Zia Ul Rehman^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Ghulam Mustafa², Hira Fayyaz⁴, Muhammad Hanif^1,2, Muhammad Anwar Saeed⁵

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1075-1090, 2021, DOI:10.32604/cmc.2020.012770

Abstract The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new… More >

Can Eyupoglu

Computer Systems Science and Engineering, Vol.34, No.3, pp. 113-121, 2019, DOI:10.32604/csse.2019.34.113

Abstract In recent years, the topic of Natural Language Processing (NLP) has attracted increasing interest. Many NLP applications including machine translation, machine learning, speech recognition, sentiment analysis, semantic search and natural language generation have been developed for most of the existing languages. Besides, two-level morphological description of the language to be used is required for these applications. However, there is no comprehensive study of Bashkir Turkish in the literature. In this paper, a two-level description of Bashkir Turkish morphology is described. The description based on a root word lexicon of Bashkir Turkish is implemented using Extensible Markup Language (XML) and appended… More >

Tao Jiang

Computer Systems Science and Engineering, Vol.33, No.5, pp. 299-308, 2018, DOI:10.32604/csse.2018.33.299

Abstract Many Domain-Specific Modelling Languages (DSML) can not formally define their semantics, which inevitably brings many problems, such as accurate description and automatic verification of model properties. In this paper, we propose a formal description method of the structural semantics of DSML for verifying consistency of models built based on DSML. Firstly, domain indicating structural semantics of DSML is formally defined based on algebra, and then, we briefly describe our framework for formalizing DSML and verifying consistency of DSML and its models and use a classic case to illustrate our approach; based on this, we construct an automatic translator for formalizing… More >

Yong Zhang^1,2, Jing Cai¹, Quanlin Li¹

Computer Systems Science and Engineering, Vol.33, No.2, pp. 125-135, 2018, DOI:10.32604/csse.2018.33.125

Abstract Extensible Markup Language (XML) is commonly employed to represent and transmit information over the Internet. Therefore, how to effectively search for keywords of massive XML data becomes a new issue. In this paper, we first present four properties to improve the classical ILE algorithm. Then, a kind of parallel XML keyword search algorithm, based on intelligent grouping to calculate SLCA, is proposed and realized under MapReduce programming model. At last, a series of experiments are implemented on 7 datasets of different sizes. The obtained results indicate that the proposed algorithm has high execution efficiency and is applicable to keyword search… More >

Farhan Ullah^1,4,*, Abdullah Bajahzar², Hamza Aldabbas³, Muhammad Farhan⁴, Hamad Naeem¹, S. Sabahat H. Bukhari^4,5, Kaleem Razzaq Malik⁶

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 169-180, 2020, DOI:10.31209/2019.100000138

Abstract This research aims to an electronic assessment (e-assessment) of students’ replies in response to the standard answer of teacher’s question to automate the assessment by WordNet semantic similarity. For this purpose, a new methodology for Semantic Similarity through WordNet Semantic Similarity Techniques (SS-WSST) has been proposed to calculate semantic similarity among teacher’ query and student’s reply. In the pilot study-1 42 words’ pairs extracted from 8 students’ replies, which marked by semantic similarity measures and compared with manually assigned teacher’s marks. The teacher is provided with 4 bins of the mark while our designed methodology provided an exact measure of… More >

Xu Zhang¹, Wenpeng Lu^1,*, Fangfang Li^2,3, Xueping Peng³, Ruoyu Zhang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 601-616, 2019, DOI:10.32604/cmc.2019.06045

Abstract Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, matching layer and prediction layer.… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866

Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More >

Zeyu Xiong^1,*, Qiangqiang Shen¹, Yueshan Xiong¹, Yijie Wang¹, Weizi Li²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 259-273, 2019, DOI:10.32604/cmc.2019.05155

Abstract Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. Recently, neural-network language models CBOW and Skip-Gram are developed as continuous-space language models for words representation in high dimensional real-valued vectors. These vector representations have recently demonstrated promising results in various NLP tasks because of their superiority in capturing syntactic and contextual regularities in language. In this paper, we propose a new strategy based on optimization in contiguous… More >

Dun Li¹, Hong Wu¹, Jinzhu Gao², Zhuoyun Liu¹, Lun Li¹, Zhiyun Zheng^1,*

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 301-321, 2019, DOI:10.32604/cmc.2019.05953

Abstract With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly,… More >

Zhijuan Wang^1,2, Xiaobin Zhao^1,2, Wei Song^1,*, Antai Wang³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 213-225, 2019, DOI:10.32604/cmc.2019.05690

Abstract Readability is a fundamental problem in textbooks assessment. For low re-sources languages (LRL), however, little investigation has been done on the readability of textbook. In this paper, we proposed a readability assessment method for Tibetan textbook (a low resource language). We extract features based on the information that are gotten by Tibetan segmentation and named entity recognition. Then, we calculate the correlation of different features using Pearson Correlation Coefficient and select some feature sets to design the readability formula. Fit detection, F test and T test are applied on these selected features to generate a new readability assessment formula. Experiment… More >