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Symmetry, Volume 11, Issue 7 (July 2019)

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Open AccessArticle
Sasaki-Einstein 7-Manifolds, Orlik Polynomials and Homology
Symmetry 2019, 11(7), 947; https://doi.org/10.3390/sym11070947
Received: 4 June 2019 / Revised: 17 July 2019 / Accepted: 18 July 2019 / Published: 23 July 2019
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Abstract
In this article, we give ten examples of 2-connected seven dimensional Sasaki-Einstein manifolds for which the third homology group is completely determined. Using the Boyer-Galicki construction of links over particular Kähler-Einstein orbifolds, we apply a valid case of Orlik’s conjecture to the links [...] Read more.
In this article, we give ten examples of 2-connected seven dimensional Sasaki-Einstein manifolds for which the third homology group is completely determined. Using the Boyer-Galicki construction of links over particular Kähler-Einstein orbifolds, we apply a valid case of Orlik’s conjecture to the links so that one is able to explicitly determine the entire third integral homology group. We give ten such new examples, all of which have the third Betti number satisfy 10 b 3 ( L f ) 20 . Full article
(This article belongs to the Special Issue Geometry of Submanifolds and Homogeneous Spaces)
Open AccessArticle
Multilevel and Multiscale Deep Neural Network for Retinal Blood Vessel Segmentation
Symmetry 2019, 11(7), 946; https://doi.org/10.3390/sym11070946
Received: 21 May 2019 / Revised: 13 July 2019 / Accepted: 17 July 2019 / Published: 22 July 2019
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Abstract
Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared [...] Read more.
Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared to the classical segmentation algorithms. CNN does not need any artificial handcrafted features to train the network. In the proposed deep neural network (DNN), a better pre-processing technique and multilevel/multiscale deep supervision (DS) layers are being incorporated for proper segmentation of retinal blood vessels. From the first four layers of the VGG-16 model, multilevel/multiscale deep supervision layers are formed by convolving vessel-specific Gaussian convolutions with two different scale initializations. These layers output the activation maps that are capable to learn vessel-specific features at multiple scales, levels, and depth. Furthermore, the receptive field of these maps is increased to obtain the symmetric feature maps that provide the refined blood vessel probability map. This map is completely free from the optic disc, boundaries, and non-vessel background. The segmented results are tested on Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), High-Resolution Fundus (HRF), and real-world retinal datasets to evaluate its performance. This proposed model achieves better sensitivity values of 0.8282, 0.8979 and 0.8655 in DRIVE, STARE and HRF datasets with acceptable specificity and accuracy performance metrics. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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Open AccessArticle
A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment
Symmetry 2019, 11(7), 945; https://doi.org/10.3390/sym11070945
Received: 15 June 2019 / Revised: 17 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the [...] Read more.
With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems. Full article
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Open AccessArticle
Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling
Symmetry 2019, 11(7), 944; https://doi.org/10.3390/sym11070944
Received: 21 May 2019 / Revised: 19 June 2019 / Accepted: 16 July 2019 / Published: 20 July 2019
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Abstract
In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation [...] Read more.
In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments. Full article
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Open AccessArticle
Some Novel Picture 2-Tuple Linguistic Maclaurin Symmetric Mean Operators and Their Application to Multiple Attribute Decision Making
Symmetry 2019, 11(7), 943; https://doi.org/10.3390/sym11070943
Received: 15 June 2019 / Revised: 5 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
When solving multiple attribute decision making (MADM) problems, the 2-tuple linguistic variable is an effective tool that can not only express complex cognitive information but also prevent loss of information in calculation. The picture fuzzy set (PFS) has three degrees and has more [...] Read more.
When solving multiple attribute decision making (MADM) problems, the 2-tuple linguistic variable is an effective tool that can not only express complex cognitive information but also prevent loss of information in calculation. The picture fuzzy set (PFS) has three degrees and has more freedom to express cognitive information. In addition, Archimedean t-conorm and t-norm (ATT) can generalize most existing t-conorms and t-norms and Maclaurin symmetric mean (MSM) operators can catch the relationships among the multi-input parameters. Therefore, we investigate several novel aggregation operators, such as the picture 2-tuple linguistic MSM (2TLMSM) operator based on the ATT (ATT-P2TLMSM) and the picture 2-tuple linguistic generalized MSM (2TLGMSM) operator based on ATT (ATT-P2TLGMSM). Considering that the input parameters have different importance, we proposed picture 2-tuple linguistic weighted MSM (2TLWMSM) operators based on ATT (ATT-P2TLWMSM) and picture 2-tuple linguistic weighted generalized MSM (2TLWGMSM) operators based on ATT (ATT-P2TLWGMSM). Finally, a MADM method is introduced, and an expositive example is presented to explain the availability and applicability of the developed operators and methods. Full article
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Open AccessArticle
An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning
Symmetry 2019, 11(7), 942; https://doi.org/10.3390/sym11070942
Received: 8 June 2019 / Revised: 16 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
A The learning process of machine learning consists of finding values of unknown weights in a cost function by minimizing the cost function based on learning data. However, since the cost function is not convex, it is conundrum to find the minimum value [...] Read more.
A The learning process of machine learning consists of finding values of unknown weights in a cost function by minimizing the cost function based on learning data. However, since the cost function is not convex, it is conundrum to find the minimum value of the cost function. The existing methods used to find the minimum values usually use the first derivative of the cost function. When even the local minimum (but not a global minimum) is reached, since the first derivative of the cost function becomes zero, the methods give the local minimum values, so that the desired global minimum cannot be found. To overcome this problem, in this paper we modified one of the existing schemes—the adaptive momentum estimation scheme—by adding a new term, so that it can prevent the new optimizer from staying at local minimum. The convergence condition for the proposed scheme and the convergence value are also analyzed, and further explained through several numerical experiments whose cost function is non-convex. Full article
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Open AccessArticle
Smart Contract-Based Pool Hopping Attack Prevention for Blockchain Networks
Symmetry 2019, 11(7), 941; https://doi.org/10.3390/sym11070941
Received: 27 May 2019 / Revised: 5 July 2019 / Accepted: 16 July 2019 / Published: 19 July 2019
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Abstract
Pool hopping attack is the result of miners leaving the pool when it offers fewer financial rewards and joining back when the rewards of mining yield higher rewards in blockchain networks. This act of leaving and rejoining the pool only during the good [...] Read more.
Pool hopping attack is the result of miners leaving the pool when it offers fewer financial rewards and joining back when the rewards of mining yield higher rewards in blockchain networks. This act of leaving and rejoining the pool only during the good times results in the miner receiving more rewards than the computational power they contribute. Miners exiting the pool deprive it of its collective hash power, which leaves the pool unable to mine the block successfully. This results in its competitors mining the block before they can finish mining. Existing research shows pool hopping resistant measures and detection strategies; however, they do not offer any robust preventive solution to discourage miners from leaving the mining pool. To prevent pool hopping attacks, a smart contract-based pool hopping attack prevention model is proposed. The main objective of our research is maintaining the symmetrical relationship between the miners by requiring them all to continually contribute their computational power to successfully mine a block. We implement a ledger containing records of all miners, in the form of a miner certificate, which tracks the history of the miner’s earlier behavior. The certificate enables a pool manager to better initiate terms of the smart contract, which safeguards the interests of existing mining pool members. The model prevents frequent mine hoppers from pool hopping as they submit coins in the form of an escrow and risk losing them if they abandon the pool before completing mining of the block. The key critical factors that every pool hopping attack prevention solution must address and a study of comparative analysis with existing solutions are presented in the paper. Full article
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Open AccessArticle
Structural Monitoring and Performance Assessment of Shield Tunnels during the Operation Period, Based on Distributed Optical-Fiber Sensors
Symmetry 2019, 11(7), 940; https://doi.org/10.3390/sym11070940
Received: 21 June 2019 / Revised: 11 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
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Abstract
The weak parts of shield tunnels are not obvious, so it is urgently necessary to implement distributed monitoring based on an advanced sensing method. As the horizontal loads at both sides of the shield tunnel present a type of symmetric distribution, the deformation [...] Read more.
The weak parts of shield tunnels are not obvious, so it is urgently necessary to implement distributed monitoring based on an advanced sensing method. As the horizontal loads at both sides of the shield tunnel present a type of symmetric distribution, the deformation parameters under the vertical loads are often selected as the key monitored parameters, such as convergence, settlement, and seam opening. In this paper, the monitoring of the proposed deformation parameters is innovatively implemented with only one sensing technology, namely distributed optical-fiber strain sensing technology. First, the improved distributed optical-fiber sensors are introduced with the sensing performance. Second, a structural health monitoring (SHM) system for operational shield tunnels is proposed, including optical-fiber sensor installation, data logging and saving, key parameter analysis, and structural health assessment. The key monitoring theory and technology are also proposed. The proposed system has been verified by experiments at the Nanjing Yangtze River tunnel. In the experiments, the proposed optical-fiber sensors were installed on the surface of a selected tunnel ring, with a longitudinal span of approximately 90 m long. The experiments were conducted over 55 days to measure the distributed strain and temperature. Then the key parameters were obtained from the measurements, with which the structural health was assessed. The possibility that the shield tunnel SHM system can be constructed with the improved distributed optical-fiber sensors, monitoring theory, and technology is proven. Full article
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Open AccessArticle
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection
Symmetry 2019, 11(7), 939; https://doi.org/10.3390/sym11070939
Received: 24 June 2019 / Revised: 16 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
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Abstract
Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant [...] Read more.
Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%. Full article
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Open AccessArticle
An Optimization Framework for Codes Classification and Performance Evaluation of RISC Microprocessors
Symmetry 2019, 11(7), 938; https://doi.org/10.3390/sym11070938
Received: 24 June 2019 / Revised: 5 July 2019 / Accepted: 6 July 2019 / Published: 19 July 2019
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Abstract
Pipelines, in Reduced Instruction Set Computer (RISC) microprocessors, are expected to provide increased throughputs in most cases. However, there are a few instructions, and therefore entire assembly language codes, that execute faster and hazard-free without pipelines. It is usual for the compilers to [...] Read more.
Pipelines, in Reduced Instruction Set Computer (RISC) microprocessors, are expected to provide increased throughputs in most cases. However, there are a few instructions, and therefore entire assembly language codes, that execute faster and hazard-free without pipelines. It is usual for the compilers to generate codes from high level description that are more suitable for the underlying hardware to maintain symmetry with respect to performance; this, however, is not always guaranteed. Therefore, instead of trying to optimize the description to suit the processor design, we try to determine the more suitable processor variant for the given code during compile time, and dynamically reconfigure the system accordingly. In doing so, however, we first need to classify each code according to its suitability to a different processor variant. The latter, in turn, gives us confidence in performance symmetry against various types of codes—this is the primary contribution of the proposed work. We first develop mathematical performance models of three conventional microprocessor designs, and propose a symmetry-improving nonlinear optimization method to achieve code-to-design mapping. Our analysis is based on four different architectures and 324,000 different assembly language codes, each with between 10 and 1000 instructions with different percentages of commonly seen instruction types. Our results suggest that in the sub-micron era, where execution time of each instruction is merely in a few nanoseconds, codes accumulating as low as 5% (or above) hazard causing instructions execute more swiftly on processors without pipelines. Full article
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Open AccessArticle
Reduction of Order: Analytical Solution of Film Formation in the Electrostatic Rotary Bell Sprayer
Symmetry 2019, 11(7), 937; https://doi.org/10.3390/sym11070937
Received: 9 July 2019 / Accepted: 16 July 2019 / Published: 18 July 2019
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Abstract
This brief paper explains the slight differences in governing equations for a fluid film in a spinning cone, and the mechanism that reduces the order of a solution. Spinning cones with a centrally supplied fluid that spreads over its inner surface as a [...] Read more.
This brief paper explains the slight differences in governing equations for a fluid film in a spinning cone, and the mechanism that reduces the order of a solution. Spinning cones with a centrally supplied fluid that spreads over its inner surface as a thin film have been the subject of interest for many years. Though often cast as a mathematical analysis, understanding this process is important, especially in the application of automotive painting. The analysis consists of a system of equations obtained from the Navier–Stokes equations along with simple boundary conditions that describe radial and tangential momentum conservation. Solutions to this system of equations are shown using several techniques. The connection between these techniques is slightly subtle. However, the conditions that enable reduction of order are clear once they are exposed. Directional velocity profiles in the film can be a combination of four roots in the complex plane. This system of roots also contains two diagonal axes of symmetry that are offset by 90 degrees. Alternatively, if the radial and tangential velocity profiles are expressed as a single complex function, a reduced order solution that is a combination of one set of diagonal set of roots can be found. Full article
Open AccessArticle
Updated Constraints on the Variations of the Fine-Structure Constant from an Analysis of White-Dwarf Spectra
Symmetry 2019, 11(7), 936; https://doi.org/10.3390/sym11070936
Received: 25 June 2019 / Revised: 9 July 2019 / Accepted: 16 July 2019 / Published: 18 July 2019
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Abstract
I fused observed spectra from the white-dwarf star G191-B2B to constrain the spatial and temporal variation of the fine-structure constant, α=e24πε0c. The analysis was combined with laboratory-measured and astronomically observed lines in [Ni [...] Read more.
I fused observed spectra from the white-dwarf star G191-B2B to constrain the spatial and temporal variation of the fine-structure constant, α = e 2 4 π ε 0 c . The analysis was combined with laboratory-measured and astronomically observed lines in [Ni V] to find Δ α / α = ( 0.003 ± 0.072 ) × 10 6 . The obtained result allows a symmetry of the related comparison with previous studies looking for cosmological variations of α using spectra from Quasi Stellar Objects (QSOs). In this way, we can expect higher sensitivity from white-dwarf spectra than QSO spectra. Therefore, this study should have orders-of-magnitude higher sensitivity per system than previous quasar studies, and we should reduce statistical and systematic errors. The results of this study place a more stringent limit on Δ α / α than previous studies using the same data. Full article
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Open AccessArticle
The Recognition of the Bifurcation Problem with Trivial Solutions
Symmetry 2019, 11(7), 935; https://doi.org/10.3390/sym11070935
Received: 15 April 2019 / Revised: 11 July 2019 / Accepted: 11 July 2019 / Published: 17 July 2019
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Abstract
This paper studies the recognition criterion of the bifurcation problem with trivial solution. The t-equivalence is different from the strong equivalence studied by Golubitsky et al. The difference is that the second component of the differential homeomorphism is not identical. Consider the [...] Read more.
This paper studies the recognition criterion of the bifurcation problem with trivial solution. The t-equivalence is different from the strong equivalence studied by Golubitsky et al. The difference is that the second component of the differential homeomorphism is not identical. Consider the normal subgroup of t-equivalence group, we obtain the characterization of higher order terms P ( h ) . In addition, we also explore the properties of intrinsic submodules and the finite determinacy of the bifurcation problem. Full article
Open AccessArticle
Definable Transformation to Normal Crossings over Henselian Fields with Separated Analytic Structure
Symmetry 2019, 11(7), 934; https://doi.org/10.3390/sym11070934
Received: 16 May 2019 / Revised: 13 July 2019 / Accepted: 16 July 2019 / Published: 17 July 2019
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Abstract
We are concerned with rigid analytic geometry in the general setting of Henselian fields K with separated analytic structure, whose theory was developed by Cluckers–Lipshitz–Robinson. It unifies earlier work and approaches of numerous mathematicians. Separated analytic structures admit reasonable relative quantifier elimination in [...] Read more.
We are concerned with rigid analytic geometry in the general setting of Henselian fields K with separated analytic structure, whose theory was developed by Cluckers–Lipshitz–Robinson. It unifies earlier work and approaches of numerous mathematicians. Separated analytic structures admit reasonable relative quantifier elimination in a suitable analytic language. However, the rings of global analytic functions with two kinds of variables seem not to have good algebraic properties such as Noetherianity or excellence. Therefore, the usual global resolution of singularities from rigid analytic geometry is no longer at our disposal. Our main purpose is to give a definable version of the canonical desingularization algorithm (the hypersurface case) due to Bierstone–Milman so that both of these powerful tools are available in the realm of non-Archimedean analytic geometry at the same time. It will be carried out within a category of definable, strong analytic manifolds and maps, which is more flexible than that of affinoid varieties and maps. Strong analytic objects are those definable ones that remain analytic over all fields elementarily equivalent to K. This condition may be regarded as a kind of symmetry imposed on ordinary analytic objects. The strong analytic category makes it possible to apply a model-theoretic compactness argument in the absence of the ordinary topological compactness. On the other hand, our closedness theorem enables application of resolution of singularities to topological problems involving the topology induced by valuation. Eventually, these three results will be applied to such issues as the existence of definable retractions or extending continuous definable functions. The established results remain valid for strictly convergent analytic structures, whose classical examples are complete, rank one valued fields with the Tate algebras of strictly convergent power series. The earlier techniques and approaches to the purely topological versions of those issues cannot be carried over to the definable settings because, among others, non-Archimedean geometry over non-locally compact fields suffers from lack of definable Skolem functions. Full article
(This article belongs to the Special Issue Mirror Symmetry and Algebraic Geometry)
Open AccessArticle
Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning
Symmetry 2019, 11(7), 933; https://doi.org/10.3390/sym11070933
Received: 27 June 2019 / Revised: 14 July 2019 / Accepted: 15 July 2019 / Published: 17 July 2019
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Abstract
The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as [...] Read more.
The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to other structures in the eye. This paper proposes a new network architecture we call Segmentation-ResNet Seg-ResNet that takes a residual network structure as the main body, introduces a channel weighting structure that automatically adjusts the dependence of the feature channels, re-calibrates the feature channels, and introduces a set of low-level features that are combined with high-level features to improve network performance. Pre-fusion features and fused features are symmetrical. Hence, this work correlates with the concept of symmetry. Combined with the training strategy of migration learning, the segmentation accuracy is improved while speeding up network convergence. The robustness and effectiveness of the proposed method are demonstrated by testing data from the GlaucomaRepo and Drishti-GS fundus image databases. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
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Open AccessArticle
Disjunctive Representation of Triangular Bipolar Neutrosophic Numbers, De-Bipolarization Technique and Application in Multi-Criteria Decision-Making Problems
Symmetry 2019, 11(7), 932; https://doi.org/10.3390/sym11070932
Received: 22 April 2019 / Revised: 14 May 2019 / Accepted: 29 May 2019 / Published: 16 July 2019
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Abstract
This research paper adds to the theory of the generalized neutrosophic number from a distinctive frame of reference. It is universally known that the concept of a neutrosophic number is generally associated with and strongly related to the concept of positive, indeterminacy and [...] Read more.
This research paper adds to the theory of the generalized neutrosophic number from a distinctive frame of reference. It is universally known that the concept of a neutrosophic number is generally associated with and strongly related to the concept of positive, indeterminacy and non-belongingness membership functions. Currently, all membership functions always lie within the range of 0 to 1. However, we have generated bipolar concept in this paper where the membership contains both positive and negative parts within the range −1 to 0 and 0 to 1. We describe different structures of generalized triangular bipolar neutrosophic numbers, such as category-1, category-2, and category-3, in relation to the membership functions containing dependency or independency with each other. Researchers from different fields always want to observe the co-relationship and interdependence between fuzzy numbers and crisp numbers. In this platform, we also created the perception of de-bipolarization for a triangular bipolar rneutrosophic number with the help of well-known techniques so that any bipolar neutrosophic fuzzy number of any type can be smoothly converted into a real number instantly. Creating a problem using bipolar neutrosophic perception is a more reliable, accurate, and trustworthy method than others. In this paper, we have also taken into account a multi-criteria decision-making problem (MCDM) for different users in the bipolar neutrosophic domain. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Aid methods in fuzzy decision problems)
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Open AccessArticle
A Detailed Examination of Sphicas (2014), Generalized EOQ Formula Using a New Parameter: Coefficient of Backorder Attractiveness
Symmetry 2019, 11(7), 931; https://doi.org/10.3390/sym11070931
Received: 27 June 2019 / Revised: 8 July 2019 / Accepted: 13 July 2019 / Published: 16 July 2019
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Abstract
Researchers have used analytic methods (calculus) to solve inventory models with fixed and linear backorder costs. They have found conditions to partition the feasible domain into two parts. For one part, the system of the first partial derivatives has a solution. For the [...] Read more.
Researchers have used analytic methods (calculus) to solve inventory models with fixed and linear backorder costs. They have found conditions to partition the feasible domain into two parts. For one part, the system of the first partial derivatives has a solution. For the other part, the inventory model degenerates to the inventory model without shortages. A scholar tried to use the algebraic method to solve this kind of model. The scholar mentioned the partition of the feasible domain. However, other researchers cannot understand why the partition appears, even though the scholar provided two motivations for his derivations. After two other researchers provided their derivations by algebraic methods, the scholar showed a generalized solution to combine inventory models with and without shortages together. In this paper, we will point out that this generalized solution approach not only did not provide explanations for his previous partition but also contained twelve questionable results. Recently, an expert indicated questionable findings from two other researchers. Hence, we can claim that solving inventory models with fixed and linear backorder costs is still an open problem for future researchers. Full article
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Open AccessArticle
On the Assumption of the Independence of Thermodynamic Properties on the Gravitational Field
Symmetry 2019, 11(7), 930; https://doi.org/10.3390/sym11070930
Received: 31 May 2019 / Revised: 9 July 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
A reversible cyclic process is analyzed in which the center of mass of an ideal gas is raised in a gravitational field during both an expansion phase and a subsequent contraction phase, with the gas returning to its initial height in a final [...] Read more.
A reversible cyclic process is analyzed in which the center of mass of an ideal gas is raised in a gravitational field during both an expansion phase and a subsequent contraction phase, with the gas returning to its initial height in a final step. When the properties of the gas are taken as uniform, the thermodynamic efficiency of this cycle is able to exceed that of a corresponding Carnot cycle, which is a violation of the second law of thermodynamics. The source of this discrepancy was previously claimed, when analyzing a similar heating and cooling of a sphere, to be the assumed independence of the internal energy on the gravitational field. However, this violation is only apparent since all of the effects of the gravitational field were not incorporated fully into the thermodynamic analysis of the cycle. When all the influences of the gravitational field are considered, no possible violation of the second law can occur. The evaluation of the entropy changes of the gas throughout the cycle also highlights other key inconsistencies that arise when the full effects of the gravitational field are neglected. As the analysis of the cycle provided here shows, the assumption of the independence of the internal energy, as well as other thermodynamic properties, on the gravitational field strength can still be invoked. Full article
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Open AccessArticle
Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors
Symmetry 2019, 11(7), 929; https://doi.org/10.3390/sym11070929
Received: 7 June 2019 / Revised: 7 July 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. [...] Read more.
With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
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Open AccessArticle
Nonlinear Rayleigh Quotients and Nonlinear Spectral Theory
Symmetry 2019, 11(7), 928; https://doi.org/10.3390/sym11070928
Received: 21 June 2019 / Revised: 10 July 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
We give a new and simplified definition of spectrumfor a nonlinear operator F acting in a real Banach space X, and study some of its features in terms of (qualitative and) quantitative properties of F such as the measure of noncompactness, [...] Read more.
We give a new and simplified definition of spectrumfor a nonlinear operator F acting in a real Banach space X, and study some of its features in terms of (qualitative and) quantitative properties of F such as the measure of noncompactness, α ( F ) , of F. Then, using as a main tool the Ekeland Variational Principle, we focus our attention on the spectral properties of F when F is a gradient operator in a real Hilbert space, and in particular on the role played by its Rayleigh quotient R ( F ) and by the best lower and upper bounds, m ( F ) and M ( F ) , of R ( F ) . Full article
(This article belongs to the Special Issue Nonlinear, Convex, Nonsmooth, Functional Analysis in Symmetry)
Open AccessArticle
Series of Semihypergroups of Time-Varying Artificial Neurons and Related Hyperstructures
Symmetry 2019, 11(7), 927; https://doi.org/10.3390/sym11070927
Received: 30 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 16 July 2019
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Abstract
Detailed analysis of the function of multilayer perceptron (MLP) and its neurons together with the use of time-varying neurons allowed the authors to find an analogy with the use of structures of linear differential operators. This procedure allowed the construction of a group [...] Read more.
Detailed analysis of the function of multilayer perceptron (MLP) and its neurons together with the use of time-varying neurons allowed the authors to find an analogy with the use of structures of linear differential operators. This procedure allowed the construction of a group and a hypergroup of artificial neurons. In this article, focusing on semihyperstructures and using the above described procedure, the authors bring new insights into structures and hyperstructures of artificial neurons and their possible symmetric relations. Full article
Open AccessArticle
Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores
Symmetry 2019, 11(7), 926; https://doi.org/10.3390/sym11070926
Received: 5 June 2019 / Revised: 10 July 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic [...] Read more.
Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform graph partitioning. It creates a cluster by grouping RDF subgraphs with higher usage frequency while creating a subcluster with lower usage frequency. These clusters and subclusters conduct load balancing by using the mean frequency of queries for the distributed server and conduct graph data partitioning by considering the size of the data stored in each distributed server. It also minimizes the number of edge-cuts connected to clusters and subclusters to minimize communication costs between servers. This solves the problem of data concentration to specific servers due to ongoing data changes and additions and allows efficient load balancing among servers. The performance results show that the proposed method significantly outperforms the existing partitioning methods in terms of query performance time in a distributed server. Full article
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Open AccessArticle
An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors
Symmetry 2019, 11(7), 925; https://doi.org/10.3390/sym11070925
Received: 29 May 2019 / Revised: 29 June 2019 / Accepted: 13 July 2019 / Published: 15 July 2019
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Abstract
This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is [...] Read more.
This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 classical engineering design optimizations. The simulation results show that the LAFBA has stronger optimization performance and higher optimization efficiency than basic bat algorithm and other bio-inspired algorithms. Furthermore, the results of the real-world engineering problems demonstrate the superiority of LAFBA in solving challenging problems with constrained and unknown search spaces. Full article
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Open AccessArticle
Hysteretically Symmetrical Evolution of Elastomers-Based Vibration Isolators within α-Fractional Nonlinear Computational Dynamics
Symmetry 2019, 11(7), 924; https://doi.org/10.3390/sym11070924
Received: 8 June 2019 / Revised: 4 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
This study deals with computational analysis of vibration isolators’ behavior, using the fractional-order differential equations (FDE). Numerical investigations regarding the influences of α-fractional derivatives have been mainly focused on the dissipative component within the differential constitutive equation of rheological model. Two classical models [...] Read more.
This study deals with computational analysis of vibration isolators’ behavior, using the fractional-order differential equations (FDE). Numerical investigations regarding the influences of α-fractional derivatives have been mainly focused on the dissipative component within the differential constitutive equation of rheological model. Two classical models were considered, Voigt-Kelvin and Van der Pol, in order to develop analyses both on linear and nonlinear formulations. The aim of this research is to evaluate the operational capability, provided by the α-fractional derivatives within the viscous component of certain rheological model, to enable an accurate response regarding the realistic behavior of elastomeric-based vibration isolators. The hysteretic response followed, which has to be able to assure the symmetry of dynamic evolution under external loads, and at the same time, properly providing dissipative and conservative characteristics in respect of the results of experimental investigations. Computational analysis was performed for different values of α-fractional order, also taking into account the integer value, in order to facilitate the comparison between the responses. The results have shown the serviceable capability of the α-fractional damping component to emulate, both a real dissipative behavior, and a virtual conservative characteristic, into a unitary way, only by tuning the α-order. At the same time, the fractional derivative models are able to preserve the symmetry of hysteretic behavior, comparatively, e.g., with rational-power nonlinear models. Thereby, the proposed models are accurately able to simulate specific behavioral aspects of rubber-like elastomers-based vibration isolators, to the experiments. Full article
(This article belongs to the Special Issue Symmetry in Applied Continuous Mechanics)
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Open AccessArticle
A Study of Boundedness in Fuzzy Normed Linear Spaces
Symmetry 2019, 11(7), 923; https://doi.org/10.3390/sym11070923
Received: 18 June 2019 / Revised: 6 July 2019 / Accepted: 8 July 2019 / Published: 15 July 2019
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Abstract
In the present paper some different types of boundedness in fuzzy normed linear spaces of type (X,N,), where ∗ is an arbitrary t-norm, are considered. These boundedness concepts are very general and some of them have [...] Read more.
In the present paper some different types of boundedness in fuzzy normed linear spaces of type ( X , N , ) , where ∗ is an arbitrary t-norm, are considered. These boundedness concepts are very general and some of them have no correspondent in the classical topological metrizable linear spaces. Properties of such bounded sets are given and we make a comparative study among these types of boundedness. Among them there are various concepts concerning symmetrical properties of the studied objects arisen from the classical setting appropriate for this journal topics. We establish the implications between them and illustrate by examples that these concepts are not similar. Full article
Open AccessArticle
Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String
Symmetry 2019, 11(7), 922; https://doi.org/10.3390/sym11070922
Received: 29 June 2019 / Revised: 11 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified [...] Read more.
The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV). Full article
(This article belongs to the Special Issue Applied Designs in Chemical Structures with High Symmetry)
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Open AccessArticle
Topologically Protected Duality on The Boundary of Maxwell-BF Theory
Symmetry 2019, 11(7), 921; https://doi.org/10.3390/sym11070921
Received: 4 June 2019 / Revised: 5 July 2019 / Accepted: 10 July 2019 / Published: 15 July 2019
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Abstract
The Maxwell-BF theory with a single-sided planar boundary is considered in Euclidean four-dimensional spacetime. The presence of a boundary breaks the Ward identities, which describe the gauge symmetries of the theory, and, using standard methods of quantum field theory, the most general boundary [...] Read more.
The Maxwell-BF theory with a single-sided planar boundary is considered in Euclidean four-dimensional spacetime. The presence of a boundary breaks the Ward identities, which describe the gauge symmetries of the theory, and, using standard methods of quantum field theory, the most general boundary conditions and a nontrivial current algebra on the boundary are derived. The electromagnetic structure, which characterizes the boundary, is used to identify the three-dimensional degrees of freedom, which turn out to be formed by a scalar field and a vector field, related by a duality relation. The induced three-dimensional theory shows a strong–weak coupling duality, which separates different regimes described by different covariant actions. The role of the Maxwell term in the bulk action is discussed, together with the relevance of the topological nature of the bulk action for the boundary physics. Full article
(This article belongs to the Special Issue Duality Symmetry)
Open AccessArticle
The Influence of a Network’s Spatial Symmetry, Topological Dimension, and Density on Its Percolation Threshold
Symmetry 2019, 11(7), 920; https://doi.org/10.3390/sym11070920
Received: 15 June 2019 / Revised: 7 July 2019 / Accepted: 10 July 2019 / Published: 15 July 2019
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Abstract
Analyses of the processes of information transfer within network structures shows that the conductivity and percolation threshold of the network depend not only on its density (average number of links per node), but also on its spatial symmetry groups and topological dimension. The [...] Read more.
Analyses of the processes of information transfer within network structures shows that the conductivity and percolation threshold of the network depend not only on its density (average number of links per node), but also on its spatial symmetry groups and topological dimension. The results presented in this paper regarding conductivity simulation in network structures show that, for regular and random 2D and 3D networks, an increase in the number of links (density) per node reduces their percolation threshold value. At the same network density, the percolation threshold value is less for 3D than for 2D networks, whatever their structure and symmetry may be. Regardless of the type of networks and their symmetry, transition from 2D to 3D structures engenders a change of percolation threshold by a value exp{−(d − 1)} that is invariant for transition between structures, for any kind of network (d being topological dimension). It is observed that in 2D or 3D networks, which can be mutually transformed by deformation without breaking and forming new links, symmetry of similarity is observed, and the networks have the same percolation threshold. The presence of symmetry axes and corresponding number of symmetry planes in which they lie affects the percolation threshold value. For transition between orders of symmetry axes, in the presence of the corresponding planes of symmetry, an invariant exists which contributes to the percolation threshold value. Inversion centers also influence the value of the percolation threshold. Moreover, the greater the number of pairs of elements of the structure which have inversion, the more they contribute to the fraction of the percolation threshold in the presence of such a center of symmetry. However, if the center of symmetry lies in the plane of mirror symmetry separating the layers of the 3D structure, the mutual presence of this group of symmetry elements do not affect the percolation threshold value. The scientific novelty of the obtained results is that for different network structures, it was shown that the percolation threshold for the blocking of nodes problem could be represented as an additive set of invariant values, that is, as an algebraic sum, the value of the members of which is stored in the transition from one structure to another. The invariant values are network density, topological dimension, and some of the elements of symmetry (axes of symmetry and the corresponding number of symmetry planes in which they lie, centers of inversion). Full article
(This article belongs to the Special Issue Recent Advances in the Application of Symmetry Group)
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Open AccessFeature PaperReview
Origin of Terrestrial Bioorganic Homochirality and Symmetry Breaking in the Universe
Symmetry 2019, 11(7), 919; https://doi.org/10.3390/sym11070919
Received: 5 June 2019 / Revised: 3 July 2019 / Accepted: 5 July 2019 / Published: 15 July 2019
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Abstract
The origin of terrestrial bioorganic homochirality is one of the most important and unresolved problems in the study of chemical evolution prior to the origin of terrestrial life. One hypothesis advocated in the context of astrobiology is that polarized quantum radiation in space, [...] Read more.
The origin of terrestrial bioorganic homochirality is one of the most important and unresolved problems in the study of chemical evolution prior to the origin of terrestrial life. One hypothesis advocated in the context of astrobiology is that polarized quantum radiation in space, such as circularly polarized photons or spin-polarized leptons, induced asymmetric chemical and physical conditions in the primitive interstellar media (the cosmic scenario). Another advocated hypothesis in the context of symmetry breaking in the universe is that the bioorganic asymmetry is intrinsically derived from the chiral asymmetric properties of elementary particles, that is, parity violation in the weak interaction (the intrinsic scenario). In this paper, the features of these two scenarios are discussed and approaches to validate them are reviewed. Full article
(This article belongs to the Special Issue Possible Scenarios for Homochirality on Earth)
Open AccessArticle
Fake Bitrate Detection of HEVC Videos Based on Prediction Process
Symmetry 2019, 11(7), 918; https://doi.org/10.3390/sym11070918
Received: 29 June 2019 / Revised: 10 July 2019 / Accepted: 11 July 2019 / Published: 15 July 2019
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Abstract
In order to defraud click-through rate, some merchants recompress the low-bitrate video to a high-bitrate one without improving the video quality. This behavior deceives viewers and wastes network resources. Therefore, a stable algorithm that detects fake bitrate videos is urgently needed. High-Efficiency Video [...] Read more.
In order to defraud click-through rate, some merchants recompress the low-bitrate video to a high-bitrate one without improving the video quality. This behavior deceives viewers and wastes network resources. Therefore, a stable algorithm that detects fake bitrate videos is urgently needed. High-Efficiency Video Coding (HEVC) is a worldwide popular video coding standard. Hence, in this paper, a robust algorithm is proposed to detect HEVC fake bitrate videos. Firstly, five effective feature sets are extracted from the prediction process of HEVC, including Coding Unit of I-picture/P-picture partitioning modes, Prediction Unit of I-picture/P-picture partitioning modes, Intra Prediction Modes of I-picture. Secondly, feature concatenation is adopted to enhance the expressiveness and improve the effectiveness of the features. Finally, five single feature sets and three concatenate feature sets are separately sent to the support vector machine for modeling and testing. The performance of the proposed algorithm is compared with state-of-the-art algorithms on HEVC videos of various resolutions and fake bitrates. The results show that the proposed algorithm can not only can better detect HEVC fake bitrate videos, but also has strong robustness against frame deletion, copy-paste, and shifted Group of Picture structure attacks. Full article
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