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  • Open Access

    ARTICLE

    An Empirical Comparison on Multi-Target Regression Learning

    Xuefeng Xi1, Victor S. Sheng1,2,*, Binqi Sun2, Lei Wang1, Fuyuan Hu1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI: 10.3970/cmc.2018.03694

    Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target learning. Among them, MTS performs… More >

  • Open Access

    ARTICLE

    A Highly Effective DPA Attack Method Based on Genetic Algorithm

    Shuaiwei Zhang1, Xiaoyuan Yang1,*, Weidong Zhong1, Yujuan Sun2

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 325-338, 2018, DOI:10.3970/cmc.2018.03611

    Abstract As one of the typical method for side channel attack, DPA has become a serious trouble for the security of encryption algorithm implementation. The potential capability of DPA attack induces researchers making a lot of efforts in this area, which significantly improved the attack efficiency of DPA. However, most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise. If large deviation happens in part of the power consumption data sample, the efficiency of DPA attack will be reduced rapidly. In this work, a highly efficient… More >

  • Open Access

    ARTICLE

    Research on Trust Model in Container-Based Cloud Service

    Xiaolan Xie1,2, Tianwei Yuan1,*, Xiao Zhou3, Xiaochun Cheng4

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 273-283, 2018, DOI: 10.3970/cmc.2018.03587

    Abstract Container virtual technology aims to provide program independence and resource sharing. The container enables flexible cloud service. Compared with traditional virtualization, traditional virtual machines have difficulty in resource and expense requirements. The container technology has the advantages of smaller size, faster migration, lower resource overhead, and higher utilization. Within container-based cloud environment, services can adopt multi-target nodes. This paper reports research results to improve the traditional trust model with consideration of cooperation effects. Cooperation trust means that in a container-based cloud environment, services can be divided into multiple containers for different container nodes. When multiple target nodes work for one… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

    Yuyu Chen1, Bangxu Yin2, Hongjie He2, Shu Yan2, Fan Chen2,*, Hengming Tai3

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 299-312, 2018, DOI: 10.3970/cmc.2018.03179

    Abstract To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in… More >

  • Open Access

    ARTICLE

    A Proxy Re-Encryption with Keyword Search Scheme in Cloud Computing

    Yongli Tang1, Huanhuan Lian1, Zemao Zhao2, Xixi Yan1,*

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 339-352, 2018, DOI: 10.3970/cmc.2018.02965

    Abstract With the widespread use of cloud computing technology, more and more users and enterprises decide to store their data in a cloud server by outsourcing. However, these huge amounts of data may contain personal privacy, business secrets and other sensitive information of the users and enterprises. Thus, at present, how to protect, retrieve, and legally use the sensitive information while preventing illegal accesses are security challenges of data storage in the cloud environment. A new proxy re-encryption with keyword search scheme is proposed in this paper in order to solve the problem of the low retrieval efficiency of the encrypted… More >

  • Open Access

    ARTICLE

    Speech Resampling Detection Based on Inconsistency of Band Energy

    Zhifeng Wang1, Diqun Yan1,*, Rangding Wang1, Li Xiang1, Tingting Wu1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 247-259, 2018, DOI: 10.3970/cmc.2018.02902

    Abstract Speech resampling is a typical tempering behavior, which is often integrated into various speech forgeries, such as splicing, electronic disguising, quality faking and so on. By analyzing the principle of resampling, we found that, compared with natural speech, the inconsistency between the bandwidth of the resampled speech and its sampling ratio will be caused because the interpolation process in resampling is imperfect. Based on our observation, a new resampling detection algorithm based on the inconsistency of band energy is proposed. First, according to the sampling ratio of the suspected speech, a band-pass Butterworth filter is designed to filter out the… More >

  • Open Access

    ARTICLE

    A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM

    Yu Yang1,2,*, Yuwei Chen1,2, Yuling Chen2, Wei Bi3,4

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 261-272, 2018, DOI: 10.3970/cmc.2018.02736

    Abstract The state-of-the-art universal steganalysis method, spatial rich model (SRM), and the steganalysis method using image quality metrics (IQM) are both based on image residuals, while they use 34671 and 10 features respectively. This paper proposes a novel steganalysis scheme that combines their advantages in two ways. First, filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods. In addition, a total variant (TV) filter is also used due to its good performance of preserving image edge properties during filtering. Second, due to each type… More >

  • Open Access

    ARTICLE

    Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

    Taoyun Zhou1,2, Baowang Lian1, Siqing Yang2,*, Yi Zhang1, Yangyang Liu1,3

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 225-245, 2018, DOI: 10.3970/cmc.2018.02671

    Abstract For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve high-precision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the… More >

  • Open Access

    ARTICLE

    Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network

    Yuhong Zhang1,*, Qinqin Wang1, Yuling Li1, Xindong Wu2

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 285-297, 2018, DOI: 10.3970/cmc.2018.02604

    Abstract Recently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, these vectors are segmented according… More >

  • Open Access

    ARTICLE

    Automatic Mining of Security-Sensitive Functions from Source Code

    Lin Chen1,2, Chunfang Yang1,2,*, Fenlin Liu1,2, Daofu Gong1,2, Shichang Ding3

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 199-210, 2018, DOI: 10.3970/cmc.2018.02574

    Abstract When dealing with the large-scale program, many automatic vulnerability mining techniques encounter such problems as path explosion, state explosion, and low efficiency. Decomposition of large-scale programs based on safety-sensitive functions helps solve the above problems. And manual identification of security-sensitive functions is a tedious task, especially for the large-scale program. This study proposes a method to mine security-sensitive functions the arguments of which need to be checked before they are called. Two argument-checking identification algorithms are proposed based on the analysis of two implementations of argument checking. Based on these algorithms, security-sensitive functions are detected based on the ratio of… More >

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