Method to Remove Handwritten Texts Using Smart Phone

To remove handwritten texts from an image of a document taken by smart phone,an intelligent removal method was proposed that combines dewarping and Fully Convolutional Network with Atrous Convolutional and Atrous Spatial Pyramid Pooling(FCN-AC-ASPP).For a picture taken by a smart phone,firstly,the image is transformed into a regular image by the dewarping algorithm.Secondly,the FCN-AC-ASPP is used to classify printed texts and handwritten texts.Lastly,handwritten texts can be removed by a simple algorithm.Experiments show that the classification accuracy of the FCN-AC-ASPP is better than FCN,DeeplabV3+,FCN-AC.For handwritten texts removal effect,the method of combining dewarping and FCN-AC-ASPP is superior to FCN-AC-ASP alone.

A Comparative Study of Artificial Intelligence and Translation Software in Chinese-English Translation:A Focus on Literary and Technical Texts

In recent years,the domain of machine translation has experienced remarkable growth,particularly with the emergence of neural machine translation,which has significantly enhanced both the accuracy and fluency of translation.At the same time,AI also showed its tremendous advancement,with its capabilities now extending to assisting users in a multitude of tasks,including translation,garnering attention across various sectors.In this paper,the author selects representative sentences from both literary and scientific texts,and translates them using two translation software and two AI tools for comparison.The results show that all four translation tools are very efficient and can help with simple translation tasks.However,the accuracy of terminology needs to be improved,and it is difficult to make adjustments based on the characteristics of the target language.It is worth mentioning that one of the advantages of AI is its interactivity,which allows it to modify the translation according to the translator’s needs.

From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Study on the Textual Coherence Function of Conjunctions in Political Texts and Their Translation Reconstruction

The assessment of translation quality in political texts is primarily based on achieving effective communication.Throughout the translation process,it is essential to not only accurately convey the original content but also effectively transform the structural mechanisms of the source language.In the translation reconstruction of political texts,various textual cohesion methods are often employed,with conjunctions serving as a primary means for semantic coherence within text units.

Assessing Text-grading of the Introduced Graded Readers in China

An increasing number of graded readers thus come out for pedagogical purposes. However, the reliability of text-grading for graded readers remains questionable. Whether they show linguistic difference across levels needs further research given a shifted emphasis on multidimensional features. The study used Co-Metrix to give an assessment on graded readers’text-grading both under unidimensional and multidimensional indices, providing a testimony to publishers’original design and to the feasibility against the new trend. It indicated that across-grade difference only partly corresponded with publishers’text-grading rationale that focused on surface features. But it made no sense in linguistics at a deeper level since no overall difference was found. Therefore, the reassessment and design of graded readers were suggested to take account of multidimensional features for across-grade difference.

Analysis and Design of a Quadrature Down-Conversion Mixer for UHF RFID Readers

A quadrature mixer with a shared transconductor stage is analyzed,including voltage conversion gain, linearity, noise figure, and image rejection. The analysis indicates it has better performance than a conventional Gilbert mixer pair in commutating mode. A quadrature down-conversion mixer based on this topology is designed and optimized for an ultra high frequency RFID reader. Operating in the 915MHz ISM band, the presented quadrature mixer measures a conversion gain of 12.5dB,an IIP3 of 10dBm, an IIP2 of 58dBm, and an SSB noise figure of 17.6riB. The chip was fabricated in a 0. 18μm 1P6M RF CMOS process and consumes only 3mA of current from a 1.8V power supply.

针对文本情感分类任务的textSE-ResNeXt集成模型

针对深度学习方法中文本表示形式单一,难以有效地利用语料之间细化的特征的缺陷,利用中英文语料的不同特性,有区别地对照抽取中英文语料的特征提出了一种新型的textSE-ResNeXt集成模型。通过PDTB语料库对语料的显式关系进行分析,从而截取语料主要情感部分,针对不同中、英文情感词典进行情感程度关系划分以此获得不同情感程度的子数据集。在textSE-ResNeXt神经网络模型中采用了动态卷积核策略,以此对文本数据特征进行更为有效的提取,模型中融合了SEnet和ResNeXt,有效地进行了深层次文本特征的抽取和分类。将不同情感程度的子集上对textSE-ResNeXt模型采用投票集成的方法进一步提高分类效率。分别在中文酒店评论语料和六类常见英文分类数据集上进行实验。实验结果表明了本模型的有效性。

A Functionalist Approach to the Translation of Allusions in Chinese Tourism Texts

Allusions are very common in Chinese tourism texts. When translating them, translators should make the translated version both understandable and acceptable. With Nord's documentary translation and instrumental translation, this paper explores how to apply these two translation processes to the translation of allusions in Chinese tourism texts.

On Creative Strategies for Translating Guangxi Tourism Texts into English under the Perspective of ASEAN

Guangxi tourism texts are a kind of tool to show China's image.However,there are still lots of problems despite certain achievements in recent years in Chinese-to-English(C-E) translation of tourism texts.So,how to improve the quality of tourism materials is of great significance practically.The aim is to adopt the"Creation"Thought of Guo Moruo that emphasizes creation,charming translation,having empathy with the source language and experience,aiming at discovering proper and feasible translation standards and strategies and making it better serve for the tourism development between Guangxi and ASEAN countries.

基于改进TextRank的科技文本关键词抽取方法

针对科技文本关键词抽取任务中抽取出现次数少但能较好表达文本主旨的词语效果差的问题,提出一种基于改进TextRank的关键词抽取方法。首先,利用词语的词频-逆文档频率(TF-IDF)统计特征和位置特征优化共现图中词语间的概率转移矩阵,通过迭代计算得到词语的初始得分;然后,利用K-Core(K-Core decomposition)算法挖掘KCore子图得到词语的层级特征,利用平均信息熵特征衡量词语的主题表征能力;最后,在词语初始得分的基础上融合层级特征和平均信息熵特征,从而确定关键词。实验结果表明,在公开数据集上,与TextRank方法和OTextRank(Optimized TextRank)方法相比,所提方法在抽取不同关键词数量的实验中,F1均值分别提高了6.5和3.3个百分点;在科技服务项目数据集上,与TextRank方法和OTextRank方法相比,所提方法在抽取不同关键词数量的实验中,F1均值分别提高了7.4和3.2个百分点。实验结果验证了所提方法抽取出现频率低但较好表达文本主旨关键词的有效性。

Sensitivity and inter-observer variability for capsule endoscopy image analysis in a cohort of novice readers

AIM： To determine the performance of novice readers （4^th year medical students） for detecting capsule endoscopy findings. METHODS： Ten capsule endoscopy cases of small bowel lesions were administered to the readers. Gold standard findings were pre-defined by gastroenterologists. Ten gold standard ＂targets＂ were identified among the 10 cases. Readers were given a 30-min overview of Rapid Reader software and instructed to mark any potential areas of abnormalities. A software program was developed using SAS to analyze the thumbnailed findings. RESULTS： The overall sensitivity for detecting the gold standard findings was 80%. As a group, at least 5 out of 10 readers detected each gold standard finding per recording. All the gold standard targets were identified when the readers＇ results were combined. Incidental finding/false positive rate ranged between 8.2-59.8 per reader. CONCLUSION： A panel of medical students with minimal endoscopic experience can achieve high sensitivity in detecting lesions on capsule endoscopy. A group of novice readers can pre-screen recordings to thumbnail potential areas of small bowel lesions for further review. These thumbnails must be reviewed to determine the clinical relevance. Further studies are ongoing to assess other cohorts.

The Organizational Patterns of English and Chinese Texts:A Contrastive Study

Thispapercontrastivelystudies,basedonthetheoreticframeworkof'topicanalysis'byvanDijkandsomeothers,theunderlyingarrangementpatternsoftopicsofEnglishandChinesetexts.TheoverallobjectiveistoexaminethediscoursepatternsofarepresentativesampleofEnglishandCh...

What Eye Movements Tell About Identifying Compound Words in Reading and Top-Down Effects in Reading Long Texts

Two lines of research on eye movements in reading are summarized. One line of research examines how adult readers identify compound words during reading. The other line of research deals with how a specific reading goal influences the way long expository texts are read. Both lines of research are conducted using Finnish as the source language. With respect to the first research question, it is demonstrated that compound words are recognized either holistically or via their components, depending on the length of the compound word. Readers begin to process whatever information is readily available in the foveal vision(i.e., either the whole-word form or the initial component). The second line of research demonstrates that(1)a specific reading goal is capable of exerting an early effect on readers’ eye fixation patterns,(2)time course analyses based on eye movement patterns can reveal interesting individual differences, and(3)working memory capacity is linked to the efficiency to strategically allocate attention as well as to encode information to and retrieve it from the long-term memory. It is concluded that the eye-tracking technique is an excellent research tool to tap into the workings of the human mind during the comprehension of written texts.

Sentiment Analysis of Short Texts Based on Parallel DenseNet

Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.

Intercultural Communication and Chinese Tourism Texts Translation

Intercultural communication language plays a crucial role in our global tourism.When we are doing translation we are doing intercultural communication in a sense,so it is necessary for translators to have intercultural communication awareness and be sensitive to the cultural elements in translation.Taking the perspective of intercultural communication,this paper analyses the cultural elements in Chinese tourism material translation in terms of culturally-loaded words and terms,and presents certain translation techniques a translator can use to deal with culturally-loaded words in their translation.

Interdiscursivity and Promotional Discourse:A Corpus-Assisted Genre Analysis of About Us Texts on Chinese University Websites

The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line has focused on marketized discourses such as advertisements,little attention has been paid to promotional discourse in public institutions such as the About us texts on Chinese university websites.The goal of the present study is twofold:first,to describe the generic features of the university About us texts in China;and second,to analyze how promotional discourse is interdiscursively incorporated in the discourse by referring to the broader sociopolitical context.Findings have indicated five main moves:giving an overview,stressing historical status,displaying strengths,pledging political and ideological allegiance,and communicating goals and visions.Move 3,displaying strengths,has the greatest amount of information and can be further divided into six sub-moves which presents information on campus facilities,faculty team,talent cultivation,disciplinary fields construction,academic research,and international exchange.The main linguistic and rhetorical strategies used in these moves are analyzed and discussed.

What are the Differences between Good and Poor Readers?

Reading is a very essential skill in the process of knowledge aqusition,while,effective reading ability is the outcome of the good combination of both time and skills.The differences between good readers and poor reader will be studied in terms of comparisons towards their reading speed,efficiency and reasons behind in the domain of psychological perspectives.

An Analysis of the Readers' Roles in the Construction of Meaning in John Donne's The Flea under Reader-Oriented Theory

Among the sundry studies in China on John Donne's The Flea,there has been none done from the perspective of Reader-Oriented Theory to probe into the readers' roles in the construction of meaning.The present article under Wolfgang Iser's theories,in a further pursuit of the expansion of the gestalt of such studies on the poem,aims at exploring the readers' active roles in complementing,completing and recreating the textual meaning,and differentiating the implied reader and the actual reader through the poem as well as attempting to fulfill the construction of the reading cognitive model through the interpretation of the conceit the flea.

Implications of Reportage Texts in EFL Teaching

In this paper, the author discusses implications of original reportage texts in EFL teaching and learning from the perspectives of socio-linguistics and pragmatic competence. The author also discusses a proper approach for teaching medical postgraduate students, which is centered on students and based on research. This approach for teaching and leaning has been proved effective and students have improved in their socio-linguistic and pragmatic competences. This approach should have implications to EFL teachers who teach students of other specialties.

Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.