-free network with different coupling
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Sukriye Nihal Agaoglu, Ali Calim, Philipp Hövel, Mahmut Ozer, Muhammet Uzuntarla We investigate the phenomenon of vibrational resonance (VR) in neural populations, whereby weak low-frequency signals below the excitability threshold can be detected with the help of additional high-frequency driving. The considered dynamical elements consist of excitable FitzHugh–Nagumo neurons connected by electrical gap junctions and chemical synapses. The VR performance of these populations is studied in unweighted and weighted scale-free networks. We find that although the characteristic network features – coupling strength and average degree – do not dramatically affect the signal detection quality in unweighted electrically coupled neural populations, they have a strong influence on the required energy level of the high-frequency driving force. On the other hand, we observe that unweighted chemically coupled populations exhibit the opposite behavior, and the VR performance is significantly affected by these network features whereas the required energy remains on a comparable level. Furthermore, we show that the observed VR performance for unweighted networks can be either enhanced or worsened by degree-dependent coupling weights depending on the amount of heterogeneity.
  • Gating mechanism based Natural Language Generation for spoken dialogue
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Van-Khanh Tran, Le-Minh Nguyen Recurrent Neural Network (RNN) based approaches have recently shown promising in tackling Natural Language Generation (NLG) problems. This paper presents an approach to leverage gating mechanisms, in which we incrementally propose three additional semantic cells into a traditional RNN model: a Refinement cell to filter the sequential inputs before RNN computations, an Adjustment cell, and an Output cell to select semantic elements and gate a feature vector during generation. The proposed gating-based generators can learn from unaligned data by jointly training both sentence planning and surface realization to generate natural language utterances. We conducted extensive experiments on four different NLG domains in which the results empirically show that the proposed methods not only achieved better performance on all the NLG domains in comparison with previous gating-based, attention-based methods, but also obtained highly competitive results compared to a hybrid generator.
  • Distributed leader-following consensus of heterogeneous second-order
           time-varying nonlinear multi-agent systems under directed switching
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Yuliang Cai, Huaguang Zhang, Kun Zhang, Yuling Liang This paper focuses on studying the distributed leader-following consensus problem of heterogeneous second-order time-varying nonlinear multi-agent systems (HSTN_MASs) under directed switching topology. Firstly, based on the relative position and relative velocity measurements among neighborhood agents, a class of distributed tracking protocols are proposed. Then the state transformations are provided to solve the time-varying nonlinearity and translate the leader-following problem into the stabilization control problem. By constructing the topology-dependent Lyapunov function and choosing appropriate time-varying regulation factor and coupling strength, it is shown that the exponential tracking of HSTN_MASs can be achieved. Moreover, this paper discusses the distributed leaderless consensus problem of HSTN_MASs. Finally, several simulation examples are given to illustrate the validity of theoretical results.
  • Fast and scalable distributed deep convolutional autoencoder for fMRI big
           data analytics
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided shallow models based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Given the Convolutional Neural Network (CNN) successes in learning hierarchical abstractions from low-level data such as tfMRI time series, in this work we propose a novel scalable distributed deep CNN autoencoder model and apply it for fMRI big data analysis. This model aims to both learn the complex hierarchical structures of the tfMRI big data and to leverage the processing power of multiple GPUs in a distributed fashion. To deploy such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow, leveraging from a large cluster of GPU nodes over cloud. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed model is efficient and scalable toward tfMRI big data modeling and analytics, thus enabling data-driven extraction of hierarchical neuroscientific information from massive fMRI big data.
  • On extreme learning machines in sequential and time series prediction: A
           non-iterative and approximate training algorithm for recurrent neural
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Yara Rizk, Mariette Awad Recurrent neural networks (RNN) are a type of artificial neural networks (ANN) that have been successfully applied to many problems in artificial intelligence. However, they are expensive to train since the number of learned weights grows exponentially with the number of hidden neurons. Non-iterative training algorithms have been proposed to reduce the training time, mainly on feedforward ANN. In this work, the application of non-iterative randomized training algorithms to various RNN architectures, including Elman RNN, fully connected RNN, and long short-term memory (LSTM), are investigated. The mathematical formulation and theoretical computational complexity of the proposed algorithms are presented. Finally, their performance is empirically compared to other iterative RNN training algorithms on time series prediction and sequential decision-making problems. Non-iteratively-trained RNN architectures showed promising results as significant training speedup of up to 99%, and improved repeatability were achieved compared to backpropagation-trained RNN. Although the decrease in prediction accuracy was found to be statistically significant based on Friedman and ANOVA testing, some applications like real-time embedded systems can tolerate and make use of that.
  • A novel variational model for noise robust document image binarization
    • Abstract: Publication date: 24 January 2019Source: Neurocomputing, Volume 325Author(s): Shu Feng Binarization is a fundamental problem in document image analysis systems. Different from current thresholding techniques, a novel variational model is proposed for noise robust document image binarization in this work, which is inspired by two classical variational models that have been successfully applied in image segmentation and denoising. In our variational model, the energy functional consists of three terms: data fidelity term, binary classification term and regularization term. As a result, the proposed model is capable of performing image binarization and suppressing noise simultaneously. Concretely, we firstly design a variational model for the original document image, the minimizer of which is our desired binarization result. Secondly, the gradient descent flow equation of our model is derived by means of variational principle. Lastly, a simple finite difference scheme, time forward and space center difference, is employed to solve the gradient descent flow equation. Extensive experiments are conducted on three types of document image datasets to validate our model qualitatively and quantitatively. The experiment results not only demonstrate the effectiveness of our approach, but also verify its noise and illumination robustness.
  • Comprehensive Survey of Image Steganography: Techniques, Evaluations, and
           Trends in Future Research
    • Abstract: Publication date: Available online 9 November 2018Source: NeurocomputingAuthor(s): Inas Jawad Kadhim, Prashan Premaratne, Peter James Vial, Brendan Halloran Storing and communicating secret and/or private information has become part of our daily life whether it is for our employment or personal well-being. Therefore, secure storage and transmission of the secret information have received the undivided attention of many researchers. The techniques for hiding confidential data in inconspicuous digital media such as video, audio, and image are collectively termed as Steganography. Among various media types used, the popularity and availability of digital images are high and in this research work and hence, our focus is on implementing digital image steganography. The main challenge in designing a steganographic system is to maintain a fair trade-off between robustness, security, imperceptibility and higher bit embedding rate. This research article provides a thorough review of existing types of image steganography and the recent contributions in each category in multiple modalities. The article also provides a complete overview of image steganography including general operation, requirements, different aspects, different types and their performance evaluations. Different performance analysis measures for evaluating steganographic system are also discussed here. Moreover, we also discuss the strategy to select different cover media for different applications and a few state-of-the-art steganalysis systems.
  • DCT-CNN-based Classification Method for the Gongbi and Xieyi Techniques of
           Chinese Ink-wash Paintings
    • Abstract: Publication date: Available online 8 November 2018Source: NeurocomputingAuthor(s): Wei Jiang, Zheng Wang, Jesse S. Jin, Yahong Han, Meijun Sun Different from the western paintings, Chinese ink-wash paintings (IWPs) have own distinctive art styles. Furthermore, Chinese IWPs can be divided into two classes, Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style). The extraction of Chinese IWP features with good classification results is challenging because of similar content. This paper presents a novel framework by combining a discrete cosine transformation (DCT) and convolutional neural networks (CNNs). In this framework, a CNN automatically extracts Chinese IWP features from a small subset of the DCT coefficients of an image instead of raw pixels commonly because of its good performance. We evaluate the proposed framework on a dataset including 1400 Chinese IWPs. Experimental results show that the proposed framework achieves competitive classification performance compared to existing benchmark methods.
  • A Multimodal Fusion Approach for Image Captioning
    • Abstract: Publication date: Available online 8 November 2018Source: NeurocomputingAuthor(s): Dexin Zhao, Zhi Chang, Shutao Guo Deep convolution neural networks connected with the recurrent neural networks are potent models that have achieved excellent performance on image caption task. Although many methods based on the neural network can generate fluent and complete sentences, the image feature vectors extracted by the convolution neural network can only retain a few significant features of the original image, which will lose a lot of useful image information. Moreover, RNNs have a gradient vanishing problem with the growth of RNNs time step, and the generation of sentences will lack the guidance of previous information. In this paper, we introduce a multimodal fusion method for generating descriptions to explain the content of images. Our model consists of four sub-networks: a convolutional neural network for image feature extraction, a ATTssd model for image attributes extraction, a language CNN model CNNm for sentence modeling and a recurrent network (e.g., GRU, LSTM, etc.) for word prediction. Compared with existing methods which predict next word based on one previous word and hidden state, our model uses image attributes information to enhance the image representation and handles all the previous words to modeling the long-term dependencies of history words. The methods are evaluated on the Flickr8k, Flickr30k and MSCOCO datasets. We prove that our model combined with ATTssd and CNNm can significantly enhance the performance, and achieve the competitive results.
  • Distributed Optimization for Deep Learning with Gossip Exchange
    • Abstract: Publication date: Available online 8 November 2018Source: NeurocomputingAuthor(s): Michael Blot, David Picard, Nicolas Thome, Matthieu Cord We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way of sharing information between different threads based on gossip algorithms that show good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized.
  • Trajectory Tracking of Constrained Robotic Systems via a Hybrid Control
    • Abstract: Publication date: Available online 8 November 2018Source: NeurocomputingAuthor(s): Weiwei Sun, You Wu, Liping Wang In this paper, a novel hybrid coordinated control scheme is proposed for robotic systems with full-state constraints. Asymmetric barrier Lyapunov functions (ABLFs) in backstepping design procedure are employed and corresponding backstepping controller is presented to prevent full-state constraints violation. Energy-based Hamilton control is utilized to provide Hamilton controller. Hybrid control method, which includes both backstepping and Hamilton control, is considered for improving asymptotic position tracking performance. Asymptotically stability of the closed-loop system is analyzed in Lyapunov sense. It is shown that proposed hybrid controller can effectively enhance response speed and tracking accuracy while ensuring that full-state constraints are not violated. Simulation example is provided to illustrate the feasibility and advantage of control algorithm.
  • Neural network-based adaptive output feedback fault-tolerant control for
           nonlinear systems with prescribed performance
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Cai-Cheng Wang, Guang-Hong Yang In this paper, an adaptive fault-tolerant control (FTC) approach is proposed for nonlinear systems involving input saturation and unknown control directions. In the framework of dynamic output feedback FTC, neural networks (NNs) are used to approximate the unknown nonlinear functions, and then a novel adaptive controller is further designed by combining the Nussbaum function property. Unlike some existing works incorporating an auxiliary state variable, the asymmetric actuator saturation is handled by the Nussbaum function in this approach. Furthermore, it is proven that all signals in the closed-loop system are bounded and the output tracking error satisfies the prescribed performance condition through using the barrier Lyapunov function method. Finally, simulation examples are presented to illustrate the effectiveness of the proposed control method.
  • A Survey of Teaching–Learning-Based Optimization
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Feng Zou, Debao Chen, Qingzheng Xu Over past few decades, swarm intelligent algorithms based on the intelligent behaviors of social creatures have been extensively studied and applied for all kinds of optimization areas. Teaching–learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of TLBO is divided into two phases and each phase executes iterative learning operation. Since its introduction by Rao and his team in 2010, TLBO has attracted more and more researchers' attention because of some of its strengths such as simple concept, without algorithm-specific parameters, rapid convergence and easy implementation yet effectiveness. In this paper we attempt to provide a brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far. We hope that this survey can be very beneficial for the researchers engaged in the study of TLBO.
  • On The Use of DAG-CNN Architecture for Age Estimation with Multi-stage
           Features Fusion
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Shahram Taheri, Önsen Toygar Accurate facial age estimation is quite challenging, since ageing process is dependent on gender, ethnicity, lifestyle and many other factors, therefore actual age and apparent age can be quite different. In this paper, we propose a new architecture of deep neural networks namely Directed Acyclic Graph Convolutional Neural Networks (DAG-CNNs) for age estimation which exploits multi-stage features from different layers of a CNN. Two instants of this system are constructed by adding multi-scale output connections to the underlying backbone from two well-known deep learning architectures, namely VGG-16 and GoogLeNet. DAG-CNNs not only fuse the feature extraction and classification stages of the age estimation into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Fine-tuning such models helps to increase the performance and we show that even “off-the-shelf” multi-scale features perform quite well. Experiments on the publicly available Morph-II and FG-NET databases prove the effectiveness of our novel method.
  • Possibilistic Fuzzy Clustering With High-Density Viewpoint
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Yiming Tang, Xianghui Hu, Witold Pedrycz, Xiaocheng Song Fuzzy clustering algorithms are usually data-driven. Recently, knowledge has been introduced into these methods to form knowledge-driven and data-driven fuzzy clustering algorithms. However, these algorithms still have the problems of sensitivity to clustering center initialization and a lack of robustness, in general. There is a genuine need for a sound acquisition of viewpoints. In this study, a new fuzzy clustering algorithm driven by data and knowledge named Density Viewpoint-induced Possibilistic Fuzzy C-Means (DVPFCM) is put forward. To begin with, we propose a new method to calculate the density radius, which determines the density range of each data point. Based on this, we establish a Hypersphere Density-based Clustering Center Initialization method (HDCCI), which can obtain the initial clustering centers located in the denser region of the dataset. Furthermore, the high density point obtained by the HDCCI method is taken as a new viewpoint and integrated into the clustering algorithm. The new viewpoint helps to speed up the convergence of the algorithm. It can also guide the clustering algorithm to discover the data structure. Finally, on the basis of the HDCCI method, the idea of high-density viewpoint is introduced, and the advantages of FCM (Fuzzy C-Means) and PFCM (Possibilistic Fuzzy C-Means) are combined, and then the DVPFCM algorithm is proposed. Through experimental studies including some comparative analyses, it is demonstrated that the DVPFCM algorithm is better in several different ways in terms of initializing clustering centers and values of some performance indexes. It also exhibits better performance in determining the distance between the computed clustering centers and the reference centers.
  • Research on virtual haptic disassembly platform considering disassembly
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): YanFang Yang, Pu Yang, Jia Li, Fan Zeng, Miao Yang, Rui Wang, Qiang Bai the speed of equipment repair and maintenance becomes a key factor in improving the production efficiency. Though the process of equipment disassembly is complex, long cycle and high cost, the combination of disassembly sequence planning and virtual disassembly platform can solve this problem effectively. The main contents of this paper include: (1) Petri net and genetic algorithm is used to solve the disassembly sequence planning and design a variety of disassembly paths. (2) In order to develop a virtual disassembly platform with more real experience, the physical properties of the equipment and parts are analyzed in the process of disassembly, and the real-time construction method of the physical model is studied; (3) Taking the reducer as an example, the platform of virtual disassembly with force feedback is developed and enhanced the real sense of virtual disassembly by added haptic interaction, which can finish the virtual disassembly under the guidance of the disassembly sequence optimized. Thus, virtual haptic disassembly platform considering disassembly process improves the effectiveness and authenticity of training, learning, and process validation of disassembly.
  • Advances in Data Representation and Learning for Pattern Analysis
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Professor C.L. Philip Chen, Professor Xinge You, Professor Xinbo Gao, Dr. Tongliang Liu, Professor Fionn Murtagh, Professor Weifeng Liu
  • Spiking Pattern Recognition Using Informative Signal of Image and
           Unsupervised Biologically Plausible Learning
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Soheila Nazari, Karim faez The recent progress of low-power neuromorphic hardware provides exceptional conditions for applications where their focus is more on saving power. However, the design of spiking neural networks (SNN) to recognize real-world patterns on such hardware remains a major challenge ahead of the researchers. In this paper, SNN inspired by the model of local cortical population as a biological neuro-computing resource for digit recognition was presented. SNN was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP). This biologically plausible learning enables neurons to make decisions and learns the structure of the input examples. There are two main reasons why this structure is state of the art compared to previous works: learning process is compatible with many experimental observations on induction of long-term potentiation and long-term depression, image to signal mapping created an informative signal of the image based on sequences of prolate spheroidal wave functions (PSWFs). The proposed image mapping translates the pixels attributes to the frequency, phase, and amplitude of a sinusoidal signal. This mapping enables the SNN to generalize better to the realistic sized images and significantly decreases the size of the input layer. Cortical SNN compared to earlier related studies recognized MNIST digits more accurate and achieved 96.1% classification accuracy with unsupervised learning based on sparse spike activity.
  • Integrated design of fault estimation and fault-tolerant control for
           linear multi-agent systems using relative outputs
    • Abstract: Publication date: Available online 7 November 2018Source: NeurocomputingAuthor(s): Yan Liu, Guang-Hong Yang In this paper, the problem of fault estimation and fault-tolerant control is investigated for linear multi-agent systems with mismatched uncertainty. Different from the existing results, an integrated design method is proposed to solve the problem of bi-directional uncertainty which arises from the combination of fault estimation and control. In order to realize a distributed integrated design, spectral decomposition and coordinate transformation are introduced so that the leader-following multi-agent systems can be decoupled into independent observable subsystems. For this new subsystem, an unknown input observer and a fault-tolerant controller are constructed by only using the relative outputs, and an integrated design condition is given in terms of linear matrix inequality. Finally, two examples are utilized to illustrate the validity of this method.
  • A novel deep model with multi-loss and efficient training for person
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Di Wu, Si-Jia Zheng, Wen-Zheng Bao, Xiao-Ping Zhang, Chang-An Yuan, De-Shuang Huang The purpose of Person re-identification (PReID) is to identify the same individual from the non-overlapping cameras, the task has been greatly promoted by the deep learning system. In this study, we review two widely-used CNN frameworks in the PReID community: identification model and triplet model. We provide a comprehensive overview of the advantages and limitations of the two models and present a hybrid model that combines the advantages of both identification and triplet models. Specifically, the proposed model employs triplet loss, identification loss and center loss to simultaneously train the carefully designed network. Furthermore, the dropout scheme is adopted by its identification subnetwork. Given a triplet unit images, the model can output the identities of the three input images and force the Euclidean distance between the mismatched pairs to be larger than those between the matched pairs as well as reduce the variance of the same class at the same time. Extensive comparative experiments on three PReID benchmark datasets (CUHK01, CUHK03, Market-1501) show that our proposed architecture outperforms many state of the art methods in most cases.
  • Classification of autism spectrum disorder by combining brain connectivity
           and deep neural network classifier
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Yazhou Kong, Jianliang Gao, Yunpei Xu, Yi Pan, Jianxin Wang, Jin Liu Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that seriously affects communication and sociality of patients. It is crucial to accurately identify patients with ASD from typical controls (TC). Conventional methods for the classification of ASD/TC mainly extract morphological features independently at different regions of interest (ROIs), rarely considering the connectivity between these ROIs. In this study, we construct an individual brain network as feature representation, and use a deep neural network (DNN) classifier to perform ASD/TC classification. Firstly, we construct an individual brain network for each subject, and extract connectivity features between each pair of ROIs. Secondly, the connectivity features are ranked in descending order using F-score, and the top ranked features are selected. Finally, the selected 3000 top features are used to perform ASD/TC classification via a DNN classifier. An evaluation of the proposed method has been conducted with T1-weighted MRI images from the Autism Brain Imaging Data Exchange I (ABIDE I) by using ten-fold cross validation. Experimental results show that our proposed method can achieve the accuracy of 90.39% and the area under receiver operating characteristic curve (AUC) of 0.9738 for ASD/TC classification. Comparison of experimental results illustrates that our proposed method outperforms some state-of-the-art methods in ASD/TC classification.
  • Automatic ICD-9 coding via deep transfer learning
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Min Zeng, Min Li, Zhihui Fei, Ying Yu, Yi Pan, Jianxin Wang ICD-9 codes have been widely used to describe a patient's diagnosis. Accurate automatic ICD-9 coding is important because manual coding is expensive, time-consuming. Inspired by the recent successes of deep transfer learning, in this study, we propose a deep transfer learning framework for automatic ICD-9 coding. Our proposed method makes use of transferring MeSH domain knowledge to improve automatic ICD-9 coding. We demonstrate its effectiveness by achieving state-of-the-art performance with a value of 0.420 for Micro-average F-measure on MIMIC-III dataset, which indicates that our method outperforms hierarchy-based SVM and flat-SVM. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of our proposed method. Our experimental results indicate that transfer learning is the key component to improve the performance of automatic ICD-9 coding and deep learning approach is the foundation in the success of our proposed model. In addition, to explore the best network architecture, we also compare the performance of multi-scale and sequential network architectures and find that using multi-scale network is better. Finally, we investigate the effects of transferring different percentage of samples on transfer learning and the results show that the best performance of target domain task can be obtained when 100% number samples are transferred.
  • Wave2Vec: Deep representation learning for clinical temporal data
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Ye Yuan, Guangxu Xun, Qiuling Suo, Kebin Jia, Aidong Zhang Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs). However, existing models cannot deal with continuous physiological records, which are often included in EHRs. The major challenges for this task are to model non-obvious representations from observed high-resolution biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec, an end-to-end deep representation learning model, to bridge the gap between biosignal processing and semantic learning. Wave2Vec not only jointly learns both inherent and temporal representations of biosignals, but also allows us to interpret the learned representations reasonably over time. We propose two embedding mechanisms to capture the temporal knowledge within signals, and discover latent knowledge from signals in time-frequency domain, namely component-based motifs. To validate the effectiveness of our model in clinical task, we carry out experiments on two real-world benchmark biosignal datasets. Experimental results demonstrate that the proposed Wave2Vec model outperforms six feature learning baselines in biosignal processing. Analytical results show that the proposed model can incorporate both motif co-occurrence information and time series information of biosignals, and hence provides clinically meaningful interpretation.
  • Identification of cancer subtypes by integrating multiple types of
           transcriptomics data with deep learning in breast cancer
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Yang Guo, Xuequn Shang, Zhanhuai Li The identification of cancer subtypes is vital to advance the precision of cancer disease diagnosis and therapy. Several works had been done to integrate multiple types of genomics data to investigate cancer subtypes. However, (1) few of them particularly considered the intrinsic correlations in each type of data; (2) to the best of our knowledge, none of them considered transcriptome alternative splicing regulation in data integration. It has been demonstrated that many cancers are related to abnormal alternative splicing regulations in recent years. In this paper, we propose a hierarchical deep learning framework, HI-SAE, to integrate gene expression and transcriptome alternative splicing profiles data to identify cancer subtypes. We adopt the stacked autoencoder (SAE) neural network to learn high-level representations in each type of data, respectively, and then integrate all the learned high-level representations by another learning layer to learn more complex data representations. Based on the final learned data representations, we cluster patients into different cancer subtype groups. Comprehensive experiments based on TCGA breast cancer data demonstrate that our model provides an effective and useful approach to integrate multiple types of transcriptomics data to identify cancer subtypes and the transcriptome alternative splicing data offers distinguishable clues of cancer subtypes.
  • Protein–protein interactions prediction based on ensemble deep
           neural networks
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Long Zhang, Guoxian Yu, Dawen Xia, Jun Wang Protein–protein interactions (PPIs) are of vital importance to most biological processes. Plenty of PPIs have been identified by wet-lab experiments in the past decades, but there are still abundant uncovered PPIs. Furthermore, wet-lab experiments are expensive and limited by the adopted experimental protocols. Although various computational models have been proposed to automatically predict PPIs and provided reliable interactions for experimental verification, the problem is still far from being solved. Novel and competent models are still anticipated. In this study, a neural network based approach called EnsDNN (Ensemble Deep Neural Networks) is proposed to predict PPIs based on different representations of amino acid sequences. Particularly, EnsDNN separately uses auto covariance descriptor, local descriptor, and multi-scale continuous and discontinuous local descriptor, to represent and explore the pattern of interactions between sequentially distant and spatially close amino acid residues. It then trains deep neural networks (DNNs) with different configurations based on each descriptor. Next, EnsDNN integrates these DNNs into an ensemble predictor to leverage complimentary information of these descriptors and of DNNs, and to predict potential PPIs. EnsDNN achieves superior performance with accuracy of 95.29%, sensitivity of 95.12%, and precision of 95.45% on predicting PPIs of Saccharomyces cerevisiae. Results on other five independent PPI datasets also demonstrate that EnsDNN gets better prediction performance than other related comparing methods.
  • Integration of deep feature representations and handcrafted features to
           improve the prediction of N6-methyladenosine sites
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Leyi Wei, Ran Su, Bing Wang, Xiuting Li, Quan Zou, Xing Gao N6-methyladenosine (m6A), as one of the most well-studied RNA modifications, has been found to be involved with a wide range of biological processes. Recently, diverse computational methods have been developed for automated identification of m6A sites within RNAs. To identify m6A sites accurately, one of the major challenges is to extract informative features to describe characteristics of m6A sites. However, existing feature representation methods are usually hand-crafted based, and cannot capture discriminative information of m6A sites. In this paper, we develop a m6A site predictor, named DeepM6APred. In this predictor, we propose to use a deep learning based feature descriptor with deep belief network (DBN) to extract high-level latent features. By integrating the deep features with traditional handcrafted features, we train a classification model based on support vector machine and successfully improve the predictive ability of m6A sites. Experimental results on a benchmark dataset show that our proposed method outperforms the state-of-the-art predictors, at least 2% higher in terms of Matthew's correlation coefficient (MCC). Moreover, a webserver that implements the DeepM6APred is established, which is currently available at the website: It is expected to be a useful tool to assist biologists to reveal the functional mechanisms of m6A sites.
  • Deep learning for biological/clinical data
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Fang-Xiang Wu, Min Li
  • Personalized prediction of genes with tumor-causing somatic mutations
           based on multi-modal deep Boltzmann machine
    • Abstract: Publication date: 9 January 2019Source: Neurocomputing, Volume 324Author(s): Yifeng Li, François Fauteux, Jinfeng Zou, André Nantel, Youlian Pan When diagnosed at an advanced stage, most cancer patients suffer from treatment failure, recurrences and low survival. Taking advantage of high-throughput sequencing and deep learning techniques, we developed an early cancer monitoring method based on multi-modal deep Boltzmann machine to (1) learn association between matched germline and somatic mutations captured by whole exome sequencing from available samples of cancer patients, and (2) predict patient-specific high-risk genes whose somatic mutations are required to drive normal tissues to a tumor state. Our experiments on a set of breast cancer samples show that our method significantly outperforms the currently used frequency-based method in the personalized prediction of genes carrying critical mutations.
  • Video Steganography: A Review
    • Abstract: Publication date: Available online 6 November 2018Source: NeurocomputingAuthor(s): Yunxia Liu, Shuyang Liu, Yonghao Wang, Hongguo Zhao, Si Liu Video steganography is becoming an important research area in various data hiding technologies, which has become a promising tool because not only the security requirement of secret message transmission is becoming stricter but also video is more favored. In this paper, according to the embedded position of secret message, video steganography is divided into three categories: intra-embedding, pre-embedding and post-embedding. Intra-embedding methods are categorized according to the video compression stages such as intra-prediction, motion vectors, pixels interpolation, transform coefficients. Pre-embedding methods are manipulated on the raw video, which can be classified into spatial and transform domains. Post-embedding methods are mainly focused on the bitstreams, which means the procedure of embedding and extraction of video steganography are all manipulated on the compressed bit stream. Then we introduce the performance assessment for video steganography and the future popular video steganography including H.265 video steganography, robust video steganography and reversible video steganography. And challenges are finally discussed in this paper.
  • Special Issue on Advances in Graph Algorithm and Applications
    • Abstract: Publication date: Available online 6 November 2018Source: NeurocomputingAuthor(s): Zhi-Yong Liu, Kai-Zhu Huang, Xu Yang, Cheng-Lin Liu
  • Class-Specific Synthesized Dictionary Model for Zero-Shot Learning
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Zhong Ji, Junyue Wang, Yunlong Yu, Yanwei Pang, Jungong Han Zero-shot learning (ZSL) aims at recognizing unseen classes that are absent during the training stage. Unlike the existing approaches that learn a visual-semantic embedding model to bridge the low-level visual space and the high-level class prototype space, we propose a novel synthesized approach for addressing ZSL within a dictionary learning framework. Specifically, it learns both a dictionary matrix and a class-specific encoding matrix for each seen class to synthesize pseudo instances for unseen classes with auxiliary of seen class prototypes. This allows us to train the classifiers for the unseen classes with these pseudo instances. In this way, ZSL can be treated as a traditional classification task, which makes it applicable for traditional and generalized ZSL settings simultaneously. Extensive experimental results on four benchmark datasets (AwA, CUB, aPY, and SUN) demonstrate that our method yields competitive performances compared to state-of-the-art methods on both settings.
  • A Survey on Laplacian Eigenmaps Based Manifold Learning Methods
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Bo Li, Yan-Rui Li, Xiao-Long Zhang As a well known nonlinear dimensionality reduction method, Laplacian Eigenmaps (LE) aims to find low dimensional representations of the original high dimensional data by preserving the local geometry between them. LE has attracted great attentions because of its capability of offering useful results on a broader range of manifolds. However, when applying it to some real-world data, several limitations have been exposed such as uneven data sampling, out-of-sample problem, small sample size, discriminant feature extraction and selection, etc. In order to overcome these problems, a large number of extensions to LE have been made. So in this paper, we make a systematical survey on these extended versions of LE. Firstly, we divide these LE based dimensionality reduction approaches into several subtypes according to different motivations to address the issues existed in the original LE. Then we successively discuss them from strategies, advantages or disadvantages to performance evaluations. At last, the future works are also suggested after some conclusions are drawn.
  • Deep Convolutional Extreme Learning Machines: filters combination and
           error model validation
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Michel M. dos Santos, Abel G. da Silva Filho, Wellington P. dos Santos In recent years, deep convolutional neural network models have been increasingly used in various computer vision tasks, like plate number recognition, object recognition, automatic digit recognition, and medical applications supporting diagnosis by signals or images. A disadvantage of these networks is the long training time. It can take days to adjust weights with iterative methods based on gradient descent. This can be an obstacle in applications that need frequent training or in real time. Fast convolutional networks avoid gradient-based methods by efficiently defining filters in feature extraction and weights in classification. The issue is how to set the convolutional filter banks, since they are not learned by the backpropagation of gradients' In this work we propose a deep fast convolutional neural network based on extreme learning machine and a fixed bank of filters. We demonstrate that our model is feasible to be used in cost-effective non-specialized computer hardware, performing the training task faster than models running on GPUs. Results were generated on EMNIST dataset representing the widely studied problem of digit recognition. We provide a deep convolutional extreme learning machine (CELM) with two feature extraction stages and combinations of selected filters. For the proposed network, we find that the empirical generalization error is explained by the error model based on a theorem by Rahimi and Retch. In comparison to the state-of-the-art, the proposed network resulted in superior accuracy as well as competitive training time, even in relation to approaches that employ processing in GPUs.
  • Identifying cluster centroids from decision graph automatically using a
           statistical outlier detection method
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Huanqian Yan, Lei Wang, Yonggang Lu Cluster centroid identification is a crucial step for many clustering methods. Recently, Alex Rodriguez et al. have proposed an effective density-based clustering method called Density Peak Clustering (DPC), in which the density value of each data point and the minimum distance from the points with higher density values are used to identify cluster centroids from the decision graph. However, there is still a lack of automatic methods for the identification of cluster centroids from the decision graph. In this work, a novel statistical outlier detection method is designed to identify cluster centroids automatically from the decision graph, so that the number of clusters is also automatically determined. In the proposed method, one-dimensional probability density functions at specific density values in the decision graph are estimated using two-dimensional Gaussian kernel functions. Then the cluster centroids are identified automatically as outliers in the decision graph using expectation values and standard deviations computed at specific density values. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation.
  • Multinode Implementation of An Extended Hodgkin-Huxley Simulator
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): G. Chatzikonstantis, H. Sidiropoulos, C. Strydis, M. Negrello, G. Smaragdos, C.I. De Zeeuw, D.J. Soudris Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin-Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model’s computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.
  • Multi-orientation and multi-scale features discriminant learning for
           palmprint recognition
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Fei Ma, Xiaoke Zhu, Cailing Wang, Huajun Liu, Xiao-Yuan Jing Palmprint contains stable and effective features, Especially the orientation and scale features of palm lines, and has now become an important identity recognition technique for surveillance and safety applications. Existing palmprint recognition methods using texture features can be roughly divided into two categories: coding-based and local descriptor based methods. As compared with the latter category, the former one can make full use of the palmprint specific features and acquire fast matching speed. However, most existing coding-based methods are based on the competitive coding scheme, in which the scale features of palmprint cannot be well exploited. In this work, we propose a discriminant orientation and scale features learning (DOSFL) for palmprint recognition. By introducing the idea of discriminant analysis into palmprint coding, DOSFL can extract the orientation and scale features with more favorable discriminability. Then, DOSFL utilizes four code bits to represent both the orientation and scale features of palmprint, and employs the Hamming distance for code matching. To make better use of the orientation and scale information contained in palmprint samples, we further propose a multi-orientation and multi-scale features discriminant learning (MOSDL) approach for palmprint recognition, which can fuse different orientation and scale feature information effectively in the discriminant learning process. Experimental results on two publicly available palmprint databases, including the HK PolyU database and UST palmprint image database, demonstrate that our proposed approach can achieve better recognition results than the compared methods.
  • REDPC: A Residual Error-based Density Peak Clustering Algorithm
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Milan Parmar, Di Wang, Xiaofeng Zhang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering algorithm named REDPC to better handle datasets comprising various data distribution patterns. Specifically, REDPC adopts the residual error computation to measure the local density within a neighbourhood region. As such, comparing to DPC, our REDPC algorithm provides a better decision graph for the identification of cluster centroids and better handles the low-density data points. Experimental results on both synthetic and real-world datasets show that REDPC performs better than DPC and other algorithms.
  • Bilateral Structure based Matrix Regression Classification for Face
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Jian-Xun Mi, Zhiheng Luo, Li-Fang Zhou, Fujin Zhong Robust sparse representation is a well-known method in computer vision. Many sparse representation models have been proposed and perform well in face recognition. Most of them use transformed images of one dimensional vector, and such implementation ignores structural information between features. To make use of this structural information, this paper presents a novel model for face recognition. Unlike traditional sparse regression measuring differences between test sample and predicted response by vector norm, our model uses matrix norm, l2, 1, to calculate residual. Instead of treating each pixel independently, the residual of a pixel is connected with all other pixels from the same line and column by means of double direction l2, 1-norm. Moreover, we incorporate the specific class information into the representation model by introducing group-sparse regularizer. And then, we use the alternating direction method of multipliers approach to optimize proposed model. The extensive experiments have been conducted to evaluate the proposed methods by comparing with many sparse representation based techniques.
  • Several Robust Extensions of Collaborative Representation for Image
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Jianping Gou, Bing Hou, Weihua Ou, Qirong Mao, Hebiao Yang, Yong Liu Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ2-norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ1-norm and the ℓ2-norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ1-norm of coding residual, jointing both the ℓ1-norm and the ℓ2-norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively.
  • Dynamic Neural Networks Based Kinematic Control for Redundant Manipulators
           with Model Uncertainties
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Zhihao Xu, Shuai Li, Xuefeng Zhou, Wu Yan, Taobo Cheng, Dan Huang Redundant design can greatly improve the flexibility of robot manipulators, but may suffer from potential limitations such as system complicity, model uncertainties, physical limitations, which make it challenging to achieve accurate tracking. In this paper, we propose a novel kinematic controller based on a recurrent neural network(RNN) which is competent in model adaption. An identifier which is related to joint velocity and tracking error is designed to learn the kinematic parameters online. In the inner loop, the redundancy resolution is formulated as a quadratic optimization problem, and a RNN is built to obtain the optimal solution recurrently, and the minimum norm of joint velocity is derived as the secondary task. Theoretical analysis demonstrates the global convergence of tracking error. Compared with existing methods, uncertain kinematic model of the robot is allowed in this paper, and pseudo-inverse of Jacobian matrix is avoided, with the consideration of physical limitations in a joint framework. Numerical and actual experiments based on a serial robot Kinova JACO2 show the effectiveness of the proposed controller.
  • Human Action Recognition Based on Spatio-temporal Three-Dimensional
           Scattering Transform Descriptor and An Improved VLAD Feature Encoding
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Bo Lin, Bin Fang, Weibin Yang, Jiye Qian The local spatio-temporal descriptor and feature encoding algorithm are two crucial key steps for human action recognition based on spatio-temporal interest points (STIP). Since the local descriptors for STIP are essentially a type of motion information based on the texture, the key point of local feature description is to extract invariable, robust and distinguishable local texture features and motion information in reference spatio-temporal volume. Scattering transform is an image transform method based on directional wavelet transform and scale convolution, which has local translation invariance, rotation invariance and elastic deformation stability for local texture features. A novel local descriptor for STIP based on spatio-temporal three-dimensional scattering transform is proposed in this paper, which extends the original scattering transform to spatio-temporal three-dimensional space. Compared to the traditional descriptors, such as HOG, HOF and so on, the proposed scattering transform coefficients based histogram of oriented gradients (STC-HOG) descriptor can capture more robust and distinguishable motion information of local texture for STIP. In order to incorporate the local descriptors into action video representation, the feature encoding algorithm is indispensable. For the problem that vector of locally aggregated descriptors (VLAD) loses feature distribution location information during feature encoding, a histogram of distribution vector of locally aggregated descriptors (HOD-VALD) based on Gaussian kernel is proposed. We validated the proposed algorithm for human action recognition on multiple public available datasets, such as KTH, UCF Sports, HMDB51 and so on. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
  • Multi-task Deep Convolutional Neural Network for Cancer Diagnosis
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Qing Liao, Ye Ding, Zoe L. Jiang, Xuan Wang, Chunkai Zhang, Qian Zhang Using computational techniques especially deep learning methods to facilitate and enhance cancer detection and diagnosis is a promising and important area. Nowadays, gene expression data has been widely used to train an effective deep neural network for precise cancer diagnosis. However, if a particular tumor has insufficient gene expressions, the trained deep neural networks may lead to a bad cancer diagnosis performance. In this paper, we propose a novel multi-task deep learning (MTDL) method to solve the data insufficiency problem. Since MTDL leverages the knowledge among the expression data of multiple cancers to learn a more stable representation for rare cancers, it can boost cancer diagnosis performance even if their expression data are inadequate. The experimental results show that MTDL significantly improves the performance of diagnosing every type of cancer when it learns from the aggregation of the expression data of twelve types of cancers.
  • Robust Pixelwise Saliency Detection via Progressive Graph Rankings
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Lihua Wang, Bo Jiang, Zhengzheng Tu, Amir Hussain, Jin Tang In this paper, we propose a novel saliency detection method based on superpixel-to-pixel level optimization. First, we segment the input image into superpixels under four scales. For each scale, we construct a k-regular basic graph with these superpixels as nodes. Furthermore, we enlarge the basic graph with virtual absorbing nodes and utilize absorbing Markov chain ranking to calculate background-based saliency. Second, for each scale, we obtain robust foreground queries from the previous result, and use manifold ranking to obtain foreground-based saliency. Third, a regularized random walk ranking based on the pixelwise graph for each scale is used to diffuse the saliency values among pixels. Finally, we obtain four saliency maps for the input image and integrate them together for the final saliency map. Extensive experiments on several challenging datasets reveal that the proposed method performs better in terms of precision, recall and F-measure values. Despite complex backgrounds, our method performs better in detecting small and/or multiple salient objects than other state-of-the-art methods as a whole.
  • Kernel Recursive Maximum Correntropy with Nyström Approximation
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Shiyuan Wang, Lujuan Dang, Guobing Qian, Yunxiang Jiang Kernel adaptive filters (KAFs) with growing network structures incur high computational burden. Generally, sparsification methods are introduced to curb the growth of the filter structure under some threshold rules, resulting in a variable structure. Unlike the sparsification methods, the Nyström method uses a subset of data samples to form the filter structure of a fixed size. In this paper, to combat the large outliers efficiently, the kernel recursive maximum correntropy with Nyström approximation (KRMC-NA) is proposed to achieve desirable filtering performance under a fixed and efficient filter structure. In addition, the theoretical analysis on the convergence characteristics of KRMC-NA is provided. Simulation results illustrate that the proposed KRMC-NA has better filtering accuracy than KAFs with sparsification, and approaches the filtering accuracy of the kernel recursive maximum correntropy using lower computational complexity.
  • Low-Rank Projection Learning via Graph Embedding
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Yingyi Liang, Lei You, Xiaohuan Lu, Zhenyu He, Hongpeng Wang With robustness to various corruptions, it is the local geometrical relationship among data that plays an important role in the recognition/clustering task of subspace learning (SL). However, a lot of previous SL methods cannot take into consideration both of the local neighborhood and the robustness, which results in poor performance in image classification and feature extraction. In this paper, a robust SL method is proposed to solve the feature extraction problem, named as Low-Rank Projection Learning via Graph Embedding (LRP-GE). The proposed algorithm enjoys two merits. First, it preserves the local neighborhood information among data by introducing the graph embedding (GE). Second, it alleviates the impact of noise and corruption by learning a robust subspace based on the low-rank projection. We cast the problem as a convex optimization problem and provide an iterative solution that can be solved efficiently in polynomial time. Extensive experiments performed on four benchmark data sets demonstrate that the proposed method performs favorably against other well-established SL methods in image classification.
  • Face Alignment by Component Adaptive Mechanism
    • Abstract: Publication date: Available online 5 November 2018Source: NeurocomputingAuthor(s): Jun Wan, Jing Li, Jun Chang, Yujia Wu, Yafu Xiao, Xuefei Li, Hao Zheng The new cascaded shape regression architecture proposed in this paper is actually an algorithm by Component Adaptive Mechanism (CAM) to cope with unconstrained face alignment. CAM divides the process of face alignment into two parts: the updating process of face box and the cascaded shape regression process by Component Adaptive Mechanism. The former process first adjusts face box by training different classifiers to estimate its transformation parameters and thereby outputs more accurate initialized shape. The latter process uses Component Adaptive Mechanism to fuse results of different domain-specific regressors to further update the shape. The major innovation of CAM is characterized by its fault-tolerated mechanism which is shown in the following two aspects. 1) A probability-based fern classifier is adopted in the partition of the optimization space into multiple domains of homogeneous descent, which not only endows the algorithm with the fault-tolerance mechanism but also augments the available training set of each domain. 2) A training strategy based on dominant set approach is used to train a stronger domain-specific regressor by dynamically adjusting the weight of objective function corresponding to different shapes and therefore regressors derived from the training are equipped with fault-tolerance ability. Conducted on such public image datasets as AFLW-full(19-pts), COFW(29-pts) and 300-W(68-pts), experiments show that the proposed CAM: 1) can deal with the problem of face detection and face alignment simultaneously; 2) is superior to existing algorithms in solving face alignment problems with extreme variations in pose, expression, illumination and partial occlusion.
  • Bilayer Image Restoration for Finger Vein Recognition
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Wenhao You, Weikang Zhou, Jing Huang, Feng Yang, Yaqin Liu, Ziyu Chen Finger vein recognition has been widely adopted for human verification because of its high recognition rate and security. Nonetheless, due to light scattering and attenuation in biological tissues, the collected finger vein images are often seriously degraded. This makes finger vein feature representation unreliable, and inevitably impairs the accuracy of finger vein recognition. Exploring effective ways for finger vein image restoration has become an urgent and prevalent topic. In this paper, we analyze the intrinsic factors causing the degradation of finger vein images, and propose a new bilayer restoration method to deal with skin scattering and improve the visibility of finger vein images. Our research is novel in two aspects compared to previous studies. Firstly, an innovative bilayer diffusion model is proposed to precisely describe light scattering in whole finger tissues (including dorsal-side and palm-side tissues). Secondly, we creatively introduce the blur-SURE (Steins unbiased risk estimate) method to yield accurate estimation of the bilayer model’s parameters, and then adopt the multi-Wiener SURE-LET (Linear expansion thresholds) approach to improve the robustness of restoration performance. Experiments performed on two publicly available finger vein databases demonstrate that the proposed method is effective and reliable in finger vein image restoration and enhancement.
  • A Survey on Video Compression Fast Block Matching Algorithms
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): A.J. Hussain, Zaynab Ahmed Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences.Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50-80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity.This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the full search is decreased while the resolution of the predicted frames is the same as for the full search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the full search.
  • A multi-feature probabilistic graphical model for social network semantic
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Feifei Kou, Junping Du, Congxian Yang, Yansong Shi, Meiyu Liang, Zhe Xue, Haisheng Li With the rapid development of social network platforms, more and more people are using them to search for material related to their interests. As the texts of social media messages are usually so short, when traditional existing document modeling methods are used in social network search tasks, the problem of semantic sparsity arises, leading to low-quality semantic representation and low-precision social network search results. Fortunately, besides of short text, social media data also has other features, such as timestamps, locations, and its user information. In light of this, to realize precise social network search, we propose a multi-feature probabilistic graphical model (MFPGM), which can generate high-quality semantic representation. To deal with the problem of semantic sparsity, we exploit two strategies in MFPGM. First, we propose a concept named special region and utilize location information to aggregate short text into long text. Second, we introduce the biterm pattern that can generate dense semantic space by supposing that a biterm occurring in the same context has the same topic. In order to generate high-quality semantic representations, we simultaneously model multiple features (i.e. biterm, user, location and timestamp) of social network data to enhance the semantic learning process of MFPGM. We conduct a lot of experiments on real-word datasets, and the comparisons with several state-of-art baseline methods have demonstrated the superiority of our MFPGM on topic quality and search performance. Additionally, with the help of the generated semantic representations, MFPGM allows people to analyze the relationships between time and the popularities of topics.
  • Fingerprint Indexing Schemes - A Survey
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Phalguni Gupta, Kamlesh Tiwari, Geetika Arora Biometric authentication involves identification/verification of an individual based on his physiological or behavioral characteristics. Fingerprint, a physiological characteristic, is an impression developed on a surface touched by a human fingertip. As the use of fingerprint recognition grows, more and more people get registered to the system and therefore the size of biometric database increases. During identification when a new fingerprint is provided and the task is to find most similar fingerprint from the database, it essentially involves searching the entire database. Identification becomes very compute intensive for large databases. If we can somehow reduce the search space from entire database to a small size list, the identification system would become cost effective. One possibility is to use locality sensitive hashing based approaches. The key idea is to choose an appropriate representation of the fingerprint and to device an indexing approach around it. Such a technique would, instead of directly comparing the query fingerprint with all the fingerprints stored in the database, make a preprocessing to produce a small size candidate list of fingerprints and then search within the list. The candidate list contains the target fingerprint with certain probabilistic guarantees. Indexing is challenging for biometric databases due to various factors such as the presence of the variable number of features, absence of structural information like order, presence of occlusion and illumination in the image etc. This paper presents a survey on indexing schemes used for fingerprint databases. These schemes have been classified into four broad categories, namely texture-based, minutiae-based, hybrid and deep learning based schemes. The schemes broadly cover the use of Level-1, Level-2, texture, and deep features.
  • Face Recognition Using Nonnegative Matrix Factorization with Fractional
           Power Inner Product Kernel
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Wen-Sheng Chen, Jingmin Liu, Binbin Pan, Bo Chen Kernel nonnegative matrix factorization (KNMF) algorithms have been widely used to extract features for face recognition. The choice of kernel function is vital to facial feature extraction. The polynomial kernel function has been commonly used in KNMF. The power of the polynomial kernel is required to be a positive integer, thereby ensuring that the kernel generates a positive semi-definite matrix. In this paper, we investigate a new type of inner-product kernel that has a fractional power. The new kernel offers us flexibility in data representation as the power can be any positive real number. Based on the fractional power inner-product kernel, we present a novel KNMF algorithm called fractional power inner-product KNMF (FPKNMF). The FPKNMF algorithm is theoretically and experimentally validated to be convergent. The experimental results confirm that our algorithm exhibits a performance superior to the state-of-the-art methods in terms of facial representation and recognition accuracy.
  • Identifying The Most Informative Features Using A Structurally Interacting
           Elastic Net
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Lixin Cui, Lu Bai, Zhihong Zhang, Yue Wang, Edwin R. Hancock Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information loss. To address this problem, we propose a new graph-based structurally interacting elastic net method for feature selection. Specifically, we commence by constructing feature graphs that can incorporate pairwise relationship between samples. With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation. This measure is used to obtain a structural interaction matrix where the elements represent the proposed information theoretic measure between feature pairs. We then formulate a new optimization model through the combination of the structural interaction matrix and an elastic net regression model for the feature subset selection problem. This allows us to a) preserve the information of the original vectorial space, b) remedy the information loss of the original feature space caused by using graph representation, and c) promote a sparse solution and also encourage correlated features to be selected. Because the proposed optimization problem is non-convex, we develop an efficient alternating direction multiplier method (ADMM) to locate the optimal solutions. Extensive experiments on various datasets demonstrate the effectiveness of the proposed method.
  • Learn to Focus on Objects for Visual Detection
    • Abstract: Publication date: Available online 3 November 2018Source: NeurocomputingAuthor(s): Zijing Chen, Jun Li, Xinhua You State-of-art visual detectors utilize object proposals as the reference of objects to achieve higher efficiency. However, the number of the proposal to ensure full coverage of potential objects is still large because the proposals are generated with thread and thrum, exposing proposal computation as a bottleneck. This paper presents a complementary technique that aims to work with any existing proposal generating system, amending the work-flow from “propose-assess” to “propose-adjust-assess”. Inspired by the biological processing, we propose to improve the quality of object proposals by analyzing visual contexts and gradually focusing proposals on targets. In particular, the proposed method can be employed with existing proposals generation algorithms based on both hand-crafted features and Convolutional Neural Network (CNN) features. For the former, we realize the focusing function by two learning-based transformation models, which are trained for identifying generic objects using image cues. For the latter, a Focus Proposal Net (FoPN) with cascaded layers, which can be directly injected into CNN models in an end-to-end manner, is developed as the implementation of focusing operation. Experiments on real-life image data sets demonstrate that the quality of the proposal is improved by the proposed technique. Besides, it can reduce the number of proposals to achieve high recall rate of the objects based on both hand-crafted features and CNN-features, and can boost the performance of state-of-art detectors.
  • Computer-Assisted Frameworks for Classification of Liver, Breast and Blood
           Neoplasias via Neural Networks: a Survey based on Medical Images
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Antonio Brunetti, Leonarda Carnimeo, Gianpaolo Francesco Trotta, Vitoantonio Bevilacqua Computer Aided Diagnosis (CAD) systems can support physicians in classifying different kinds of breast cancer, liver cancer and blood tumours also revealed by images acquired via Computer Tomography, Magnetic Resonance, and Blood Smear systems. In this regard, this survey focuses on papers dealing with the description of existing CAD frameworks for the classification of the three mentioned diseases, by detailing existing CAD workflows based on the same steps for supporting the diagnosis of these tumours. In detail, after an appropriate acquisition of the images, the fundamental steps carried out by a CAD framework can be identified as image segmentation, feature extraction and classification. In particular, in this work, specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods. The cited methodology is focused on sharing and reviewing an amount of specific works. Then, the performance of three selected case studies are carefully reported, designed with the aim of showing how final outcomes can vary according to different choices in each step of the adopted workflow. More in detail, these case studies concern with breast images acquired by Tomosynthesis and Magnetic Resonance, hepatocellular carcinoma images acquired by Computed Tomography and enhanced by a triphasic protocol with a contrast medium, peripheral blood smear images for cellular blood tumours and are used to compare their performance.
  • Robust Geometric Model Fitting Based on Iterative Hypergraph Construction
           and Partition
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Guobao Xiao, Hanzi Wang, Yan Yan, Liming Zhang In this paper, we propose a novel Iterative Hypergraph Construction and Partition based model fitting method (termed IHCP), for handling multiple-structure data. Specifically, IHCP initially constructs a small-sized hypergraph, and then it performs hypergraph partition. Based on the partitioning results, IHCP iteratively updates the hypergraph by a novel guided sampling algorithm, and performs hypergraph partition. After a few iterations, IHCP is able to construct an effective hypergraph to represent the complex relationship between data points and model hypotheses, and obtain good partitioning results for model fitting as well. IHCP is very efficient since it avoids generating a large number of model hypotheses, and it is also very effective due to the excellent ability of the novel iterative strategy. Experimental results on real images show the superiority of the proposed IHCP method over several state-of-the-art model fitting methods.
  • A Preliminary Geometric Structure Simplification for Principal Component
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Huamao Gu, Tong Lin, Xun Wang Real world data are commonly geometrically nonlinear and thus are not easy to be processed by the traditional linear methods. Many existing techniques for nonlinear dimensionality reduction need careful parameter tuning and cannot be applied to real data stably and consistently. In this article we propose an efficient data preprocessing algorithm, called Curve Straightening Transformation (CST), to flatten the nonlinear geometric structure of data. Then Principal Component Analysis (PCA) and other linear projection methods are adequate to perform the dimensionality reduction task in most cases. In this aspect, the proposed CST algorithm can be regarded as a geometric preprocessing step tailored for PCA. The comprehensive experiments on both artificial and real datasets demonstrate that the proposed preprocessing algorithm is able to simplify the nonlinear geometric structures, and the flattened data are suitable for further dimensionality reduction by linear methods such as PCA.
  • A Survey on Metaheuristic Optimization for Random Single-hidden Layer
           Feedforward Neural Network
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Fei Han, Jing Jiang, Qing-Hua Ling, Ben-Yue Su Random single-hidden layer feedforward neural network (RSLFN) is currently a popular learning algorithm proposed for improving traditional gradient-based model due to its fast learning speed and acceptable performance. For RSLFN, the input weights and/or other parameters are randomly initialized in advance, and the remained are iteratively or non-iteratively trained. However, the performance of RSLFN is highly sensitive to the number of hidden neurons and randomly initialized parameters. Numerous methods have been successfully employed to improve the RSLFN from various perspectives. Because of their favourable search ability, metaheuristic optimization approaches gradually attract more and more attentions. Metaheuristic algorithms usually formulate the random parameters of RSLFN into an optimization model, and then provide a near-optimum solution which could be converted into RSLFN with better generalization performance. The hybrid method for optimizing RSLFN therefore shows considerable potential in intelligent computing and artificial intelligence. However, there is no comprehensive survey on RSLFN with metaheuristic in the research area, which ultimately leads to lost opportunities for an advancement. This paper firstly introduces the basic principles of RSLFN along with several metaheuristic algorithms. Secondly, it provides a comprehensive survey of the state-of-the-art contributions in the area. Finally, current challenges are highlighted and promising research directions are also presented.
  • Finite-time Consensus Control of Heterogeneous Nonlinear MASs with
           Uncertainties Bounded by Positive Functions
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Wei Liu, Qian Ma, Qiang Wang, Hongyan Feng In this paper, we investigate the finite-time consensus control problem for heterogeneous nonlinear multi-agent systems (MASs) subject to nonlinear uncertainties. In the case of a connected and undirected communication graph, a new finite-time consensus control algorithm for leaderless MASs is presented. The designed consensus protocol can guarantee that the steady state error of every two agents converge to zero in a finite-time. Compared with some existing studies, in which the upper bounds of uncertainties are assumed to be some positive constants, the upper bound assumption conditions of uncertainties are relaxed to be some positive functions. Finally, a numerical example and a practical model are presented to demonstrate the effectiveness of the proposed consensus control method.
  • A Review of Image Set Classification
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Zhong-Qiu Zhao, Shou-Tao Xu, Dian Liu, Wei-Dong Tian, Zhi-Da Jiang In computer vision, we generally solve a classification problem by a single image. With the video cameras being widely used in our real life, it is a nature choice to solve a classification problem by image sets. Compared with the single image based methods, the image set classification deals with severe changes of appearance and makes decisions by comparing the query set with gallery sets. So the image set classification offers more promises and has therefore attracted significant research attention in recent years. In this paper, we provide a review on image set classification. Our review begins with an overview of the direction of image set classification. Then we detail some classic algorithms. Experimental analyses are provided in corresponding subsection to compare classification performance of various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided as guidelines for future work.
  • Deep Depth-based Representations of Graphs through Deep Learning Networks
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Lu Bai, Lixin Cui, Xiao Bai, Edwin R. Hancock Graph-based representations are powerful tools in structural pattern recognition and machine learning. In this paper, we propose a framework of computing the deep depth-based representations for graph structures. Our work links the ideas of graph complexity measures and deep learning networks. Specifically, for a set of graphs, we commence by computing depth-based representations rooted at each vertex as vertex points. In order to identify an informative depth-based representation subset, we employ the well-known k-means method to identify M dominant centroids of the depth-based representation vectors as prototype representations. To overcome the burdensome computation of using depth-based representations for all graphs, we propose to use the prototype representations to train a deep autoencoder network, that is optimized using Stochastic Gradient Descent together with the Deep Belief Network for pretraining. By inputting the depth-based representations of vertices over all graphs to the trained deep network, we compute the deep representation for each vertex. The resulting deep depth-based representation of a graph is computed by averaging the deep representations of its complete set of vertices. We theoretically demonstrate that the deep depth-based representations of graphs not only reflect both the local and global characteristics of graphs through the depth-based representations, but also capture the main structural relationship and information content over all graphs under investigations. Experimental evaluations demonstrate the effectiveness of the proposed method.
  • Ranking nodes in complex networks based on local structure and improving
           closeness centrality
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Chiman Salavati, Alireza Abdollahpouri, Zhaleh Manbari In complex networks, the nodes with most spreading ability are called influential nodes. In many applications such as viral marketing, identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality is one of the most commonly used methods to identify influential spreaders in social networks. However, this method is time-consuming for dynamic large-scale networks and has high computational complexity. In this paper, we propose a novel ranking algorithm which improves closeness centrality by taking advantage of local structure of nodes and aims to decrease the computational complexity. In our proposed method, at first, a community detection algorithm is applied to extract community structures of the network. Thereafter, after ignoring the relationship between communities, one best node as local critical node for each community is extracted according to any centrality measure. Then, with the consideration of interconnection links between communities, another best node as gateway node is found. Finally, the nodes are sorted and ranked based on computing the sum of the shortest path length of nodes to obtained critical nodes. Our method can detect the most spreader nodes with high diffusion ability and low time complexity, which make it appropriately applicable to large-scale networks. Experiments on synthetic and real-world connected networks under common diffusion models demonstrate the effectiveness of our proposed method in comparison with other methods.
  • A Survey on Deep Learning based Bearing Fault Diagnosis
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Duy-Tang Hoang, Hee-Jun Kang Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units, Deep Learning has become a promising tool for intelligent bearing fault diagnosis. This survey paper intends to provide a systematic review of Deep Learning based bearing fault diagnosis. The three popular Deep Learning algorithms for bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced. And their applications are reviewed through publications and research works on the area of bearing fault diagnosis. Further applications and challenges in this research area are also discussed.
  • Mining Concise Patterns on Graph-Connected Itemsets
    • Abstract: Publication date: Available online 2 November 2018Source: NeurocomputingAuthor(s): Di Zhang, Yunquan Zhang, Qiang Niu, Xingbao Qiu The itemset is a basic and usual form of data. People can obtain new insights into their business by discovering its implicit regularities through pattern mining. In some real applications, e.g., network alarm association, the itemsets usually have the following two characteristics: 1) The observed samples come from different entities, with inherent structural relationships implied in their static properties; 2) the samples are scarce, which may lead to incomplete pattern extraction. This paper considers how to efficiently find a concise set of patterns on such kind of data. Firstly, we use a graph to express the entities and their interconnections and propagate every sample to every node with a weight, determined by the pre-defined combination of kernel functions based on the similarities of the nodes and patterns. Next, the weight values can be naturally imported into the MDL-based filtering process and bring a differentiated pattern set for each node. Experiments show that the solution can outperform the global solution (trading all nodes as one) and isolated solution (removing all edges) on simulated and real data, and its effectiveness and scalability can be further verified in the application of large-scale network operation&maintenance.
  • Class imbalance learning using UnderBagging based Kernelized Extreme
           Learning Machine
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Bhagat Singh Raghuwanshi, Sanyam Shukla Many real-life problems can be described as imbalanced classification problems, where the number of samples belonging to one of the classes is heavily outnumbered than the numbers in other classes. The samples with larger and smaller class proportion are referred to as the majority and the minority class respectively. Traditional extreme learning machine (ELM) and Support Vector Machine (SVM) provides equal importance to all the samples leading to results biased towards the majority class. Many variants of ELM-like Weighted ELM (WELM), Boosting WELM (BWELM) etc. are designed to solve the class imbalance problem effectively. This work develops a novel UnderBagging based kernelized ELM (UBKELM) to address the class imbalance problem more effectively. In this paper, an UnderBagging ensemble is proposed which employs kernelized ELM as the component classifier. UnderBagging ensemble incorporates the strength of random undersampling and the bagging. This work creates several balanced training subsets by random undersampling of the majority class samples. The number of training subsets depending on the degree of the class imbalance. This work uses kernelized ELM as the component classifier to make the ensemble as it is stable and has the promising generalization performance. The computational cost of UBKELM is significantly lower than BWELM, BalanceCascade, EasyEnsemble, hybrid artificial bee colony WELM. The final outcome is computed by the majority voting and the soft voting of these classification models. The proposed work is assessed by employing benchmark real-world imbalanced datasets taken from the KEEL dataset repository. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers for class imbalance learning.
  • Multi-resolution Attention Convolutional Neural Network for Crowd Counting
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Youmei Zhang, Chunluan Zhou, Faliang Chang, Alex C. Kot Estimating crowd counts remains a challenging task due to the problems of scale variations, non-uniform distribution and complex backgrounds. In this paper, we propose a multi-resolution attention convolutional neural network (MRA-CNN) to address this challenging task. Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. Besides, we utilize a multi-resolution attention (MRA) model to generate score maps, where head locations are with higher scores to guide the network to focus on head regions and suppress non-head regions regardless of the complex backgrounds. During the generation of score maps, atrous convolution layers are used to expand the receptive field with fewer parameters, thus getting higher-level features and providing the MRA model more comprehensive information. Experiments on ShanghaiTech, WorldExpo’10 and UCF datasets demonstrate the effectiveness of our method.
  • Density-Sensitive Robust Fuzzy Kernel Principal Component Analysis
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): A. Xinmin Tao, B. Rui Chang, C. Chenxi Li, D. Ruotong Wang, E. Rui Liu In order to deal with the sensitivity of traditional kernel principal component analysis (KPCA) to the outliers and high computational complexity of the other existing robust KPCAs, a novel density-sensitive robust fuzzy kernel principal component analysis (DRF-KPCA) is proposed in this paper. First, the initial membership degree of the sample is determined by introducing the relative density. Secondly, the membership degree is formulated based on reconstruction error and updated by the optimal gradient descent approach, which can effectively solve the problem of the principal component skewing caused by the sensitivity of KPCA to the outliers. Ultimately, the computational complexity and running time of DRF-KPCA are significantly reduced by simplifying the calculation formula of the reconstruction error. In the experiments, as compared with KPCA and other modified approaches on both the datasets with outliers and the datasets without outliers, DRF-KPCA is evaluated to effectively eliminate the impacts of the outliers on the performance with low computational complexity. In addition, the influence of parameters on the performance of DRF-KPCA is also analyzed in detail and the suggestions of determination on the optimal coefficients are given. Eventually, the comparison results with the-state-of-art techniques on UCI benchmark datasets and other high-dimensional classification datasets demonstrate that the performance of DRF-KPCA is significantly improved.
  • Integration Enhanced and Noise Tolerant ZNN for Computing Various
           Expressions Involving Outer Inverses
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Predrag S. Stanimirović, Shuai Li, Vasilios N. Katsikis An integration-enhanced noise-tolerant zeroing neural network (IENTZNN) model for computing various expressions involving outer inverses is defined and considered. The model assumes an input matrix A, two appropriately selected matrices F, G, a regularization parameter, and will be shortly denoted by IENTZNN(A, F, G). Particularly, IENTZNN(A, F, G) is applicable in computing real-time-varying matrix outer inverse with prescribed range and null space under the presence of various kinds of noises. The model is an extension of the IEZNN model for solving the problem of real-time matrix inversion as well as an integration-enhanced and noise-tolerant extension of the ZNNATS2 model for approximating time-varying outer inverse with prescribed range and null space. Theoretical analyses show that the proposed integration-enhanced model globally and exponentially converges to the theoretical solution. Moreover, the proposed IENTZNN(A, F, G) model is proven to have a very good performance in the presence of various kinds of noise. Finally, illustrative simulation examples are presented to testify the efficacy of the proposed model.
  • 3G Structure for Image Caption Generation
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Aihong Yuan, Xuelong Li, Xiaoqiang Lu It is a big challenge of computer vision to make machine automatically describe the content of an image with a natural language sentence. Previous works have made great progress on this task, but they only use the global or local image feature, which may lose some important subtle or global information of an image. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. The model mainly has three gated structures. 1) Gate for the global image feature, which can adaptively decide when and how much the global image feature should be imported into the sentence generator. 2) The gated recurrent neural network (RNN) is used as the sentence generator. 3) The gated feedback method for stacking RNN is employed to increase the capability of nonlinearity fitting. More specially, the global and local image features are combined together in this paper, which makes full use of the image information. The global image feature is controlled by the first gate and the local image feature is selected by the attention mechanism. With the latter two gates, the relationship between image and text can be well explored, which improves the performance of the language part as well as the multi-modal embedding part. Experimental results show that our proposed method outperforms the state-of-the-art for image caption generation.
  • Time Delay Chebyshev Functional Link Artificial Neural Network
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Lu Lu, Yi Yu, Xiaomin Yang, Wei Wu In real applications, a time delay in the parameter update of the neural network is sometimes required. In this paper, motivated by the Chebyshev functional link artificial neural network (CFLANN), a new structure based on the time delay adaptation is developed for nonlinear system identification. Particularly, we present a new CFLANN-delayed recursive least square (CFLANN-DRLS), as an online learning algorithm for parameter adaptation in CFLANN. The CFLANN-DRLS algorithm exploits the time delayed error signal with the gain vector delayed by D cycles to form the weight increment term, which provides potential implementation in the filter with pipelined structure. However, it suffers from the instability problems under imperfect network delay estimate. To overcome this problem, we further propose a modified CFLANN-DRLS (CFLANN-MDRLS) algorithm by including a compensation term to the error signal. We analyze the stability and convergence of the proposed algorithm. Simulations in nonlinear system identification contexts reveal that the newly proposed CFLANN-MDRLS algorithm can effectively compensate the time delay of system and it is even superior to the algorithm without delay in some cases.
  • Finite-Time and Fixed-Time Anti-Synchronization of Neural Networks with
           Time-Varying Delays
    • Abstract: Publication date: Available online 1 November 2018Source: NeurocomputingAuthor(s): Lili Wang, Tianping Chen In this paper, the finite-time and fixed-time anti-synchronization of master-slave dynamical systems with time-varying delays are investigated. The feedback controller is designed only depending on the system state at present time t, but independent of the delayed states, which would be much easier to be verified and realized in practice. Rigorous analysis is developed in two cases with respect to the different ranges of initial state of error systems. Then the absolute value of each error state component can be considered as that, flowing from the initial value to 1 first, then from 1 to 0, and the time it needs in this whole process is finite. As special cases, several neural network models with unbounded time delays are addressed to illustrate the effectiveness and efficiency of our obtained results.
  • A Hybrid Finger Identification Pattern Using Polarized Depth-weighted
           Binary Direction Coding
    • Abstract: Publication date: Available online 24 October 2018Source: NeurocomputingAuthor(s): Wenming Yang, Wenyang Ji, Jing-Hao Xue, Yong Ren, Qingmin Liao Finger identification is increasingly popular in recent years. In this paper, we propose a new finger identification system to acquire a hybrid pattern of dorsal finger vein and texture in a single image by using only one camera. It effectively reduces the cost and volume of the imaging device to acquire multi-modal patterns. The hybrid pattern of dorsal finger vein and texture is both storage-saving and calculation-saving. As there was no existing method specially developed for this kind of pattern, we propose a new feature extraction method called “Polarized depth-Weighted Binary Direction Coding” (PWBDC). We also establish a new database of such hybrid images of 210 finger samples. Experimental results demonstrate that the proposed system and feature are not only storage and calculation saving, but more importantly effective for identification. The proposed PWBDC method performs well on both the newly established database of hybrid images and a popular public database of traditional finger vein images, superior to many established and state-of-the-art methods.
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