Effects of Instructional Video in College Oral English Classroom

Recently,applying instructional videos to stimulate students’motivation for English acquisition has become increasingly widespread in China.Whether utilizing instructional video in EFL teaching is instructive or not remains a hot issue.On the basis of linguistic theory of employing instructional video in EFL teaching,the paper is focused on exploring the effects of utilizing authentic video in College oral English Classroom.According to the study and analysis,the writer concludes that instructional video is fairly effective in college oral English classroom,even though there have been some problems and obstacles.It is suggested that English professors in China should make full use of instructional video to create an authentic language teaching and learning context where students can acquire English language naturally and effectively.The results from this study will offer useful information for the utilization of instructional video in EFL teaching.It is also suggested that educators and scholars in China should do more researches and studies on how to facilitate EFL teaching and learning by applying with instructional video.

Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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.

Analysis of two English Versions of Poems in Hong Lou Meng:from the Perspective of Schema Theory

The paper introduces the application of schema in translation and applies it to the analysis of two English versions of poems in HLM in terms of formal schema,language schema and content schema respectively.It probes into the reasons for three different English versions,exploring corresponding translation strategies to clear away the comprehension obstruction by the difference of schema during translating.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language.Sentiment is classified as a negative or positive assessment articulated through language.SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been reviewed.Deep learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise SA.With this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)technique.The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data.Primarily,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process.For the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this work.At last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different datasets.The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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.

Recombinant Vaccinia Virus is an Effective and Non-perturbing Vector for Human Dendritic Cells Transfected with Epstein-Barr Virus Latent Membrane Protein 2A

ObjectiveTo study the effects of dendritic cells (DC) transfected with recombinant vaccinia virus encoding Epstein Barr virus (EBV) latent membrane protein 2A(LMP2A) gene,and to provide evidence for further investigation on the therapeutic vaccines against EBV associated malignancies. MethodsMature DC were transfected with EBV LMP2A recombinant vaccinia virus (rVV LMP2A). Before and after the transfection,the expression of surface antigens on mature DC including CD1a,CD83,CD40,CD80,HLA DR was measured by fluorescence activated cell sorter (FACS) and the function of DC to stimulate allogeneic T cells proliferation was measured by mixed leukocyte reactions (MLR). ResultsLMP2A protein was highly expressed (66.1 %) in DC after the transfection of rVV LMP2A. No significant changes in the primary surface antigens expression and in the MLR were detected during the transfection. Transfected DC still had strong potential in stimulating the proliferation of allogeneic T cells. ConclusionRecombinant vaccinia virus was an effective and non perturbing vector to mediate the transfection of LMP2A into DC. The functions of mature DC were not affected significantly by the transfection of Vac LMP2A. This study could provide evidence for the further immunotherapy of EBV associated malignancies,e.g. nasopharyngeal carcinoma (NPC).

Clinical results of tricuspid valve replacement—a 21-case report

Objective： To summarize the clinical experiences of 21 patients treated with tricuspid valve replacement （TVR） and investigate the surgical indications and methods. Methods： Data from 21 patients who underwent TVR from December 2002 to March 2009 were retrospectively collected and analyzed. The mean age was 48.86± 15.37 years （range： 20-72 years）. The underlying disease of the patients was classified as rheumatic （n = 10）, congenital （n = 8）, endocarditis （n = 2） or chest trauma （n = 1）. Previous cardiac surgery had been performed in 12 patients （57.14%）. Results： In-hospital death occurred in two patients （9.52%）. Postoperative morbidities included cardiac failure （n = 2）, bleeding related re-operation （n = 1）, and plural effusion （n = 2）. Conclusion： The early outcomes of TVR were acceptable. At the present time TVR can be performed through optimal perioperative management.

The Use of Group Work in ESL Classrooms- Discourse Features and Implications

Group work has many advantages from language acquisition point of view. However, some teachers are unwilling to introduce group work in their own classroom because group work is very difficult to manage. The author of this essay explores the discourse features of successful and unsuccessful group work using discourse analysis in the hope that the results of this language classroom research can help language teachers gain some insight into the use of group work and make more informed decisions about group work management.

Study on the Formation and Construction of the Cultural Empathy in the Cross- cultural Communication

The development of globalization has promoted different regions to be closely linked and the boundaries between different cultural cycles to be gradually broken,so that the communication and integration between different cultures emerge. In cross-cultural communication,cultural empathy is not only a problem and necessarily implemented strategy,but also plays an important role,so people must improve their own cultural sensitivity,set up the awareness in cultural empathy on the basis of recognizing cultural diversity and equality,break the thinking inertia in the local culture and increase the consciousness in cognizing cultural empathy,put aside the idea of cultural centralism and strengthen the cultural inclusivity,and widely understand the basic cultures of other countries and reduce cultural empathy.

Tess - A Pure but Tragic Woman

Thomas Hardy had a developing conception of tragedy in the process of his novel creation. When he wrote Tess of D'Urbervilles, he became fully aware that the human tragedy resulted far more from society than characters themselves. The article is to elaborate how society imposes on Tess the tragedy in a planned way.

Conveying Traditional Chinese Medicine to Europe in the 17^(th)-18^(th) Centuries from the Tradition of Natural History

The translation efforts and missionary works by the Society of Jesus(or Jesuits)in China between the 16^(th) and 18^(th) centuries is a significant part of the history between China and the Western world.The Jesuits were instrumental in the transmission of knowledge,science,and culture between China and the West,which had an impact on the Chinese society and has continued to this day.

Effect of rural household participation and targeted tourism poverty alleviation

In areas with abundant tourism resources,it is possible for the poor to get rid of poverty by developing tourism.Differences in the extent to which farmers participate in the division of the tourism industry chain lead to differences in the effectiveness of poverty alleviation.This paper conducts in-depth interviews on the participation of rural households in the division of labor in the tourism industry chain in Qianshan city,and studies the degree to which rural households participate in the division of labor in the tourism industry chain,as well as the differences in the increment of tourism income brought about by different degrees.The survey found that the reasons for the differences in the degree of participation of farmers in tourism:off-season crops are not developed,and seasonal crops are competing for sale;Middlemen buy agricultural products from farmers at low prices.Farmers’organic products are not recognized and unable to enter the organic market.From the perspective of the government and farmers,the paper puts forward countermeasures for solving the above predicament.

An Optimized Deep Learning Model for Emotion Classification in Tweets

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural Network.Furthermore,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.