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.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

Preliminary results on a near-real-time rock slope damage monitoring system based on relative velocity changes following the September 5,2022 M_(S) 6.8 Luding,China earthquake

Relative seismic velocity change(dv/v)is important for monitoring changes in subsurface material properties and evaluating earthquake-induced rock slope damage in a geological disaster-prone region.In this paper,we present a rapid damage assessment on three slow-moving rock slopes by measuring dv/v decrease caused by the 2022 M_(S) 6.8 Luding earthquake in Southwest China.By applying the stretching method to the cross-correlated seismic wavefields between sensors installed on each slope,we obtain earthquake-induced dv/v decreases of~2.1%,~0.5%,and~0.2%on three slopes at distances ranging from~86 to~370 km to the epicenter,respectively.Moreover,based on seismic data recorded by 16 sensors deployed on the rock slope at a distance of~370 km away from the epicenter,a localized dv/v decease region was observed at the crest of the slope by calculating the spatial dv/v images before and after the earthquake.We also derive an empirical in situ stress sensitivity of -7.29×10^(-8)/Pa by relating the dv/v change to the measured peak dynamic stresses.Our results indicate that a rapid dv/v assessment not only can help facilitate on-site emergency response to earthquakeinduced secondary geological disasters but also can provide a better understanding of the subsurface geological risks under diverse seismic loadings.

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.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

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.

Generativity of Self-Organizing Processes and Their Correlative Description in Terms of a Formal Language of Meta-Ordinal Generative Nature, in the Light of the Maximum Ordinality Principle and the Explicit Solution to the “Three-Body Problem”

The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

Effect of scalp acupuncture combined with language rehabilitation training in treating post-stroke motor aphasia:A systematic review and meta-analysis

Objective:To evaluate the efficacy of scalp acupuncture combined with language rehabilitation training in the treatment of motor aphasia.Methods:CNKI,VIP,Wan Fang Database,MEDLINE,Embase,Web of Science and Cochrane Library were searched for published researches up to March,2021.Randomized controlled trials RCTs that focused on scalp acupuncture combined with language rehabilitation training in the treatment of motor aphasia were included.We managed the data analysis with RevMan 5.3 software.Results:A total of 16 RCTs with 1323 patients were involved.The results of meta-analysis showed that:①The effective rate of scalp acupuncture combined with language rehabilitation training in the treatment of motor aphasia after stroke was significantly better than that of simple language rehabilitation training[OR=3.94,95%CI(2.73,5.68),P<0.00001];②In the evaluation of language function,compared with the language rehabilitation training,the scalp acupuncture combined with language rehabilitation training can significantly improve the reading ability of the patients with motor aphasia after stroke[MD=7.22,95%CI(3.55,10.89),P=0.0001],writing ability[MD=6.51,95%CI(3.61,9.41),P<0.0001],expressive ability[MD=4.13,95%CI(2.37,5.89),P<0.0001],retelling ability[MD=5.00,95%CI(2.38,7.63),P=0.0002],listening comprehension ability[MD=5.36,95%CI(3.12,7.61),P<0.00001]and naming ability[MD=5.60,95%CI(4.20,7.00),P<0.00001];③Compared with simple language rehabilitation training,scalp acupuncture combined with language rehabilitation can significantly improve the daily life language communication ability of patients with motor aphasia,and the difference was statistically significant[MD=30.01,95%CI(11.30,48.72),P=0.002].Conclusion:Scalp acupuncture combined with language rehabilitation training has a significant effect on motor aphasia.However,due to the small sample size,more RCTs are needed to confirm that.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

A Natural Language Generation Algorithm for Greek by Using Hole Semantics and a Systemic Grammatical Formalism

This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.

Complementary monogamy and polygamy properties among multipartite systems

Monogamy and polygamy relations are essential properties of quantum entanglement,which characterize the distributions of entanglement in multipartite systems.In this paper,we establish the general monogamy relations forγ-th(0≤γ≤α,α≥1)power of quantum entanglement based on unified-(q,s)entanglement and polygamy relations forδ-th(δ≥β,0≤β≤1)power of entanglement of assistance based on unified-(q,s)entanglement of assistance,which provides a complement to the previous research in terms of different parameter regions ofγandδ.These results are then applied to specific quantum correlations,e.g.,entanglement of formation,Renyi-q entanglement of assistance and Tsallis-q entanglement of assistance to get the corresponding monogamy and polygamy inequalities.Moreover,typical examples are presented for illustration.

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.

The chemical characteristics of different sodium iron ethylenediaminetetraacetate sources and their relative bioavailabilities for broilers fed with a conventional corn‑soybean meal diet

Background Our previous studies demonstrated that divalent organic iron(Fe)proteinate sources with higher complexation or chelation strengths as expressed by the greater quotient of formation(Qf)values displayed higher Fe bioavailabilities for broilers.Sodium iron ethylenediaminetetraacetate(NaFeEDTA)is a trivalent organic Fe source with the strongest chelating ligand EDTA.However,the bioavailability of Fe when administered as NaFeEDTA in broilers and other agricultural animals remains untested.Herein,the chemical characteristics of 12 NaFeEDTA products were determined.Of these,one feed grade NaFeEDTA(Qf=2.07×10^(8)),one food grade NaFeEDTA(Qf=3.31×10^(8)),and one Fe proteinate with an extremely strong chelation strength(Fe-Prot ES,Qf value=8,590)were selected.Their bioavailabilities relative to Fe sulfate(FeSO_(4)·7H_(2)O)for broilers fed with a conventional corn-soybean meal diet were evaluated during d 1 to 21 by investigating the effects of the above Fe sources and added Fe levels on the growth performance,hematological indices,Fe contents,activities and gene expressions of Fe-containing enzymes in various tissues of broilers.Results NaFeEDTA sources varied greatly in their chemical characteristics.Plasma Fe concentration(PI),transferrin saturation(TS),liver Fe content,succinate dehydrogenase(SDH)activities in liver,heart,and kidney,catalase(CAT)activity in liver,and SDH mRNA expressions in liver and kidney increased linearly(P<0.05)with increasing levels of Fe supplementation.However,differences among Fe sources were detected(P<0.05)only for PI,liver Fe content,CAT activity in liver,SDH activities in heart and kidney,and SDH mRNA expressions in liver and kidney.Based on slope ratios from multiple linear regressions of the above indices on daily dietary analyzed Fe intake,the average bioavailabilities of Fe-Prot ES,feed grade NaFeEDTA,and food grade NaFeEDTA relative to the inorganic FeSO_(4)·7H_(2)O(100%)for broilers were 139%,155%,and 166%,respectively.Conclusions The bioavailabilities of organic Fe sources relative to FeSO_(4)·7H_(2)O were closely related to their Qf values,and NaFeEDTA sources with higher Qf values showed higher Fe bioavailabilities for broilers fed with a conventional corn-soybean meal diet.

Optimal investment based on relative performance and weighted utility

This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.

A Systematic Review of Language MOOCs: Research Design, Evidence, and Implications

This study conducts a systematic literature review to investigate Language MOOC(LMOOC),a newly-emerged research field,and aims to explore two aspects of LMOOC:the effectiveness of LMOOCs and factors influencing language learning in LMOOCs.This study reviews 24 empirical studies from the Web of Science.After analyzing and integrating research findings,this paper mainly draws two conclusions:(1)as for the learning outcomes,LMOOCs can improve learners’overall language proficiency,oral competence,vocabulary knowledge,and capability to apply the learned knowledge to an unknown situation;besides,the fact that most learners take a positive attitude towards LMOOC also demonstrates the excellent learning outcomes of LMOOC;(2)to facilitate and prevent the language learning in LMOOC,five factors should be considered:participants’autonomy,course organization,intrinsic motivation and external conditions of participants,cultural and contextual support of course content,language and technological ability.The research design and implications of LMOOCs are also discussed in this study.

Impacts of high temperature,relative air humidity,and vapor pressure deficit on the seed set of contrasting maize genotypes during flowering

Heat stress is a major constraint to current and future maize production at the global scale.Male and female reproductive organs both play major roles in increasing seed set under heat stress at flowering,but their relative contributions to seed set are unclear.In this study,a 2-year field experiment including three sowing dates in each year and 20 inbred lines was conducted.Seed set,kernel number per ear,and grain yield were all reduced by more than 80%in the third sowing dates compared to the first sowing dates.Pollen viability,silk emergence ratio,and anthesis-silking interval were the key determinants of seed set under heat stress;and their correlation coefficients were 0.89^(***),0.65^(***),and-0.72^(***),respectively.Vapor pressure deficit(VPD)and relative air humidity(RH)both had significant correlations with pollen viability and the silk emergence ratio.High RH can alleviate the impacts of heat on maize seed set by maintaining high pollen viability and a high silk emergence ratio.Under a warming climate from 2020 to 2050,VPD will decrease due to the increased RH.Based on their pollen viability and silk emergence ratios,the 20 genotypes fell into four different groups.The group with high pollen viability and a high silk emergence ratio performed better under heat stress,and their performance can be further improved by combining the improved flowering pattern traits.

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.

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.