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  • 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

    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

    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

    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

    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

    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

    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

    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

    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

    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 >

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