Lejun Zhang¹, Tianwen Huang¹, Xiaoyan Hu¹, Zhijie Zhang¹, Weizheng Wang², Donghai Guan^{3, *}, Chunhui Zhao^{1, 4}, Seokhoon Kim⁵

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2013-2030, 2020, DOI:10.32604/cmc.2020.011219 - 30 June 2020

Abstract Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In… More >

Ye Zhai^{1, *}, Dongsheng Liu¹, Celimuge Wu², Rongrong She¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1999-2012, 2020, DOI:10.32604/cmc.2020.09950 - 30 June 2020

Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that More >

Dunhong Yao^{1, 2, 3}, Shijun Li^{4, *}, Ang Li⁵, Yu Chen⁶

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1959-1975, 2020, DOI:10.32604/cmc.2020.010186 - 30 June 2020

Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and… More >

Jinhui Ge¹, Xiaoliang Liu^{2, *}, Guo Liang³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1945-1958, 2020, DOI:10.32604/cmc.2020.010977 - 30 June 2020

Abstract With the expansion of the application scope of social computing problems, many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes, cultures, and the emotional needs of customers. The actual soft time window vehicle routing problem, speeding up the response of customer needs, improving distribution efficiency, and reducing operating costs is the focus of current social computing problems. Therefore, designing fast and effective algorithms to solve this problem has certain theoretical and practical significance. In this paper, considering the time delay… More >

Tingxin Wei^{1, 2}, Weiguang Qu^{2, 3, *}, Junsheng Zhou³, Yunfei Long⁴, Yanhui Gu³, Zhentao Xia³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1897-1913, 2020, DOI:10.32604/cmc.2020.010813 - 30 June 2020

Abstract The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning More >

Wu Zeng¹, Zhanxiong Huo^{1, *}, Yuxuan Xie², Yingxiang Jiang¹, Kun Hu¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1869-1883, 2020, DOI:10.32604/cmc.2020.010139 - 30 June 2020

Abstract Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features More >

Zhaoquan Gu¹, Yinyin Cai¹, Sheng Wang¹, Mohan Li^{1, *}, Jing Qiu¹, Shen Su¹, Xiaojiang Du¹, Zhihong Tian¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739 - 30 June 2020

Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks More >

Chuqing Zhang¹, Jiangyuan Yao^{2, *}, Guangwu Hu³, Thomas Schøtt⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1741-1753, 2020, DOI:10.32604/cmc.2020.010306 - 30 June 2020

Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved More >

Meichen Xia^{1, *}, Shiliang Li^1,, Liu Liu²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1673-1689, 2020, DOI:10.32604/cmc.2020.010177 - 30 June 2020

Abstract Multi-server authenticated key agreement schemes have attracted great attention to both academia and industry in recent years. However, traditional authenticated key agreement schemes in the single-server environment are not suitable for the multi-server environment because the user has to register on each server when he/she wishes to log in various servers for different service. Moreover, it is unreasonable to consider all servers are trusted since the server in a multi-server environment may be a semi-trusted party. In order to overcome these difficulties, we designed a secure threefactor multi-server authenticated key agreement protocol based on elliptic More >

Ningcheng Yuan¹, Chao Jia^{2, *}, Jizhao Lu³, Shaoyong Guo¹, Wencui Li³, Xuesong Qiu¹, Lei Shi⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1657-1671, 2020, DOI:10.32604/cmc.2020.09840 - 30 June 2020

Abstract Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose… More >