A Framework Based on the DAO and NFT in Blockchain for Electronic Document Sharing

In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge sharing.However,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative enthusiasm.Therefore,it is crucial to establish an e-document sharing system that enforces copyright protection.However,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the text.Digital watermark technology is only used as a means of infringement tracing.This paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in blockchain.The use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized systems.The e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document quality.The mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document sharing.Moreover,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization solution.Through experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in assessments.

An explorative study on document type assignment of review articles in Web of Science,Scopus and journals’websites

Purpose:Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS)and Scopus is important.This study aims to investigate the document type assignation of review articles in Web of Science,Scopus and Publisher’s websites on a large scale.Design/methodology/approach:27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed.The document types of these papers labeled on journals’websites,and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning.For the document type labeled on the website,we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not.Findings:Overall,WoS and Scopus performed similarly,with an average precision of about 99% and recall of about 80%.However,there were some differences between WoS and Scopus across different journal series and within the same journal series.The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly,especially for Scopus.Research limitations:The document types we used as the gold standard were based on the journal websites’labeling which were not manually validated one by one.We only studied the labeling performance for review articles published during 2017-2018 in review journals.Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear.Practical implications:This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus,and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites,WoS and Scopus.Originality/value:This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

Multimodal Deep Neural Networks for Digitized Document Classification

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement.Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years ago.Deep learning aids in comprehending input patterns so that object classes may be predicted.The segmentation process divides the input image into separate segments for a more thorough image study.This study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher education.To further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student orders.Subsequently,a deep learning model based on Conv2D layers is proposed for the document classification process.In the final part of this research,the proposed model is evaluated and compared with other machine-learning techniques.The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,respectively.The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

Pre-training transformer with dual-branch context content module for table detection in document images

Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction.However,because of the diversity in the shapes and sizes of tables,existing table detection methods adapted from general object detection algorithms,have not yet achieved satisfactory results.Incorrect detection results might lead to the loss of critical information.Methods Therefore,we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections.To better deal with table areas of different shapes and sizes,we added a dualbranch context content attention module(DCCAM)to high-dimensional features to extract context content information,thereby enhancing the network's ability to learn shape features.For feature fusion at different scales,we replaced the original 3×3 convolution with a multilayer residual module,which contains enhanced gradient flow information to improve the feature representation and extraction capability.Results We evaluated our method on public document datasets and compared it with previous methods,which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score.https://github.com/Yong Z-Lee/TD-DCCAM.

Research and Analysis of Grammatical Error Correction Technology for Chinese Documents

With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.

Research on overlapping structures and evolution properties of co-citation network

Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.

Evolution of the Socio-cognitive Structure of Knowledge Management(1986–2015): An Author Co-citation Analysis

Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.

Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

Local-to-Global Causal Reasoning for Cross-Document Relation Extraction

Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts.Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity.However,similarity-based methods cannot distinguish valid information from highly similar retrieved documents well.How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue.To address this problem,we design a novel local-toglobal causal reasoning(LGCR)network for cross-document RE,which enables efficient distinguishing,filtering and global reasoning on complex information from a causal perspective.Specifically,we propose a local causal estimation algorithm to estimate the causal effect,which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE.Furthermore,based on the causal effect,we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning.Experimental results under the closed and the open settings of the large-scale dataset Cod RED demonstrate our LGCR network significantly outperforms the state-ofthe-art methods and validate the effectiveness of causal reasoning in confusing information processing.

Restoration of Folk Document Covers from the Qing Dynasty to the Republic of China

The covers of booklets and books in folk documents primarily serve to protect the pages.Owing to long-term storage limitations,a considerable number of book covers have suffered varying degrees of damage.Following the principles of restoration,a comparative analysis and restoration of folk document covers were conducted,selecting four different types of carriers from the Taihang Mountain Documents,ranging from the Qing dynasty to the Republican Era.These carriers included hemp,mulberry bark,and machinemade paper,and cotton blue cloth.Each cover type was matched with an appropriate restoration paper,and different methods were employed during the restoration process.Through restoration,the previously damaged document covers can continue to fulfill their role in protecting the books,thereby extending the lifespan of these four folk documents.

Functions of Karez to Xinjiang Agriculture in the Qing Dynasty from the Perspective of Historical Documents

Desertification is increasingly serious in Xinjiang,and the construction of water conservancy is a precondition for the development of agriculture.The main project for the development of agriculture and water conservancy in Xinjiang is to build Karez,which played a vital role in the development of Xinjiang agriculture in the Qing Dynasty.It has been recorded many times in historical documents of the Qing Dynasty,such as Lin Zexu s Diary,Tao Baolian s Diary,Xinjiang Atlas and Zuo Zongtang s Memorial to the Emperor,etc.,which recorded the situation and historical origin of Karez.Karez made a significant contribution to the development of agriculture in the Qing Dynasty.It increased the cultivated land in Xinjiang at that time,and increased the types and yields of crops.It is conducive to the stability and development of Xinjiang s economy.Until today,Karez is still an important water source for agricultural irrigation in Xinjiang.

Human Rights in Civil Judicial Documents:Conception and Function

Traditional human rights theory tends to hold that human rights should be aimed at defending public authority and that the legal issue of human rights is a matter of public law.However,the development of human rights concepts and practices is not just confined to this.A textual search shows that the term“human rights”exists widely in China’s civil judicial documents.Among the 3,412 civil judicial documents we researched,the concept of“human rights”penetrates all kinds of disputes in lawsuits,ranging from property rights,contracts,labor,and torts to marital property,which is embedded in both the claims of the parties concerned and the reasoning of judges.Human rights have become the discourse and yardstick for understanding and evaluating social behavior.The widespread use of the term“human rights”in civil judicial documents reflects at least three concepts related to human rights:first,the rights to subsistence and development are the primary basic human rights;second,the judicial protection of human rights is a bottom-line guarantee;third,the protection of human rights aims to achieve equal rights.Today,judges quote the theory of human rights in judicial judgments from time to time,evidencing that human rights have a practical function in judicial adjudication activities,and in practice this is mainly manifested in declaring righteous values and strengthening arguments with the values and ideas related to human rights,using the provisions concerning human rights in the Constitution to interpret the constitutionality,and using the principles of human rights to interpret blurred rules and rank the importance of different rights.

Fostering Critical Thinking Skills in the EFL College-Level Classroom Through Online Collaborative Document Tools

This paper explores the potential of applying online collaborative documents to foster critical thinking skills in EFL college-level classrooms.Considering the limitations of traditional teacher-centered approaches and the need for innovative methods,the study examines the integration of online collaborative tools,using Tencent Docs as an example.The discussion highlights the importance of critical thinking in the academic and professional spheres and introduces the concept of online collaborative documents for enhancing this cognitive skill.Through a detailed exploration,the paper presents a model of employing collaborative documents within a college English class,demonstrating how students collaboratively learning an article.Then,the paper discusses the pros and cons of employing this technology in classroom.The conclusion emphasizes the transformative potential of integrating technology into pedagogy and its role in creating a dynamic learning environment.The paper underscores the importance of striking a balance between technology and traditional methods,foreseeing avenues for further research and development.

关于加强我国古籍保护法治建设的若干思考

做好古籍保护与利用,对赓续中华文脉、弘扬民族精神、增强国家文化软实力、建设社会主义文化强国具有重要意义。近年来,国家高度重视古籍工作,古籍保护立法工作已经取得一定成效,但仍存在文物保护相关法律法规无法完全适用、古籍相关规定较为宏观或效力较低、实践中仍有大量实际问题需要法律保障等问题。基于此,建议优先选择行政法规作为古籍保护立法的突破口,尽快推动《古籍保护条例》出台;平衡与已有法律法规和政策文件之间的关系;针对古籍保护利用中的实际需求和问题进行立法。表2。参考文献14。

Fully Automated Paper Document Sorting Robot Design

A fully automated paper document sorting robot was developed in this project.This robot classifies documents efficiently and accurately.The objective of this project was to improve the efficiency of classifying or sorting paper documents,reduce costs,and save time.The robot can classify documents according to user-defined rules,such as keywords,dates,serial numbers,bar codes,and the meaning of paragraphs.Since it can classify or sort documents intelligently,it can complete large-scale document classification quickly.The robot is constructed using an aluminum profile to create a box-type truss gantry structure frame.It was built on the LubanCat 4 motherboard and controlled through Python language programming.Driven by a stepper motor to move the manipulator.The camera module is combined with an artificial intelligence algorithm to recognize paper in real time,and the text is recognized after taking pictures of the paper.The sorting function is performed by several sensors.In addition,a web-based human-computer interaction platform was developed using the Flask web framework in Python.Users could access this platform in a variety of ways,allowing them to easily and swiftly configure parameters and send operational instructions to perform various functions.

不同分子质量水溶性壳聚糖在纸质文献加固中的应用研究

本研究采用乙酸酐制备了不同分子质量的水溶性壳聚糖(WSC-3、WSC-10、WSC-20、WSC-30和WSC-60)并用作修复液,探究其对纸质文献加固及耐老化的效果。结果表明,与未经加固处理的纸张(原纸)相比,采用不同分子质量的水溶性壳聚糖加固处理后,纸张的强度性能有所提升,白度下降幅度较小,色差变化较小,pH值和碱储量均增加。纸张加速老化后,与原纸相比,加固处理后纸张的强度性能下降幅度明显较小,其中WSC-30加固处理后纸张的抗张指数、撕裂指数、零距抗张强度的保留率分别提升了9.7、7.4、4.5个百分点,外观形貌、白度和色差等相差较小。

基于融合矩阵的文本相似度计算实现检索结果聚类

目的/意义弥补医学文本语义表示方面的不足,实现PubMed数据库检索结果聚类。方法/过程采用Jaccard系数和TF-IDF构建融合矩阵方法,建立短语间、文档间、短语与文档内容间的相似性关系融合矩阵,训练聚类算法,将PubMed数据库检索结果集合分组,随后生成类别标签,描述每一类簇文档的含义。结果/结论基于融合矩阵的聚类效果较好,提取出描述类别的高频词能很好地区分类别含义,对检索结果文本聚类任务有效。

行政复议规范性文件审查的困境及功能优化

行政复议法规定了规范性文件审查制度,但该制度在实践中呈现出当事人申请意愿低、复议机关抵触情绪大、对规范性文件的监督效果差等诸多问题。上述问题产生的直接原因包括审查主体定位不当、审查标准与行政诉讼趋同、审查机制与审查后果不匹配等,而根本原因则在于该制度的功能定位偏离了行政复议制度的功能定位。根据行政复议法,复议规范性文件审查的首要功能是预防和纠正不合法、不适当的规范性文件,而不是辅助行政争议的解决;同时,该制度还需兼顾保护当事人合法权益和保障行政机关依法行使职权的目标。这与行政诉讼中的规范性文件审查制度以及备案审查制度的功能定位存在明显差异。复议规范性文件审查制度的功能应当与复议制度的整体功能保持协同。以此为指引,复议规范性文件审查主体应被限缩解释为对规范性文件有监督权的上级机关,审查标准应被扩张解释为广义合法性审查标准,审查程序应在考虑有效监督规范性文件的基础上作平衡性设计。