Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced h...Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.展开更多
The main goal of English teaching in colleges and universities is to cultivate students’ability to use the language,but many students are still unable to complete oral communication fluently after years of study.For ...The main goal of English teaching in colleges and universities is to cultivate students’ability to use the language,but many students are still unable to complete oral communication fluently after years of study.For this reason,teachers need to deeply analyze and study the linguistic features of oral English corpora and formulate reasonable teaching strategies to improve students’oral expression skills.This paper outlines the linguistic features of oral English corpora,comparatively analyzes the differences between oral English corpora and written English corpora,and explores effective teaching strategies,hoping to provide guidelines for relevant teachers.展开更多
To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,...To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.展开更多
A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic information.In this paper,the notion of a Boolean element is proposed in a 2DL-L...A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic information.In this paper,the notion of a Boolean element is proposed in a 2DL-LIA and some properties of Boolean elements are discussed.Then derivations on 2DL-LIAs are introduced and the related properties of derivations are investigated.Moreover,it proves that the derivations on 2DL-LIAs can be constructed by Boolean elements.展开更多
The journal Chinese Journal of Applied Linguistics (CJAL) is the official journal of China English Language Education Association (CELEA),the Chinese affiliate of AILA (International Association of Applied Linguistics...The journal Chinese Journal of Applied Linguistics (CJAL) is the official journal of China English Language Education Association (CELEA),the Chinese affiliate of AILA (International Association of Applied Linguistics).It was initiated in 1978 as the only English language journal in China in the area of applied linguistics.Prof.Wen Qiufang from Beijing Foreign Studies University,former President of CELEA,serves as the current editor-in-chief.展开更多
Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19)...Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.展开更多
Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development st...Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country.Therefore,graduate education is of great remarkably to the development of national education.As an important manifestation of graduate education,the quality of a graduate thesis should receive more attention.It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis.For this purpose,this work is based on textmining,expert interviews,and questionnaire surveys to obtain the factors influencing the quality of a graduate thesis first.Then,through three rounds of expert consultation,a multidimensional evaluation indicator system for the graduate thesis quality is built.Furthermore,probabilistic linguistic termsets(PLTSs)are utilized to obtain the initial evaluation information and apply the stepwise weight assessment ratio analysis method to determine the weights of attributes.In the ensuing step,the novel multi-attribute border approximation area comparison based on the PLTS method is established.Finally,the proposed method is employed in a case study concerning the quality evaluation of a graduate thesis and the effectiveness of this approach is further illustrated.展开更多
Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs...Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.展开更多
Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation inform...Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.展开更多
The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increas...The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.展开更多
Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of soci...Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.展开更多
Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary classification.While i...Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary classification.While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist.In this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts.LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model.In the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis task.Besides,a shared CNN is built to capture potential interaction information and share linguistic features among all tasks.Finally,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic.Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,respectively.More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.展开更多
Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-...Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.展开更多
Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Inte...The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.展开更多
Mobile-assisted language learning(MALL)has been regarded as an excellent tool in the field of language acquisition as modern technology develops.The popularity of using mobile devices in education makes it possible fo...Mobile-assisted language learning(MALL)has been regarded as an excellent tool in the field of language acquisition as modern technology develops.The popularity of using mobile devices in education makes it possible for people to learn languages through platforms like tablets and smartphones from anywhere and at any time.However,the application of MALL in a collaborative student-centered environment has received comparatively little attention.Since spoken fluency and vocabulary size are the two crucial components of language proficiency,this study aims to investigate if young learners can improve their native language level through learning online beyond the classroom.The quantitative data reveal that MALL does make a difference in students’linguistic skills.The results show that the incorporation of mobile applications into language learning could better help learners achieve the learning outcomes and improve their communication skills than simply using conventional methods.In addition,the data of the questionnaire exposed some issues that need to be continuously improved.The viable suggestions are also discussed to share ideas about building a more sustainable learning environment in a data-driven age.展开更多
Linguistic landscape has become an emerging research topic in sociolinguistics due to the development of urban linguistic landscape.Based on the development of linguistic landscape in TaiKoo Hui,its diverse characteri...Linguistic landscape has become an emerging research topic in sociolinguistics due to the development of urban linguistic landscape.Based on the development of linguistic landscape in TaiKoo Hui,its diverse characteristics are analyzed,and the language signs,usage,and codes are summarized.It is found that there is poor transmission effect of multilingual landscape information and improper use of linguistic landscape in TaiKoo Hui.This paper concludes that the linguistic landscape of Taikoo Hui can be optimized from two aspects to enhance its attractiveness and competitiveness,including improving the informational function of foreign language signs and regulating the use of linguistic landscape.展开更多
Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 polici...Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 policies in The Economist.Transitivity,voice,and nominalization are the major analytical subjects.After China lifted the zero-COVID policy,western media began criticizing China’s lack of data sharing,with some misinformation and misleading reports.The denouncement of inertness and reluctance to fight against the pandemic disclaim the Chinese government’s efforts and depreciate China’s image.China is portrayed as the villain and destroyer of people’s health worldwide.Meanwhile,they also hold a hesitant attitude toward China’s diplomacy.The re-engaging with foreign countries and travel restrictions have been described as imprudent and rushed actions.They also consider China as the fuse of contradiction in the United Nations.What is overt is their view of breaking up China.展开更多
The silent communication of non-linguistic signs occurs in human cognition.This can be proved from four points of view:to introduce the definitions of linguistics,non-linguistic signs and meaning;all of them can commu...The silent communication of non-linguistic signs occurs in human cognition.This can be proved from four points of view:to introduce the definitions of linguistics,non-linguistic signs and meaning;all of them can communicate in human cognition through their comparison;the connotations of many kinds of non-linguistic signs in daily life;the position of non-linguistic signs in linguistic fields;from the above the conclusion can be reached.展开更多
The direct application of linguistic theories to syllabus design gives rise to frequent change of syllabus type in the history of syllabus development, which makes language teachers feel difficult to adapt to, to adop...The direct application of linguistic theories to syllabus design gives rise to frequent change of syllabus type in the history of syllabus development, which makes language teachers feel difficult to adapt to, to adopt and to implement. The recognition and popularization of the new-born discipline educational linguistics servers as a method to ease the situation, especially in the college English syllabus design in China. The development and application of the fruitful achievements in educational linguistics is bound to provide us with a more scientific approach to syllabus design in the future.展开更多
文摘Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.
文摘The main goal of English teaching in colleges and universities is to cultivate students’ability to use the language,but many students are still unable to complete oral communication fluently after years of study.For this reason,teachers need to deeply analyze and study the linguistic features of oral English corpora and formulate reasonable teaching strategies to improve students’oral expression skills.This paper outlines the linguistic features of oral English corpora,comparatively analyzes the differences between oral English corpora and written English corpora,and explores effective teaching strategies,hoping to provide guidelines for relevant teachers.
文摘To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.
基金Supported by the National Natural Science Foundation of China(11501523,61673320)。
文摘A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic information.In this paper,the notion of a Boolean element is proposed in a 2DL-LIA and some properties of Boolean elements are discussed.Then derivations on 2DL-LIAs are introduced and the related properties of derivations are investigated.Moreover,it proves that the derivations on 2DL-LIAs can be constructed by Boolean elements.
文摘The journal Chinese Journal of Applied Linguistics (CJAL) is the official journal of China English Language Education Association (CELEA),the Chinese affiliate of AILA (International Association of Applied Linguistics).It was initiated in 1978 as the only English language journal in China in the area of applied linguistics.Prof.Wen Qiufang from Beijing Foreign Studies University,former President of CELEA,serves as the current editor-in-chief.
基金The authors thank the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number(120/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.
文摘Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country.Therefore,graduate education is of great remarkably to the development of national education.As an important manifestation of graduate education,the quality of a graduate thesis should receive more attention.It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis.For this purpose,this work is based on textmining,expert interviews,and questionnaire surveys to obtain the factors influencing the quality of a graduate thesis first.Then,through three rounds of expert consultation,a multidimensional evaluation indicator system for the graduate thesis quality is built.Furthermore,probabilistic linguistic termsets(PLTSs)are utilized to obtain the initial evaluation information and apply the stepwise weight assessment ratio analysis method to determine the weights of attributes.In the ensuing step,the novel multi-attribute border approximation area comparison based on the PLTS method is established.Finally,the proposed method is employed in a case study concerning the quality evaluation of a graduate thesis and the effectiveness of this approach is further illustrated.
基金supported by National Social Science Foundation of China (Grant No.17ZDA030).
文摘Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.
文摘Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.
文摘The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR33).
文摘Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.
基金This paper is partly supported by the National Natural Science Foundation of China unde rGrants 61972057 and 62172059Hunan ProvincialNatural Science Foundation of China underGrant 2022JJ30623 and 2019JJ50287Scientific Research Fund of Hunan Provincial Education Department of China under Grant 21A0211 and 19A265。
文摘Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary classification.While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist.In this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts.LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model.In the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis task.Besides,a shared CNN is built to capture potential interaction information and share linguistic features among all tasks.Finally,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic.Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,respectively.More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4331004DSR031)supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
文摘Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-biaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR09).
文摘The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.
基金Youth Project of Philosophy and Social Science Planning of Henan Province“Study on the Influence of Foreign Language Learning on Chinese Students’Purity of Chinese Language”(Grant No.2023CYY038)General Project of Annual Research Topics of Henan Provincial Federation of Social Science and Technology“Study on Formation Factors and Cognitive Mechanisms of Network Harmonies and Chinese-English Mixed-Up Idioms”(Grant No.SKL-2023-1588).
文摘Mobile-assisted language learning(MALL)has been regarded as an excellent tool in the field of language acquisition as modern technology develops.The popularity of using mobile devices in education makes it possible for people to learn languages through platforms like tablets and smartphones from anywhere and at any time.However,the application of MALL in a collaborative student-centered environment has received comparatively little attention.Since spoken fluency and vocabulary size are the two crucial components of language proficiency,this study aims to investigate if young learners can improve their native language level through learning online beyond the classroom.The quantitative data reveal that MALL does make a difference in students’linguistic skills.The results show that the incorporation of mobile applications into language learning could better help learners achieve the learning outcomes and improve their communication skills than simply using conventional methods.In addition,the data of the questionnaire exposed some issues that need to be continuously improved.The viable suggestions are also discussed to share ideas about building a more sustainable learning environment in a data-driven age.
文摘Linguistic landscape has become an emerging research topic in sociolinguistics due to the development of urban linguistic landscape.Based on the development of linguistic landscape in TaiKoo Hui,its diverse characteristics are analyzed,and the language signs,usage,and codes are summarized.It is found that there is poor transmission effect of multilingual landscape information and improper use of linguistic landscape in TaiKoo Hui.This paper concludes that the linguistic landscape of Taikoo Hui can be optimized from two aspects to enhance its attractiveness and competitiveness,including improving the informational function of foreign language signs and regulating the use of linguistic landscape.
文摘Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 policies in The Economist.Transitivity,voice,and nominalization are the major analytical subjects.After China lifted the zero-COVID policy,western media began criticizing China’s lack of data sharing,with some misinformation and misleading reports.The denouncement of inertness and reluctance to fight against the pandemic disclaim the Chinese government’s efforts and depreciate China’s image.China is portrayed as the villain and destroyer of people’s health worldwide.Meanwhile,they also hold a hesitant attitude toward China’s diplomacy.The re-engaging with foreign countries and travel restrictions have been described as imprudent and rushed actions.They also consider China as the fuse of contradiction in the United Nations.What is overt is their view of breaking up China.
文摘The silent communication of non-linguistic signs occurs in human cognition.This can be proved from four points of view:to introduce the definitions of linguistics,non-linguistic signs and meaning;all of them can communicate in human cognition through their comparison;the connotations of many kinds of non-linguistic signs in daily life;the position of non-linguistic signs in linguistic fields;from the above the conclusion can be reached.
文摘The direct application of linguistic theories to syllabus design gives rise to frequent change of syllabus type in the history of syllabus development, which makes language teachers feel difficult to adapt to, to adopt and to implement. The recognition and popularization of the new-born discipline educational linguistics servers as a method to ease the situation, especially in the college English syllabus design in China. The development and application of the fruitful achievements in educational linguistics is bound to provide us with a more scientific approach to syllabus design in the future.
