Journals / CMC / Vol.61, No.3
Table of Content


  • ARTICLE

    Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

    Hyun Kyu Shin1, Yong Han Ahn2, Sang Hyo Lee3, Ha Young Kim4,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269
    Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.… More >

  • ARTICLE

    XML-Based Information Fusion Architecture Based on Cloud Computing Ecosystem

    I-Ching Hsu1,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 929-950, 2019, DOI:10.32604/cmc.2019.07876
    Abstract Considering cloud computing from an organizational and end user computing point of view, it is a new paradigm for deploying, managing and offering services through a shared infrastructure. Current development of cloud computing applications, however, are the lack of a uniformly approach to cope with the heterogeneous information fusion. This leads cloud computing to inefficient development and a low potential reuse. This study addresses these issues to propose a novel Web 2.0 Mashups as a Service, called WMaaS, which is a fundamental cloud service model. The WMaaS is developed based on a XML-based Mashups Architecture (XMA) that is composed of… More >

  • ARTICLE

    Forecasting Damage Mechanics By Deep Learning

    Duyen Le Hien Nguyen1, Dieu Thi Thanh Do2, Jaehong Lee2, Timon Rabczuk3, Hung Nguyen-Xuan1,4,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 951-977, 2019, DOI:10.32604/cmc.2019.08001
    Abstract We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth… More >

  • ARTICLE

    Reduced Differential Transform Method for Solving Nonlinear Biomathematics Models

    K. A. Gepreel1,2, A. M. S. Mahdy1,2,*, M. S. Mohamed1,3, A. Al-Amiri4
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 979-994, 2019, DOI:10.32604/cmc.2019.07701
    Abstract In this paper, we study the approximate solutions for some of nonlinear Biomathematics models via the e-epidemic SI1I2R model characterizing the spread of viruses in a computer network and SIR childhood disease model. The reduced differential transforms method (RDTM) is one of the interesting methods for finding the approximate solutions for nonlinear problems. We apply the RDTM to discuss the analytic approximate solutions to the SI1I2R model for the spread of virus HCV-subtype and SIR childhood disease model. We discuss the numerical results at some special values of parameters in the approximate solutions. We use the computer software package such… More >

  • ARTICLE

    SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081
    Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is applied to handwritten Arabic characters… More >

  • ARTICLE

    Automated Negotiation in E Commerce: Protocol Relevance and Improvement Techniques

    S. R. Vij1,*, D. Mukhopadhyay2, A. J. Agrawal3
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1009-1024, 2019, DOI:10.32604/cmc.2019.08417
    Abstract We all negotiate, formally or informally, in jobs, in day today lives and outcomes of negotiations affect those processes of life. Although negotiation is an intrinsic nature of human psyche, it is very complex phenomenon to implement using computing and internet for the various purposes in E Commerce. Automation of negotiation process poses unique challenges for computer scientists and researchers, so here we study how negotiation can be modeled and analyzed mathematically, what can be different techniques and strategies or set of rules/protocols to be implemented and how they can be relevantly implemented. We are in a quest to find… More >

  • ARTICLE

    A Stochastic Numerical Analysis for Computer Virus Model with Vertical Transmission Over the Internet

    Muhammad Shoaib Arif1, Ali Raza1, Wasfi Shatanawi2, 3, *, Muhammad Rafiq4, Mairaj Bibi5
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1025-1043, 2019, DOI:10.32604/cmc.2019.08405
    Abstract We are presenting the numerical analysis for stochastic SLBR model of computer virus over the internet in this manuscript. We are going to present the results of stochastic and deterministic computer virus model. Outcomes of the threshold number C* hold in stochastic computer virus model. If C*<1 then in such a condition virus controlled in the computer population while C*>1 shows virus spread in the computer population. Unfortunately, stochastic numerical techniques fail to cope with large step sizes of time. The suggested structure of the stochastic non-standard finite difference scheme (SNSFD) maintains all diverse characteristics such as dynamical consistency, bounded-ness… More >

  • ARTICLE

    Genetic-Frog-Leaping Algorithm for Text Document Clustering

    Lubna Alhenak1, Manar Hosny1,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1045-1074, 2019, DOI:10.32604/cmc.2019.08355
    Abstract In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of… More >

  • ARTICLE

    Security Analysis of Smart Speaker: Security Attacks and Mitigation

    Youngseok Park1, Hyunsang Choi1, Sanghyun Cho1, Young-Gab Kim2,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1075-1090, 2019, DOI:10.32604/cmc.2019.08520
    Abstract The speech recognition technology has been increasingly common in our lives. Recently, a number of commercial smart speakers containing the personal assistant system using speech recognition came out. While the smart speaker vendors have been concerned about the intelligence and the convenience of their assistants, but there have been little mentions of the smart speakers in security aspects. As the smart speakers are becoming the hub for home automation, its security vulnerabilities can cause critical problems. In this paper, we categorize attack vectors and classify them into hardware-based, network-based, and software-based. With the attack vectors, we describe the detail attack… More >

  • ARTICLE

    Research on Flight First Service Model and Algorithms for the Gate Assignment Problem

    Jiarui Zhang1, Gang Wang2,*, Siyuan Tong1
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1091-1104, 2019, DOI:10.32604/cmc.2019.05907
    Abstract Aiming at the problem of gate allocation of transit flights, a flight first service model is established. Under the constraints of maximizing the utilization rate of gates and minimizing the transit time, the idea of “first flight serving first” is used to allocate the first time, and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution. That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly, and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal… More >

  • ARTICLE

    MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

    Dongjie Zhu1, Yuhua Wang1, Chuiju You2,*, Jinming Qiu2,3, Ning Cao2, Chenjing Gong4, Guohua Yang5, Helen Min Zhou6
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1105-1115, 2019, DOI:10.32604/cmc.2019.06041
    Abstract With the rapid development of the mobile Internet, users generate massive data in different forms in social network every day, and different characteristics of users are reflected by these social media data. How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services, marketing, and recommendation systems. In this paper, we propose Multi-source & Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user. Firstly, we design their own feature extraction models… More >

  • ARTICLE

    Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

    Xuting Duan1,2,3, Jingyi Wei1,2,3, Daxin Tian1,2,3,*, Jianshan Zhou1,2,3,4, Haiying Xia5, Xin Li6, Kunxian Zheng1,2,3
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1117-1128, 2019, DOI:10.32604/cmc.2019.05578
    Abstract In vehicle ad-hoc networks (VANETs), the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant. Motivated by the self-adaptive paradigm of cellular attractors, this paper regards an individual communication as a cell, so that we can apply the revised attractor selection model to induce each connected vehicle. Aiming at improving the Quality of Service (QoS), we presented the bio-inspired handover decision-making mechanism. In addition, we employ the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for any vehicle to choose an access network. This paper proposes a novel framework… More >

  • ARTICLE

    A Method for Vulnerability Database Quantitative Evaluation

    Tiantian Tan1,*, Baosheng Wang1, Yong Tang1, Xu Zhou1, Jingwen Han2
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1129-1144, 2019, DOI:10.32604/cmc.2019.06051
    Abstract During system development, implementation and operation, vulnerability database technique is necessary to system security; there are many vulnerability databases but a lack of quality standardization and general evaluation method are needed. this paper summarized current international popular vulnerability databases, systematically introduced the present situation of current vulnerability databases, and found the problems of vulnerability database technology, extracted common metrics by analyzing vulnerability data of current popular vulnerability databases, introduced 4 measure indexes: the number scale of vulnerabilities, the independence level, the standardization degree and the integrity of vulnerability description, proposed a method for vulnerability database quantitative evaluation using SCAP protocol… More >

  • ARTICLE

    Quantum Communication Networks and Trust Management: A Survey

    Shibin Zhang1,*, Yan Chang1, Lili Yan1, Zhiwei Sheng1, Fan Yang1, Guihua Han1, Yuanyuan Huang1, Jinyue Xia2
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1145-1174, 2019, DOI:10.32604/cmc.2019.05668
    Abstract This paper summarizes the state of art in quantum communication networks and trust management in recent years. As in the classical networks, trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues, therefore the basic and importance of trust management in quantum communication networks should be taken more seriously. Compared with other theories and techniques in quantum communication, the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage. In this paper, the core technologies of establishing secure and reliable quantum communication… More >

  • ARTICLE

    Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

    Longqing Zhang1, Liping Bai1,*, Xinwei Zhang2, Yanghong Zhang2, Feng Sun2, Changcheng Chen2
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1175-1187, 2019, DOI:10.32604/cmc.2019.06075
    Abstract Based on the two-dimensional relation table, this paper studies the missing values in the sample data of land price of Shunde District of Foshan City. GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis; NORM software was adopted to construct the multiple imputation models; EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets. Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set, and the weight is determined according to the differences. Finally, comprehensive… More >

  • ARTICLE

    Research on Action Recognition and Content Analysis in Videos Based on DNN and MLN

    Wei Song1,2,*, Jing Yu3, Xiaobing Zhao1,2, Antai Wang4
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1189-1204, 2019, DOI:10.32604/cmc.2019.06361
    Abstract In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the development trend and direction of… More >

  • ARTICLE

    Non-Local DWI Image Super-Resolution with Joint Information Based on GPU Implementation

    Yanfen Guo1,2, Zhe Cui1,*, Zhipeng Yang3, Xi Wu2, Shaahin Madani4
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1205-1215, 2019, DOI:10.32604/cmc.2019.06029
    Abstract Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran slowly because of the joint… More >

  • ARTICLE

    Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

    Donghui Li1, Cong Shen2,*, Xiaopeng Dai1, Xinghui Zhu1, Jian Luo1, Xueting Li1, Haiwen Chen3, Zhiyao Liang4
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1217-1231, 2019, DOI:10.32604/cmc.2019.06354
    Abstract Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor. The data of water quality in the environment comes from different sensors, thus the data must be fused. In our research, self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value, temperature, oxygen dissolved and NH3 concentration of water quality environment. Based on the fusion, the Grubbs method is used to detect the abnormal data so as to provide data support for estimation, prediction and early warning of the water quality. More >

  • ARTICLE

    The Volatility of High-Yield Bonds Using Mixed Data Sampling Methods

    Maojun Zhang1,2, Jiajin Yao1, Zhonghang Xia3, Jiangxia Nan1,*, Cuiqing Zhang1
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1233-1244, 2019, DOI:10.32604/cmc.2019.06118
    Abstract It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market. However, the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear. In this paper, we employ GARCH-MIDAS models to investigate their correlation with US economic policy uncertainty index and S&P high-yield bond index. The empirical studies show that mixed volatility models can effectively capture the realized volatility of high-yield bonds, and economic policy uncertainty and macroeconomic factors have significant effects on the long-term component of high-yield bonds volatility. More >

  • ARTICLE

    Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

    Ce Li1,2,*, Xinyu Shang2, Liguo Zhang3,4, Feng Yang1,2, Jing Zheng1,5, Xianlei Xu1
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1245-1260, 2019, DOI:10.32604/cmc.2019.06159
    Abstract Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection… More >

  • ARTICLE

    Hashtag Recommendation Using LSTM Networks with Self-Attention

    Yatian Shen1, Yan Li1, Jun Sun1,*, Wenke Ding1, Xianjin Shi1, Lei Zhang1, Xiajiong Shen1, Jing He2
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1261-1269, 2019, DOI:10.32604/cmc.2019.06104
    Abstract On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and… More >

  • ARTICLE

    SNES: Social-Network-Oriented Public Opinion Monitoring Platform Based on ElasticSearch

    Chuiju You1, Dongjie Zhu2,*, Yundong Sun2, Anshan Ye3, Gangshan Wu4, Ning Cao1, Jinming Qiu1, Helen Min Zhou5
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1271-1283, 2019, DOI:10.32604/cmc.2019.06133
    Abstract With the rapid development of social network, public opinion monitoring based on social networks is becoming more and more important. Many platforms have achieved some success in public opinion monitoring. However, these platforms cannot perform well in scalability, fault tolerance, and real-time performance. In this paper, we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch (SNES). Firstly, SNES integrates the module of distributed crawler cluster, which provides real-time social media data access. Secondly, SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time. Finally, we design subscription module based on Apache Kafka… More >

  • ARTICLE

    Detecting Domain Generation Algorithms with Bi-LSTM

    Liang Ding1,*, Lunjie Li1, Jianghong Han1, Yuqi Fan2,*, Donghui Hu1
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1285-1304, 2019, DOI:10.32604/cmc.2019.06160
    Abstract Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for real-time detection. In addition, when a new family of… More >

  • ARTICLE

    A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

    Yongyun Zhu1, Tao Zhang1,*, Mohan Li2, Di Wang1, Shaoen Wu3
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1305-1321, 2019, DOI:10.32604/cmc.2019.06406
    Abstract The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments, which will slow down the convergence rate. To solve the above problem, a real-time noise reduction method, sliding fixed-interval least squares (SFI-LS), is devised to depress the noise in the observation vectors. In this paper, the least square method, improved by a sliding fixed-interval approach, is applied for the real-time noise reduction. In order to achieve a better-performed coarse alignment, the proposed method is utilized to de-noise the random noise in observation vectors. First, the principles of proposed SFI-LS algorithm and coarse alignment… More >

  • ARTICLE

    Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

    Bing Hu1, Feng Xiang2, Fan Wu3, Jian Liu4, Zhe Sun1, Zhixin Sun1,*
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1323-1344, 2019, DOI:10.32604/cmc.2019.06414
    Abstract To cope with the arbitrariness of the network delays, a novel method, referred to as the composite particle filter approach based on variational Bayesian (VB-CPF), is proposed herein to estimate the clock skew and clock offset in wireless sensor networks. VB-CPF is an improvement of the Gaussian mixture kalman particle filter (GMKPF) algorithm. In GMKPF, Expectation-Maximization (EM) algorithm needs to determine the number of mixture components in advance, and it is easy to generate overfitting and underfitting. Variational Bayesian EM (VB-EM) algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations. Moreover,… More >

  • ARTICLE

    QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

    Wenchen He1,*, Shaoyong Guo1, Yun Liang2, Rui Ma3, Xuesong Qiu1, Lei Shi4
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1345-1364, 2019, DOI:10.32604/cmc.2019.06669
    Abstract With the popularization of terminal devices and services in Internet of things (IoT), it will be a challenge to design a network resource allocation method meeting various QoS requirements and effectively using substrate resources. In this paper, a dynamic network slicing mechanism including virtual network (VN) mapping and VN reconfiguration is proposed to provide network slices for services. Firstly, a service priority model is defined to create queue for resource allocation. Then a slice including Virtual Network Function (VNF) placement and routing with optimal cost is generated by VN mapping. Next, considering temporal variations of service resource requirements, the size… More >

  • ARTICLE

    Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

    Yu Sun1,2,*, Qiyuan Gao3, Xiaofan Du3, Zhao Gu3
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1365-1375, 2019, DOI:10.32604/cmc.2019.06294
    Abstract In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector… More >

  • ARTICLE

    Optimization of Face Recognition System Based on Azure IoT Edge

    Shen Li1, Fang Liu1,*, Jiayue Liang1, Zhenhua Cai1, Zhiyao Liang2
    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1377-1389, 2019, DOI:10.32604/cmc.2019.06402
    Abstract With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture “client-cloud” face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture.… More >

Share Link

WeChat scan