Malicious Document Detection Based on GGE Visualization

With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly urgent.Currently,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and adaptability.To address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color image.The Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images.As shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.

Chinese Documentaries

This video series is the first experimental psychology documentary made in China.It focuses on analyzing professional theories to raise people’s general understanding of basic psychology.By combining innovative audiovisual narrative with psychological experiments,it zooms in on real human nature through discussing social hotspots from the perspectives of social psychology,cognitive psychology,and personality psychology,in order to help people find answers for their current psychological difficulties.

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.

Fusion of Type-2 Neutrosophic Similarity Measure in Signatures Verification Systems: A New Forensic Document Analysis Paradigm

Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.

From Diaries to Digital:The Role of AI in Web-Mediated Documentary Analysis

This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.

明洪武朝官修礼制文献考

洪武朝是明代礼制的奠基时期,制礼作乐活动频举,在此过程中产生了众多礼制文献,可分为两类:一是礼制专书,约有近20种,既有擘划一代礼制规模的综合礼典,又有专门规范某种礼仪的礼制专书;二是不少官修政书里面也多有礼制的内容,包括法律类、综合类及一些专门类政书,展示出明代礼法、礼政合一的特点。洪武朝的礼制文献,不论是礼制专书,还是“礼政居多”的政书文献,后多被会典采辑,在明代得到长久施行,对明代的政治与社会产生了深远影响。

Storyline Extraction of Document-Level Events Using Large Language Models

This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.

分子鉴别诊断(MDD)技术在呼吸道病原体检测中的应用

目的:以靶序列富集多重PCR(Tem-PCR)和液相芯片(xMAP)检测技术结合,建立常见呼吸道病原体的分子鉴别诊断(MDD)平台,对其可靠性进行评估,并用于临床样品的检测。方法:采集苏州大学附属儿童医院呼吸科住院病例呼吸道灌洗液样品22例和苏州大学附属第一医院呼吸内科门诊病例咽拭子样品20例,使用Tem-PCR技术对样品的核酸进行多重扩增,以液相芯片技术进行高通量的多重检测。结果:对已知样品的检测表明该平台具有高度的特异性和敏感性。对42例临床样品进行检测的结果显示,22例呼吸道灌洗液的阳性检出率为63.6%。呼吸道灌洗液阳性检出率高于门诊病例咽拭子样品的阳性检出率,RNA病原体(以病毒为主)的阳性率高于DNA病原体(以细菌为主)的阳性率。结论:建立了呼吸道病原体的MDD平台,具备了对呼吸道传染性疾病的高通量快速诊断能力。

MDD法和裂变径迹法相结合模拟样品的低温热历史--以柴达木盆地北缘赛什腾山中新生代构造演化为例

MDD法和裂变径迹法是2种最常用的低温热年代学方法。通过对它们的测试结果的模拟,揭示出样品经历的热历史,可以有效地延伸这2种方法的研究范围。但是由于模拟的多解性,使得如何制约模拟过程,获得合理的模拟结果显得尤为重要。通过柴达木盆地北缘的模拟过程,展示了模拟中可能存在的问题及提高模拟结果的方法。同时揭示了柴达木盆地北缘中新生代可能存在的3次冷却事件,分别发生在距今约130～150、30～40和5～10Ma。

青藏高原北缘中生代伸展构造^(40)Ar/^(39)Ar测年和MDD模拟

沿着青藏高原北缘的阿尔金山脉东段 ,发育了长度大于 30 0km、EW走向的拉配泉断裂。中美合作阿尔金课题组的地质填图结果表明 ,该断层实际上是一条南倾的正断层 ,局部倾角可以低至 30°以下。沉积学和热年代学研究控制了拉配泉断裂的活动时代 :早—中侏罗统地层可以解释为断裂上盘的伸展盆地沉积 ;下盘岩石中钾长石40 Ar/ 3 9Ar测年和MDD模拟给出 2个阶段的冷却事件 ,早期事件出现在约 2 2 0～ 187Ma之间 ,晚期事件出现在早白垩世晚期 (约 10 0Ma)。早期事件代表了拉配泉断裂正断作用的主要阶段。

构造非平稳运动定量研究的MDD模式制约

利用ＭＤＤ模式得到的连续冷却曲线，可以定量地描述地质体的冷却（隆升）过程；采用我国７个不同地区中３８个钾长石ＭＤＤ模式研究结果证明，快速冷却事件存在的普遍性，不同的快速冷却速度可能反映了不同的构造事件；突变因子（δｖｉ）可以定量地描述地质体非平稳运动以及突变程度。

MDD模式与快速冷却事件

ＭＤＤ 模式（Ｍｕｌｔｉｐｌｅ Ｄｉｆｆｕｓｉｏｎ Ｄｏｍａｉｎ ｍｏｄｅｌ） 是Ｄｏｄｓｏｎ 模式的扩展，它可以从一个钾长石样品分析中，得到温度在≤３５０ ～１５０ ℃之间的一条连续的冷却曲线，因此，ＭＤＤ 模式的一个突出贡献是对快速冷却事件的定量制约。冈底斯岩带与哀牢山－ 红河剪切带中新世的快速冷却事件与构造含义是利用这一模式进行研究的成功实例，它不仅定量厘定了快速冷却事件的时间，证实了其存在的普遍性，而且明确指出了中新世印度和欧亚板块碰撞后岩石圈质量转移过程的时间序列，定量地指出了冈底斯岩带逆冲抬升导致的地壳增厚与沿哀牢山－

MDD模式应用中若干问题的讨论Ⅱ——活化能选取的重要性

活化能取值在ＭＤＤ模式中对冷却曲线的温度有较大的影响，其影响度大约为７～８℃／ｋｃａｌ·ｍｏｌ－１，但它并不改变冷却曲线的形状。导致活化能取值偏低的影响因素包括低温阶段的加热时间、Ｋ－长石的纯度和部分斜长石、石英的混入等。

Documentation Concordance,Sharing and Utilization of Tea Germplasm Resources in Yunnan

In this paper,the research achievements and progress of Yunnan tea germplasm resource in past sixty years are systematically reviewed from the following aspects：exploration,collecting,conservation,protection,identification,evaluation and shared utilization.Simultaneously,the current problems and the suggestions about subsequent development of tea germplasm resources in Yunnan were discussed,including superior and rare germplasm collection,tea genetic diversity research,biotechnology utilization in tea germplasm innovation,super gene exploration and function,the construction of utilization platform,biological base of species and population conservation.