Home / Journals / CMC / Vol.63, No.2, 2020
Table of Content
  • Open Access

    ARTICLE

    Analysis and Process of Music Signals to Generate TwoDimensional Tabular Data and a New Music

    Oakyoung Han1, Jaehyoun Kim2, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 553-566, 2020, DOI:10.32604/cmc.2020.09362
    Abstract The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the… More >

  • Open Access

    ARTICLE

    Hip Fracture Risk Assessment Based on Different Failure Criteria Using QCT-Based Finite Element Modeling

    Hossein Bisheh1, 2, Yunhua Luo1, 3, Timon Rabczuk4, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 567-591, 2020, DOI:10.32604/cmc.2020.09393
    Abstract Precise evaluation of hip fracture risk leads to reduce hip fracture occurrence in individuals and assist to check the effect of a treatment. A subject-specific QCT-based finite element model is introduced to evaluate hip fracture risk using the strain energy, von-Mises stress, and von-Mises strain criteria during the single-leg stance and the sideways fall configurations. Choosing a proper failure criterion in hip fracture risk assessment is very important. The aim of this study is to define hip fracture risk index using the strain energy, von Mises stress, and von Mises strain criteria and compare the calculated fracture risk indices using… More >

  • Open Access

    ARTICLE

    Functional Causality between Oil Prices and GDP Based on Big Data

    Ibrahim Mufrah Almanjahie1, 2, Zouaoui Chikr Elmezouar1, 2, 3, *, Ali Laksaci1, 2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 593-604, 2020, DOI:10.32604/cmc.2020.08901
    Abstract This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We… More >

  • Open Access

    ARTICLE

    Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

    Junhua Xi1, *, Kouquan Zheng1, Jianfeng Ma1, Jungang Yang1, Zhiyao Liang2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 605-619, 2020, DOI:10.32604/cmc.2020.06343
    Abstract Intuitionistic fuzzy Petri net is an important class of Petri nets, which can be used to model the knowledge base system based on intuitionistic fuzzy production rules. In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems, a new Petri net modeling method is proposed by introducing BP (Error Back Propagation) algorithm in neural networks. By judging whether the transition is ignited by continuous function, the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training, which makes Petri network have stronger generalization ability and adaptive function, and the reasoning result is… More >

  • Open Access

    ARTICLE

    Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble

    Tao Li1, Xiang Li1, *, Yongjun Ren2, Jinyue Xia3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 621-631, 2020, DOI:10.32604/cmc.2020.06463
    Abstract In view of the low accuracy of traditional ground nephogram recognition model, the authors put forward a k-means algorithm-acquired neural network ensemble method, which takes BP neural network ensemble model as the basis, uses k-means algorithm to choose the individual neural networks with partial diversities for integration, and builds the cloud form classification model. Through simulation experiments on ground nephogram samples, the results show that the algorithm proposed in the article can effectively improve the Classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP AdaBoost ensemble algorithm on classification of ground nephogram. More >

  • Open Access

    ARTICLE

    Key-Private Identity-Based Proxy Re-Encryption

    Chunpeng Ge1, *, Jinyue Xia2, Liming Fang1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 633-647, 2020, DOI:10.32604/cmc.2020.06217
    Abstract An identity-based proxy re-encryption scheme (IB-PRE) allows a semi-trusted proxy to convert an encryption under one identity to another without revealing the underlying message. Due to the fact that the proxy was semi-trusted, it should place as little trust as necessary to allow it to perform the translations. In some applications such as distributed file system, it demands the adversary cannot identify the sender and recipient’s identities. However, none of the exiting IB-PRE schemes satisfy this requirement. In this work, we first define the security model of key-private IB-PRE. Finally, we propose the first key-private IB-PRE scheme. Our scheme is… More >

  • Open Access

    ARTICLE

    Skipping Undesired High-Frequency Content to Boost DPI Engine

    Likun Liu1, Jiantao Shi1, *, Xiangzhan Yu1, Hongli Zhang1, Dongyang Zhan2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 649-661, 2020, DOI:10.32604/cmc.2020.07736
    Abstract Deep Packet Inspection (DPI) at the core of many monitoring appliances, such as NIDS, NIPS, plays a major role. DPI is beneficial to content providers and censorship to monitor network traffic. However, the surge of network traffic has put tremendous pressure on the performance of DPI. In fact, the sensitive content being monitored is only a minority of network traffic, that is to say, most is undesired. A close look at the network traffic, we found that it contains many undesired high frequency content (UHC) that are not monitored. As everyone knows, the key to improve DPI performance is to… More >

  • Open Access

    ARTICLE

    Multi-Factor Password-Authenticated Key Exchange via Pythia PRF Service

    Zengpeng Li1, Jiuru Wang2, *, Chang Choi3, Wenyin Zhang2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 663-674, 2020, DOI:10.32604/cmc.2020.06565
    Abstract Multi-factor authentication (MFA) was proposed by Pointcheval et al. [Pointcheval and Zimmer (2008)] to improve the security of single-factor (and two-factor) authentication. As the backbone of multi-factor authentication, biometric data are widely observed. Especially, how to keep the privacy of biometric at the password database without impairing efficiency is still an open question. Using the vulnerability of encryption (or hash) algorithms, the attacker can still launch offline brute-force attacks on encrypted (or hashed) biometric data. To address the potential risk of biometric disclosure at the password database, in this paper, we propose a novel efficient and secure MFA key exchange… More >

  • Open Access

    ARTICLE

    Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

    Junshan Tan1, Rong Duan1, Jiaohua Qin1, *, Xuyu Xiang1, Yun Tan1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 675-689, 2020, DOI:10.32604/cmc.2020.07730
    Abstract Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system, making it more and more widely used in image retrieval. Multi-view data describes image information more comprehensively than traditional methods using a single-view. How to use hashing to combine multi-view data for image retrieval is still a challenge. In this paper, a multi-view fusion hashing method based on RKCCA (Random Kernel Canonical Correlation Analysis) is proposed. In order to describe image content more accurately, we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT (Bag-of-Words… More >

  • Open Access

    ARTICLE

    Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

    Xiangmao Chang1, 2, *, Yuan Qiu1, Shangting Su1, Deliang Yang3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 691-703, 2020, DOI:10.32604/cmc.2020.07923
    Abstract Wireless sensor networks are increasingly used in sensitive event monitoring. However, various abnormal data generated by sensors greatly decrease the accuracy of the event detection. Although many methods have been proposed to deal with the abnormal data, they generally detect and/or repair all abnormal data without further differentiate. Actually, besides the abnormal data caused by events, it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources. Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.… More >

  • Open Access

    ARTICLE

    A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

    Qiang Wei1, 2, *, Guangmin Hu1, Chao Shen3, Yunfei Yin4, 5
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 705-724, 2020, DOI:10.32604/cmc.2020.07467
    Abstract Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring. However, the existing methods are time-consuming for even moderate-scale networks. In this paper, we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries. The effectiveness of the proposed method is validated through synthetic and real-world networks. The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path… More >

  • Open Access

    ARTICLE

    An Efficient Certificateless Aggregate Signature Scheme Designed for VANET

    Cui Li1, *, Gang Wu1, Lipeng Xing1, Feng Zhu1, Liang Zhao2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 725-742, 2020, DOI:10.32604/cmc.2020.07188
    Abstract The Vehicular Ad-hoc Network (VANET) is the fundamental of smart transportation system in the future, but the security of the communication between vehicles and vehicles, between vehicles and roadside infrastructures have become increasingly prominent. Certificateless aggregate signature protocol is used to address this security issue, but the existing schemes still have many drawbacks in terms of security and efficiency: First, many schemes are not secure, and signatures can be forged by the attacker; Second, even if some scheme are secure, many schemes use a large number of bilinear pairing operation, and the computation overhead is large. At the same time,… More >

  • Open Access

    ARTICLE

    Expanding Hot Code Path for Data Cleaning on Software Graph

    Guang Sun1, 2, *, Xiaoping Fan1, Wangdong Jiang1, Hangjun Zhou1, Fenghua Li1, Rong Yang1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 743-753, 2020, DOI:10.32604/cmc.2020.05564
    Abstract Graph analysis can be done at scale by using Spark GraphX which loading data into memory and running graph analysis in parallel. In this way, we should take data out of graph databases and put it into memory. Considering the limitation of memory size, the premise of accelerating graph analytical process reduces the graph data to a suitable size without too much loss of similarity to the original graph. This paper presents our method of data cleaning on the software graph. We use SEQUITUR data compression algorithm to find out hot code path and store it as a whole paths… More >

  • Open Access

    ARTICLE

    Improvement of Stochastic Competitive Learning for Social Network

    Wenzheng Li1, Yijun Gu1, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 755-768, 2020, DOI:10.32604/cmc.2020.07984
    Abstract As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the… More >

  • Open Access

    ARTICLE

    OTT Messages Modeling and Classification Based on Recurrent Neural Networks

    Guangyong Yang1, Jianqiu Zeng1, Mengke Yang2, *, Yifei Wei3, Xiangqing Wang3, Zulfiqar Hussain Pathan4
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 769-785, 2020, DOI:10.32604/cmc.2020.07528
    Abstract A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model.… More >

  • Open Access

    ARTICLE

    Energy Efficiency in Internet of Things: An Overview

    Wuxiong Zhang1, 2, Weidong Fang1, 2, *, Qianqian Zhao1, 2, Xiaohong Ji3, Guoqing Jia3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 787-811, 2020, DOI:10.32604/cmc.2020.07620
    Abstract Energy efficiency is very important for the Internet of Things (IoT), especially for front-end sensed terminal or node. It not only embodies the node’s life, but also reflects the lifetime of the network. Meanwhile, it is also a key indicator of green communications. Unfortunately, there is no article on systematic analysis and review for energy efficiency evaluation in IoT. In this paper, we systemically analyze the architecture of IoT, and point out its energy distribution, Furthermore, we summarized the energy consumption model in IoT, analyzed the pros and cons of improving energy efficiency, presented a state of the art the… More >

  • Open Access

    ARTICLE

    Molecular Dynamics Simulations for Anisotropic Thermal Conductivity of Borophene

    Yue Jia1, Chun Li1, *, Jinwu Jiang2, Ning Wei3, Yang Chen4, Yongjie Jessica Zhang5
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 813-823, 2020, DOI:10.32604/cmc.2020.07801
    Abstract The present work carries out molecular dynamics simulations to compute the thermal conductivity of the borophene nanoribbon and the borophene nanotube using the Muller-Plathe approach. We investigate the thermal conductivity of the armchair and zigzag borophenes, and show the strong anisotropic thermal conductivity property of borophene. We compare results of the borophene nanoribbon and the borophene nanotube, and find the thermal conductivity of the borophene is orientation dependent. The thermal conductivity of the borophene does not vary as changing the width of the borophene nanoribbon and the perimeter of the borophene nanotube. In addition, the thermal conductivity of the borophene… More >

  • Open Access

    ARTICLE

    Laboratory Model Tests and DEM Simulations of Unloading- Induced Tunnel Failure Mechanism

    Abierdi1, Yuzhou Xiang2, Haiyi Zhong2, Xin Gu2, Hanlong Liu2, 3, Wengang Zhang2, 3, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 825-844, 2020, DOI:10.32604/cmc.2020.07946
    Abstract Tunnel excavation is a complicated loading-unloading-reloading process characterized by decreased radial stresses and increased axial stresses. An approach that considers only loading, is generally used in tunnel model testing. However, this approach is incapable of characterizing the unloading effects induced by excavation on surrounding rocks and hence presents radial and tangential stress paths during the failure process that are different from the actual stress state of tunnels. This paper carried out a comparative analysis using laboratory model testing and particle flow code (PFC2D)-based numerical simulation, and shed light upon the crack propagation process and, microscopic stress and force chain variations… More >

  • Open Access

    ARTICLE

    Post-Quantum Blockchain over Lattice

    Xiao Zhang1, 2, 3, Faguo Wu1, 2, 3, Wang Yao1, 2, 3, *, Wenhua Wang4, Zhiming Zheng1, 2, 3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 845-859, 2020, DOI:10.32604/cmc.2020.08008
    Abstract Blockchain is an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, and has recently attracted intensive attention from governments, financial institutions, high-tech enterprises, and the capital markets. Its cryptographic security relies on asymmetric cryptography, such as ECC, RSA. However, with the surprising development of quantum technology, asymmetric cryptography schemes mentioned above would become vulnerable. Recently, lattice-based cryptography scheme was proposed to be secure against attacks in the quantum era. In 2018, with the aid of Bonsai Trees technology, Yin et al. [Yin, Wen, Li et al. (2018)] proposed a lattice-based authentication method which can extend a… More >

  • Open Access

    ARTICLE

    A Temporal Multi-Tenant RBAC Model for Collaborative Cloud Services

    Zhengtao Liu1, *, Yi Ying1, Yaqin Peng1, Jinyue Xia2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 861-871, 2020, DOI:10.32604/cmc.2020.07142
    Abstract Multi-tenant collaboration brings the challenge to access control in cloud computing environment. Based on the multi-tenant role-based access control (MTRBAC) model, a Temporal MT-RBAC (TMT-RBAC) model for collaborative cloud services is proposed. It adds the time constraint between trusted tenants, including usable role time constraint based on both calendar and interval time. Analysis shows that the new model strengthens the presentation ability of MT-RBAC model, achieves the finergrained access control, reduces the management costs and enhances the security of multitenant collaboration in cloud computing environment. More >

  • Open Access

    ARTICLE

    A Novel Quantum-Behaved Particle Swarm Optimization Algorithm

    Tao Wu1, Lei Xie1, Xi Chen2, Amir Homayoon Ashrafzadeh3, Shu Zhang4, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 873-890, 2020, DOI:10.32604/cmc.2020.07478
    Abstract The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling. This paper analyzed the motion pattern of particles in a square potential well, given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well (BCQSPSO). In this novel algorithm, the intrinsic cognitive link between particles’ experience information and group sharing information was created by using normal Copula function. After… More >

  • Open Access

    ARTICLE

    Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

    Kun Zhu1, Nana Zhang1, Shi Ying1, *, Xu Wang2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 891-910, 2020, DOI:10.32604/cmc.2020.08096
    Abstract With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding Based on Run-Level Coding in H.264/AVC Video Streams

    Yi Chen1, Hongxia Wang2, *, Xuyun Zhang3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 911-922, 2020, DOI:10.32604/cmc.2020.08027
    Abstract This paper presents a reversible data hiding (RDH) method, which is designed by combining histogram modification (HM) with run-level coding in H.264/advanced video coding (AVC). In this scheme, the run-level is changed for embedding data into H.264/AVC video sequences. In order to guarantee the reversibility of the proposed scheme, the last nonzero quantized discrete cosine transform (DCT) coefficients in embeddable 4×4 blocks are shifted by the technology of histogram modification. The proposed scheme is realized after quantization and before entropy coding of H.264/AVC compression standard. Therefore, the embedded information can be correctly extracted at the decoding side. Peak-signal-noise-to-ratio (PSNR) and… More >

  • Open Access

    ARTICLE

    Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network

    Xiaolei Ma1, 2, Yang Lu1, 2, Yinan Lu1, *, Zhili Pei2, Jichao Liu3
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 923-941, 2020, DOI:10.32604/cmc.2020.07711
    Abstract Supervised machine learning approaches are effective in text mining, but their success relies heavily on manually annotated corpora. However, there are limited numbers of annotated biomedical event corpora, and the available datasets contain insufficient examples for training classifiers; the common cure is to seek large amounts of training samples from unlabeled data, but such data sets often contain many mislabeled samples, which will degrade the performance of classifiers. Therefore, this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data. First, we construct the mislabeled dataset through error data analysis with the development… More >

  • Open Access

    ARTICLE

    Sentence Similarity Measurement with Convolutional Neural Networks Using Semantic and Syntactic Features

    Shiru Zhang1, Zhiyao Liang1, *, Jian Lin2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 943-957, 2020, DOI:10.32604/cmc.2020.08800
    Abstract Calculating the semantic similarity of two sentences is an extremely challenging problem. We propose a solution based on convolutional neural networks (CNN) using semantic and syntactic features of sentences. The similarity score between two sentences is computed as follows. First, given a sentence, two matrices are constructed accordingly, which are called the syntax model input matrix and the semantic model input matrix; one records some syntax features, and the other records some semantic features. By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices, we adopt the most effective way of constructing… More >

  • Open Access

    ARTICLE

    Visualization Analysis for Business Performance of Chinese Listed Companies Based on Gephi

    Guang Sun1, Hongzhang Lv1, *, Dianyu Wang2, Xiaoping Fan1, 3, Yi Zuo1, Yanfei Xiao4, Xu Liu1, Wenqian Xiang1, Ziyi Guo1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 959-977, 2020, DOI:10.32604/cmc.2020.08619
    Abstract When conducting company performance evaluations, the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market. Gephi is an efficient tool for data analysis and visualization in the era of big data. It can convert the evaluation results of all listed companies into nodes and edges, and directly display them in the form of graphs, thus making up for the defects of traditional methods. This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object. First uses tushare and web crawlers to collect the financial… More >

  • Open Access

    ARTICLE

    Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

    Dongjie Zhu1, Haiwen Du6, Yundong Sun1, Xiaofang Li2, Rongning Qu2, Hao Hu1, Shuangshuang Dong1, Helen Min Zhou3, Ning Cao4, 5, *,
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 979-993, 2020, DOI:10.32604/cmc.2020.06478
    Abstract In distributed storage systems, file access efficiency has an important impact on the real-time nature of information forensics. As a popular approach to improve file accessing efficiency, prefetching model can fetches data before it is needed according to the file access pattern, which can reduce the I/O waiting time and increase the system concurrency. However, prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching. In the massive small file situation, the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining. In this paper, we propose a… More >

  • Open Access

    ARTICLE

    Multi-Task Learning Using Attention-Based Convolutional Encoder-Decoder for Dilated Cardiomyopathy CMR Segmentation and Classification

    Chao Luo1, Canghong Shi1, Xiaojie Li1, *, Xin Wang4, Yucheng Chen3, Dongrui Gao1, Youbing Yin4, Qi Song4, Xi Wu1, Jiliu Zhou1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 995-1012, 2020, DOI:10.32604/cmc.2020.07968
    Abstract Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease. Dilated Cardiomyopathy (DCM) is a kind of common chronic and life-threatening cardiopathy. Early diagnostics significantly increases the chances of correct treatment and survival. However, accurate and rapid diagnosis of DCM is still challenge due to high variability of cardiac structure, low contrast cardiac magnetic resonance (CMR) images, and intrinsic noise in synthetic CMR images caused by motion artifact and cardiac dynamics. Moreover, visual assessment and empirical evaluation are widely used in routine clinical diagnosis, but they are subject to high inter-observer variability and are both subjective… More >

  • Open Access

    ARTICLE

    A Security Sensitive Function Mining Approach Based on Precondition Pattern Analysis

    Zhongxu Yin1, *, Yiran Song2, Huiqin Chen3, Yan Cao4
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1013-1029, 2020, DOI:10.32604/cmc.2020.09345
    Abstract Security-sensitive functions are the basis for building a taint-style vulnerability model. Current approaches for extracting security-sensitive functions either don’t analyze data flow accurately, or not conducting pattern analyzing of conditions, resulting in higher false positive rate or false negative rate, which increased manual confirmation workload. In this paper, we propose a security sensitive function mining approach based on preconditon pattern analyzing. Firstly, we propose an enhanced system dependency graph analysis algorithm for precisely extracting the conditional statements which check the function parameters and conducting statistical analysis of the conditional statements for selecting candidate security sensitive functions of the target program.… More >

  • Open Access

    ARTICLE

    An Efficient Ciphertext-Policy Attribute-Based Encryption Scheme with Policy Update

    Changji Wang1, *, Yuan Yuan2
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1031-1041, 2020, DOI:10.32604/cmc.2020.06278
    Abstract Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic solution to the problem for enforcing fine-grained access control over encrypted data in the cloud. However, when applying CP-ABE to data outsourcing scenarios, we have to address the challenging issue of policy updates because access control elements, such as users, attributes, and access rules may change frequently. In this paper, we propose a notion of access policy updatable ciphertext-policy attribute-based encryption (APU-CP-ABE) by combining the idea of ciphertext-policy attribute-based key encapsulation and symmetric proxy re-encryption. When an access policy update occurs, data owner is no longer required to download any data for… More >

  • Open Access

    ARTICLE

    An Adaptive Power Allocation Scheme for Performance Improvement of Cooperative SWIPT NOMA Wireless Networks

    Manimekalai Thirunavukkarasu1, Romera Joan Sparjan1, *, Laxmikandan Thangavelu1
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1043-1064, 2020, DOI:10.32604/cmc.2020.09819
    Abstract Future wireless networks demand high spectral efficiency, energy efficiency and reliability. Cooperative non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) is considered as one of the novel techniques to meet this demand. In this work, an adaptive power allocation scheme called SWIPT based adaptive power allocation (SWIPT-APA-NOMA) is proposed for a power domain NOMA network. The proposed scheme considers the receiver sensitivity of the end users while calculating the power allocation coefficients in order to prevent wastage of power allocated to user in outage and by offering priority to any one of the users to use… More >

  • Open Access

    ARTICLE

    Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation

    Waqar Ali1, 2, Salah Ud Din1, Abdullah Aman Khan1, Saifullah Tumrani1, Xiaochen Wang1, Jie Shao1, 3, *
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017
    Abstract Recommender systems are rapidly transforming the digital world into intelligent information hubs. The valuable context information associated with the users’ prior transactions has played a vital role in determining the user preferences for items or rating prediction. It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades. This paper presents a novel Context Based Rating Prediction (CBRP) model with a unique similarity scoring estimation method. The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential… More >

  • Open Access

    RETRACTION

    Retraction Notice to: Automatic Arrhythmia Detection Based on Convolutional Neural Networks

    Zhong Liu, Xin’an Wang, Kuntao Lu, David Su
    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1079-1079, 2020, DOI:10.32604/cmc.2020.04882
    Abstract This article has no abstract. More >

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