Paolina Centonze^1,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 361-374, 2019, DOI:10.32604/cmc.2019.06223

Abstract In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed Big Data (BD) processing cluster frameworks, which are adopted to manage yottabyte of unstructured sensitive data. For instance, Big Data systems’ privacy and security restrictions are most likely to failure due to the malformed AC policy configurations. Furthermore, BD systems were initially developed toped to take care of some of the DB issues to address BD challenges and many of these dealt with… More >

Qun Mo¹, Heng Yao¹, Fang Cao², Zheng Chang³, Chuan Qin^1,*

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 119-133, 2019, DOI:10.32604/cmc.2019.05770

Abstract Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a novel RDHEI scheme based on block classification and permutation is proposed. Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively. After block classification, content owner utilizes a specific encryption method, including stream cipher encryption and block permutation to protect image content securely. For the encrypted image, data hider embeds additional secret information in the most significant bits… More >

Khader M. Hamdia², Hamid Ghasemi³, Xiaoying Zhuang^4,5, Naif Alajlan¹, Timon Rabczuk^1,2,*

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 79-87, 2019, DOI:10.32604/cmc.2019.05882

Abstract In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction… More >

Manal Hazazi¹, Afnan Almousa¹, Heba Kurdi^1,2,*, Shiroq Al-Megren¹, Shada Alsalamah¹

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 15-29, 2019, DOI:10.32604/cmc.2019.06221

Abstract The underlying premise of peer-to-peer (P2P) systems is the trading of digital resources among individual peers to facilitate file sharing, distributed computing, storage, collaborative applications and multimedia streaming. So-called free-riders challenge the foundations of this system by consuming resources from other peers without offering any resources in return, hindering resource exchange among peers. Therefore, immense effort has been invested in discouraging free-riding and overcoming the ill effects of such unfair use of the system. However, previous efforts have all fallen short of effectively addressing free-riding behaviour in P2P networks. This paper proposes a novel approach based on utilising a credit… More >

Cosmin Anitescu¹, Elena Atroshchenko², Naif Alajlan³, Timon Rabczuk^3,*

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 345-359, 2019, DOI:10.32604/cmc.2019.06641

Abstract We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy. In this procedure, a coarse grid of training points is used at the initial training stages, while more points are added at later stages based on the value of the residual at a larger set of evaluation points. This method increases the robustness of the neural network approximation and can result in significant computational savings, particularly when the solution is non-smooth. Numerical results are presented for benchmark problems for scalar-valued PDEs, namely Poisson and Helmholtz equations, as well as for an inverse… More >

Mohan Li¹, Yanbin Sun^{1, *}, Shen Su¹, Zhihong Tian¹, Yuhang Wang^{1, *}, Xianzhi Wang²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 331-344, 2019, DOI:10.32604/cmc.2019.05379

Abstract Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can… More >

Yuhan Kang¹, Fenlin Liu¹, Chunfang Yang^1,*, Xiangyang Luo¹, Tingting Zhang²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 315-329, 2019, DOI:10.32604/cmc.2019.05242

Abstract This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate… More >

Lili Pan¹, Jiaohua Qin^1,*, Hao Chen², Xuyu Xiang¹, Cong Li¹, Ran Chen¹

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 297-313, 2019, DOI:10.32604/cmc.2019.04097

Abstract Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep… More >

Zhiliang Wang^1,*, Nuocheng Tian², Jianguo Wang³, Shengqi Yang³, Guang Liu¹

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 275-296, 2019, DOI:10.32604/cmc.2019.04247

Abstract The mechanical behaviors and energy dissipation characteristics of heat-treated granite were investigated under repeated impact loading. The granite samples were firstly heat-treated at the temperature of 20°C, 200°C, 400°C, and 600°C, respectively. The thermal damage characteristics of these samples were then observed and measured before impact tests. Dynamic impact compression tests finally were carried out using a modified split-Hopkinson pressure bar under three impact velocities of 12 m/s, 15 m/s, and 18 m/s. These test results show that the mineral composition and the main oxides of the granite do not change with these treatment temperatures. The number of microcracks and… More >

Xianyi Chen^1,2,*, Haidong Zhong^1,2, Zhifeng Bao³

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 239-255, 2019, DOI:10.32604/cmc.2019.03572

Abstract Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved… More >