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Symmetry, Volume 10, Issue 5 (May 2018)

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Cover Story (view full-size image) We have formulated a numerical application of linear-molecule symmetry properties, described by the [...] Read more.
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Open AccessArticle Network Embedding via a Bi-Mode and Deep Neural Network Model
Symmetry 2018, 10(5), 180; https://doi.org/10.3390/sym10050180
Received: 6 April 2018 / Revised: 10 May 2018 / Accepted: 21 May 2018 / Published: 22 May 2018
Cited by 1 | Viewed by 467 | PDF Full-text (416 KB) | HTML Full-text | XML Full-text
Abstract
Network embedding (NE) is an important method to learn the representations of a network via a low-dimensional space. Conventional NE models focus on capturing the structural information and semantic information of vertices while neglecting such information for edges. In this work, we propose
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Network embedding (NE) is an important method to learn the representations of a network via a low-dimensional space. Conventional NE models focus on capturing the structural information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structural and semantic information of edges. BimoNet is composed of two parts; i.e., the bi-mode embedding part and the deep neural network part. For the bi-mode embedding part, the first mode—named the add-mode—is used to express the entity-shared features of edges and the second mode—named the subtract-mode—is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For the deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then, we take the nodes’ adjacent matrix as the input of the deep neural network, as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structural information of edges. In experiments, we evaluate BimoNet on three real-world datasets and the task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently. Full article
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Open AccessFeature PaperArticle Axisymmetric Arc Sliding Method of Basal Heave Stability Analysis for Braced Circular Excavations
Symmetry 2018, 10(5), 179; https://doi.org/10.3390/sym10050179
Received: 4 May 2018 / Revised: 18 May 2018 / Accepted: 19 May 2018 / Published: 22 May 2018
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Abstract
On the basis of the circular arc sliding model of the limit equilibrium method, an axisymmetric arc sliding method (AASM) is proposed to analyze the basal heave stability of braced circular excavations. The proposed method considers the stiffness of the enclosure structure and
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On the basis of the circular arc sliding model of the limit equilibrium method, an axisymmetric arc sliding method (AASM) is proposed to analyze the basal heave stability of braced circular excavations. The proposed method considers the stiffness of the enclosure structure and spatial effects. The AASM was applied to check basal heave stability in a design example and provided results that were more reasonable than those obtained using other methods. The radii effects in theory and numerical simulation, and the enclosure structure stiffness effects on the basal heave stability safety factor were discussed. Additionally, the effects of the embedded depth on the basal heave stability of a braced circular excavation were analyzed. The safety factor of basal heave stability for a braced circular excavation will be larger when calculated with the AASM than when calculated with the circular arc sliding method, and the optimized embedded depth of the enclosure structure may therefore be reduced by 4∼5 m to lower the cost of the enclosure structure. Full article
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Open AccessArticle Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings
Symmetry 2018, 10(5), 178; https://doi.org/10.3390/sym10050178
Received: 16 April 2018 / Revised: 13 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
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Abstract
With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering
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With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering the decision makers are bounded rational, this paper proposes a novel decision support model for product selection based on online ratings, in which the regret aversion behavior of consumers is formulated. Massive online ratings provided by experienced consumers for alternative products associated with several evaluation attributes are obtained by software finders. Then, the evaluations of alternative products in format of stochastic variables are conducted. To select a desirable alternative product, a novel method is introduced to calculate gain and loss degrees of each alternative over others. Considering the regret behavior of consumers in the product selection process, the regret and rejoice values of alternative products for consumer are computed to obtain the perceived utility values of alternative products. According to the prior order of the evaluation attributes provided by the consumer, the prior weights of attributes are determined based on the perceived utility values of alternative products. Furthermore, the overall perceived utility values of alternative products are obtained to generate a ranking result. Finally, a practical example from Zol.com.cn for tablet computer selection is used to demonstrate the feasibility and practically of the proposed model. Full article
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Open AccessArticle A Novel Comparison of Probabilistic Hesitant Fuzzy Elements in Multi-Criteria Decision Making
Symmetry 2018, 10(5), 177; https://doi.org/10.3390/sym10050177
Received: 26 April 2018 / Revised: 18 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
Cited by 2 | Viewed by 472 | PDF Full-text (425 KB) | HTML Full-text | XML Full-text
Abstract
The probabilistic hesitant fuzzy element is a common tool to deal with multi-criteria decision-making problems when the decision makers are irresolute in providing their evaluations. The existing methods for ranking probabilistic hesitant fuzzy elements are limited and not reasonable in practical applications. The
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The probabilistic hesitant fuzzy element is a common tool to deal with multi-criteria decision-making problems when the decision makers are irresolute in providing their evaluations. The existing methods for ranking probabilistic hesitant fuzzy elements are limited and not reasonable in practical applications. The main purpose of this paper is to find a more precise and appropriate comparison method so that we can deal with multi-criteria decision-making problems more efficiently. We first propose a chart technique to analyze the structure of a probabilistic hesitant fuzzy element. After that, we propose a novel possibility degree formula to rank probabilistic hesitant fuzzy elements. Last but not least, we provide a useful process to solve the actual multi-criteria decision-making problems, and make a real case study which demonstrates that our method is feasible and reliable. Full article
(This article belongs to the Special Issue Symmetry in Computing Theory and Application)
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Open AccessArticle Neutrosophic Weighted Support Vector Machines for the Determination of School Administrators Who Attended an Action Learning Course Based on Their Conflict-Handling Styles
Symmetry 2018, 10(5), 176; https://doi.org/10.3390/sym10050176
Received: 30 March 2018 / Revised: 15 May 2018 / Accepted: 18 May 2018 / Published: 20 May 2018
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Abstract
In the recent years, school administrators often come across various problems while teaching, counseling, and promoting and providing other services which engender disagreements and interpersonal conflicts between students, the administrative staff, and others. Action learning is an effective way to train school administrators
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In the recent years, school administrators often come across various problems while teaching, counseling, and promoting and providing other services which engender disagreements and interpersonal conflicts between students, the administrative staff, and others. Action learning is an effective way to train school administrators in order to improve their conflict-handling styles. In this paper, a novel approach is used to determine the effectiveness of training in school administrators who attended an action learning course based on their conflict-handling styles. To this end, a Rahim Organization Conflict Inventory II (ROCI-II) instrument is used that consists of both the demographic information and the conflict-handling styles of the school administrators. The proposed method uses the Neutrosophic Set (NS) and Support Vector Machines (SVMs) to construct an efficient classification scheme neutrosophic support vector machine (NS-SVM). The neutrosophic c-means (NCM) clustering algorithm is used to determine the neutrosophic memberships and then a weighting parameter is calculated from the neutrosophic memberships. The calculated weight value is then used in SVM as handled in the Fuzzy SVM (FSVM) approach. Various experimental works are carried in a computer environment out to validate the proposed idea. All experimental works are simulated in a MATLAB environment with a five-fold cross-validation technique. The classification performance is measured by accuracy criteria. The prediction experiments are conducted based on two scenarios. In the first one, all statements are used to predict if a school administrator is trained or not after attending an action learning program. In the second scenario, five independent dimensions are used individually to predict if a school administrator is trained or not after attending an action learning program. According to the obtained results, the proposed NS-SVM outperforms for all experimental works. Full article
Open AccessArticle Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification
Symmetry 2018, 10(5), 175; https://doi.org/10.3390/sym10050175
Received: 19 April 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 20 May 2018
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Abstract
Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution
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Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases. Full article
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Open AccessArticle Neutrosophic Hesitant Fuzzy Subalgebras and Filters in Pseudo-BCI Algebras
Symmetry 2018, 10(5), 174; https://doi.org/10.3390/sym10050174
Received: 29 April 2018 / Revised: 13 May 2018 / Accepted: 14 May 2018 / Published: 18 May 2018
Cited by 2 | Viewed by 489 | PDF Full-text (328 KB) | HTML Full-text | XML Full-text
Abstract
The notions of the neutrosophic hesitant fuzzy subalgebra and neutrosophic hesitant fuzzy filter in pseudo-BCI algebras are introduced, and some properties and equivalent conditions are investigated. The relationships between neutrosophic hesitant fuzzy subalgebras (filters) and hesitant fuzzy subalgebras (filters) is discussed. Five kinds
[...] Read more.
The notions of the neutrosophic hesitant fuzzy subalgebra and neutrosophic hesitant fuzzy filter in pseudo-BCI algebras are introduced, and some properties and equivalent conditions are investigated. The relationships between neutrosophic hesitant fuzzy subalgebras (filters) and hesitant fuzzy subalgebras (filters) is discussed. Five kinds of special sets are constructed by a neutrosophic hesitant fuzzy set, and the conditions for the two kinds of sets to be filters are given. Moreover, the conditions for two kinds of special neutrosophic hesitant fuzzy sets to be neutrosophic hesitant fuzzy filters are proved. Full article
Open AccessArticle On Topological Properties of Symmetric Chemical Structures
Symmetry 2018, 10(5), 173; https://doi.org/10.3390/sym10050173
Received: 16 April 2018 / Revised: 3 May 2018 / Accepted: 11 May 2018 / Published: 18 May 2018
Cited by 5 | Viewed by 669 | PDF Full-text (4138 KB) | HTML Full-text | XML Full-text
Abstract
The utilizations of graph theory in chemistry and in the study of molecule structures are more than someone’s expectations, and, lately, it has increased exponentially. In molecular graphs, atoms are denoted by vertices and bonds by edges. In this paper, we focus on
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The utilizations of graph theory in chemistry and in the study of molecule structures are more than someone’s expectations, and, lately, it has increased exponentially. In molecular graphs, atoms are denoted by vertices and bonds by edges. In this paper, we focus on the molecular graph of (2D) silicon-carbon S i 2 C 3 -I and S i 2 C 3 - I I . Moreover, we have computed topological indices, namely general Randić Zagreb types indices, geometric arithmetic index, atom–bond connectivity index, fourth atom–bond connectivity and fifth geometric arithmetic index of S i 2 C 3 -I and S i 2 C 3 - I I . Full article
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Open AccessArticle A Novel Approach to Multi-Attribute Group Decision-Making with q-Rung Picture Linguistic Information
Symmetry 2018, 10(5), 172; https://doi.org/10.3390/sym10050172
Received: 20 April 2018 / Revised: 10 May 2018 / Accepted: 14 May 2018 / Published: 18 May 2018
Cited by 4 | Viewed by 527 | PDF Full-text (1006 KB) | HTML Full-text | XML Full-text
Abstract
The proposed q-rung orthopair fuzzy set (q-ROFS) and picture fuzzy set (PIFS) are two powerful tools for depicting fuzziness and uncertainty. This paper proposes a new tool, called q-rung picture linguistic set (q-RPLS) to deal with vagueness
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The proposed q-rung orthopair fuzzy set (q-ROFS) and picture fuzzy set (PIFS) are two powerful tools for depicting fuzziness and uncertainty. This paper proposes a new tool, called q-rung picture linguistic set (q-RPLS) to deal with vagueness and impreciseness in multi-attribute group decision-making (MAGDM). The proposed q-RPLS takes full advantages of q-ROFS and PIFS and reflects decision-makers’ quantitative and qualitative assessments. To effectively aggregate q-rung picture linguistic information, we extend the classic Heronian mean (HM) to q-RPLSs and propose a family of q-rung picture linguistic Heronian mean operators, such as the q-rung picture linguistic Heronian mean (q-RPLHM) operator, the q-rung picture linguistic weighted Heronian mean (q-RPLWHM) operator, the q-rung picture linguistic geometric Heronian mean (q-RPLGHM) operator, and the q-rung picture linguistic weighted geometric Heronian mean (q-RPLWGHM) operator. The prominent advantage of the proposed operators is that the interrelationship between q-rung picture linguistic numbers (q-RPLNs) can be considered. Further, we put forward a novel approach to MAGDM based on the proposed operators. We also provide a numerical example to demonstrate the validity and superiorities of the proposed method. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Decision Making 2018)
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Open AccessFeature PaperArticle Lie Symmetries of Nonlinear Parabolic-Elliptic Systems and Their Application to a Tumour Growth Model
Symmetry 2018, 10(5), 171; https://doi.org/10.3390/sym10050171
Received: 25 April 2018 / Revised: 13 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
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Abstract
A generalisation of the Lie symmetry method is applied to classify a coupled system of reaction-diffusion equations wherein the nonlinearities involve arbitrary functions in the limit case in which one equation of the pair is quasi-steady but the other is not. A complete
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A generalisation of the Lie symmetry method is applied to classify a coupled system of reaction-diffusion equations wherein the nonlinearities involve arbitrary functions in the limit case in which one equation of the pair is quasi-steady but the other is not. A complete Lie symmetry classification, including a number of the cases characterised as being unlikely to be identified purely by intuition, is obtained. Notably, in addition to the symmetry analysis of the PDEs themselves, the approach is extended to allow the derivation of exact solutions to specific moving-boundary problems motivated by biological applications (tumour growth). Graphical representations of the solutions are provided and a biological interpretation is briefly addressed. The results are generalised on multi-dimensional case under the assumption of the radially symmetrical shape of the tumour. Full article
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Open AccessFeature PaperArticle Inflation in Mimetic f(G) Gravity
Symmetry 2018, 10(5), 170; https://doi.org/10.3390/sym10050170
Received: 12 April 2018 / Revised: 7 May 2018 / Accepted: 10 May 2018 / Published: 17 May 2018
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Abstract
Mimetic gravity is analysed in the framework of some extensions of general relativity (GR), whereby a function of the Gauss–Bonnet invariant in four dimensions is considered. By assuming the mimetic condition, the conformal degree of freedom is isolated, and a pressureless fluid naturally
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Mimetic gravity is analysed in the framework of some extensions of general relativity (GR), whereby a function of the Gauss–Bonnet invariant in four dimensions is considered. By assuming the mimetic condition, the conformal degree of freedom is isolated, and a pressureless fluid naturally arises. Then, the complete set of field equations for mimetic Gauss–Bonnet gravity is established, and some inflationary models are analysed, for which the corresponding gravitational action is reconstructed. The spectral index and tensor-to-scalar ratio are obtained and compared with observational bounds from Planck and BICEP2/Keck array data. Full agreement with the above data is achieved for several versions of the mimetic Gauss–Bonnet gravity. Finally, some extensions of Gauss–Bonnet mimetic gravity are considered, and the possibility of reproducing inflation is also explored. Full article
(This article belongs to the Special Issue Symmetry: Anniversary Feature Papers 2018)
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Open AccessArticle Efficient Superpixel-Guided Interactive Image Segmentation Based on Graph Theory
Symmetry 2018, 10(5), 169; https://doi.org/10.3390/sym10050169
Received: 25 April 2018 / Revised: 13 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
Cited by 1 | Viewed by 673 | PDF Full-text (10654 KB) | HTML Full-text | XML Full-text
Abstract
Image segmentation is a challenging task in the field of image processing and computer vision. In order to obtain an accurate segmentation performance, user interaction is always used in practical image-segmentation applications. However, a good segmentation method should not rely on much prior
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Image segmentation is a challenging task in the field of image processing and computer vision. In order to obtain an accurate segmentation performance, user interaction is always used in practical image-segmentation applications. However, a good segmentation method should not rely on much prior information. In this paper, an efficient superpixel-guided interactive image-segmentation algorithm based on graph theory is proposed. In this algorithm, we first perform the initial segmentation by using the MeanShift algorithm, then a graph is built by taking the pre-segmented regions (superpixels) as nodes, and the maximum flow–minimum cut algorithm is applied to get the superpixel-level segmentation solution. In this process, each superpixel is represented by a color histogram, and the Bhattacharyya coefficient is chosen to calculate the similarity between any two adjacent superpixels. Considering the over-segmentation problem of the MeanShift algorithm, a narrow band is constructed along the contour of objects using a morphology operator. In order to further segment the pixels around edges accurately, a graph is created again for those pixels in the narrow band and, following the maximum flow–minimum cut algorithm, the final pixel-level segmentation is completed. Extensive experimental results show that the presented algorithm obtains much more accurate segmentation results with less user interaction and less running time than the widely used GraphCut algorithm, Lazy Snapping algorithm, GrabCut algorithm and a region merging algorithm based on maximum similarity (MSRM). Full article
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Open AccessArticle Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments
Symmetry 2018, 10(5), 168; https://doi.org/10.3390/sym10050168
Received: 24 April 2018 / Revised: 11 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
Cited by 1 | Viewed by 485 | PDF Full-text (292 KB) | HTML Full-text | XML Full-text
Abstract
Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task
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Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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Open AccessArticle Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations
Symmetry 2018, 10(5), 167; https://doi.org/10.3390/sym10050167
Received: 4 April 2018 / Revised: 14 May 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
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Abstract
We proposed a new efficient image denoising scheme, which leads to four important contributions. The first is to integrate both reconstruction and learning based approaches into a single model so that we are able to benefit advantages from both approaches simultaneously. The second
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We proposed a new efficient image denoising scheme, which leads to four important contributions. The first is to integrate both reconstruction and learning based approaches into a single model so that we are able to benefit advantages from both approaches simultaneously. The second is to handle both multiplicative and additive noise removal problems. The third is that the proposed approach introduces a sparse term to reduce non-Gaussian outliers from multiplicative noise and uses a Laplacian Schatten norm to capture the global structure information. In addition, the image is represented by preserving the intrinsic local similarity via a sparse coding method, which allows our model to incorporate both global and local information from the image. Finally, we propose a new method that combines Method of Optimal Directions (MOD) with Approximate K-SVD (AK-SVD) for dictionary learning. Extensive experimental results show that the proposed scheme is competitive against some of the state-of-the-art denoising algorithms. Full article
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Open AccessArticle On the Second-Degree Exterior Derivation of Kahler Modules on XY
Symmetry 2018, 10(5), 166; https://doi.org/10.3390/sym10050166
Received: 14 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 16 May 2018
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Abstract
This article presents a new approach to stress the properties of Kahler modules. In this paper, we construct the Kahler modules of second-degree exterior derivations and we constitute an exact sequence of XY -modules. Particularly, we examine Kahler modules on X
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This article presents a new approach to stress the properties of Kahler modules. In this paper, we construct the Kahler modules of second-degree exterior derivations and we constitute an exact sequence of X Y -modules. Particularly, we examine Kahler modules on X Y , and search for the homological size of Λ 2 ( Ω 1 ( X Y ) ) . Full article
(This article belongs to the Special Issue Mathematical Physics and Symmetry)
Open AccessArticle False Data Injection Attack Based on Hyperplane Migration of Support Vector Machine in Transmission Network of the Smart Grid
Symmetry 2018, 10(5), 165; https://doi.org/10.3390/sym10050165
Received: 8 April 2018 / Revised: 13 May 2018 / Accepted: 14 May 2018 / Published: 15 May 2018
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Abstract
The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a
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The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a great impact on the safe and stable operation of the smart grid, many effective detection methods have been proposed, such as an FDI detector based on the support vector machine (SVM). In this paper, we first analyze the problem existing in the detector based on SVM. Then, we propose a new attack method to reduce the detection effect of the FDI detector based on SVM and give a proof. The core of the method is that the FDI detector based on SVM cannot detect the attack vectors which are specially constructed and can replace the attack vectors into the training set when it is updated. Therefore, the training set is changed and then the next training result will be affected. With the increase of the number of the attack vectors which are injected into the positive space, the hyperplane moves to the side of the negative space, and the detection effect of the FDI detector based on SVM is reduced. Finally, we analyze the impact of different data injection modes for training results. Simulation experiments show that this attack method can impact the effectiveness of the FDI detector based on SVM. Full article
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Open AccessArticle Integrated Hybrid Second Order Algorithm for Orthogonal Projection onto a Planar Implicit Curve
Symmetry 2018, 10(5), 164; https://doi.org/10.3390/sym10050164
Received: 17 April 2018 / Revised: 27 April 2018 / Accepted: 8 May 2018 / Published: 15 May 2018
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Abstract
The computation of the minimum distance between a point and a planar implicit curve is a very important problem in geometric modeling and graphics. An integrated hybrid second order algorithm to facilitate the computation is presented. The proofs indicate that the convergence of
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The computation of the minimum distance between a point and a planar implicit curve is a very important problem in geometric modeling and graphics. An integrated hybrid second order algorithm to facilitate the computation is presented. The proofs indicate that the convergence of the algorithm is independent of the initial value and demonstrate that its convergence order is up to two. Some numerical examples further confirm that the algorithm is more robust and efficient than the existing methods. Full article
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Open AccessArticle An Online Algorithm for Dynamic NFV Placement in Cloud-Based Autonomous Response Networks
Symmetry 2018, 10(5), 163; https://doi.org/10.3390/sym10050163
Received: 30 March 2018 / Revised: 3 May 2018 / Accepted: 10 May 2018 / Published: 15 May 2018
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Abstract
Autonomous response networks are becoming a reality thanks to recent advances in cloud computing, Network Function Virtualization (NFV) and Software-Defined Networking (SDN) technologies. These enhanced networks fully enable autonomous real-time management of virtualized infrastructures. In this context, one of the major challenges is
[...] Read more.
Autonomous response networks are becoming a reality thanks to recent advances in cloud computing, Network Function Virtualization (NFV) and Software-Defined Networking (SDN) technologies. These enhanced networks fully enable autonomous real-time management of virtualized infrastructures. In this context, one of the major challenges is how virtualized network resources can be effectively placed. Although this issue has been addressed before in cloud-based environments, it is not yet completely resolved for the online placement of virtual machines. For such a purpose, this paper proposes an online heuristic algorithm called Topology-Aware Placement of Virtual Network Functions (TAP-VNF) as a low-complexity solution for such dynamic infrastructures. As a complement, we provide a general formulation of the network function placement using the service function chaining concept. Furthermore, two metrics called consolidation and aggregation validate the efficiency of the proposal in the experimental simulations. We have compared our approach with optimal solutions, in terms of consolidation and aggregation ratios, showing a more suitable performance for dynamic cloud-based environments. The obtained results show that TAP-VNF also outperforms existing approaches based on traditional bin packing schemes. Full article
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
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Open AccessFeature PaperArticle Sampling Based Histogram PCA and Its Mapreduce Parallel Implementation on Multicore
Symmetry 2018, 10(5), 162; https://doi.org/10.3390/sym10050162
Received: 7 April 2018 / Revised: 28 April 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
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Abstract
In existing principle component analysis (PCA) methods for histogram-valued symbolic data, projection results are approximated based on Moore’s algebra and fail to reflect the data’s true structure, mainly because there is no precise, unified calculation method for the linear combination of histogram data.
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In existing principle component analysis (PCA) methods for histogram-valued symbolic data, projection results are approximated based on Moore’s algebra and fail to reflect the data’s true structure, mainly because there is no precise, unified calculation method for the linear combination of histogram data. In this paper, we propose a new PCA method for histogram data that distinguishes itself from various well-established methods in that it can project observations onto the space spanned by principal components more accurately and rapidly by sampling through a MapReduce framework. The new histogram PCA method is implemented under the same assumption of “orthogonal dimensions for every observation” with the existing literatures. To project observations, the method first samples from the original histogram variables to acquire single-valued data, on which linear combination operations can be performed. Then, the projection of observations can be given by linear combination of loading vectors and single-valued samples, which is close to accurate projection results. Finally, the projection is summarized to histogram data. These procedures involve complex algorithms and large-scale data, which makes the new method time-consuming. To speed it up, we undertake a parallel implementation of the new method in a multicore MapReduce framework. A simulation study and an empirical study confirm that the new method is effective and time-saving. Full article
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Open AccessArticle Searching on Encrypted E-Data Using Random Searchable Encryption (RanSCrypt) Scheme
Symmetry 2018, 10(5), 161; https://doi.org/10.3390/sym10050161
Received: 5 March 2018 / Revised: 16 April 2018 / Accepted: 26 April 2018 / Published: 15 May 2018
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Abstract
Cloud computing is intensifying the necessity for searchable encryption (SE) for data protection in cloud storage. SE encrypts data to preserve its confidentiality while offering a secure search facility on the encrypted data. Typical index-based SEs in data sharing scenarios can effectively search
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Cloud computing is intensifying the necessity for searchable encryption (SE) for data protection in cloud storage. SE encrypts data to preserve its confidentiality while offering a secure search facility on the encrypted data. Typical index-based SEs in data sharing scenarios can effectively search secure keyword indexes. However, due to the smaller size of the keyword space, SEs using a public key are susceptible to a Keyword Guessing Attack (KGA) and other statistical information leakage. In this paper, for secure search in a data sharing scenario, we propose Random Searchable enCryption (RanSCrypt) that adds randomness to a transformed keyword to increase its space and aspires to make it irreversible. At the core of the mechanism, two keywords are garbled with randomness, still enabling another party to determine if the two garbled keywords (RanSCrypt’s terms REST and Trapdoor) are the same or not without knowing the actual keywords. As SE in a public key setting suffers from vulnerability to KGA, RanSCrypt transfers into a symmetric key setting with minimum overhead and without losing the features of a data sharing scenario. RanSCrypt also adulterates the search result to add perplexity and provides full control of access only to the data receiver. The receiver can cull out the erroneous results from the search result locally. Finally, we introduce a new type of attack on SE, namely, the Keyword Luring Attack (KLA), and show that RanSCrypt is safe from KLA attack due to adulteration of the result. Our security analysis proves RanSCrypt is invulnerable against KGA and leaks no information. Full article
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Open AccessArticle Linguistic Neutrosophic Generalized Partitioned Bonferroni Mean Operators and Their Application to Multi-Attribute Group Decision Making
Symmetry 2018, 10(5), 160; https://doi.org/10.3390/sym10050160
Received: 10 April 2018 / Revised: 24 April 2018 / Accepted: 26 April 2018 / Published: 14 May 2018
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Abstract
To solve the problems related to inhomogeneous connections among the attributes, we introduce a novel multiple attribute group decision-making (MAGDM) method based on the introduced linguistic neutrosophic generalized weighted partitioned Bonferroni mean operator (LNGWPBM) for linguistic neutrosophic numbers (LNNs). First of
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To solve the problems related to inhomogeneous connections among the attributes, we introduce a novel multiple attribute group decision-making (MAGDM) method based on the introduced linguistic neutrosophic generalized weighted partitioned Bonferroni mean operator (LNGWPBM) for linguistic neutrosophic numbers (LNNs). First of all, inspired by the merits of the generalized partitioned Bonferroni mean (GPBM) operator and LNNs, we combine the GPBM operator and LNNs to propose the linguistic neutrosophic GPBM (LNGPBM) operator, which supposes that the relationships are heterogeneous among the attributes in MAGDM. Then, we discuss its desirable properties and some special cases. In addition, aimed at the different importance of each attribute, the weighted form of the LNGPBM operator is investigated, which we call the LNGWPBM operator. Then, we discuss some of its desirable properties and special examples accordingly. In the end, we propose a novel MAGDM method on the basis of the introduced LNGWPBM operator, and illustrate its validity and merit by comparing it with the existing methods. Full article
Open AccessFeature PaperReview The Local Theory for Regular Systems in the Context of t-Bonded Sets
Symmetry 2018, 10(5), 159; https://doi.org/10.3390/sym10050159
Received: 14 February 2018 / Revised: 17 April 2018 / Accepted: 7 May 2018 / Published: 14 May 2018
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Abstract
The main goal of the local theory for crystals developed in the last quarter of the 20th Century by a geometry group of Delone (Delaunay) at the Steklov Mathematical Institute is to find and prove the correct statements rigorously explaining why the crystalline
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The main goal of the local theory for crystals developed in the last quarter of the 20th Century by a geometry group of Delone (Delaunay) at the Steklov Mathematical Institute is to find and prove the correct statements rigorously explaining why the crystalline structure follows from the pair-wise identity of local arrangements around each atom. Originally, the local theory for regular and multiregular systems was developed with the assumption that all point sets under consideration are ( r , R ) -systems or, in other words, Delone sets of type ( r , R ) in d-dimensional Euclidean space. In this paper, we will review the recent results of the local theory for a wider class of point sets compared with the Delone sets. We call them t-bonded sets. This theory, in particular, might provide new insight into the case for which the atomic structure of matter is a Delone set of a “microporous” character, i.e., a set that contains relatively large cavities free from points of the set. Full article
(This article belongs to the Special Issue Mathematical Crystallography)
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Open AccessFeature PaperArticle Blast Damage Assessment of Symmetrical Box-Shaped Underground Tunnel According to Peak Particle Velocity (PPV) and Single Degree of Freedom (SDOF) Criteria
Symmetry 2018, 10(5), 158; https://doi.org/10.3390/sym10050158
Received: 26 April 2018 / Revised: 4 May 2018 / Accepted: 4 May 2018 / Published: 14 May 2018
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Abstract
This study aimed to determine the reliability of the damage criteria that was adopted by the peak particle velocity (PPV) method and the single degree of freedom (SDOF) approach to assess the damage level of a box-shaped underground tunnel. An advanced arbitrary Lagrangian
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This study aimed to determine the reliability of the damage criteria that was adopted by the peak particle velocity (PPV) method and the single degree of freedom (SDOF) approach to assess the damage level of a box-shaped underground tunnel. An advanced arbitrary Lagrangian Eulerian (ALE) technique available in LS-DYNA software was used to simulate a symmetrical underground tunnel that was subjected to a surface detonation. The validation results of peak pressure into the soil revealed a good consistency with the TM5-855-1 manual within differences that were much less than previous numerical studies. The pressure contours revealed that the blast waves travelled into the soil in a hemispherical shape and the peak reflected the pressure of the tunnel that occurred immediately before the incident pressure reached its highest value. The assessment results proved that the criteria of the above methods could efficiently predict the damage level of a box-shaped tunnel under different circumstances of explosive charge weight and lining thickness at a depth of 4 m within slight differences that were observed during van and small delivery truck (SDT) explosions. However, the efficiency of both the methods was varied with the increase of burial depth. Whereas, using the PPV method significantly underestimated or overestimated the damage level of the tunnel, especially during SDT and container explosions with a lining thickness of 250 mm at burial depths of 6 and 8 m, respectively, the damage level that was obtained by the SDOF method greatly matched with the observed failure modes of the tunnel. Furthermore, new boundary conditions and equations were proposed for the damage criteria of the PVV method. Full article
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Open AccessArticle Total Variation Based Neural Network Regression for Nonuniformity Correction of Infrared Images
Symmetry 2018, 10(5), 157; https://doi.org/10.3390/sym10050157
Received: 26 March 2018 / Revised: 3 May 2018 / Accepted: 12 May 2018 / Published: 14 May 2018
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Abstract
Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in
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Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in least mean square (LMS) regression of traditional neural network NUC methods, which is realized by employing a total variation penalty in the cost function and redesigning the processing architecture. Moreover, an adaptive gated learning rate is presented to further reduce the ghosting artifacts and guarantee fast convergence. The performance of the proposed algorithm is comprehensively investigated with artificially corrupted test sequences and real infrared image sequences, respectively. Experimental results show that the proposed algorithm can effectively accelerate the convergence speed, suppress ghosting artifacts, and promote correction precision. Full article
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Open AccessArticle Symmetry Breakings in Dual-Core Systems with Double-Spot Localization of Nonlinearity
Symmetry 2018, 10(5), 156; https://doi.org/10.3390/sym10050156
Received: 29 April 2018 / Revised: 10 May 2018 / Accepted: 10 May 2018 / Published: 13 May 2018
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Abstract
We introduce a dual-core system with double symmetry, one between the cores, and one along each core, imposed by the spatial modulation of local nonlinearity in the form of two tightly localized spots, which may be approximated by a pair of ideal delta-functions.
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We introduce a dual-core system with double symmetry, one between the cores, and one along each core, imposed by the spatial modulation of local nonlinearity in the form of two tightly localized spots, which may be approximated by a pair of ideal delta-functions. The analysis aims to investigate effects of spontaneous symmetry breaking in such systems. Stationary one-dimensional modes are constructed in an implicit analytical form. These solutions include symmetric ones, as well as modes with spontaneously broken inter-core and along-the-cores symmetries. Solutions featuring the simultaneous (double) breaking of both symmetries are produced too. In the model with the ideal delta-functions, all species of the asymmetric modes are found to be unstable. However, numerical consideration of a two-dimensional extension of the system, which includes symmetric cores with a nonzero transverse thickness, and the nonlinearity-localization spots of a small finite size, produces stable asymmetric modes of all the types, realizing the separate breaking of each symmetry, and states featuring simultaneous (double) breaking of both symmetries. Full article
(This article belongs to the Special Issue Broken Symmetry)
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Open AccessArticle Force Loading Tracking Control of an Electro-Hydraulic Actuator Based on a Nonlinear Adaptive Fuzzy Backstepping Control Scheme
Symmetry 2018, 10(5), 155; https://doi.org/10.3390/sym10050155
Received: 14 February 2018 / Revised: 27 April 2018 / Accepted: 3 May 2018 / Published: 11 May 2018
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Abstract
In this article, a nonlinear adaptive fuzzy backstepping controller combined with an adaptive backstepping controller and an adaptive fuzzy controller is proposed for real-time tracking control of an electro-hydraulic force loading system. Firstly, a nonlinear dynamic model for the electro-hydraulic force loading system
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In this article, a nonlinear adaptive fuzzy backstepping controller combined with an adaptive backstepping controller and an adaptive fuzzy controller is proposed for real-time tracking control of an electro-hydraulic force loading system. Firstly, a nonlinear dynamic model for the electro-hydraulic force loading system is built with consideration of parameter uncertainties and external disturbances. Then, the adaptive backstepping controller is employed to obtain desired control output for the force loading control system considering parameter uncertainties and external disturbances. Furthermore, an adaptive fuzzy control scheme is designed to adjust uncertain control parameters based on adaptive fuzzy system to cope with the chattering condition that results from the overwhelming external disturbances. The stability of the overall system with the proposed control algorithm can be proved by Lyapunov stability theory. Finally, an electro-hydraulic force loading experimental system with xPC rapid prototyping technology is carried out to verify the effectiveness of the proposed nonlinear adaptive fuzzy backstepping controller. Experimental results verify that the proposed control method exhibit excellent performances on force loading tracking control of the electro-hydraulic force loading experimental system compared with a conventional proportional-integral-derivative (PID) controller with velocity feedforward and adaptive backstepping control schemes. Full article
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Open AccessArticle The Cosine Measure of Single-Valued Neutrosophic Multisets for Multiple Attribute Decision-Making
Symmetry 2018, 10(5), 154; https://doi.org/10.3390/sym10050154
Received: 29 April 2018 / Revised: 7 May 2018 / Accepted: 10 May 2018 / Published: 11 May 2018
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Abstract
Based on the multiplicity evaluation in some real situations, this paper firstly introduces a single-valued neutrosophic multiset (SVNM) as a subclass of neutrosophic multiset (NM) to express the multiplicity information and the operational relations of SVNMs. Then, a cosine measure between SVNMs and
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Based on the multiplicity evaluation in some real situations, this paper firstly introduces a single-valued neutrosophic multiset (SVNM) as a subclass of neutrosophic multiset (NM) to express the multiplicity information and the operational relations of SVNMs. Then, a cosine measure between SVNMs and weighted cosine measure between SVNMs are presented to measure the cosine degree between SVNMs, and their properties are investigated. Based on the weighted cosine measure of SVNMs, a multiple attribute decision-making method under a SVNM environment is proposed, in which the evaluated values of alternatives are taken in the form of SVNMs. The ranking order of all alternatives and the best one can be determined by the weighted cosine measure between every alternative and the ideal alternative. Finally, an actual application on the selecting problem illustrates the effectiveness and application of the proposed method. Full article
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Open AccessArticle New Holographic Dark Energy Model in Brans-Dicke Theory
Symmetry 2018, 10(5), 153; https://doi.org/10.3390/sym10050153
Received: 17 April 2018 / Revised: 4 May 2018 / Accepted: 4 May 2018 / Published: 11 May 2018
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Abstract
We study the cosmic evolution of the Bianchi type I universe by using new holographic dark energy model in the context of the Brans-Dicke theory for both non-interacting and interacting cases between dark energy and dark matter. We evaluate the equation of state
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We study the cosmic evolution of the Bianchi type I universe by using new holographic dark energy model in the context of the Brans-Dicke theory for both non-interacting and interacting cases between dark energy and dark matter. We evaluate the equation of state for dark energy ω D and draw the ω D ω ˙ D plane, where the dot denotes the time derivative. It is found that a stage in which the cosmic expansion is accelerating can be realized in both cases. In addition, we investigate the stability of the model by analyzing the sound speed. As a result, it is demonstrated that for both cases, the behavior of the sound speed becomes unstable. Furthermore, with the Om-diagnostic tool, it is shown that the quintessence region of the universe can exist. Full article
(This article belongs to the Special Issue Cosmological Inflation, Dark Matter and Dark Energy)
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Open AccessArticle Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries
Symmetry 2018, 10(5), 152; https://doi.org/10.3390/sym10050152
Received: 25 February 2018 / Revised: 23 April 2018 / Accepted: 9 May 2018 / Published: 10 May 2018
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Abstract
In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped
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In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped countries. In this study, we built a system model for the drug therapy, relevance parameter analysis, and data decision making in non-small cell lung cancer. Based on the probability analysis and status decision, the optimized therapeutic schedule can be calculated and selected, and then effective drug therapy methods can be determined to improve relevance parameters. Statistical analysis of clinical data proves that the model of the probability analysis and decision making can provide fast and accurate clinical data. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle An Intelligent Improvement of Internet-Wide Scan Engine for Fast Discovery of Vulnerable IoT Devices
Symmetry 2018, 10(5), 151; https://doi.org/10.3390/sym10050151
Received: 31 March 2018 / Revised: 7 May 2018 / Accepted: 7 May 2018 / Published: 10 May 2018
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Abstract
Since 2016, Mirai and Persirai malware have infected hundreds of thousands of Internet of Things (IoT) devices and created a massive IoT botnet, which caused distributed denial of service (DDoS) attacks. IoT malware targets vulnerable IoT devices, which are vulnerable to security risks.
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Since 2016, Mirai and Persirai malware have infected hundreds of thousands of Internet of Things (IoT) devices and created a massive IoT botnet, which caused distributed denial of service (DDoS) attacks. IoT malware targets vulnerable IoT devices, which are vulnerable to security risks. Techniques are needed to prevent IoT devices from being exploited by attackers. However, unlike high-performance PCs, IoT devices are lightweight, low-power, and low-cost, having performance limitations regarding processing and memory, which makes it difficult to install security and anti-malware programs. Recently, several studies have been attempted to quickly search for vulnerable internet-connected devices to solve this real issue. Issues yet to be studied still exist regarding these types of internet-wide scan technologies, such as filtering by security devices and a shortage of collected operating system (OS) information. This paper proposes an intelligent internet-wide scan model that improves IP state scanning with advanced internet protocol (IP) randomization, reactive protocol (port) scanning, and OS fingerprinting scanning, applying k* algorithm in order to find vulnerable IoT devices. Additionally, we describe the experiment’s results compared to the existing internet-wide scan technologies, such as ZMap and Shodan. As a result, the proposed model experimentally shows improved performance. Although we improved the ZMap, the throughput per minute (TPM) performance is similar to ZMap without degrading the IP scan throughput and the performance of generating a single IP address is about 118% better than ZMap. In the protocol scan performance experiments, it is about 129% better than the Censys based ZMap, and the performance of OS fingerprinting is better than ZMap, with about 50% accuracy. Full article
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
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