Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

Chinese Clinical Named Entity Recognition Using Multi-Feature Fusion and Multi-Scale Local Context Enhancement

Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

Experimental Study on the Influence of Cognition and Emotion on Moral Judgment of College Students in Dilemma Situation

Objective:To study the influence of cognition and emotion on moral judgment of college students under the circumstance of whether the cognitive resources are occupied and whether the emotion is induced.Methods:This experiment uses a multi-factor mixed experiment method to divide experiments and groups.Experiment 1 uses a two-factor mixed experimental design of 2(cognitive resource occupancy group,cognitive resource non-occupied group)×3(difficult situation type).Experiment 2 uses a two-factor mixed experimental design of 2(emotion induction group,emotion induction and cognitive resource occupation group)×3(three types of dilemma situation types)is adopted.The dependent variable of this experiment(including Experiment 1 and Experiment 2)is the judgment response time and the judgment result is“Yes”(F)or“No”(J).Results:(1)The reaction time of the cognitive resource occupancy group was significantly higher than that of the cognitive resource non-occupied group,and the cognitive resource occupancy group in the three types of dilemma situations of high personal involvement,low personal involvement,and non-personal participation.There is no significant difference between the results of moral judgments and the cognitive resource non-occupied group.(2)In the three dilemmas of high personal involvement,low personal involvement,and non-personal participation,the emotion-induced group and the emotion-induced and cognitive resource occupation group have no significant differences in reaction time and moral judgment results.(3)In the three dilemmas of high personal involvement,low personal involvement,and non-personal participation,the reaction time difference between the cognitive resource occupation group and the emotionally induced and cognitive resource occupation is not significant,while in the dilemma of low personal involvement,the number of people in the cognitive resource occupation group whose moral judgment is“Yes”was significantly higher than that in the emotionally induced and cognitive resource occupation group.(4)In the three dilemmas of high personal involvement,low personal involvement,and non-personal participation,the reaction time of the emotionally induced group was significantly higher than that of the cognitive resource non-occupied group,and the moral judgment results of the two groups were both found no significant difference.Conclusion:When the occupation of cognitive resources and the induction of emotions will significantly affect the response of individual moral judgments,different types of dilemmas will significantly affect the results of individual moral judgments.

Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.

企业诚信建设与注册会计师审计意见

在全社会强调传承诚信文化的背景下,已有文献研究了诚信建设带给企业的诸多后果,却鲜有研究从审计市场角度分析企业诚信的影响。本文基于诚信的三个基础因素“言”“行”和“言行不一致”,利用年报文本和监管处罚数据构建了企业诚信指标,分析了企业诚信对审计意见的影响与机制。研究发现,企业诚信降低了注册会计师出具非标意见的可能性,该现象在非国有企业中更加突出。企业诚信通过减少财务报告错报、抑制盈余操纵和加强内部控制的方式,最终影响了其对审计意见的判断。进一步地,当客户的应收账款和存货占比越高、客户越重要时,企业诚信更能减少非标审计意见出具。本研究有助于促进企业从审计市场角度理解诚信建设的重要性。

基于情感和认知协同的道德判断方法

随着大语言模型的迅速发展,大语言模型的安全性逐渐引起了研究者和公众的密切关注.为了防止大语言模型在与人类协作中对人类产生伤害,如何确保大语言模型在日常场景中的判断能与人类道德观念相符成为了一个重要问题.其中一个关键的挑战是,如何确保大语言模型在道德判断方面,能够像人类那样,针对不同的情境,灵活地调整或重新考虑预定的规则,从而使其判断与人类的道德观念保持一致.受心理学和认知科学中关于人类道德判断的情感和认知影响因素研究的启发,结合大语言模型在认知推理和情感分析能力上的优势,设计了一种模仿人类道德判断过程中情感判断和认知判断能力交互的方法,从而提升了大语言模型的道德判断表现.实验结果证明了所提方法在该任务上的有效性.总的来说,不仅为大语言模型的道德判断提供了一种创新的方法,也强调了心理学与认知科学理论在此领域的重要性,为未来的进一步研究奠定基础.

教师的教育判断力:内涵要义、价值表征及发展策略

教育是中国式现代化的关键组成,面对教育的快速发展以及教育数字化、人工智能的到来,教育判断力是教师减少迷茫、寻求立命之所的重要支点,也是提升教师教育质量的关键。教师的教育判断力就是在教育情境中合目的性的指向下,将各种教育现象、事例与一般规则交互作用而进行的“归摄”“运用”“反思”的判断能力,它是教育“尝试”与“提升”特质的诉求、教育思想形成与创生的支撑、教师生命意义获得与更新的助力。然而,当下教师的教育判断力面临缺乏生长土壤、教师教育者的判断力不足等困境。提升教师的教育判断力,应以深度学习为理念,以案例教学为依托,以教育情景为中介,建立跨界教师教育师资团队。

指导性案例制度的运行思路再定位

指导性案例制度旨在通过各级法院参照适用前案的方式,推进法律适用的统一。但该制度的运行存在效力不明确、供给量不足、适用范围不广等诸多问题。鉴于遵循先例以实现同案同判的运行机制与指导性案例以确保法律适用统一的运行机制具有相似性,通过分析判例法上遵循先例的运行条件,融入指导性案例的运行环境观察其运行效果,发现由于两者的法律适用逻辑存在根本性差异,指导性案例制度实现法律适用统一的效率性、稳定性、权威性和发展性等方面皆不尽如人意。指导性案例应当回归其指导示范价值而非硬性拘束,并围绕覆盖司法分歧和增加案例竞争,完善相应的遴选与退出机制。

国内外主流药典中可见异物检查方法的比较与分析

目的:探析国内外各主流药典中可见异物检查的分析方法,为《中国药典》中可见异物检查法的升版提出一些思路和建议。方法:通过查阅国内外各主流药典,分别从可见异物的定义、人工灯检法、可见异物的判定标准等方面进行分析比对,总结国内外各主流药典的异同点。结果:各主流药典对可见异物的定义、判定标准等方面均存在差异,对于人工灯检法的检验人员、光源、光照强度、检查数量的要求以及具体的操作方法等均有不同,各主流药典均有所长及所短,检验参数相辅相成,并相互制约。结论:建议《中国药典》结合各主流药典的优缺点和中国企业的现状,对可见异物检查法的内容进行完善,从而更好地保证药品的质量以及人民的用药安全。

个体共情能力对不同道德行为取向自动驾驶汽车接受意愿的影响

研究通过两项实验考察了共情对不同道德取向自动驾驶汽车接受意愿的影响。结果发现:(1)低共情者在不涉及自身利益时不在意自动驾驶汽车的道德取向(功利主义/道义论),当涉及自身利益时,更倾向于购买自我保护的汽车。(2)当不涉及自身时,高共情者更倾向于购买功利主义自动驾驶汽车;但也可以接受牺牲自我的汽车,对两类自动驾驶汽车的购买意愿相当。

乘型模糊判断矩阵排序向量的递推方法

文章首先在模糊判断矩阵乘型一致性以及矩阵元素和权重之间关系的基础上,结合模糊判断矩阵的上三角矩阵元素,构建了一个关于权重和矩阵上三角元素的方程组,并证明了该方程组存在唯一的正解。随后指出方程组的证明过程就是模糊判断矩阵排序向量的求解过程,从而给出了乘型一致性模糊判断矩阵排序向量的一种递推方法。然后,在偏差函数基础上,通过构造并求解一个优化模型,求出了非乘型一致性模糊判断矩阵的排序向量,结果显示,其解的形式与采用乘型一致性模糊判断矩阵递推方法得到的排序向量完全一样。最后,通过实例以及相关方法对比说明排序向量递推方法是可行有效的。

论康德的良知观

在康德伦理学中,良知是一个充满着诸多争议的问题。对康德而言,良知属于理性还是情感?与此相关,它属于理性慎思还是直觉性反应?就此,学界大体上可以分为情感主义与理性主义两种立场。其实,良知应该属于一种道德直觉,其中蕴含着理性与情感的交融。就产生根源而言,良知在康德那里是一种判断力,也属于一般性的实践理性,但仍然主要呈现为一种道德情感,包含了理性与情感两种心灵状态。作为道德直觉的良知最好从一种自然主义的角度加以理解,在具体的运用过程中,它既有敏锐性、瞬时性的优势,但也由于各种原因而会导致模糊的、歧义的判断。

诗人与循吏的融合:《张船山判牍》审美价值探析

性灵诗人张船山不仅以卓著的诗书画屹立于“儒林”,同时也是一代“循吏”,清正廉明,敏于听断,素有“神断”之称,有襟霞阁主编的《张船山判牍》留存于世,流传甚广,影响颇大。《张船山判牍》不仅题材丰富,广泛反映嘉庆时期的社会生活;而且词章华美,事理通达,情理融合,兼具历史上“骈判”与“散判”之优长,具有较高的审美价值。本文试从审美角度探寻判牍的实用美、质实美、词章美、事理美和人情美。

基于因果图分析的可解释司法判决预测方法研究

随着人工智能技术的发展和海量司法数据的公开,面向“智慧司法”服务的司法判决预测(legal judgment prediction,LJP)任务受到了学术界和工业界的广泛关注,该任务旨在根据有限的案件事实描述文本来预测案件的罪名、法条和刑期。然而,现有工作缺乏对易混淆司法案件的智能决策的研究,且相关模型通常缺乏可解释性,这会导致模型预测严重依赖领域专家,阻碍LJP在不同法律体系中的应用。为此,提出了一种基于因果图分析的司法判决预测(prediction of legal judgment based on causal graph analysis,CGLJ)方法,首先从非结构化的法律事实描述文本中挖掘要素之间的因果关系,然后采用易混淆罪名聚类的构图方法构建因果图,既考虑了相似事实描述之间的差异,又增强了事实描述和法律法规之间的相互作用,最后将构建好的因果图融入深度神经网络进行联合推理,得到判决预测结果。此外,还对模型预测过程中的因果图推理过程进行了可视化,为判决结果提供了更好的可解释性。在2018中国“法研杯”司法人工智能挑战赛(CAIL2018)司法判决预测数据集上的实验结果表明,该方法相比基线模型取得了更好的效果。

由“体验式”到“验证式”:“新文科”背景下法学实验教学的模式优化

“新文科”的内涵可以被解构为学科交叉之新理念、科教融合之新目标与文理交融之新方法;法学实验教学作为法学教育体系中最具实践性、前沿性与创新性的环节,应当及时回应上述新诉求。目前,法学实验教学模式仍表现出“体验式”路径依赖,即便是融入科技元素的虚拟仿真也是以体验各类诉讼程序或非诉流程为目标,未能体现出“假设与验证”的实验教学本质。将类案司法裁判文书作为大数据素材,有助于在法学教育中融入计算科学的思维与方法,以一种科学发现的姿态去探索与验证所学法学理论,呈现法学实验教学的“验证式”面孔。依托于“构想提出、确立假设、文书检索、数据分析、理论检验与方法总结”等核心环节,可以确立“验证式”法学实验教学方案。

基于条件生成对抗网络的无线传感网络多节点失效修复研究

当前主流的传感节点失效修复主要通过纠删码完成,修复后节点具有更高的空间利用率,但无法有效提升网络寿命。为此,提出基于条件生成对抗网络的无线传感网络多节点数据重构方法,完成失效修复。感知无线传感网络节点,对失效节点展开裁决,确定失效节点位置,并重构节点内数据;将获取的失效节点用于条件生成对抗网络(CGAN)框架中生成器与节点替换网络的训练,通过训练好的生成器,以失效节点为条件,生成未失效节点;为提升修复性能,使用粒子群算法寻优节点替换网络参数,完成节点重构数据置换,实现失效节点的有效修复。结果表明:利用所提方法进行修复时,能耗最高仅为17 J,剩余寿命最低可达到300 h,连通度最高可达到99.2%,具有较好的修复效果。

基于模糊综合评判和本体的测量仪器智能优选研究

针对当前测量仪器的种类逐渐增多且各仪器都有不同的性能影响因素问题,提出了一种基于模糊综合评判和本体的测量仪器智能优选研究。首先,利用模糊综合评判对测量仪器的性能影响因素进行模糊划分;其次,依据本体领域知识,将模糊划分模型转化为测量仪器智能优选的本体模型,运用SWRL规则和protégé工具推理出最优的测量仪器;最后,通过工程实例展示该方法的工作过程,验证了该方法的可行性。实验结果表明,提出的方法可以为仪器选择提供参考,并可自动推理结果。