Special Issue:Questions&Data for Better Science and Innovation Call for submissions

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?

MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Dual modality prompt learning for visual question-grounded answering in robotic surgery

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image.This limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image regions.To address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer prediction.Drawing inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information interactions.Specifically,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate localization.The textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the answer.Additionally,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded answers.The experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

试析科举考试的题型结构及其功能

历史上随着科举考试科目和场次的不断变化,考试题型也在不断发展和改进。从题型总体结构来看,以主观题型为主。曾经使用和经常出现过的科举考试题型包括:帖经;墨义、口义、经义、经问、经疑;诗赋、词赋、试帖诗、律赋;杂文、诏、诰、箴、表、赞、判;四书文、五经文、八股文;策、论、对策、策问、时务策等。另外,武举、童子举等也各有题型。不同的题型显示不同的人才考选作用,进而发挥不同的功能。科举考试借助确立衡文标准,力图把知识和能力的测评相结合,共同为科学选才和巩固儒学本位的命题立意服务。

基于知识图谱和大语言模型的口述历史资源的问答应用研究

档案馆和图书馆等人文机构逐渐形成了丰富多样的有序化整理后的口述历史档案集合。引入问答系统,通过互动方式可展示档案单元内容的知识推理能力。本研究融合知识图谱和大语言模型,充分发挥知识图谱的准确性、内容透明度等优势,降低大语言模型带来应答幻觉、建设成本高等问题,尝试构造面对口述历史档案资源的问答系统。文章详细阐述了系统设计思路与构建过程,以及核心部件的关键技术要点等,并以李政道图书馆馆藏的CUSPEA主题的口述历史为研究对象,进行问答应用实践。实践验证了问答系统的可行性,能实现口述历史档案资源的知识融汇与知识挖掘,能有效辅助人文学者和历史爱好者理解与洞悉口述历史本质。

学会提问:大学生与生成式人工智能协同学习模式的研究

以ChatGPT为代表的生成式人工智能的问世,给传统的学习模式带来了巨大的机遇与挑战。学生如何运用生成式人工智能促进学习成为教育教学改革亟待探索的问题。本研究在大学生课程学习过程中引入GAI,对学生与GAI的话语类型、提问水平、提问策略以及自我报告进行编码分析,探究了大学课堂中学生如何与GAI进行协同学习。研究发现,在学生与GAI的对话中,学生是对话的发起主体,单个对话构成的对话单元居多,持续性的讨论较少。学生话语主要以初始提问、拓展提问和改述提问为主,评价和继续指令话语较少。同时学生提问的认知水平较低,以知识水平、理解水平提问为主,提问策略单一,较少使用角色提问、材料提问、方案提问等策略。在不同任务阶段、不同使用经验的学生与GAI的对话存在差异性,在任务后期人智之间展开更高频、更持续的互动对话,且提问认知水平更高、提问策略使用更熟练。使用GAI经验越丰富的学生产生更多的高认知水平对话。在呈现出不同话语特征的对话过程中,学生对在大学课堂教学中引入GAI整体上持积极态度但有所分化。学生普遍认为,GAI能够积极地辅助学习,具有回应优势、能够为学生提供信息价值、处理多类任务和促进学生能力发展,但同时也存在技术局限,引发对学生主体、学习评价和教育生态的挑战。在此基础上,本研究从提供提问训练、丰富提问场景、加强回答反思三个方面为进一步在课堂教学过程中引入生成性人工智能提供了有效的建议。

情境“载体”与“问题”融合度的4个层级——以2023年高考物理试题为例

《中国高考评价体系》将情境问题分为“生活实践类情境”与“学习探索类情境”,主要是基于命题素材的来源进行分类,尝试从问题与情境融合度的角度对情境化试题进行分类,并以2023年高考试题作为案例进行简要分析.

人们对什么终极问题感兴趣?

人们最感兴趣的问题都是没有必然答案或唯一答案的终极问题。无论多么困难,终究能够解决的问题属于科学,永远不能解决的问题就成为哲学问题。终极问题通常被认为属于哲学,但由于无法超越无穷性而不存在找到终极答案的方法,哲学反而回答不了哲学里的终极问题。如果非想要一个答案不可,就变成了神学。哲学必须是反思的,否则不是哲学,而反思的底色是怀疑论。怀疑论的基本方法就是悬搁,即对绝对或终极答案不置可否,以便理解本来就能够理解的事情。维特根斯坦提示了,哲学思考不是要去远方,而是留在原地。但人类还是永远向往不可能的“远方”。在很多时候,人们想要的就是令人满意的错误答案。

习近平生态文明思想的世界观和方法论:出场、逻辑与旨归

习近平生态文明思想的世界观和方法论具有独特的出场语境、逻辑体系与价值旨归。从出场语境来看,其继承了辩证唯物主义与历史唯物主义,吸收了中国传统以民为本、道法自然与天人合一的思想,借鉴了西方生态中心主义与弱式人类中心主义的有益内容。从逻辑体系来看,其以人民至上为根本宗旨;以自信自立为立论根基,以守正创新为方向引领,以问题导向为内生动力,以系统观念为思维力量,以胸怀天下为崇高理想,并呈现由内及外、首尾呼应的特点。从价值旨归来看,其以人与自然和谐共生的现代化回答了中国之问,以共建生态良好的地球美好家园回答了世界之问,以解决影响人民群众健康的环境问题回答了人民之问,以贯彻以绿色发展为导向的战略路径回答了时代之问。

基于临床思维培养的中药学课程教学实践

中药学课程对高校培养中医学专业人才中医临床思维具有重要意义。教师需不断提升教学能力和专业素养,重新认识中药学课程地位,积极采用线上线下相融合的混合式教学模式。教材内容整合重排,融入中医经典与名家经验及医案,传承中医药认知思维及文化,补充教材内容之不足,并积极开展第二课堂,提高应用题型测试考核比例,以优化拓展教学内容,增强教学内容的临床实用性、引导性,提升学生的思想、思考、思辨能力,提高中医思维的高度、宽度及深度。

苏格拉底式问题支架:促进学生向AI大模型提出高质量问题

向AI大模型提出高质量问题已成为数智时代学习者的必备技能与元素养。然而,当前学生向AI大模型提问的能力较低,所提问题存在浅表化、思维含量不高等现象,导致大模型的响应往往无法满足预期需求。苏格拉底式提问为有效提升学生提问质量提供了有益参考,基于此设计的苏格拉底式问题支架从“认知—元认知”双重维度提供了提示性、指导性、解释性三类支架,能够激发学生向AI大模型主动提问和持续追问的能力,有效促进学生认知发展。面向教育技术学专业44名本科生开展的准实验研究发现,使用该支架前后学生在问题深度、问题序列和问题架构方面具有明显变化。苏格拉底式问题支架能显著提高学生的深度提问能力,有效促进高阶问题序列转换,让学生形成分析创造式、深度理解式、评价创造式三种典型的转换循环,还能引导学生构建基于不同观点的星型、链式和闭环的问题架构逻辑。未来,教师应借助多样化的问题支架,引导学生向AI大模型提出高阶问题,并在追问中建立问题逻辑,以高质量的人机对话促进深度学习和学生高阶思维发展。

基于Question Point的合作虚拟咨询服务

介绍了Question Point合作虚拟咨询服务产生的背景、功能、系统工作流程以及我国开展合作虚拟参考咨询的情况。

临床实践指南制订过程中临床问题的构建——基于病例引导式方法

在临床实践指南制订过程中,临床问题的构建存在技术难点。临床医生或指南制订者在构建临床问题时较少基于真实病例情景,导致结构化临床问题与实际临床内涵之间存在一定的信息缺失。为解决此难点,本文提出病例引导式临床问题构建方法,并在指南制订过程中进行了实例验证,发现其可更高效、科学地辅助临床问题构建,为临床医生或指南制订者提供参考。

Question classification in question answering based on real-world web data sets

To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.

Question Point——基于网络参考咨询系统的探讨

简要概述了Question Point的由来,讨论了其功能和在图书馆中的运作,同时提出了合作化数字参考咨询实施过程中的一些问题及未来的发展趋势。

回返起源:中国教育学的意涵与路径

教育理论研究需要不断地回到教育的基本问题,需要我们在历史与现实之中、在古今中西之际、在理想与现实之间不断地回答这些基本问题,不断地追溯起源,不断地探寻教育的基本问题,由此而让我们的教育思考成为有根有源的思考。回返根本,意在厘清个体成人之民族历史文化的本源,由此而给予个体以民族的文化根脉,成为有根有魂之人。所谓中国教育学不仅是有“人”的教育学,还是有“中国人”的教育学。与此同时,我们需要在回返起源的过程中积极回应当代中国人的生命路向及其教育机理,返本以开新,开启中国教育学的内在可能性。

面向区块链漏洞知识库的大模型增强知识图谱问答模型

大语言模型(LLM)在专业领域特别是区块链漏洞领域应用时存在局限性,如专业术语噪声干扰和细粒度信息过重导致理解不足。为此,构建一种面向区块链漏洞知识库的增强型知识图谱问答模型(LMBK_KG)。通过整合大模型和知识图谱来增强知识表示和理解能力,同时利用多粒度语义信息进行专业问题的过滤和精准匹配。研究方法包括使用集成的多粒度语义信息和知识图谱来过滤专业术语噪声,以及采用大模型生成的回答与专业知识图谱进行结构化匹配和验证,以提高模型的鲁棒性和安全性。实验结果表明,所提出的模型在区块链漏洞领域问答的准确率比单独使用大模型提高26%。