Hadoop-Based Similarity Computation System for Composed Documents

There exist a large number of composed documents in universities in the teaching process. Most of them are required to check the similarity for validation. A kind of similarity computation system is constructed for composed documents with images and text information. Firstly, each document is split and outputs two parts as images and text information. Then, these documents are compared by computing the similarities of images and text contents independently. Through Hadoop system, the text contents are easily and quickly separated. Experimental results show that the proposed system is efficient and practical.

EDCMS:A Content Management System for Engineering Documents

Engineers often need to look for the right pieces of information by sifting through long engineering documents, It is a very tiring and time-consuming job. To address this issue, researchers are increasingly devoting their attention to new ways to help information users, including engineers, to access and retrieve document content. The research reported in this paper explores how to use the key technologies of document decomposition （study of document structure）, document mark-up （with EXtensible Mark- up Language （XML）, HyperText Mark-up Language （HTML）, and Scalable Vector Graphics （SVG））, and a facetted classification mechanism. Document content extraction is implemented via computer programming （with Java）. An Engineering Document Content Management System （EDCMS） developed in this research demonstrates that as information providers we can make document content in a more accessible manner for information users including engineers.The main features of the EDCMS system are： 1） EDCMS is a system that enables users, especially engineers, to access and retrieve information at content rather than document level. In other words, it provides the right pieces of information that answer specific questions so that engineers don＇t need to waste time sifting through the whole document to obtain the required piece of information. 2） Users can use the EDCMS via both the data and metadata of a document to access engineering document content. 3） Users can use the EDCMS to access and retrieve content objects, i.e. text, images and graphics （including engineering drawings） via multiple views and at different granularities based on decomposition schemes. Experiments with the EDCMS have been conducted on semi-structured documents, a textbook of CADCAM, and a set of project posters in the Engineering Design domain. Experimental results show that the system provides information users with a powerful solution to access document content.

Evaluation System for Reasoning Description of Judgment Documents Based on TensorFlow CNN

In order to improve the quality of the judgment documents, the state and government have introduced laws and regulations. However, the current status of trials in our country is that the number of cases is very large. Using system to verify the documents can reduce the burden on the judges and ensure the accuracy of the judgment. This paper describes an evaluation system for reasoning description of judgment documents. The main evaluation steps include: segmenting the front and back of the law;extracting the key information in the document by using XML parsing technology;constructing the legal exclusive stop word library and preprocessing inputting text;entering the text input into the model to get the text matching result;using the “match keyword, compare sentencing degree” idea to judge whether the logic is consistent if it is the evaluation of “law and conclusion”;integrating the calculation results of each evaluation subject and feeding clear and concise results back to the system user. Simulation of real application scenarios was conducted to test whether the reasoning lacks key links or is insufficient or the judgment result is unreasonable. The result show that evaluation speed of each document is relatively fast and the accuracy of the evaluation of the common nine criminal cases is high.

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.

Smart Client与Office System整合应用研究

Smart Client既可以运用网络和Web Services存取各种外部系统的资源,又能使用本地计算机的所有资源,充分发挥客户端的运算能力,是一种全新软件模式。Office 2003 System中提供了一个Smart Document的功能,使用以文档为基础的方式扩充智能感知的XML标记功能,和用户交互更容易。微软还提出了一套InformationBridge Framework(IBF),来协助开发人员建立方案来连接Microsoft Office应用程序与Web服务。应用Microsoft的Smart Client,结合Office 2003 System以及.NET技术,构建一种部署成本低、维护容易、构架轻小、健壮的应用系统。

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields.This also helps in having a better collaboration with governments and businesses.This study aims to investigate the development of research fields over time,translating it into a topic detection problem.Design/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents.Document embedding approaches are utilized to transform documents into vector-based representations.The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(i.e.LDA)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI publications.Findings:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset.Using the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the topics.Research limitations:We noticed that it is not possible to generalize one solution for all downstream tasks.Hence,it is required to fine-tune or optimize the solutions for each task and even datasets.In addition,interpretation of cluster labels can be subjective and vary based on the readers’opinions.It is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further limited.Practical implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and reviewers of the academic research to detect,summarize,analyze,and visualize research topics from decades of academic documents.This helps the scientific community and all related organizations in fast and effective analysis of the fields,by establishing and explaining the topics.Originality/value:In this study,we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction.We also use a concept extraction method as a labeling approach in this study.The effectiveness of the method has been evaluated in a case study of AI publications,where we analyze the AI topics during the past three decades.

On the study of ecosystem health:state of the art

Ecosystem health is a newly proposed concept that sets new goals for environmental management. Its definition, assessment indicators, and assessment methods are reviewed in this paper. Literature shows that the definitions and the assessment indicators cover a wide range of ecosystem health, and they differ in terms of researchers different scientific background. It is concluded that the concept of ecosystem health cannot be defined or understood simply in biological or ethical or aesthetic or historical terms and the assessment should be based on applying several indicators simultaneously to get overall picture of the health or integrity state of an ecosystem.

INFORMATION RETRIEVAL FOR SHORT DOCUMENTS

The major problem of the most current approaches of information models lies in that individual words provide unreliable evidence about the content of the texts. When the document is short, e.g. only the abstract is available, the word-use variability problem will have substantial impact on the Information Retrieval （IR） performance. To solve the problem, a new technology to short document retrieval named Reference Document Model （RDM） is put forward in this letter. RDM gets the statistical semantic of the query/document by pseudo feedback both for the query and document from reference documents. The contributions of this model are three-fold： （1） Pseudo feedback both for the query and the document; （2） Building the query model and the document model from reference documents; （3） Flexible indexing units, which can be ally linguistic elements such as documents, paragraphs, sentences, n-grams, term or character. For short document retrieval, RDM achieves significant improvements over the classical probabilistic models on the task of ad hoc retrieval on Text REtrieval Conference （TREC） test sets. Results also show that the shorter the document, the better the RDM performance.

Study on Multi-Label Classification of Medical Dispute Documents

The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.

Performance Assessment of Nanocellulose Hydroxypropyl Methyl Cellulose Composite on Role of Nano-CaCO_(3) for the Preservation of Paper Documents

Deacidification and self-cleaning are important for the preservation of paper documents.In this study,nano-CaCO_(3) was used as a deacidification agent and stabilized by nanocellulose(CNC)and hydroxypropyl methylcellulose(HPMC)to form a uniform dispersion.Followed by polydimethylsiloxane(PDMS)treatment and chemical vapor deposition(CVD)of methyltrimethoxysilane(MTMS),a hydrophobic coating was constructed for self-cleaning purposes.The pH value of the treated paper was approximately 8.20,and the static contact angle was as high as 152.29°.Compared to the untreated paper,the tensile strength of the treated paper increased by 12.6%.This treatment method endows the paper with a good deacidification effect and self-cleaning property,which are beneficial for its long-term preservation.

Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classicatio

Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for controlling data behavior.This paper presents a document classication multimodal for categorizing textual semi-structured and unstructured documents.The multimodal implements several individual deep learning models such as Deep Neural Networks(DNN),Recurrent Convolutional Neural Networks(RCNN)and Bidirectional-LSTM(Bi-LSTM).The Stacked Ensemble based meta-model technique is used to combine the results of the individual classiers to produce better results,compared to those reached by any of the above mentioned models individually.A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques.These techniques include using Term Frequency Inverse Term Frequency(TFIDF)or Continuous Bag of Word(CBOW)to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models.Moreover,this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class.In addition,the experimental results prove that the proposed model has achieved effective performance.Besides,upon investigating the PDF Documents classication,the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features,respectively.Moreover,concerning the JSON Documents classication,the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features,respectively.Furthermore,as for the XML Documents classication,the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features,respectively.

A METHODOLOGY FOR INTEGRATED MANAGEMENT SYSTEM

ISO 9000 certification has been considered as an effective competitive edge for manufacturing industries. In Hong Kong, the ISO 9000 certification has been widely adopted and extended to service industries for the sole purpose of enhancing corporate image. However, complaints have been voiced by many certified organizations about increasing paperwork, non direct labour and excessive resources channelled into non core business. This paper is to propound the integrated management system, a single management system focusing on business purpose yet being flexible enough to integrate the requirements of various management standards such as the ISO 9000, ISO 14000 and future ISO 18000. This paper describes how the proposed system could be implemented to achieve the desired business results.

Automatic Table Recognition and Extraction from Heterogeneous Documents

This paper examines automatic recognition and extraction of tables from a large collection of het-erogeneous documents. The heterogeneous documents are initially pre-processed and converted to HTML codes, after which an algorithm recognises the table portion of the documents. Hidden Markov Model (HMM) is then applied to the HTML code in order to extract the tables. The model was trained and tested with five hundred and twenty six self-generated tables (three hundred and twenty-one (321) tables for training and two hundred and five (205) tables for testing). Viterbi algorithm was implemented for the testing part. The system was evaluated in terms of accuracy, precision, recall and f-measure. The overall evaluation results show 88.8% accuracy, 96.8% precision, 91.7% recall and 88.8% F-measure revealing that the method is good at solving the problem of table extraction.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

High-speed broadband data acquisition system based on FPGA

A field-programmable gate array(FPGA)based high-speed broadband data acquisition system is designed.The system has a dual channel simultaneous acquisition function.The maximum sampling rate is 500 MSa/s and bandwidth is200 MHz,which solves the large bandwidth,high-speed signal acquisition and processing problems.At present,the data acquisition system is successfully used in broadband receiver test systems.

Roll angle measurement system based on triaxial magneto-resistive sensor

Aiming at large error when inertial devices measure the roll angle under high overload conditions,the article designs one kind of roll angle measurement system based on magneto-resistive sensor which calculates the roll angle by micro controller STM32.Experiment results of a triaxial turntable show that using magneto-resistive sensor to measure roll angle is feasible and of high accuracy,and it can calculate the roll angle of the conventional projectile with the error in 1°.

Stochastic Model for Multiple Classes and Subclasses Simple Documents Processing

The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian ML×K/ML×K/1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. In fine, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.

Synchronizing Fractional Unified Hyperchaotic System via Impulsive Control

This work investigates synchronization of two fractional unified hyperchaotic systems via impulsive control.The stable theory about impulsive fractional equation is studied based on the stable theory about fractional linear system.Then according to the theorem proposed the sufficient condition on feedback strength and impulsive interval are established to guarantee the synchronization.Numerical simulations show the effectiveness of the theorem.

Integrated management system of agro-fishery products based on QR-code

Recently,both the importance and awareness relating to food safety relating to agrochemicals,chemical fertilizers,the pollution of ecosystem and unwholesome foods are growing rapidly.For the same reason,the purchaser's curiosity increases to know about the production process which can be accomplished by production traceability system.The traceability system can store and superintend the specific attribute information about products regarding the productive centre,producer and production finish data.The products undergo various circulation processes and they are being managed by individually constructed traceability system.Thus,it is difficult for the producer and the consumer to guarantee the safety from transpirable issues about the falsification of production traceability because the traceability information can be re-produced by forging through various ways.So in this paper,we proposed a system for integrated management of agro-fishery products based on quick response code(QR-code)for forgery prevention which may help to solve these issues.The proposed system makes QR-code using production information provided by producer.And then it is provided to the distributor and the consumer through integrated service model using the QR-code.Therefore,this system make sure to provide the same production traceability about the products to the distributor and the consumer and always keeps both updated with the same information.

Supporting B2B Business Documents in XML Web Services

While XML web services become recognized as a solution to business-to-business transactions, there are many problems that should be solved. For example, it is not easy to manipulate business documents of existing standards such as RosettaNet and UN/EDIFACT EDI, traditionally regarded as an important resource for managing B2B relationships. As a starting point for the complete implementation of B2B web services, this paper deals with how to support B2B business documents in XML web services. In the first phase, basic requirements for driving XML web services by business documents are introduced. As a solution, this paper presents how to express B2B business documents in WSDL, a core standard for XML web services. This kind of approach facilitates the reuse of existing business documents and enhances interoperability between implemented web services. Furthermore, it suggests how to link with other conceptual modeling frameworks such as ebXML/UMM, built on a rich heritage of electronic business experience.