Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspec...Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.展开更多
在小学高年级英语语篇教学中,存在学生思维浅表化、问题设计碎片化、旧版教材适配难这三个痛点。以译林版英语教材六年级上册Unit 4 Then and now中Story time的教学为例,教师立足教材文本,构建“课前定问—课初引链—课中解链—课后拓...在小学高年级英语语篇教学中,存在学生思维浅表化、问题设计碎片化、旧版教材适配难这三个痛点。以译林版英语教材六年级上册Unit 4 Then and now中Story time的教学为例,教师立足教材文本,构建“课前定问—课初引链—课中解链—课后拓链—全程评链”的五步闭环,用大问题拉主线、小问题搭台阶,能激活学生语篇学习内驱力,实现英语教学从“知识传递”到“素养培养”的转变。展开更多
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions to...Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?展开更多
Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CASTopic of the Special Issue What are the top questions tow...Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CASTopic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?展开更多
This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering(VQA)systems.The method focuses on identifying false answer predictions by the model a...This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering(VQA)systems.The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the input space,thus facilitating dataset curation.The decisionmaking process of the model is demonstrated by highlighting certain internal states of a graph neural network(GNN).The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset.The authors evaluated their tool through the demonstration of identified use cases,quantitative measures,and a user study conducted with experts from machine learning,visualization,and natural language processing domains.The authors’findings highlight the prominence of their implemented features in supporting the users with incorrect prediction identification and identifying the underlying issues.Additionally,their approach is easily extendable to similar models aiming at graph-based question answering.展开更多
Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capab...Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.展开更多
The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach ...The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions.However,the current structured degree of the URT emergency knowledge base remains low,and the domain questions lack labeled datasets,resulting in a large deviation between the consultation outcomes and the intended objectives.To address this issue,this paper proposes a question intention recognition model for the URT emergency domain,leveraging knowledge graph(KG)and data enhancement technology.First,a structured storage of emergency cases and emergency plans is realized based on KG.Subsequently,a comprehensive question template is developed,and the labeled dataset of emergency domain questions in URT is generated through the KG.Lastly,data enhancement is applied by prompt learning and the NLP Chinese Data Augmentation(NLPCDA)tool,and the intention recognition model combining Generalized Auto-regression Pre-training for Language Understanding(XLNet)and Recurrent Convolutional Neural Network for Text Classification(TextRCNN)is constructed.Word embeddings are generated by XLNet,context information is further captured using Bidirectional Long Short-Term Memory Neural Network(BiLSTM),and salient features are extracted with Convolutional Neural Network(CNN).Experimental results demonstrate that the proposed model can enhance the clarity of classification and the identification of domain questions,thereby providing supportive knowledge for emergency decision-making in URT.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
基金supported by the Ningxia Natural Science Foundation(2025AAC050001)the Scientific Research Startup Project for Full-Time Introduced High-Level Talents in Ningxia(2024BEH04130)+2 种基金the National Natural Science Foundation of China(32460444)the Ningxia Hui Autonomous Region Key Research and Development Program(2024BBF0101302,2023BDE02001)Supported by the Special Fund for Basic Research Business of Central Universities of North Minzu University(2025BG234,2023ZRLG12).
文摘Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.
文摘在小学高年级英语语篇教学中,存在学生思维浅表化、问题设计碎片化、旧版教材适配难这三个痛点。以译林版英语教材六年级上册Unit 4 Then and now中Story time的教学为例,教师立足教材文本,构建“课前定问—课初引链—课中解链—课后拓链—全程评链”的五步闭环,用大问题拉主线、小问题搭台阶,能激活学生语篇学习内驱力,实现英语教学从“知识传递”到“素养培养”的转变。
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
文摘Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?
文摘Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CASTopic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy,No.EXC-2075-390740016.
文摘This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering(VQA)systems.The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the input space,thus facilitating dataset curation.The decisionmaking process of the model is demonstrated by highlighting certain internal states of a graph neural network(GNN).The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset.The authors evaluated their tool through the demonstration of identified use cases,quantitative measures,and a user study conducted with experts from machine learning,visualization,and natural language processing domains.The authors’findings highlight the prominence of their implemented features in supporting the users with incorrect prediction identification and identifying the underlying issues.Additionally,their approach is easily extendable to similar models aiming at graph-based question answering.
基金Supported by the Program for Liaoning Excellent Talents in University(No.LR15045)the Liaoning Provincial Science and Technology Department Applied Basic Research Plan(No.101300243).
文摘Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.
基金supported in part by the National Natural Science Foundation of China.The funding numbers 62433005,62272036,62132003,and 62173167.
文摘The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions.However,the current structured degree of the URT emergency knowledge base remains low,and the domain questions lack labeled datasets,resulting in a large deviation between the consultation outcomes and the intended objectives.To address this issue,this paper proposes a question intention recognition model for the URT emergency domain,leveraging knowledge graph(KG)and data enhancement technology.First,a structured storage of emergency cases and emergency plans is realized based on KG.Subsequently,a comprehensive question template is developed,and the labeled dataset of emergency domain questions in URT is generated through the KG.Lastly,data enhancement is applied by prompt learning and the NLP Chinese Data Augmentation(NLPCDA)tool,and the intention recognition model combining Generalized Auto-regression Pre-training for Language Understanding(XLNet)and Recurrent Convolutional Neural Network for Text Classification(TextRCNN)is constructed.Word embeddings are generated by XLNet,context information is further captured using Bidirectional Long Short-Term Memory Neural Network(BiLSTM),and salient features are extracted with Convolutional Neural Network(CNN).Experimental results demonstrate that the proposed model can enhance the clarity of classification and the identification of domain questions,thereby providing supportive knowledge for emergency decision-making in URT.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
