Chuanlong Li^1,2,*, Yumeng Jiang³, Marta Cheslyar¹

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 313-324, 2018, DOI: 10.3970/cmc.2018.03950

Abstract Deep neural network has proven to be very effective in computer vision fields. Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods. Generative adversarial network (GAN) is becoming one of the highlights among these deep neural networks. GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks. One promising application of using GAN generated images would be image concealing which requires the embedded image looks… More >

Yu Yang^1,2,*, Yuwei Chen^1,2, Yuling Chen², Wei Bi^3,4

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 >

Wenjie Liu^1,2,*, Zhenyu Chen², Jinsuo Liu³, Zhaofeng Su⁴, Lianhua Chi⁵

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 211-223, 2018, DOI: 10.3970/cmc.2018.02288

Abstract The delegating private quantum computation (DQC) protocol with the universal quantum gate set {X,Z,H,P,R,CNOT} was firstly proposed by Broadbent et al. [Broadbent (2015)], and then Tan et al. [Tan and Zhou (2017)] tried to put forward a half-blind DQC protocol (HDQC) with another universal set {H,P,CNOT,T}. However, the decryption circuit of Toffoli gate (i.e. T) is a little redundant, and Tan et al.’s protocol [Tan and Zhou (2017)] exists the information leak. In addition, both of these two protocols just focus on the blindness of data (i.e. the client’s input and output), but do not consider the blindness of computation… 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 >

Daojian Zeng^1,3, Yuan Dai^1,3, Feng Li^1,3, R. Simon Sherratt², Jin Wang^3,*

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121

Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute… More >