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.

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.

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.

一种引入核心实体关注度评估的KBQA算法

目前针对复杂语义和复杂句法的知识库问答(Knowledge Base Question Answering,KBQA)研究层出不穷,但它们多以已知问题的主题实体为前提,对问题中多意图和多实体重视不足,而问句中对核心实体的识别是理解自然语言的关键。针对此问题,提出了一种引入核心实体关注度的KBQA模型。该模型基于注意力机制及注意力增强技术,对识别到的实体引用(Mention)进行重要性评估,得到实体引用关注度,去除潜在干扰项,捕获用户提问的核心实体,解决了多实体、多意图问句的语义理解问题。此外,还将评估的结果作为重要权重引入后续的问答推理中。在英文MetaQA数据集、多实体问句MetaQA数据集、多实体问句HotpotQA数据集上,与KVMem,GraftNet,PullNet等模型进行了对比实验。结果表明,针对多实体问句,所提模型在Hits@n、准确率、召回率等评估指标上均取得了更好的实验效果。

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

ArcCHECK Machine QA工具在医用直线加速器质量保证中的应用效果

目的探讨ArcCHECK Machine QA工具在医用直线加速器质量保证中的应用效果。方法利用ArcCHECK Machine QA工具和ArcCHECK体模对医用直线加速器进行性能测试,项目包括机架角度、机架旋转速度、机架旋转中心、多叶准直器和铅门位置的一致性、机架旋转出束时的平坦度和对称性,评估该工具在医用直线加速器质量保证中的应用效果。结果旋转模式下机架平均旋转速度为3.6 deg/s,最大偏差约0.5 deg/s;机架旋转等中心形成的平均半径为0.4 mm,多叶准直器与铅门的最大距离正、负差异平均值分别为0.7 mm、-0.7 mm;旋转出束模式下Y方向的平坦度为1.8%,Y方向的对称性为1.1%,X方向的对称性为4.3%。结论ArcCHECK Machine QA工具可用于医用直线加速器常规及容积调强出束性能质量保证。

A Domain Question Answering Algorithm Based on the Contrastive Language-Image Pretraining Mechanism

Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.

Impact of Data Quality on Question Answering System Performances

In contrast with the research of new models,little attention has been paid to the impact of low or high-quality data feeding a dialogue system.The present paper makes thefirst attempt tofill this gap by extending our previous work on question-answering(QA)systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses.Instead of using large language models trained on huge datasets,we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model.It is important to identify the features that modify the agent performance because a high rate of wrong answers can make the students lose their interest in using the QA agent as an additional tool for distant learning.The results demonstrate the accuracy of the proposed context simplification exceeds 85%.Thesefindings shed light on the importance of question data quality and context complexity construct as key dimensions of the QA system.In conclusion,the experimental results on questions and contexts showed that controlling and improving the various aspects of data quality around the QA system can significantly enhance his robustness and performance.

Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore,recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction(IE)for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.

Development of a Best Answer Recommendation Model in a Community Question Answering (CQA) System

In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.

Improved Blending Attention Mechanism in Visual Question Answering

Visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks.Analysis of all features may cause information redundancy and heavy computational burden.Attention mechanism is a wise way to solve this problem.However,using single attention mechanism may cause incomplete concern of features.This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method.In the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as compensation.For the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall model.The results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

Deep Multi-Module Based Language Priors Mitigation Model for Visual Question Answering

The original intention of visual question answering(VQA)models is to infer the answer based on the relevant information of the question text in the visual image,but many VQA models often yield answers that are biased by some prior knowledge,especially the language priors.This paper proposes a mitigation model called language priors mitigation-VQA(LPM-VQA)for the language priors problem in VQA model,which divides language priors into positive and negative language priors.Different network branches are used to capture and process the different priors to achieve the purpose of mitigating language priors.A dynamically-changing language prior feedback objective function is designed with the intermediate results of some modules in the VQA model.The weight of the loss value for each answer is dynamically set according to the strength of its language priors to balance its proportion in the total VQA loss to further mitigate the language priors.This model does not depend on the baseline VQA architectures and can be configured like a plug-in to improve the performance of the model over most existing VQA models.The experimental results show that the proposed model is general and effective,achieving state-of-the-art accuracy in the VQA-CP v2 dataset.

基于知识表示学习的KBQA答案推理重排序算法

现有的知识库问答(KBQA)研究通常依赖于完善的知识库,忽视了实际应用中知识图谱稀疏性这一关键问题。为了弥补该不足,引入了知识表示学习方法,将知识库转换为低维向量,有效摆脱了传统模型中对子图搜索空间的依赖,并实现了对隐式关系的推理,这是以往研究所未涉及到的。其次,针对传统KBQA在信息检索中常见的问句语义理解错误对下游问答推理的错误传播,引入了一种基于知识表示学习的答案推理重排序机制。该机制使用伪孪生网络分别对知识三元组和问句进行表征,并融合上游任务核心实体关注度评估阶段的特征,以实现对答案推理结果三元组的有效重排序。最后,为了验证所提算法的有效性,在中国移动RPA知识图谱问答系统与英文开源数据集下分别进行了对比实验。实验结果显示,相比现有的同类模型,该算法在hits@n、准确率、F_(1)值等多个关键评估指标上均表现更佳,证明了基于知识表示学习的KBQA答案推理重排序算法在处理稀疏知识图谱的隐式关系推理和KBQA答案推理方面的优越性。

PBS质子治疗系统快速日检QA方案的应用研究

目的:通过分析IBA Sphinx Compact设备在迈胜紧凑型笔形束扫描质子治疗系统上的日检QA测量结果,评价该方案在质子治疗中的临床应用价值。方法:采用Sphinx Compact设备对迈胜S250i质子治疗系统进行连续30 d的日检QA测量,分析测量结果。结果:30 d内摆位激光灯与影像中心平均偏差为(0.42±0.27)mm;高能和低能笔形束的近端及远端深度误差均在0.50 mm以内;测量的所有光斑位置偏差不超过1.00 mm,尺寸偏差不超过7.5%;影像中心与射束中心偏差不超过0.75 mm;矩形射野平坦度相对偏差在0.5%左右;方野输出剂量偏差在1.0%以内。结论:Sphinx Compact设备能准确并快速测量AAPM TG-224号报告推荐的质子系统日检QA项目,提供实用且高效的解决方案,具有很好的临床实用价值。

五没食子酰葡萄糖靶向补体分子C1qa修复糖尿病小鼠主动脉内皮损伤的机制研究

糖尿病常伴随血管内皮损伤,显著加速血管病理进程,但其分子机制尚未阐明。已有报道证实,特殊的补体分子在血管损伤中扮演重要角色。该实验室既往研究发现,补体分子C1qa(Component 1,q Subcomponent,A chain)在糖尿病小鼠主动脉内皮层高表达,并且表达量随着心血管疾病的发展而增多。因此,该研究利用链脲佐菌素(streptozocin,STZ)诱导糖尿病小鼠模型,并研究了C1qa分子的食源性抑制剂,即五没食子酰葡萄糖(1,2,3,4,6-O-pentagalloylglucose,PGG)对糖尿病血管内皮的修复作用。结果表明,外源C1qa刺激会显著增加血管内皮的活性氧(reactive oxygen species,ROS)水平,同时降低主动脉内皮依赖性舒张功能。相似地,糖尿病模型中,血管内皮C1qa的高含量伴随着ROS的过量合成,主动脉血管段的内皮依赖性舒张显著降低。而PGG灌胃3周后,糖尿病小鼠血糖下降至15.36 mmol/L,主动脉内皮C1qa表达量显著降低,ROS生成量减少了52.41%,血管内皮舒张率由47.06%升到68.41%。同时,NADPH氧化酶(NOX_(2)/NOX_(4))通路表达下调,提示ROS生成的关键通路被抑制,血管内皮氧化应激损伤显著减轻。因此,活性分子PGG靶向抑制C1qa,有利于修复糖尿病小鼠主动脉内皮损伤。

奇安信面向全行业发售新版QAX-GPT安全机器人

3月20日,奇安信集团宣布,新版QAX-GPT安全机器人面向全行业正式发售。据了解,经过持续打磨和优化迭代,新版安全机器人不仅对智能研判和智能问答进行了升级,还新增了智能调查、智能任务、智能报告和智能驾驶舱四项功能。相对于前一版,新版安全机器人有三大亮点。

Improving VQA via Dual-Level Feature Embedding Network

Visual Question Answering(VQA)has sparked widespread interest as a crucial task in integrating vision and language.VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions.The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively.However,it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details,which is the advantage of grid-based features.In this paper,we propose a Dual-Level Feature Embedding(DLFE)network,which effectively integrates grid-based and detection-based image features in a unified architecture to realize the complementary advantages of both features.Specifically,in DLFE,In DLFE,firstly,a novel Dual-Level Self-Attention(DLSA)modular is proposed to mine the intrinsic properties of the two features,where Positional Relation Attention(PRA)is designed to model the position information.Then,we propose a Feature Fusion Attention(FFA)to address the semantic noise caused by the fusion of two features and construct an alignment graph to enhance and align the grid and detection features.Finally,we use co-attention to learn the interactive features of the image and question and answer questions more accurately.Our method has significantly improved compared to the baseline,increasing accuracy from 66.01%to 70.63%on the test-std dataset of VQA 1.0 and from 66.24%to 70.91%for the test-std dataset of VQA 2.0.