Xintao Duan^1,*, Haoxian Song¹, Chuan Qin², Muhammad Khurram Khan³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 483-493, 2018, DOI: 10.3970/cmc.2018.01798

Abstract In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image.… More >

Qijun Wu¹, Limin Han¹, Lingxuan Wang², Xun Gong^3,*

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 393-403, 2018, DOI: 10.3970/cmc.2018.01740

Abstract We optimized the ground-state stable configuration of CoS molecule in different external radiation fields (0-0.04 atomic units (a.u.)) at the basis set level of 6-311G++ (d, p) using the B3LYP density functional theory. On this basis, the molecular structure, total energy, energy gap, and the intensities of infrared ray (IR) spectra, Raman spectra, and ultraviolet-visible (UV-Vis) absorption spectra of CoS molecule were computed using the same method. The results showed that the molecular structure changed greatly under the effect of the external radiation fields and had significant dependency on the radiation fields. The total energy of CoS molecule grew slightly… More >

Meijuan Wang^1,2,*, Jian Wang¹, Lihong Guo^1,3, Lein Harn⁴

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 465-482, 2018, DOI: 10.3970/cmc.2018.02568

Abstract In the era of big data, the conflict between data mining and data privacy protection is increasing day by day. Traditional information security focuses on protecting the security of attribute values without semantic association. The data privacy of big data is mainly reflected in the effective use of data without exposing the user’s sensitive information. Considering the semantic association, reasonable security access for privacy protect is required. Semi-structured and self-descriptive XML (eXtensible Markup Language) has become a common form of data organization for database management in big data environments. Based on the semantic integration nature of XML data, this paper… More >

Shengqun Fang¹, Zhiping Cai^1,*, Wencheng Sun¹, Anfeng Liu², Fang Liu³, Zhiyao Liang⁴, Guoyan Wang⁵

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI: 10.3970/cmc.2018.02289

Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories, and compare this method… More >

Ming Wan¹, Jiangyuan Yao^2,*, Yuan Jing¹, Xi Jin^3,4

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 447-463, 2018, DOI: 10.3970/cmc.2018.02195

Abstract As the main communication mediums in industrial control networks, industrial communication protocols are always vulnerable to extreme exploitations, and it is very difficult to take protective measures due to their serious privacy. Based on the SDN (Software Defined Network) technology, this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols, and this approach can be installed in SDN switches as a security software appliance in SDN-based control systems. Furthermore, aiming at the unknown protocol specification and message format, this approach first restructures the industrial communication sessions and merges the payloads from industrial communication… More >

Jixin Liu^1,*, Ning Sun^1,2, Xiaofei Li¹, Guang Han¹, Haigen Yang¹, Quansen Sun³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 435-446, 2018, DOI: 10.3970/cmc.2018.02177

Abstract Rare bird has long been considered an important in the field of airport security, biological conservation, environmental monitoring, and so on. With the development and popularization of IOT-based video surveillance, all day and weather unattended bird monitoring becomes possible. However, the current mainstream bird recognition methods are mostly based on deep learning. These will be appropriate for big data applications, but the training sample size for rare bird is usually very short. Therefore, this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning. There are two achievements in our work: (1) after the… More >

Xiaorui Zhang^1,2,3,*, Pengpai Wang¹, Wei Sun², Norman I. Badler³

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 297-319, 2018, DOI:10.3970/cmc.2018.01764

Abstract Real-time performance and accuracy are two most challenging requirements in virtual surgery training. These difficulties limit the promotion of advanced models in virtual surgery, including many geometric and physical models. This paper proposes a physical model of virtual soft tissue, which is a twist model based on the Kriging interpolation and membrane analogy. The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method. The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach… More >

Xian Tang^1,*, Ziyang Chen², Haiyan Zhang³, Xiang Liu¹, Yunyu Shi¹, Asad Shahzadi⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 267-283, 2018, DOI:10.3970/cmc.2018.01839

Abstract Answering reachability queries is one of the fundamental graph operations. Existing approaches either accelerate index construction by constructing an index that covers only partial reachability relationship, which may result in performing cost traversing operation when answering a query; or accelerate query answering by constructing an index covering the complete reachability relationship, which may be inefficient due to comparing the complete node labels. We propose a novel labeling scheme, which covers the complete reachability relationship, to accelerate reachability queries processing. The idea is to decompose the given directed acyclic graph (DAG) G into two subgraphs, G1 and G2. For G1, we… More >

Ya Tu¹, Yun Lin¹, Jin Wang^2,3,*, Jeong-Uk Kim⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter… More >

Qi Cui^1,2,*, Suzanne McIntosh³, Huiyu Sun³

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 229-241, 2018, DOI:10.3970/cmc.2018.01693

Abstract Currently, some photorealistic computer graphics are very similar to photographic images. Photorealistic computer generated graphics can be forged as photographic images, causing serious security problems. The aim of this work is to use a deep neural network to detect photographic images (PI) versus computer generated graphics (CG). In existing approaches, image feature classification is computationally intensive and fails to achieve real-time analysis. This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks (DCNNs). Compared with some existing methods, the proposed method achieves real-time forensic tasks by deepening the network structure. Experimental results… More >