Shuai Yuan¹, Tingting He^{2, 3, *}, Huan Huang⁴, Rui Hou⁵, Meng Wang⁶

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 817-833, 2020, DOI:10.32604/cmc.2020.010471 - 23 July 2020

Abstract Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students. Manually grading student essays is a time-consuming task; however, it is necessary. An automated essay scoring system can not only greatly improve the efficiency of essay scoring, but also provide more objective score. Therefore, many researchers have been exploring automated essay scoring techniques and tools. However, the technique of scoring Chinese essays is still limited, and its accuracy needs to be enhanced further. To improve the accuracy of the scoring model for a… More >

Linguo Li^{1, 2}, Lijuan Sun¹, Jian Guo¹, Shujing Li^{2, *}, Ping Jiang³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158 - 23 July 2020

Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, More >

Jun Li^{1, 2}, Jieren Cheng^{2, *}, Naixue Xiong³, Lougao Zhan⁴, Yuan Zhang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272 - 23 July 2020

Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data… More >

Jinrong Hu¹, Zhiqin Lei¹, Xiaoying Li^{2, *}, Yongqun He³, Jiliu Zhou¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 705-722, 2020, DOI:10.32604/cmc.2020.09878 - 23 July 2020

Abstract The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to More >

Fei Li^{1, *}, Jiayan Zhang¹, Edward Szczerbicki², Jiaqi Song¹, Ruxiang Li ¹, Renhong Diao¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264 - 23 July 2020

Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to More >

Xiujuan Wang¹, Qianqian Zheng^{1, *}, Kangfeng Zheng², Yi Sui¹, Jiayue Zhang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165 - 23 July 2020

Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in… More >

Lejun Zhang^{1, 2}, Zhijie Zhang¹, Weizheng Wang³, Rasheed Waqas¹, Chunhui Zhao^{1, 4}, Seokhoon Kim⁵, Huiling Chen^{6, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 597-616, 2020, DOI:10.32604/cmc.2020.011554 - 23 July 2020

Abstract As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low… More >

Yatian Shen¹, Yubo Mai², Xiajiong Shen², Wenke Ding^{2, *}, Mengjiao Guo³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139 - 23 July 2020

Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer More >

Shengchun Wang¹, Xiaozhong Yu¹, Lianye Liu², Jingui Huang^{1, *}, Tsz Ho Wong³, Chengcheng Jiang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627 - 23 July 2020

Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address More >

Yiming Huang^{1, *}, Hang Lei¹, Xiaoyu Li^{1, *}, Qingsheng Zhu², Wanghao Ren³, Xusheng Liu^{2, 4}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390 - 23 July 2020

Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with… More >