Inheritance and Development of Three Pre-Qin Classics of Confucianism——An Application of Topic Modeling in Classical Chinese Text Analysis

The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading2”.

Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication,integrity verication,and tampering detection of the digital contents.In this paper,text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model(HMM),the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Enhancing disaster management effectiveness: An integrated analysis of key factors and practical strategies through Structural Equation Modeling (SEM)and scopus data text mining

In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andperformance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and RiskAssessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizesScopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management.IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling(SEM) for model evaluation.The research raises questions about crafting a comprehensive, adaptable model, understanding the interplaybetween vulnerability assessment, training, and disaster preparedness, and integrating effective communicationand collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model.Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.

Assessing citizen science opportunities in forest monitoring using probabilistic topic modelling

Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of ＂citizen scientists＂ can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. Methods： We conduct a systematic meta-analysis of 1015 publication abstracts addressing ＂forest monitoring＂ and ＂citizen science＂ in order to explore the combined topical landscape of these subjects. We employ ＇topic modelling an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. Results： We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on ＂forest monitoring＂ and ＂citizen science＂ and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. Conclusions： We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology.In this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach.The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text.Moreover,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any tampering.SFASCDW has been implemented using PHP with VS code IDE.The robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and deletion.The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack

In this article,a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet(HFDATAI)is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft computing.The HFDATAI solution technically integrates and senses the watermark without modifying the original text.The alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated,null-watermarking approach to enhance the proposed approach’s efficiency,accuracy,and intensity.The first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of the Arabic language.In addition,the features of the interrelationship among text contexts and characteristics of watermark information extraction that is used later validated for detecting any tampering of the Arabic-text attacked.The HFDATAI strategy was introduced based on PHP with included IDE of VS code.Experiments of four separate duration datasets in random sites illustrate the fragility,efficacy,and applicability of HFDATAI by using the three common tampering attacks i.e.,insertion,reorder,and deletion.The HFDATAI was found to be effective,applicable,and very sensitive for detecting any possible tampering on Arabic text.

An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques

In this paper,the text analysis-based approach RTADZWA(Reliable Text Analysis and Digital Zero-Watermarking Approach)has been proposed for transferring and receiving authentic English text via the internet.Second level order of alphanumeric mechanism of hidden Markov model has been used in RTADZWA approach as a natural language processing to analyze the English text and extracts the features of the interrelationship between contexts of the text and utilizes the extracted features as watermark information and then validates it later with attacked English text to detect any tampering occurred on it.Text analysis and text zero-watermarking techniques have been integrated by RTADZWA approach to improving the performance,accuracy,capacity,and robustness issues of the previous literature proposed by the researchers.The RTADZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark.RTADZWA has been implemented using PHP with VS code IDE.The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion,reorder,and deletion attacks,e.g.,Comparison results with baseline approaches also show the advantages of the proposed approach.

The Chinese Image on Twitter： An Empirical Study Based on Text Mining

The study use crawler to get 842,917 hot tweets written in English with keyword Chinese or China. Topic modeling and sentiment analysis are used to explore the tweets. Thirty topics are extracted. Overall, 33% of the tweets relate to politics, and 20% relate to economy, 21% relate to culture, and 26% relate to society. Regarding the polarity, 55% of the tweets are positive, 31% are negative and the other 14% are neutral. There are only 25.3% of the tweets with obvious sentiment, most of them are joy.

基于文本挖掘的跑鞋用户评价及情感分析

为了挖掘消费者在线购买跑鞋时的关注信息,文章用大数据分析视角,以“京东商城”为例按照销量排序分析了前600款跑鞋品牌定位、价格分布、优惠信息、标签占比,使用LDA模型对10万条跑鞋在线评论进行文本挖掘,对商品评论数据进行词频共现分析、主题聚类与情感分析,从品牌、技术和售后服务的维度分析了问题的原因并提出相关建议。研究表明:国产品牌跑鞋在各价位段布局完整,销量高的跑鞋多使用满减和商品券,自营和优惠券标签对跑鞋购买具较为显著的促进作用;消费者购买跑鞋时主要关注外观细节、功能属性、性价比、穿着感受、服务优惠等方面。

基于文本挖掘的新冠肺炎疫情下医药在线消费者的需求研究

基于新冠肺炎疫情下医药电商交易规模的爆炸式增长,对医药电商在线评论进行文本分析,以某B2C医药电商平台2019—2021年在线评论数据为样本,利用LDA主题模型提取在线评论蕴含的主题,并构建情感词典融合深度学习的情感分析模型,对评论和主题词进行情感分析。研究结果显示:1)消费者网购医药商品始终关注平台的可靠性、物流服务、商品价格、药品的使用效果;2)新冠肺炎疫情爆发之前,消费者对服务态度、商品品牌、购买便捷性有很大关注度;疫情爆发后对感冒类和维生素类药品关注度更高,疫情的爆发会影响消费者的购药决策;后疫情时代,消费者更关注商品性价比、购买快捷性以及药品的品质;3)消费者对于在医药电商平台进行购药整体上表现出积极正面的情感态度;4)负面在线评论主要集中在价格、药效、处方药购买、虚假宣传、物流包装、限购等方面。本研究挖掘出疫情下消费者对于网购医药商品的需求重点和痛点,对医药电商平台改善服务质量提供建设性意见。

基于Word2Vec和LDA主题模型的中国省级五年规划“文化政策”文本研究

运用Word2Vec和LDA相结合的主题模型分析技术,对我国31个省份三个时期五年规划文本中文化政策部分进行主题识别,从时间和空间两个维度进行“文化政策”主题挖掘和演化分析。研究发现,“文化政策”主题在发展趋势、重点转移、政策导向、技术应用等方面随时间推移呈现不同演化趋势;四大区域受经济发展水平、文化资源禀赋、政策导向影响,在企业角色强调程度、地区特色旅游发展以及国家级项目和竞争力方面存在地域差异。

推行科技特派员制度政策的量化评价分析——基于PMC指数模型

推行科技特派员制度的政策是推进科技助农兴农、深入实施乡村振兴战略的重要抓手。为进一步丰富科技特派员政策体系建设,以42份中国省域推行科技特派员制度的政策文本为研究对象,综合运用文本内容分析、政策建模一致性(PMC)指数模型法等方法,构建包括9个一级变量、40个二级变量的PMC指数模型,从政策内容的全面性、政策对象的覆盖面、施策部门的协同性、对成果转化推广的引导作用等多维度评价推行科技特派员制度政策水平。研究发现:科技特派员制度政策样本的PMC指数均值为5.45,处于良好水平,其中3项政策效果为优秀、29项政策效果为良好、10项政策效果为可接受,说明深入推行科技特派员制度体系较为完善,在政策目标、政策功能等方面表现较好,但在多部门协同性、成效跟踪、平台搭建、培训交流、健全社会化服务体系等方面仍有可优化空间。并据此在完善服务机制、加强成效跟踪、健全考核体系等方面提出政策优化建议。

数字化转型背景下的人力资源管理者胜任特征模型建构

数字经济时代,人工智能、区块链、云计算、大数据等数字化技术的应用对人力资源管理者的任职条件提出了新的要求。文章基于网络文本分析法和典型案例分析法,构建了包含专业知识、职业技能、通用能力和个人特质4个因子27个条目的胜任特征模型。其中,数据分析能力、云平台及大数据操作管理技能、快速学习能力、场景迁移能力等胜任特征体现了数字化转型背景下的人力资源管理者胜任特征新要求。企业要进行数字化转型,应重视数字化实践中人力资源管理者的配置与培训,通过新的胜任特征模型建立人才画像和人才评估标准,运用大数据技术提升人力资源管理部门工作效率。

基于米特-霍恩模型的高校实验室安全管理制度执行困境及路径优化

为提高高校实验室安全管理规范相关文件的执行效果,以国家教育部、省教育厅、某高校发布的实验室安全管理相关制度为对象,引入米特-霍恩模型分析高校实验室安全管理制度的执行路径,并运用文本分析法寻找文件中与其作用因素相关的关键词,进行制度执行过程中的困境分析。研究结果表明,在实验室安全管理方面,高校可能会面临多重困境,包括实验室危险因素的多样化、实验室安全经费的不足、制度执行主体之间的交流分层化、职能重叠、制度执行者缺乏足够的实施驱动力等问题。文章对实验室安全管理制度执行进行了路径优化,旨在健全我国高校实验室安全监管机制,降低高校实验室事故发生率。

PPP政策内容分析及其市场影响

PPP政策是政府治理PPP模式的主要工具,也是社会资本方制定PPP投资策略的指导文件.从政策文本内容及其市场影响两个角度出发,分析了2014年∼2021年我国中央层面的PPP政策文本.首先,构建了LDA主题模型对PPP政策进行主题演化分析,从主题发现、主题强度和演化趋势三个方面分析PPP政策的文本内容;其次,基于LDA主题模型结果及财政部PPP项目库数据,构建了多元线性回归模型进一步研究了不同政策主题对PPP市场的影响机制.从政策内容来看,PPP政策在文本设计方面存在主题分布不均衡的问题,并且呈现出一定的波动性和矛盾性;从政策的市场影响来看,激励性政策并未达到政策预期,且PPP市场对规范性政策更加敏感,社会资本方在进行投资选择时更倾向于监管规范、信息公开的PPP市场.

人工智能与团队精神——来自中国上市公司的经验发现

优秀的企业文化体现了企业的价值观,是影响企业高质量发展的重要非正式制度因素。人工智能正在与中国经济各领域深度融合,企业文化势必也将随着组织生产方式变革而发生改变。从团队精神视角,实证研究判别了人工智能技术使用是否以及怎样影响企业文化。研究结果发现,人工智能技术的应用能显著提高企业团队精神,经过一系列内生性和稳健性检验后仍然成立。机制分析表明,人工智能从收入创造效应、人力资本升级效应以及市场关注提升效应三个方面促进企业的团队合作精神。这种促进作用在儒家文化较为薄弱的地区更加突出,并且积极的团队精神也能够提升企业的市场价值。因此,数字经济时代人工智能技术的使用能够积极塑造企业团队精神,为树立符合社会主义核心价值观的优秀企业文化提供了科学依据。

基于LDA主题模型的服务业企业创新测度研究——兼与传统创新测度方法的对比分析

服务创新是新时代服务业高质量发展的重要内涵。以R&D投入、专利申请为代表的传统创新测度方法在衡量服务业企业创新时存在明显缺陷。使用隐含狄利克雷分配(LDA)主题模型对2008-2022年间914家服务业上市公司的创新水平进行了测度,并从企业整体、行业归属、区域分布、企业规模、企业所有制维度与传统创新测度方法对比分析。研究发现:相较于传统创新测度方法,LDA方法能够①同时反映服务业企业创新中的技术和非技术创新部分,更适用于测度非研发和非专利服务业企业;②更好捕捉教育,文化、体育和娱乐业等生活性服务业企业的创新特征和形式;③向上纠偏中西部地区服务业企业创新水平,向下纠偏东部地区服务业企业创新水平,还原东部和中西部地区服务业企业真实创新差距;④向上纠偏中小型服务业企业创新水平,向下纠偏大型服务业企业创新水平;⑤深入挖掘国有和非国有服务业企业在创新词频和主题上的差异,揭示非国有服务业企业创新水平较高的本质原因。基于此,从拓展服务创新内涵新认知、加快生活性服务业企业创新、营造良好创新生态、深化数字技术渗透以及发挥差异性优势五个方面提出政策建议,以期为加快服务业企业创新发展提供政策启示。

基于文本分析的飞机牵引车作业不安全事件研究

为了研究飞机牵引车作业过程的安全性,文章对1989年至2023年间自愿报告系统(ASRS)中12537条涉及飞机牵引车作业不安全事件的叙述和简述部分进行了文本挖掘和文本分析,并结合SHELL模型识别了飞机牵引车作业关键要素,最后采用SHEL-Jaccard系数矩阵对关键要素之间的关系进行分析,提出飞机牵引车作业安全预防和控制措施。研究发现,飞机牵引车作业关键要素如下:以销、刹车、速度控制装置、连接装置、通讯设备为主的硬件要素;以牵引车检查单、机场地面引导线、机场管理为主的软件要素;以牵引车司机、引导、警戒和机组为主的主要人员和以能见度、地面状况、障碍物为主的环境要素。

企业数字化转型:概念内涵、统计测度技术路线和改进思路

数字化转型是数字经济的重要推力,是企业实现提质降本增效目标、发现并创造新价值的必由之路。高质量的统计测度是研究数字化转型作用机制的基础,学界和业界目前已经就这一问题形成成熟度模型构建、文本分析和数字化无形资产占比测算三条主要技术路线,政府部门亦在指标体系构建方面有所作为。本文首先介绍数字化转型的概念内涵和属性;再对三条统计测度技术路线进行系统概述,围绕测度框架和数据来源评价其各自优劣,选出首选路线;最后分别面向学界业界和政府统计部门提出后续改进思路。本文试图在提高企业数字化转型统计测度质量方面做出理论贡献,以期为后续有关企业数字化转型的研究提供支持。