XML Parser Research Articles

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Considering the prevailing rule of Large Language Models (LLMs) applications and the benefits of XML in a compiler context. This manuscript explores the synergistic integration of Large Language Models with XML-based compiler tools and advanced computing technologies. Marking a significant stride toward redefining compiler construction and data representation paradigms. As computing power and internet proliferation advance, XML emerges as a pivotal technology for representing, exchanging, and transforming documents and data. This study builds on the foundational work of Chomsky's Context-Free Grammar (CFG). Recognized for their critical role in compiler construction, to address and mitigate the speed penalties associated with traditional compiler systems and parser generators through the development of an efficient XML parser generator employing compiler techniques. Our research employs a methodical approach to harness the sophisticated capabilities of LLMs, alongside XML technologies. The key is to automate grammar optimization, facilitate natural language processing capabilities, and pioneer advanced parsing algorithms. To demonstrate their effectiveness, we thoroughly run experiments and compare them to other techniques. This way, we call attention to the efficiency, adaptability, and user-friendliness of the XML-based compiler tools with the help of these integrations. And the target will be the elimination of left-recursive grammars and the development of a global schema for LL(1) grammars, the latter taking advantage of the XML technology, to support the LL(1) grammars construction. The findings in this research not only underscore the signification of these innovations in the field of compilation construction but also indicate a paradigm move towards the use of AI technologies and XML in the context of the resolution of programming traditional issues. The outlined methodology serves as a roadmap for future research and development in compiler technology, which paves the way for open-source software to sweep across all fields. Gradually ushering in a new era of compiler technology featuring better efficiency, adaptability, and all CFGs processed through existing XML utilities on a global basis.

Parsing of Research Documents into XML Using Formal Grammars

Automatic information extraction of content and style format in paged documents is challenging. It requires the conversion of the original document into a granular level of details for which every document section and content is identifiable. This functionality or tool does not exist for any academic research document yet. In this paper, we present an automated process of parsing research paper documents into XML files using a formal method approach of context-free grammars (CFGs) and regular expressions (REGEXs) definable of a standard template. We created a tool for the algorithms to parse these documents into tree-like structures organized as XML files named research_XML (RX) parser. The RX tool performed the extraction of syntactic structure and semantic information of the document’s contents into XML files. These XML output files are lightweight, analyzable, query-able, and web interoperable. The RX tool has a success rate of 91% when evaluated on fifty varying research documents of 160 average pages and 8,004 total pages. The tool and test data are accessible on GitHub repo. The novelty of our process is specific to applying formal techniques for information extraction in structured multipaged documents and academic research documents thus advancing the research in automatic information extraction.

THREAT ATTRIBUTES HANGING IN THE WILD ANDROID

Android is a complicated system that applications and component are usable and support for multiple work together, giving rise to highly complex interdependence relationships. Meanwhile, the Android environment is notable for being greatlty disparate and decentralized: different Operation System version is personalized and re-personalized by different parties about fast and used by whoever that can develop an application for that version. Android secure its explanation sources over an app sandbox and permissions model, where each application execution in this part can entrance only suspectible overall assets and another application component (value providers, services, activities, publication receivers) by the appropriate liscense.
 This study uses Harehunter measurement to automatically detect Hare vulnerabilities in Android system applications. Harehunter and HareGuard performance evaluations were carried out in this study, both of which proved to be highly effective. The approach used here is divergent investigation, by searching all quoted, decompiled script, and obvious data for targeted attribute determination as an initial step, and running an XML parser. The outcome of this research show that the impact of Hares is very significant. The application of HareGuard in this study proved to be effective in detecting all attack applications that were made. Further evaluation of the performance impact on the minimum system host. For future research, to make Harehunter more effective, it is suggested to use a more qualified analyzer. So that this direction can be explored in more depth.

AtlasGrabber: a software facilitating the high throughput analysis of the human protein atlas online database

BackgroundThe human protein atlas (HPA) is an online database containing large sets of protein expression data in normal and cancerous tissues in image form from immunohistochemically (IHC) stained tissue microarrays. In these, the tissue architecture is preserved and thus provides information on the spatial distribution and localization of protein expression at the cellular and extracellular levels. The database is freely available online through the HPA website but currently without support for large-scale screening and analysis of the images in the database. Features like spatial information are typically lacking in gene expression datasets from homogenized tissues or single-cell analysis. To enable high throughput analysis of the HPA database, we developed the AtlasGrabber software. It is available freely under an open-source license. Based on a predefined gene list, the software fetches the images from the database and displays them for the user. Several filters for specific antibodies or images enable the user to customize her/his image analysis. Up to four images can be displayed simultaneously, which allows for the comparison of protein expression between different tissues and between normal and cancerous tissues. An additional feature is the XML parser that allows the extraction of a list of available antibodies, images, and genes for specific tissues or cancer types from the HPA’s database file.ResultsCompared to existing software designed for a similar purpose, ours provide more functionality and is easier to use. To demonstrate the software’s usability, we identified six new markers of basal cells of the prostate. A comparison to prostate cancer showed that five of them are absent in prostate cancer.ConclusionsThe HPA is a uniquely valuable database. By facilitating its usefulness with the AtlasGrabber, we enable researchers to exploit its full capacity. The loss of basal cell markers is diagnostic for prostate cancer and can help refine the histopathological diagnosis of prostate cancer. As proof of concept, with the AtlasGrabber we identified five new potential biomarkers specific for prostate basal cells which are lost in prostate cancer and thus can be used for prostate cancer diagnostics.

A text mining framework for screening catalysts and critical process parameters from scientific literature - A study on Hydrogen production from alcohol

Hydrogen production is an active area of research with a vast amount of available scientific literature. However, this data is unstructured and scattered, making its utilization difficult from an academic and industrial point of view. This work aims to develop a recommendation system to identify optimal process conditions and catalyst information using Natural Language Processing (NLP) tools. To this end, full-text articles were extracted using the Elsevier API key followed by a custom XML parser. Latent Dirichlet allocation (LDA) was applied on this dataset to form clusters of topics. The experimental section of each article is annotated using state-of-the-art sentiment analysis techniques and divided into four categories based on the presence of catalyst and process information. This dataset is used to develop a dedicated NLP model, “Ex-SciBERT” by performing transfer learning on the “Sci-BERT” model. This model performs classification followed by Named Entity Recognition (NER) to extract catalyst and process parameters. Ex-SciBERT model produces an accuracy score of 0.915 (train dataset) and 0.890 (test dataset) for the classification of sentences task and an excellent accuracy score of 0.998 (train dataset) and 0.997 (test dataset) for the NER task. Deployment of this model will automate and accelerate the screening of relevant information from literature by reducing manual efforts.

XAPP: An Implementation of SAX-Based Method for Mapping XML Document to and from a Relational Database

Extensible Markup Language (XML) is the standard medium for data exchange among businesses over the Internet, hence the need for effective management. However, since XML was not designed for storage and retrieval, its management has become an open research area in the database community. Existing mapping techniques for XML-to-relational database adopt either the structural mapping or the model mapping. Though numerous mapping approaches have been developed, mapping and reconstruction time had been problematic, especially when the document size is large and can hardly fit into main memory. In this research, an application codenamed XAPP, a new lightweight application that adopts a novel model mapping approach was developed using Simple API for XML (SAX) parser. XAPP accepts a document with or without Document Type Definition (DTD). It implements two algorithms: one maps XML data to a relational database and improves mapping time, and the other reconstructs an XML document from a relational database to improve reconstruction time and minimise memory usage. The performance of XAPP was analysed and compared with the Document Object Model (DOM) algorithm. XAPP proves to perform significantly better than the DOM-based algorithm in terms of mapping and reconstruction time, and memory efficiency. The correctness of XAPP was also verified.

The GeoModel tool suite for detector description

The GeoModel class library for detector description has recently been released as an open-source package and extended with a set of tools to allow much of the detector modeling to be carried out in a lightweight development environment, outside of large and complex software frameworks. These tools include the mechanisms for creating persistent representation of the geometry, an interactive 3D visualization tool, various command-line tools, a plugin system, and XML and JSON parsers. The overall goal of the tool suite is a fast geometry development cycle with quick visual feedback. The tool suite can be built on both Linux and Macintosh systems with minimal external dependencies. It includes useful command-line utilities: gmclash which runs clash detection, gmgeantino which generates geantino maps, and fullSimLight which runs GEANT4 simulation on geometry imported from GeoModel description. The GeoModel tool suite is presently in use in both the ATLAS and FASER experiments. In ATLAS it will be the basis of the LHC Run 4 geometry description.

A survey and classification of XML based attacks on web applications

ABSTRACTXML based attacks are executed in web applications through crafted XML document that forces XML parser to process un-validated documents. This leads to disclosure of sensitive information, malicious code execution and disruption of services. OWASP has included XML based attacks at number four in its top 10 list of vulnerabilities published in 2017. Most of the vulnerabilities reported using the XML document range from high to critical and require to be addressed immediately. As per the National Vulnerability Database, 152 vulnerabilities have already been reported in the first five months of the year 2019. A varied number of XML vulnerabilities and their classification exist but are limited to a specific vulnerability. In this paper, the authors have proposed a classification of XML based vulnerabilities based on exhaustive literature survey. The approach/strategies to mitigate these vulnerabilities are also presented. The work will help the web developers for proposing secure parsers that will thwart such attacks.

XML-REG: Transforming XML Into Relational Using Hybrid-Based Mapping Approach

eXtensible Markup Language (XML) is one of the most used standards for information sharing between applications and devices, both on the internet and local network. However, relational database (RDB) has been used by many enterprises as their data management system and will require an amount of cost to change the system completely, if they are to change to XML technology solely. Thus, a mapping scheme is required to provide seamless integration on bridging XML technologies and RDBs. In this paper, an efficient model-based mapping scheme named XML-REG is proposed. The XML document will first be read and parsed into the parser, namely Streaming API for XML (StAX) parser. Then, each node will then be assigned with unique identification label to show the exact position of nodes in the document. Subsequently, by employing the proposed algorithm, data will then be transformed into tables in the RDB storage. As the result, two tables, namely (i) value table to store information carried by text node of the document, and (ii) path table to store the hierarchy structure of the document will be created. Experimental evaluations demonstrated that XML-REG outperformed some existing approaches, such as Mini-XML, XAncestor, XMap and XRecursive in terms of data storage size, mapping time and query retrieval time. In addition, the scalability test has also been conducted to show the capability of these approaches in supporting huge datasets, by scaling the DBLP dataset by times 5, times 10 and times 15. The results showed that XML-REG has the closest to linear graph compared to other existing approaches. On average, XML-REG showed the best performance in terms of query retrieval time and database storage size.

Parallelism in web services: design of parallel XML parser for web services

<p>A WS provides the communication between heterogeneous systems. While performing this operation, we need to focus on QoS of consumer, provider and registry directory. There will be some parameters like WS selection, prediction and rank these are parameters need to consider while QoS implementation in web services. While performing integration in web services we need to focus on QoS requirements regarding server and network performance. Performance of WS is related to locations i.e the network distance and the Internet connections between consumer and provider. There will be more QoS approach which works on consumers collected QoS data, based on this data system can predict the QoS of WS. Throughput and response time are the QoS of WS. In this paper, we have proposed parallel XML parser, by which we can parse UDDI, WSDL and SOAP XML files parallel by which it will improve the response time and throughput of WS.</p>

Application Research of XML Parsing Technology Based on Android

Based on the research of XML core technology, this paper introduces the current mainstream XML parsing methods, XML DOM and SAX methods. For the current android system, which is used in android operating system. XML is researched in the android layout file, which is combined with the current android platform. The paper introduce application of XML pull parsing process, first study the parsing performance of XML, the second through an example to discuss these three methods, the last analysis XML file Size and compares these three methods.

Coverage Criteria for State-Based Testing

State-based testing (SBT) is known as deriving test cases from state machines and examining the dynamic behaviour of the system. It helps to identify various types of state-based faults within a system under test (SUT). For SBT, test cases are generated from state chart diagrams based on various coverage criteria such as All Transition, Round Trip Path, All Transition Pair, All Transition Pair with length 2, All Transition Pair with length 3, All Transition Pair of length 4 and Full Predicate. This article discuses a number of coverage criteria at the design level to find out various types of state-based faults in SBT. First, the intermediate graph is generated from a state chart diagram using an XML parser. The graph is traversed based on the given coverage criteria to generate a sequence of test cases. Then, mutation testing and sneak-path testing are applied on the generated test cases to check the effectiveness of the generated test suite. These two are common methods for checking the effectiveness of test cases. Mutation testing helps in the number of seeded errors covered whereas sneak-path testing basically helps to examine the unspecified behavior of the system. In round trip path (RTP), it is not possible to cover all paths. All transition is not an adequate level of fault detection with more execution time compared to all transition pair (ATP) with length 4 (LN4). In the discussion, ATP with LN4 is the best among all coverage criteria. SBT can able to detect various state-based faults-incorrect transition, missing transition, missing or incorrect event, missing or incorrect action, extra missing or corrupt state, which are difficult to detect in code-based testing. Most of these state-based faults can be avoided, if the testing is conducted at the early phase of design.

Towards efficient XML parsing through minimization of JVM parameter space

A significant increase in the usage of Extensible Markup Language (XML) data for various protocols and standards emphasizes the development of efficient XML parsers. For the Java language, the XML DOM parser despite performing in-memory operations is unable to achieve peak execution performance on modern systems, especially for parsing large XML files. The issue of inefficient execution may be mitigated by selecting appropriate runtime parameters for the Java Virtual Machine (JVM). This entails to exploring parameter space in an exhaustive manner that is not practically feasible for rapid application development. This paper aims at performance enhancement of XML parsing through selection of optimal set of JVM runtime parameters. The proposed approach works independent of parser design. It reduces JVM parameter space through machine learning-based models which are trained using profile data. The impact of parameters is determined using linear regression and artificial neural network-based models. The subsequent computation of a location-based weight vector along with a threshold value for filtration of parameters generates a set of optimal parameters for performance enhancement. The XML parsing code using the optimal parameters achieves average speedups of 13.18% and 21.42% over the standard code on Intel Xeon and Intel Core i7-based systems, respectively.

A Java Based Web Service Model for Library Information Discovery

Library information discovery is a vital aspect of education development and management. Nowadays, many libraries offer the information regarding to facilities, resources and services to users through their website. The existing library information systems discover only their information and do not integrate the related information from other libraries. Therefore, web service technology has been developed, which is uses a standard interface to enable the consumer-to-business (C2B) or business-to-business (B2B) easily interact over the website. Currently, there is no standard model is developed to facilitate the users to discover and integrate the information of facilities, resources and services that published in the libraries website. This article discussed the development of a web service model that can discover and integrate the information in the various libraries website to the users by using Java Technology application program interface and tools provided by the integrated Web Services Stack. In the modelling process, the library websites are converted into Extensive Markup Language format (XML) for examining the Simple Object Access Protocol (SOAP) of each website. The converted XML files are stored as the application data source, which lies as the core of the web service model. By using an XML parser, the XML files are parsed into XMLDOM format for manipulating its contents. XML request object hypertext protocol is used to receive or reload the data from the client to the server. Finally, HTML and cascading style sheet (CSS) are used to reload the information to the client and display it. The results show that the proposed model is able to achieve over 90% of information discovery average precision performance regarding to user’s query on facilities, resources and services provided in the library websites. The developed model can benefit library’s users to extract the resources, facilities and services provided by the library intelligently.

THE METHOD OF DATA SEARCH AND ANALYSIS FROM THE INTERNET RESOURCES FOR THE FORMATION OF ACTUAL REQUIREMENTS FOR CANDIDATES

The article deals with the issues of data extraction from Web-resources on the example of gathering information on vacancies. There are three main interacting parts of this process: data source, database, and an expert. The main problematic aspects of the data mining process are the availability of several data sources; data representation in different languages; extraction data from different file formats; multiple updating of repetitive operations and data. The advantages and disadvantages of Web Mining methods were analyzed and defined. They are DOM tree analysis, line parsing, usage of regular expressions, XML parsing and visual approach. Method of DOM tree using XPath was applied in the paper. The method of comparator identification for modeling the data extraction process was proposed. The component, which receives the search topic and the search start page, carries out a thematically directed extraction. The comparator compares the extracted word from the page with the words of the search model. The application of the above-mentioned approach is presented for identifying a vacancy on the job search site. The thesaurus of employers' requirements is developed. Words-indicators of the required vacancies are presented in three languages. The parser work was set up. The parser processes the documents and retrieves the data used to fill a particular data model. The developed module works as follows. It begins to work with obtaining an array of necessary pages from the selected Web site. The next step is the analysis of Web page‘s structure. Then it is necessary to get the content of a specific HTML page, which contains the necessary information for its further retrieval and processing. As a result ―vacancy model‖ is developed. The model should include the following elements: vacancy title; date of adding a job to the site; the city where the applicant needs to work; requirements for the candidate; applicant duties; working conditions. Extraction of requirements, liabilities, and conditions was defined as the most problematic area, whereas the same information can be presented in a different way. In order to unify requirement experts were engaged.

Biopython Project Update 2017

The Biopython Project is a long-running distributed collaborative effort, supported by the Open Bioinformatics Foundation, which develops a freely available Python library for biological computation [1]. We present here details of the Biopython releases since BOSC 2016, namely Biopython 1.68, 1.69 and 1.70. Together these had 82 named contributors including 51 newcomers which reflects our policy of trying to encourage even small contributions.Biopython 1.68 (August 2016) was a relatively small release, with the main new feature being support for RSSB’s new binary Macromolecular Transmission Format (MMTF) for structural data.Biopython 1.69 (April 2017) represents the start of our re-licensing plan, to transition away from our liberal but unique Biopython License Agreement to the similar but very widely used 3-Clause BSD License. We are reviewing the code base authorship file-by-file, in order to gradually dual license the entire project. Major new features include: a new parser for the ExPASy Cellosaurus cell line database, catalogue and ontology; support for the UCSC Multiple Alignment Format (MAF), FSA sequencing files, version 4 of the Affymetrix CEL format; updates to the REBASE February 2017 restriction enzyme list; Bio.PDB.PDBList now can download more formats including MMTF; enhanced PyPy support by taking advantage of NumPy and compiling most of the Biopython C code modules.Biopython 1.70 (July 2017) has internal changes to better support the now standard pip tool for Python package installation. Major new features include: support for Mauve’s eXtended Multi-FastA (XMFA) file format, updates to our BLAST XML and MEME parsers, ExPASy support, and phylogenetic distance matrices. This release is noteworthy for our new logo, contributed by Patrick Kunzmann. This draws on our original double helix logo, and the blue and yellow colors of the current Python logo.All releases fixed miscellaneous bugs, enhanced the test suite, and continued efforts to follow the PEP8 and PEP257 coding style guidelines which is now checked automatically with GitHub-integrated continuous integration testing using TravisCI. We now also use AppVeyor for continuous integration testing under Windows. Current efforts include improving the unit test coverage, which is easily viewed online at CodeCov.io.

XML Parser를 활용한 최신 영화 정보 제공 연구

XML Parser를 활용한 최신 영화 정보 제공 연구

High-performance XML modeling of parallel queries based on MapReduce framework

With the increasing of data at an incredible rate, the development of cloud computing technologies is of critical importance to the advances of researches. MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. Traditional parallel XML parsing and indexing approaches are inadequate for processing large-scale XML datasets on clusters and; therefore, we propose an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. Our solution seamlessly integrates data storage, labeling, indexing, and parallel queries to process a massive amount of XML data. Specifically, we introduce an SDN labeling algorithm and a distributed hierarchical index using DHTs. More importantly, we design an advanced two phase MapReduce solution that is able to efficiently address the issues of labeling, indexing, and query processing on big XML data. The first MapReduce phase applies filtering, labeling, index building techniques, in which each DataNode performs elements labeling using a map function and a reduce function to merge and build indexes. In the second phase, local XML queries in multiple partitions are performed in parallel using index-table-enabled B-SLCA. Our experimental results show the efficiency and effectiveness of our proposed parallel XML data approach using MapReduce Framework.

An XML-based machining energy estimation tool from process plans

Excessive energy consumption has become a worldwide issue in today’s manufacturing industry. Software tools for estimating manufacturing energy consumption that could later be used to integrate with CAD/CAM systems are in demand. This research develops software tools for estimating manufacturing energy consumption in machining operations based on manufacturing process plans. This study first reviews literature and background in related areas and then presents the method to calculate manufacturing energy in machining operations. eXtended Markup Language (XML) has been adopted as the knowledge representation language for machining process plans and forms one of the inputs for energy estimation. Since XML plays an important role in estimating machining energy, this article introduces XML validation and parsing. The method is demonstrated via case studies for machining energy estimation in machining operations. The method will assist users who have little knowledge about energy computations to estimate energy consumption during the manufacturing phase. Such energy estimation can be used to redesign the part and assemblies, leading to products with reduced machining energy.

FPGA를 이용한 하드웨어 기반 고성능 XML 파싱 기법

다양한 웹 서비스들은 서비스의 제공을 위해서 구조화된 표준문서인 XML(eXtensible Markup Language)을 널리 사용하고 있으며, 모바일 환경에서의 전자문서 및 전자서명 그리고 메일시스템에서도 XML이 사용되고 있다. XML을 사용하기 위해서는 문서의 파싱이 요구되며, 이는 XML 처리에서 가장 계산 집중적 작업이다. 따라서, XML 파싱 성능을 높이기 위해 하드웨어 기반의 파서들이 제안되어 성능 향상에 초점을 맞추고 있지만 실제 파싱 기법들에 대한 연구는 거의 이루어지지 않았다. 본 논문에서는 파서의 종류와 상관없이 사용될 수 있는 고성능 XML 파싱 기법을 제안하고 FPGA를 이용하여 파서를 설계하여 검증하였다. 제안된 파싱 기법은 상태머신 대신에 엘리먼트 분석기들을 사용하며 다중바이트 단위 엘리먼트 매칭을 수행한다. 제안된 파싱 기법은 CPB 항목에서 약 2~4배의 소비 클록을 감소시켰으며 파싱 이전에 전처리작업을 요구하지 않는다. 다른 파서들과 비교하여 제안된 파서는 약 1.33~1.82배 속도를 향상시켰다. 따라서, 제안된 파싱 기법은 실시간 XML 파싱이 가능하며 일반적인 XML 파서들에서도 적용할 수 있는 적합한 구조를 갖는다. A structured XML has been widely used to present services on various Web-services. The XML is also used for digital documents and digital signatures and for the representation of multimedia files in email systems. The XML document should be firstly parsed to access elements in the XML. The parsing is the most compute-instensive task in the use of XML documents. Most of the previous work has focused on hardware based XML parsers in order to improve parsing performance, while a little work has studied parsing techniques. We present the high performance parsing technique which can be used all of XML parsers and design hardware based XML parser using an FPGA. The proposed parsing technique uses element analyzers instead of the state machine and performs multibyte-based element matching. As a result, our parsing technique can reduce the number of clock cycles per byte(CPB) and does not need to require any preprocessing, such as loading XML data into memory. Compared to other parsers, our parser acheives 1.33~1.82 times improvement in the system performance. Therefore, the proposed parsing technique can process XML documents in real time and is suitable for applying to all of XML parsers.