The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effec...The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.展开更多
The advantages and disadvantages for learning English in the Network-based environment attract most researchers’concern nowadays.This study profiles college English teachers’beliefs about the networkbased language l...The advantages and disadvantages for learning English in the Network-based environment attract most researchers’concern nowadays.This study profiles college English teachers’beliefs about the networkbased language learning.The main finding is that teachers’beliefs about network-based language learning are heterogeneous and thus reflect a wide range in terms of the evolution of approaches and technology use.展开更多
The emergence of embodied cognition theory has altered our traditional understanding of children’s language learning,emphasizing the close connection between the body,environment,and movement.This paper discusses the...The emergence of embodied cognition theory has altered our traditional understanding of children’s language learning,emphasizing the close connection between the body,environment,and movement.This paper discusses the opportunities,challenges,and future directions of research on children’s language learning from the perspective of embodied cognition.It concludes that multisensory engagement can greatly improve children’s comprehension and memorization of language knowledge and that language acquisition is intimately tied to bodily perception,movement,and emotional experience.In addition,children’s language acquisition can also be effectively aided by embodied cognition techniques as multimedia aids,gesture and enactment,and imagery.Based on previous evidence,we propose an integrated language learning framework and a new relevance-integration taxonomy for children’s language learning from the perspectives of embodied cognition and cognitive load theories.In order to support the long-term growth of children’s language education,future research should focus more on the requirement of embodied language learning in the preschool-primary transition and optimize the teaching objectives and contents.展开更多
The paper aims to examine the application of multimedia technology in expanding vocabulary in second language acquisition.Incorporating innovative technology such as mobile applications,gaming applications,websites,an...The paper aims to examine the application of multimedia technology in expanding vocabulary in second language acquisition.Incorporating innovative technology such as mobile applications,gaming applications,websites,and other related online tools has increased learners’vocabulary mastery,engagement,and motivation levels.Interactional processes like media-embedded objects,teach-learning capacity algorithms,and feedback help learners receive the course in a personalized way that considers individual learning patterns or abilities.However,there are the following challenges:accessibility issues,total reliance on technology,and issues related to privacy.The following challenges affecting learning that arise from using gadgets:the digital divide,limited device access,and environmental issues that may distract a learner in a technology-enabled environment.Moreover,the security issue for data and the ethical question of users’information remain important too.Hence,the paper provides arguments that although these technologies contribute significantly to vocabulary acquisition,the challenge that emerges should be addressed by integrating technology in teaching and learning alongside conventional methods for vocabulary acquisition,which is a practical language acquisition tool that should not be monopolized.展开更多
Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the in...Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.展开更多
This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These ca...This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.展开更多
Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte...Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.展开更多
This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretic...This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretical evolution,methodological innovations,key influencing factors and proposed suggestion for further research on student agency.Future research should prioritize,longitudinal studies,culturally comparative designs,validity constructs and ethical evaluations of artificial intelligence’s impact on learner autonomy.This review calls for a holistic approach to FL education,where agentic engagement bridges individual initiative,pedagogical innovation,and sociocultural responsiveness to empower learners in multilingual global contexts.展开更多
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 automa...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.展开更多
With the swift development of network technology, research on how to integrate network technology into language learning has become a trend. This paper examines whether the application of language learning strategy(LL...With the swift development of network technology, research on how to integrate network technology into language learning has become a trend. This paper examines whether the application of language learning strategy(LLS) in the networkbased environment has incomparable superiority. Beginning with the literature review, it presents an analysis on similarities and differences between network-based language learning strategy(NBLLS) and non-NBLLS, and then expounds the characteristics,the influencing factors and teachers' role of NBLLS. Taking 25 participants in the group of non-NBLL and 34 in the NBLL group as the comparative survey study, the empirical result shows the new evidence that there is a little difference between the two groups in the use of LLS. The findings of this study have implications for the application of NBLLS.展开更多
Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learn...Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.展开更多
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 Prof...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.展开更多
Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentime...Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.展开更多
Against the backdrop of the digital transformation of vocational education,informal digital learning of foreign language(hereinafter referred to as IDLFL)is an emerging learning model with significant potential for en...Against the backdrop of the digital transformation of vocational education,informal digital learning of foreign language(hereinafter referred to as IDLFL)is an emerging learning model with significant potential for enabling individualized and autonomous learning and improving workplace English proficiency among Higher Vocational Colleges(hereinafter referred to as HVC)students.This study explores how Chinese foreign language teachers in HVC perceive and understand HVC students’IDLFL.Primary data are collected through questionnaires and interviews,and qualitative analysis is conducted based on grounded theory to reveal effective ways to improve the foreign language learning outcomes of HVC students through IDLFL and the corresponding strategies that HVC language teachers should adopt.However,this study also emphasizes the need to be wary of overstating the impact and role of IDLFL in teaching practice.展开更多
Native language has been rejected for a long time in the foreign language class, which results from a misunderstanding to native language transfer by most teachers. Actually, it is an effective learning strategy to co...Native language has been rejected for a long time in the foreign language class, which results from a misunderstanding to native language transfer by most teachers. Actually, it is an effective learning strategy to complete the communication task under the help of native language, which should be acknowledged. Using native language to explain specific words or grammar rules can take advantage of the limited class-time efficiently and increase the class efficiency; using native language to discuss teaching method and solve the problems for the students can promote their enthusiasm. In these specific teaching processes, native language is an important teaching resource. Instances identify that native language has positive effects in the foreign language learning and teaching and it should have its own standpoint.展开更多
As the ultimate goal of education, autonomy in language learning has aroused a lot of attention from scholars at home and abroad. While in universities of China, students do not have strong autonomy in English languag...As the ultimate goal of education, autonomy in language learning has aroused a lot of attention from scholars at home and abroad. While in universities of China, students do not have strong autonomy in English language learning. The author tries to adopt specific meta-cognitive strategies to facilitate students＇ autonomy in learning by improving learners＇ capacities in study planning or management, monitoring and evaluating in learning to raise their consciousness and ability in autonomy, and lay a foundation for life-long learning.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different ...Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.展开更多
Independent language learning is one aspect of what the research literature calls learner autonomy, in other words, the ability and willingness of learners to study foreign languages on their own. This article illustr...Independent language learning is one aspect of what the research literature calls learner autonomy, in other words, the ability and willingness of learners to study foreign languages on their own. This article illustrates the strategies I used to design, carry out and sustain my independent language learning project.展开更多
文摘The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.
文摘The advantages and disadvantages for learning English in the Network-based environment attract most researchers’concern nowadays.This study profiles college English teachers’beliefs about the networkbased language learning.The main finding is that teachers’beliefs about network-based language learning are heterogeneous and thus reflect a wide range in terms of the evolution of approaches and technology use.
基金supported by the Humanities and Social Science Fund of the Ministry of Education of China(23YJA190012)Guangdong Provincial College Student Innovation and Entrepreneurship Training Program(S202410577105)+1 种基金Huizhou Philosophy and Social Sciences Discipline Co-Construction Project(HZ2023GJ128)Characteristic Innovation Project of Colleges and Universities in Guangdong Province(2021WTSCX090).
文摘The emergence of embodied cognition theory has altered our traditional understanding of children’s language learning,emphasizing the close connection between the body,environment,and movement.This paper discusses the opportunities,challenges,and future directions of research on children’s language learning from the perspective of embodied cognition.It concludes that multisensory engagement can greatly improve children’s comprehension and memorization of language knowledge and that language acquisition is intimately tied to bodily perception,movement,and emotional experience.In addition,children’s language acquisition can also be effectively aided by embodied cognition techniques as multimedia aids,gesture and enactment,and imagery.Based on previous evidence,we propose an integrated language learning framework and a new relevance-integration taxonomy for children’s language learning from the perspectives of embodied cognition and cognitive load theories.In order to support the long-term growth of children’s language education,future research should focus more on the requirement of embodied language learning in the preschool-primary transition and optimize the teaching objectives and contents.
基金Interim Achievements of the“Yingying Technology Empowerment–Application-Oriented Talent Enhancement Project at Changchun College of Electronic Technology”under the Fourth Phase of the 2024 Ministry of Education’s Employment-Education Collaboration Project(Project Number:2024121188944Project Leader:Chunhua Ren)+3 种基金Interim Achievements of the“Directional Cultivation Project for Composite Talents at Changchun College of Electronic Technology”under the Fourth Phase of the 2024 Ministry of Education’s Supply-Demand Matching and Employment-Education Cultivation Program(Project Number:2024121107571Project Leader:Chunhua Ren)Interim Achievements of the“Research on the Cultivation Path of Craftsmanship Spirit among University Teachers in the Context of Industry-University Collaboration”under the 2025 Ministry of Education’s Industry-University Cooperative Education Project(Project Number:2505164755Project Leader:Chunhua Ren)。
文摘The paper aims to examine the application of multimedia technology in expanding vocabulary in second language acquisition.Incorporating innovative technology such as mobile applications,gaming applications,websites,and other related online tools has increased learners’vocabulary mastery,engagement,and motivation levels.Interactional processes like media-embedded objects,teach-learning capacity algorithms,and feedback help learners receive the course in a personalized way that considers individual learning patterns or abilities.However,there are the following challenges:accessibility issues,total reliance on technology,and issues related to privacy.The following challenges affecting learning that arise from using gadgets:the digital divide,limited device access,and environmental issues that may distract a learner in a technology-enabled environment.Moreover,the security issue for data and the ethical question of users’information remain important too.Hence,the paper provides arguments that although these technologies contribute significantly to vocabulary acquisition,the challenge that emerges should be addressed by integrating technology in teaching and learning alongside conventional methods for vocabulary acquisition,which is a practical language acquisition tool that should not be monopolized.
文摘Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant No.12202451).
文摘This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.
基金supported by the National Natural Science Foundation of China(Grant No.:62101087)the China Postdoctoral Science Foundation(Grant No.:2021MD703942)+2 种基金the Chongqing Postdoctoral Research Project Special Funding,China(Grant No.:2021XM2016)the Science Foundation of Chongqing Municipal Commission of Education,China(Grant No.:KJQN202100642)the Chongqing Natural Science Foundation,China(Grant No.:cstc2021jcyj-msxmX0834).
文摘Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.
文摘This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretical evolution,methodological innovations,key influencing factors and proposed suggestion for further research on student agency.Future research should prioritize,longitudinal studies,culturally comparative designs,validity constructs and ethical evaluations of artificial intelligence’s impact on learner autonomy.This review calls for a holistic approach to FL education,where agentic engagement bridges individual initiative,pedagogical innovation,and sociocultural responsiveness to empower learners in multilingual global contexts.
基金supported from the National Philosophy and Social Sciences Foundation(Grant No.20BTQ065).
文摘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.
文摘With the swift development of network technology, research on how to integrate network technology into language learning has become a trend. This paper examines whether the application of language learning strategy(LLS) in the networkbased environment has incomparable superiority. Beginning with the literature review, it presents an analysis on similarities and differences between network-based language learning strategy(NBLLS) and non-NBLLS, and then expounds the characteristics,the influencing factors and teachers' role of NBLLS. Taking 25 participants in the group of non-NBLL and 34 in the NBLL group as the comparative survey study, the empirical result shows the new evidence that there is a little difference between the two groups in the use of LLS. The findings of this study have implications for the application of NBLLS.
基金This Research is funded by Researchers Supporting Project Number(RSPD2024R947),King Saud University,Riyadh,Saudi Arabia.
文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
文摘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.
文摘Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.
文摘Against the backdrop of the digital transformation of vocational education,informal digital learning of foreign language(hereinafter referred to as IDLFL)is an emerging learning model with significant potential for enabling individualized and autonomous learning and improving workplace English proficiency among Higher Vocational Colleges(hereinafter referred to as HVC)students.This study explores how Chinese foreign language teachers in HVC perceive and understand HVC students’IDLFL.Primary data are collected through questionnaires and interviews,and qualitative analysis is conducted based on grounded theory to reveal effective ways to improve the foreign language learning outcomes of HVC students through IDLFL and the corresponding strategies that HVC language teachers should adopt.However,this study also emphasizes the need to be wary of overstating the impact and role of IDLFL in teaching practice.
文摘Native language has been rejected for a long time in the foreign language class, which results from a misunderstanding to native language transfer by most teachers. Actually, it is an effective learning strategy to complete the communication task under the help of native language, which should be acknowledged. Using native language to explain specific words or grammar rules can take advantage of the limited class-time efficiently and increase the class efficiency; using native language to discuss teaching method and solve the problems for the students can promote their enthusiasm. In these specific teaching processes, native language is an important teaching resource. Instances identify that native language has positive effects in the foreign language learning and teaching and it should have its own standpoint.
文摘As the ultimate goal of education, autonomy in language learning has aroused a lot of attention from scholars at home and abroad. While in universities of China, students do not have strong autonomy in English language learning. The author tries to adopt specific meta-cognitive strategies to facilitate students＇ autonomy in learning by improving learners＇ capacities in study planning or management, monitoring and evaluating in learning to raise their consciousness and ability in autonomy, and lay a foundation for life-long learning.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金supported by the ONR Vannevar Bush Faculty Fellowship(Grant No.N00014-22-1-2795).
文摘Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.
文摘Independent language learning is one aspect of what the research literature calls learner autonomy, in other words, the ability and willingness of learners to study foreign languages on their own. This article illustrates the strategies I used to design, carry out and sustain my independent language learning project.
