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.

ANSWERS模型及其应用

ANSWERS模型主要是针对欧洲平原地区研发的分散型物理模型。介绍了模型的研发历史、结构、输入和输出信息以及模型的应用。ANSWERS主要适用于缓坡地形区的径流模拟、侵蚀模拟和农业污染物运移模拟。如何根据中国的实际合理确定模型参数,使模型在我国复杂地形区应用,尚有许多问题需要研究。

ANSWER2000在小流域土壤侵蚀过程模拟中的应用研究

ANSWERS2 0 0 0是一个用于流域土壤侵蚀过程模拟的分散型物理模型 ,将此模型运用于三峡库区小流域侵蚀产沙、地表径流以及不同土地利用类型水沙分布状况的模拟中。通过两个不同小流域模拟结果的对比 ,采用误差百分比、线性回归以及 Nash- Sutcliffe效率 3种方法 ,分析和评价了模型的模拟效果。结果表明 ,模型在应用于我国三峡库区小流域土壤侵蚀模拟时 ,其模拟结果与实测结果具有较高的吻合度 ,模拟结果基本可信。但是 ,对于一些陡坡林地等特殊地类 ,模型的模拟误差较大 ,其模拟精度还有待于进一步提高。

土壤侵蚀建模中ANSWERS及地理信息系统ARC/INFO^R的应用研究

研究了土壤侵蚀模型ＡＮＳＷＥＲＳ和地理信息系统（ＧＩＳ）ＡＲＣ／ＩＮＦＯ之间的连结。采用ＡＲＣ／ＩＮＦＯ建立数据库和ＡＮＳＷＥＲＳ进行实际操作，加强了该模型在制定水保措施中的应用。同时，研究出的ＡＲＣＡＮＳ模型，使ＡＲＣ／ＩＮＦＯ和ＡＮＳＷＥＲＳ之间的连结更为容易、有效。最后，对四川紫色丘陵区的一个小流域实施了模拟，以展示连结情况和一些值得注意的问题。

AnswerSeeker:基于互联网挖掘的智能问答系统

智能问答系统是一种处理自然语言的新型的信息检索系统。介绍了AnswerSeeker智能问答系统,该系统采用了模块化和可扩展的框架,以便整合多种智能问答技术和多样化数据源。通过将与语言无关的代码和语言相关的代码分离,并且将语言相关的代码封装为组件,只要替换相应的组件,该系统可以适用于多种语言。由于很多自然语言处理技术还没有针对中文的,目前为止,我们系统的内核只支持英文,所以将以英语自然语言为例介绍AnswerSeeker的架构和工作原理。该系统采用了两种互联网挖掘的方法来寻找问题的答案:知识挖掘和知识诠释。AnswerSeeker使用网络作为一个知识源,当然它也可以使用其他小的语料库或面向专业领域的知识库作为知识源。此外,提出了一种新的问题的表示和答案提取的方法一文本模式,文本模式分为问题模式和答案模式;其中问题模式用来表示问题,答案模式用来提取精确的答案。AnswerSeeker通过将问题-答案对作为训练数据,自动学习答案模式。实验表明将互联网作为知识源,将模式学习和知识诠释的技术集成在同一系统中进行答案挖掘是一种这种很有前途的方法。

Google Answers及其对图书馆工作的启示

GoogleAnswers是Google推出的一项新服务。它将传统图书馆参考咨询服务技术有机的运用到网络中去并得到了用户的广泛好评。论文通过介绍GoogleAnswers的相关工作流程并将GoogleAn-swers与传统参考咨询进行对比,以期得到对当前图书馆事业发展的一些启示。

借鉴Google Answers构建高校图书馆咨询专家队伍

分析Google Answers,借鉴它的问答模式、用人政策等,从而构想依托高校专家教授的人力资源来建立高校图书馆的咨询专家队伍。

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.

The Answer is 9 or 10?

Below is Problem 9 in Singapore MathematicalOlympiad 2001:Five people jointly bought a lottery ticketwhich won the first prize.They decided to keeptheir ticket in a locker installed with a number oflocks.Each person may hold the keys to more thanone lock.What is the minimum number of locks nee-ded to serve the purpose that any 3 together will beable to open the locker but any 2 of them togetherwill not be able to open it ?A.6 B.9 C.10 D.

评短片《Answer》:如果精神被取代了,我们何以为人?

《Answer》是一部略带魔幻现实风格的短片,剧情聚焦于一个有着手机依赖症的大学生,而主题实际指向现代科技的发展与传统伦理、人性美好的背离。

Einstein's Answer

One day when Einstein was 1(?) his way home, a young man stopped him and wished 2(?) a word with him. Einstein granted his request. The young man asked, "How, Mr Einstein, can you 3(?) your fame? " The scientist said, "It seems that you have been thinking of 4(?) famous every day." The young man nodded.

Chinese Culture in ELT Classroom—The Influence of Chinese Culture on Students' Answering Questions

Based on personal observation and students’ self-reports, this article studies the characteristics presented by students in answering questions. It is found that Chinese culture plays an important part in explaining their specific behaviors. Hence, in order to lessen the negative effect of it, the author offers her own suggestions.

ANSWER模型评估新疆咸水灌溉棉花产量与效益

利用咸水或微咸水进行农田灌溉是缓解中国新疆地区农业水资源供需矛盾从而保障当地棉花产业可持续发展的主要途径之一。为了明确不同咸水灌溉措施对棉花产量及经济效益的影响,该研究通过2 a的棉花膜下滴灌大田试验和文献检索获取了新疆9个不同试验地点的土壤、作物及灌溉等数据资料,评估作物产量-水盐胁迫响应分析模型(ANalytical Salt WatER,ANSWER)在新疆棉花产量评估中的适用性和可靠性,并结合经济收支平衡方法,模拟分析不同咸水灌溉措施(包括不同灌溉定额和灌溉水电导率的组合)对棉花产量与经济效益的影响。采用决定系数(R2)、均方根误差(root mean squared error,RMSE)、相对均方根误差(relative root mean squared error,RRMSE)评价模型精度。结果表明,在9个不同试验地点,ANSWER模型均可较准确地估算棉花的相对产量,其估算值与实测值之间的R^(2)≥0.54,RMSE≤0.14,RRMSE≤0.16;不同试验地点,优化获得的各个模型生物参数(与棉花根系吸水的水盐胁迫响应相关的参数)差异较小,变异系数的绝对值处于0.08~0.37之间;基于不同试验地点优化的各生物参数均值估算各地的棉花相对产量,其与实测值仍然吻合良好(R^(2)为0.59,RMSE为0.06,RRMSE为0.07);此外,当灌溉水电导率一定时,棉花净收益随灌溉定额增加呈先增后降的趋势,净收益达到峰值所需的灌溉定额随灌溉水电导率升高而迅速增加;当灌溉水电导率不大于10 dS/m时,通过加大供水量均可获得与淡水灌溉相当的净收益。研究可为新疆地区棉花产量与效益评估以及咸水资源合理开发利用提供理论依据。

非点源污染模型ANSWERS-2000的水文子模型研究

非点源污染的前期过程，是在流域面上污染物质或是营养元素随着降雨径流的产生而产生。利用实验室大型土槽试验，研究ANSWERS-2000模型的水文子模型对于人工降雨事件的模拟精度，探索ANSWERS-2000在理想坡面上的适用情况，并且尽可能准确地给出其水文模型的部分参数的取值。经过计算发现该水文子模型模拟理想坡面的误差较小，但是存在系统偏大的情况；整个计算单元的Manning糙率取0．03～0．07。存在的问题有参数土表面结皮层厚度只能定性无法准确的定量描述；降雨装置测流装置对于径流模拟产生的影响较大。

Noether symmetry and conserved quantity for dynamical system with non-standard Lagrangians on time scales

This paper focuses on studying the Noether symmetry and the conserved quantity with non-standard Lagrangians, namely exponential Lagrangians and power-law Lagrangians on time scales. Firstly, for each case, the Hamilton prin- ciple based on the action with non-standard Lagrangians on time scales is established, with which the corresponding Euler-Lagrange equation is given. Secondly, according to the invariance of the Hamilton action under the infinitesimal transformation, the Noether theorem for the dynamical system with non-standard Lagrangians on time scales is established. The proof of the theorem consists of two steps. First, it is proved under the infinitesimal transformations of a special one-parameter group without transforming time. Second, utilizing the technique of time-re-parameterization, the Noether theorem in a general form is obtained. The Noether-type conserved quantities with non-standard Lagrangians in both clas- sical and discrete cases are given. Finally, an example in Friedmann-Robertson-Walker spacetime and an example about second order Duffing equation are given to illustrate the application of the results.

Automatic Question Answering from Web Documents

A passage retrieval strategy for web-based question answering （QA） systems is proposed in our QA system. It firstly analyzes the question based on semantic patterns to obtain its syntactic and semantic information and then form initial queries. The queries are used to retrieve documents from the World Wide Web （WWW） using the Google search engine. The queries are then rewritten to form queries for passage retrieval in order to improve the precision. The relations between keywords in the question are employed in our query rewrite method. The experimental result on the question set of the TREC-2003 passage task shows that our system performs well for factoid questions.