Visualizing medicine:The case for implementing graphical abstracts in clinical reporting

Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant advantages of GAs,including improved clarity,increased reader engagement,and enhanced visibility of research findings.By transforming intricate scientific data into accessible visual formats,these abstracts facilitate quick and effective knowledge transfer,crucial in clinical decision-making and patient care.However,challenges such as potential data misrepresentation due to oversimplification,the skill gap in graphic design among researchers,and the lack of standardized creation guidelines pose barriers to their widespread adoption.Additionally,while software such as Adobe Illustrator,BioRender,and Canva are commonly employed to create these visuals,not all researchers may be proficient in their use.To address these issues,we recommend that academic journals establish clear guidelines and provide necessary design training to researchers.This proactive approach will ensure the creation of high-quality GAs,promote their standardization,and expand their use in clinical reporting,ultimately benefiting the medical community and improving healthcare outcomes.

Research on the Novel Mode of Plane Graphic Design from the Angle of Visual Language Transmitting

In this paper, we conduct research on the novel mode of plane graphic design from the core angle of the primary visual language transmitting. Relationship between nationality and cosmopolitan processing in graphic design in the design of image symbol expression and significance of the symbol on the one hand require symbolic form must have enough openness and the cognitive function on the other hand with semiotics method can let the designer to extract the most representative and symbolic notation style to carry on the design expression. As a non- verbal symbols, from the perspective of the view, the design image is no national boundaries, but the human form to beauty and should express the symbolism of cognition is the same. In the form of the ethnic groups of the cosmopolitan codes are for identification of Chinese contemporary design possible effective methods. Our research proposes novel perspective of the design which is meaningful.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Modelling Design of Color Graphics Books Using Visual Vocabulary Based on Children’s Color Language Preferences

Color language has important meaning for children’s picture books.Understanding children’s preferences in terms of color language will be helpful when designers model computerized picture books in order to adapt to children’s visual senses and effectively stimulate children’s interest in reading.In this study,we aimed to further explore the general characteristics of children’s preference for colors,color depth and color matching forms in picture books.For the study,256 children between the ages of three and six were selected and divided into four groups.According to the implementation needs of the children’s color preference test,these childrenwere asked to complete a series of tasks so that their choices in the tasks could subsequently be analyzed.The tasks included making the following choices:selecting their preferred colors from nine options(red,orange,yellow,green,cyan,blue,purple,black and white);selecting their preferred color brightness fromthree different color shades;and indicating their preferences in relation to complementary colors,matching colors and pure colors.The experimental results showed that children aged between three and six mainly favor red,blue and yellow,and colors with a high degree of brightness.As children grow older,boys tend to prefer blue,and girls tend to prefer pink,with red remaining the favorite color of both genders.As for complementary colors,matching colors and solid colors,three-to six-year-old children prefer solid colors.In conclusion,it seems that color language preferences in children between the ages of three and six are very similar,which could be of use when designing children’s picture books to match their visual vocabulary and psychological cognition.

Recommender System for Information Retrieval Using Natural Language Querying Interface Based in Bibliographic Research for Naïve Users

With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

FL-Online:An x-ray crystallographic web-server for atomic-scale structure analysis of biomolecule

FL-Online(http://fanlab.ac.cn) is an out-of-box modern web service featuring a user-friendly interface and simplified parameters, providing academic users with access to a series of online programs for biomolecular crystallography, including SAPI-online, OASIS-online, C-IPCAS-online and a series of upcoming software releases. Meanwhile, it is a highly scalable and maintainable web application framework that provides a powerful and flexible solution for academic web development needs. All the codes are open-source under MIT licenses in GitHub.

Plain language in the healthcare of Japan:a systematic review of“plain Japanese”

Objective:Despite the decrease in the number of foreign visitors and residents in Japan due to the coronavirus disease 2019,a resurgence is remarkable from 2022.However,Japan's medical support system for foreign patients,especially residents,is inadequate,with language barriers potentially causing health disparities.Comprehensive interpretation and translation services are challenging,but“plain Japanese”may be a viable alternative for foreign patients with basic Japanese language skills.This study explores the application and obstacles of plain Japanese in the medical sector.Methods:A literature review was performed across these databases:Web of Science,PubMed,Google Scholar,Scopus,CINAHL Plus,Springer Link and Ichushi-Web(Japanese medical literature).The search covered themes related to healthcare,care for foreign patients,and scholarly articles,and was conducted in July 2023.Results:The study incorporated five papers.Each paper emphasized the language barriers foreign residents in Japan face when accessing healthcare,highlighting the critical role and necessity of plain Japanese in medical environments.Most of the reports focused on the challenges of delivering medical care to foreign patients and the training of healthcare professionals in using plain Japanese for communication.Conclusion:The knowledge and application of plain Japanese among healthcare professionals are inadequate,and literature also remains scarce.With the increasing number of foreign residents in Japan,the establishment of a healthcare system that effectively uses plain Japanese is essential.However,plain Japanese may not be the optimal linguistic assistance in certain situations,thus it is imperative to encourage more research and reports on healthcare services using plain Japanese.

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

Foreign language teaching practice is developing rapidly,but research on foreign language teacher learning is currently relatively fragmented and unstructured.The book Foreign Language Teacher Learning,written by Professor Kang Yan from Capital Normal University,published in September 2022,makes a systematic introduction to foreign language teacher learning,which to some extent makes up for this shortcoming.Her book presents the lineage of foreign language teacher learning research at home and abroad,analyzes both theoretical and practical aspects,reviews the cuttingedge research results,and foresees the future development trend,painting a complete research picture for researchers in the field of foreign language teaching and teacher education as well as front-line teachers interested in foreign language teacher learning.This is an important inspiration for conducting foreign language teacher learning research in the future.And this paper makes a review of the book from aspects such as its content,major characteristics,contributions and limitations.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Incorporating Technology into English Language Learners’Classroom—A Case Study of Using Wiki,Podcast,and Computer Graphic Organizer as Tools

The technology has been utilized by many teachers to provide English language learners with better learning experience.But it can not be denied that the technology is a two edged sword.The technology could facilitate the students’learning if it is used in a proper way.Otherwise,the technology may even have adverse effects.The paper will illustrate how the teacher could succeed in adapting the technology to meet specific demand of the students through case study.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.

Visualizing Language and Aging From 2013-2022

Declining cognitive abilities can be a concomitant of advanced age.As language is closely associated with cognitive abilities,changes in language abilities can be an important marker of changes in cognitive abilities.The current study is to review cognitive studies of language and aging by first identifying and exploring the major clusters and pivotal articles and then detecting emerging trends.Data of 3,266 articles on language and aging from 2013 to 2022 were collected from the Web of Science Core Collection database.Adopting Document Co-citation Analysis,Freeman’s betweenness centrality metric(Freeman,2002)and Kleinberg’s burst detection algorithm(Kleinberg,2002),we explored major clusters,pivotal articles and emerging trends in this field.Cognition appears to be the most remarkable cluster.Bilingualism,speech production,listening effort,and reading comprehension are other major active clusters in a certain period.The most recent active cluster concerns the studies of Alzheimer’s disease.Articles serving as pivotal points concentrate on cognitive studies of the Framework for Understanding Effortful Listening(FUEL),the new Ease of Language Understanding model(EUL)and a hierarchical multi-representational generative framework of language comprehension.The progress in statistical methods,the relationship between language and cognitive impairment and the relationship between language abilities and cognition are the emerging trends.These emerging trends will provide some insights into how cognitive abilities influence language abilities in aging.

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

Recent Advances on Deep Learning for Sign Language Recognition

Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.

Application of Perceived Teaching with Moderate Elimination of“Graphics”in Architectural Design Courses

Starting from the traditional form of“graphic”architectural education,this paper explores a teaching method of entity construction experience,instrument assistance,and digital virtual expression symbiosis symbiosis from three aspects:environmental perception,ontology perception,and extension perception.Using physical perception as a medium,and gradually rising from active perception to a comprehensive expression of visual audience perception through practical operation,it allows students to have a more comprehensive understanding of the meaning of architectural design on the basis of the“graphic”expression paradigm.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

A large language model-powered literature review for high-angle annular dark field imaging

High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemical information through Z-contrast.This study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.