Development of a Lightweight Model for Handwritten Dataset Recognition: Bangladeshi City Names in Bangla Script

The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.

Something About Chinese Names

Dear Jack,I'm very glad to know that you'll come to China to learn Chinese.And you want to know about Chinese names.Now,I'd like to tell you something about them.Chinese names are different from English names.In Chinese,family names always come first and given names come last Given names usually have some special meanings.We also had informal names when we were little kids,such as Congcong,Nana and so on.

U.Taxonstand:An R package for standardizing scientific names of plants and animals

The scientific names of organisms are key identifiers of plants and animals.Correctly treating scientific names is a prerequisite for biodiversity research and documentation.Here,we present an R package,’U.Taxonstand’,which can standardize and harmonize scientific names in plant and animal species lists at a fast speed and at a high rate of matching success.Unlike most of other similar R packages each of which works with only one taxonomic database,U.Taxonstand can work with all taxonomic databases,as long as they are properly formatted.Multiple databases for plants and animals that can be directly used by U.Taxonstand,which include bryophytes,vascular plants,amphibians,birds,fishes,mammals,and reptiles,are available online.U.Taxonstand can be a very useful tool for botanists,zoologists,ecologists and biogeographers to standardize and harmonize scientific names of organisms.

Translation of Metro Station Names in Guangzhou and Foshan from the Perspective of Symbolic Function of Translation Landscape

Guangzhou and Foshan enjoy relatively mature metro network.However,some names of metro stations are over-transliterated in Pinyin.Such a translation method is used in translating general names,nouns of locality and some names of tourist destinations.With translation landscape and linguistic landscape theories,the reasons and impacts of over-transliteration in Guangzhou and Foshan metro will be discussed from the perspective of symbolic function.English names of Metro stations in other cities serve as a reference so as to appropriate solutions.

SciCN:A Scientific Dataset for Chinese Named Entity Recognition

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://github.com/yangjingla/SciCN.git.

Membrane vesicles derived from Streptococcus suis serotype 2 induce cell pyroptosis in endothelial cells via the NLRP3/Caspase-1/GSDMD pathway

Streptococcus suis serotype 2(S.suis 2)is a zoonotic pathogen that clinically causes severe swine and human infections(such as meningitis,endocarditis,and septicemia).In order to cause widespread diseases in different organs,S.suis 2 must colonize the host,break the blood barrier,and cause exaggerated inflammation.In the last few years,most studies have focused on a single virulence factor and its influences on the host.Membrane vesicles(MVs)can be actively secreted into the extracellular environment contributing to bacteria-host interactions.Gram-negative bacteria-derived outer membrane vesicles(OMVs)were recently shown to activate host Caspase-11-mediated non-canonical inflammasome pathway via deliverance of OMV-bound lipopolysaccharide(LPS),causing host cell pyroptosis.However,little is known about the effect of the MVs from S.suis 2(Gram-positive bacteria without LPS)on cell pyroptosis.Thus,we investigated the molecular mechanism by which S.suis 2 MVs participate in endothelial cell pyroptosis.In this study,we used proteomics,electron scanning microscopy,fluorescence microscope,Western blotting,and bioassays,to investigate the MVs secreted by S.suis 2.First,we demonstrated that S.suis 2 secreted MVs with an average diameter of 72.04 nm,and 200 proteins in MVs were identified.Then,we showed that MVs were transported to cells via mainly dynamin-dependent endocytosis.The S.suis 2 MVs activated NLRP3/Caspase-1/GSDMD canonical inflammasome signaling pathway,resulting in cell pyroptosis,but it did not activate the Caspase-4/-5 pathway.More importantly,endothelial cells produce large amounts of reactive oxygen species(ROS)and lost their mitochondrial membrane potential under induction by S.suis 2 MVs.The results in this study suggest for the first time that MVs from S.suis 2 were internalized by endothelial cells via mainly dynamin-dependent endocytosis and might promote NLRP3/Caspase-1/GSDMD pathway by mitochondrial damage,which produced mtDNA and ROS under induction,leading to the pyroptosis of endothelial cells.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

Mathematical Named Entity Recognition Based on Adversarial Training and Self-Attention

Mathematical named entity recognition(MNER)is one of the fundamental tasks in the analysis of mathematical texts.To solve the existing problems of the current neural network that has local instability,fuzzy entity boundary,and long-distance dependence between entities in Chinese mathematical entity recognition task,we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long shortterm memory-Selfattention Conditional random field(AT-BSAC)model.In our model,the mathematical text was vectorized by the word embedding technique,and small perturbations were added to the word vector to generate adversarial samples,while local features were extracted by Bi-directional Long Short-Term Memory(BiLSTM).The self-attentive mechanism was incorporated to extract more dependent features between entities.The experimental results demonstrated that the AT-BSAC model achieved a precision(P)of 93.88%,a recall(R)of 93.84%,and an F1-score of 93.74%,respectively,which is 8.73%higher than the F1-score of the previous Bi-directional Long Short-Term Memory Conditional Random Field(BiLSTM-CRF)model.The effectiveness of the proposed model in mathematical named entity recognition.

A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models

Named Entity Recognition(NER)is crucial for extracting structured information from text.While traditional methods rely on rules,Conditional Random Fields(CRFs),or deep learning,the advent of large-scale Pre-trained Language Models(PLMs)offers new possibilities.PLMs excel at contextual learning,potentially simplifying many natural language processing tasks.However,their application to NER remains underexplored.This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning.We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity.Our experimental results demonstrate the feasibility of this approach,suggesting a promising direction for harnessing PLMs in NER.

基于盆底超声波的SUI肛提肌生物力学评估及其应用研究

关于SUI(压力性尿失禁)患者肛提肌生物力学特征的研究目标,旨在运用盆底超声波技术进行评估及实际应用,细究SUI之缘起,赋予了独到的观察与防范视点。该项探究运用了盆腹超声技术,针对压力性尿失禁(SUI)的女性,研究人员对其肛提肌生物力学进行了探讨。深入研究超声波图像,探寻肛提肌的构造与运动规律,对生物力学参数予以探究与剖析。有关SUI患者的现象,研究揭示出其特殊性,盆底肌肉力量失衡,运动能力衰退与肌肉组织层密度降低存在关联。此外,生物力学异变与病患病情深刻相连。调查成果表明,肛提肌的生物力学特性与压力性尿失禁(SUI)的发生及发展存在一定联系。运用盆底超声波之法,深入研究肛提肌之生物力学特性,SUI的诊断获得了实证,关于尿失禁(SUI)的全新应对策略已成功实施。

基于GeoNames和Solr的地名数据全文检索

地名数据是一种重要的地理信息资源,目前我国的地名数据库建设多局限于国内地名,缺少国外数据。传统地名数据的检索多为关键字查询,查询效率低且无法用于复杂地名查询。文中提出一种基于开源地名数据库GeoNames和开源搜索引擎Solr的地名数据全文检索方法,通过分析GeoNames的数据类型和结构,构建MySQL地名数据库,并利用Solr建立地名索引,提供基于Web服务的地名数据全文检索。实验表明,基于Solr的地名数据全文检索能够显著提高地名数据检索效率,对于复杂地名查询,也能进行有效地检索。

A Cognitive Study of the Naming Elements of Chinese Dish Names Based on the Prominence Principle

The assortedness of Chinese food,together with the complexity of their naming elements,has ignited numerous scholars' interests in this field and prompted them to make abundant analyses of Chinese dish names.Most of them,however,were done in studies of traditional linguistics,rhetoric,translatology and cross-cultural communication.And studies,based on corpus,on the naming elements of Chinese dishes under cognitive linguistic theories almost remain a blank.This paper aims to conduct a quantitative analysis of 4,000 Chinese dish names(500 ones selected freely from each of the eight cuisines),based on the Prominence Principle,in order to identify the specific naming elements of Chinese dishes and forward related statistics and ratios.

On Generalities of Chinese Names and English Names

Name system has developed simultaneously with the change of world language.Generalities of Chinese names and English names are discussed from 8 aspects.Meanwhile,the structural changes of names in Chinese and English are also contrasted in order to make some further exploration.Readers may more deeply understand the specific regularity of the individual languages after knowing several generalities.

Characteristics Studies on Uyghur Place Names in Ili Kazak Autonomous Prefecture

Because of its peculiar language environment with multi ethnicities and the One Belt and One Road initiative,the studies of place names in Ili Kazak Autonomous Prefecture attract many linguists’interests.This paper focuses on characteristics studies of Uyghur place names in Ili Kazak Autonomous Prefecture based on Universal Principles of Tendencies proposed by the Polish linguist Witold Manczak.Through careful and thorough study,this paper found that because of different traditional lifestyles and different political strategies through different historical periods,as urban people,the Uyghur place names highlighted the Uyghur people’s great contributions to artificial constructions.

Characteristics Studies on Kazak Place Names in Ili Kazak Autonomous Prefecture

This paper focuses on characteristics studies of Kazak place names in Ili Kazak Autonomous Prefecture based on Univer-sal Principles of Tendencies proposed by the Polish linguist Witold Manczak.Through careful and thorough study,this paper foundthat because of different traditional lifestyles and different political strategies through different historical periods,this paper provedthat the Kazaks as the nomads were proficient in observing the environmental conditions,and made full use of them to name places.

Road or Lu, Is It Still a Question?—On Translation of Road and Street Names from the Perspective of Functional Theories

The translation of road names from Chinese into English applies no specific standards up to date. Whether the roads should be re ferred in accordance with the nearby position or some symbolic buildings or simply be represented by a phonetic pronunciation which has no specific meaning in English has provoked some heated discussion. In this paper I employ the skopos theory and other relevant theories to put forward a new perspective of translating road and street names, with same emphasis on that of generic name and on specific name. Some illustrations will be made on the defects of the writing standard set by relevant Chinese laws and regulations as well as resolutions of the United Nations Conference on the Standardization of Geographical Names, i.e. the adoption of a Single Romanization System, and Lu to be the generic name in China's case. Also, I will further probe the existing translations of road and street names in major Chinese cities, most of which to be Road, either go against relevant laws or fail to inform foreign visitors. Finally, I will come up with the suggestion of ap plication of a combination of Lu and Road in translation of road and street names in accordance with functional approaches.

Principles of Translating Brand Names and Cultural Taboos in Translating Brand Names

Translation of brand names is a form of intercultural communication. Whether we translate the Chinese brand names into the English ones or translate the English brand names into the Chinese ones, language laws, cultural psychology, aesthetic interest and some other factors will be involved. According to the principle of equivalence theory, the translated brand names should achieve a perfect linguistic unity among sound, form and meaning. Based on the translation principles, the translators should also pay attention to some forbidden zones.

Translation of Brand Names Based on Adaptation Theory

This paper,from the perspective of Verschueren’s adaptation theory,explores how a translator should adapt to the properties of products,different language customs,and consumers’psychology during the translation of brand names.First,a gen eral introduction is made on adaptation theory.Then,the application of adaptation theory in brand name translation is illustrated.Finally it is found that adaptation theory is very helpful for the translation of brand names.