Chao Luo¹, Canghong Shi¹, Xiaojie Li^{1, *}, 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 >

Dongjie Zhu¹, Haiwen Du⁶, Yundong Sun¹, Xiaofang Li², Rongning Qu², Hao Hu¹, Shuangshuang Dong¹, Helen Min Zhou³, Ning Cao^{4, 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 >

Guang Sun¹, Hongzhang Lv^{1, *}, Dianyu Wang², Xiaoping Fan^{1, 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 >

Shiru Zhang¹, Zhiyao Liang^{1, *}, 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 >

Xiaolei Ma^{1, 2}, Yang Lu^{1, 2}, Yinan Lu^{1, *}, 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 >

Yi Chen¹, Hongxia Wang^{2, *}, 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 >

Kun Zhu¹, Nana Zhang¹, Shi Ying^{1, *}, 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 >

Tao Wu¹, Lei Xie¹, Xi Chen², Amir Homayoon Ashrafzadeh³, Shu Zhang^{4, *}

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 >

Zhengtao Liu^{1, *}, 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 >

Xiao Zhang^{1, 2, 3}, Faguo Wu^{1, 2, 3}, Wang Yao^{1, 2, 3, *}, Wenhua Wang⁴, Zhiming Zheng^{1, 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 >