Portable Document Formats Research Articles

ON CREATION OF DOGRI LANGUAGE CORPUS

The pre-requisite for any Natural Language Processing (NLP) task, is the corpus. Corpus is defined as a large collection of structured text. Dogri is one of the official languages of India but is under-resourced in terms of computational resources needed for any NLP task. This paper proposes a methodology to construct a standard corpus which can be used for performing various language processing tasks like stemming, part-of-speech tagging, information retrieval, etc. The digitized text required for creating the corpus is not available due to the scarcity of online resources containing Dogri text. The only online source which is available is the Dogri Newspaper “Jammu Prabhat". Hence, the text is to be extracted from portable document formats (pdf) of that newspaper which are first converted to images before extraction of the text. To achieve this, an open-source tool-Tesseract is used for extracting the text from images. The methodology that is used for the corpus creation of Dogri Language is discussed in detail in the paper. The challenges faced during the research and the acquired results have also been discussed

Evading Signature Validation in Digitally Signed PDF

Carefully marked Portable Document Formats (PDFs) are utilized in agreements, contracts, bills, proposals, and arrangements to ensure the genuineness and trustworthiness of their material. A normal client would accept that carefully marked PDF records are conclusive and cannot be additionally altered. Be that as it may, different changes like adding comments to a marked PDF or rounding out structure fields are permitted and do not nullify PDF marks. In this paper, we show that this adaptability permits attackers to totally change a record’s substance while keeping the first signature approval status immaculate.

Clinical accuracy of information extracted from prostate needle biopsy pathology reports using natural language processing.

1557 Background: Patients with prostate cancer are diagnosed through a prostate needle biopsy (PNB). Information contained in PNB pathology reports is critical for informing clinical risk stratification and treatment; however, patient comprehension of PNB pathology reports is low, and formats vary widely by institution. Natural language processing (NLP) models trained to automatically extract key information from unstructured PNB pathology reports could be used to generate personalized educational materials for patients in a scalable fashion and expedite the process of collecting registry data or screening patients for clinical trials. As proof of concept, we trained and tested four NLP models for accuracy of information extraction. Methods: Using 403 positive PNB pathology reports from over 80 institutions, we converted portable document formats (PDFs) into text using the Tesseract optical character recognition (OCR) engine, removed protected health information using the Philter open-source tool, cleaned the text with rule-based methods, and annotated clinically relevant attributes as well as structural attributes relevant to information extraction using the Brat Rapid Annotation Tool. Text pre-processing for classification and extraction was done using Scispacy and rule-based methods. Using a 75:25 train:test split (N = 302, 101), we tested conditional random field (CRF), support vector machine (SVM), bidirectional long-short term memory network (Bi-LSTM), and Bi-LSTM-CRF models, reserving 46 training reports as a validation subset for the latter two models. Model-extracted variables were compared with values manually obtained from the unprocessed PDF reports for clinical accuracy. Results: Clinical accuracy of model-extracted variables is reported in the Table. CRF was the highest performing model, with accuracies of 97% for Gleason grade, 82% for percentage of positive cores ( < 50% vs. ≥50%), 90% for perineural or lymphovascular invasion, and 100% for presence of non-acinar carcinoma histology. On manual review of inaccurate results, model performance was limited by PDF image quality, errors in OCR processing of tables or columns, and practice variability in reporting number of biopsy cores. Conclusions: Our results demonstrate successful proof of concept for the use of NLP models in accurately extracting information from PNB pathology reports, though further optimization is needed before use in clinical practice.[Table: see text]

The Acquisition of Structured Clinical Data from a Document-Based Electronic Medical Record System.

Our hospital stores all clinical records as Portable Document Formats (PDFs). These PDFs are delivered by each system with a document profile XML file. Using this interface, the items thought to be important for clinical studies are described in the document profile XML and delivered to the data warehouse (DWH). In case clinical data not stored in the DWH are needed, we extract the data from PDF documents. Even from scanned PDFs, the data can be extracted with high accuracy.

Work-related outcomes in randomized, double blind, placebo-controlled trials in osteoarthritis \u2013 are they adequately reported in journal publications? A systematic review

BackgroundOsteoarthritis (OA) has a high prevalence in Western societies and can affect an individual’s life in a number of domains, including work. In our experience, treatment trials on OA, however, rarely report work-related outcomes. Here we conducted a systematic review to assess the reporting of work-related outcomes in randomized, double blind, placebo-controlled trials in OA. Our systematic review also compared two search strategies for identifying eligible publications, one where work-related terms were included in the database search string (A) and one where this was not the case and work-related outcomes were identified by searches of full text Portable Document Formats (PDFs) (B). Search strategy A would conventionally be used and would only identify publications where work-related terms were mentioned in the title or abstract. Search strategy B presents the innovation of full text PDF searching and would identify publications were work-related terms were reported in the full text, regardless of whether they are mentioned in the title and abstract or not. We hypothesize that search strategy B identifies more relevant publications than search strategy A.MethodsElectronic database searching was performed in Medline (Pubmed) from database inception to February 23, 2017 to identify peer-reviewed articles of randomized, double blind, placebo-controlled treatment trials in OA of the hand, hip, or knee, available as full-text PDFs. For search strategy A, search terms to identify work-related outcomes were included in the database search string, while search strategy B did not have these terms included in the database search string, but instead involved full text PDF searching. We included English language articles only and only those articles where searchable PDFs were available, to enable a comparison between search strategies A and B. Additionally, included studies also needed to report on pain intensity in relation to the work-related outcomes.ResultsSearch strategy A yielded 50 hits combined for hand, hip or knee OA that mentioned some work-related concept in the title or abstract; 12 articles had to be excluded because they were not available as searchable PDFs. Screening of the remaining 38 articles resulted in only two articles that satisfied our inclusion criteria. Search strategy B yielded 986 hits, out of which 201 articles were excluded because searchable full text PDFs were not available. PDF full text searching and further screening resulted in 10 articles that were considered eligible for our review.ConclusionsWork-related outcomes are rarely reported in journal publication on randomized, double blind, placebo-controlled trials of hand, hip or knee OA. Searching full text PDFs yields more eligible articles than searching titles and abstracts only.

Computational Approach for Securing Radiology-Diagnostic Data in Connected Health Network using High-Performance GPU-Accelerated AES

Diagnostic radiology is a core and integral part of modern medicine, paving ways for the primary care physicians in the disease diagnoses, treatments and therapy managements. Obviously, all recent standard healthcare procedures have immensely benefitted from the contemporary information technology revolutions, apparently revolutionizing those approaches to acquiring, storing and sharing of diagnostic data for efficient and timely diagnosis of diseases. Connected health network was introduced as an alternative to the ageing traditional concept in healthcare system, improving hospital-physician connectivity and clinical collaborations. Undoubtedly, the modern medicinal approach has drastically improved healthcare but at the expense of high computational cost and possible breach of diagnosis privacy. Consequently, a number of cryptographical techniques are recently being applied to clinical applications, but the challenges of not being able to successfully encrypt both the image and the textual data persist. Furthermore, processing time of encryption-decryption of medical datasets, within a considerable lower computational cost without jeopardizing the required security strength of the encryption algorithm, still remains as an outstanding issue. This study proposes a secured radiology-diagnostic data framework for connected health network using high-performance GPU-accelerated Advanced Encryption Standard. The study was evaluated with radiology image datasets consisting of brain MR and CT datasets obtained from the department of Surgery, University of North Carolina, USA, and the Swedish National Infrastructure for Computing. Sample patients' notes from the University of North Carolina, School of medicine at Chapel Hill were also used to evaluate the framework for its strength in encrypting-decrypting textual data in the form of medical report. Significantly, the framework is not only able to accurately encrypt and decrypt medical image datasets, but it also successfully encrypts and decrypts textual data in Microsoft Word document, Microsoft Excel and Portable Document Formats which are the conventional format of documenting medical records. Interestingly, the entire encryption and decryption procedures were achieved at a lower computational cost using regular hardware and software resources without compromising neither the quality of the decrypted data nor the security level of the algorithms.

Extracting bibliographical data for PDF documents with HMM and external resources

Purpose – The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs). Design/methodology/approach – The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata. Findings – Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified. Research limitations/implications – For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity. Practical implications – For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation. Originality/value – The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

Preserving Content from Your Institutional Repository

Between institutional repositories and hosting journals, many libraries are becoming responsible for scholarly content in new ways. While Portable Document Formats (PDFs) are the most common formats today, the unique, local, serial content could be in a variety of formats. These items might be digitized text, born digital text, audio, video, images, or multimedia. This article discusses formats that will remain accessible through time (PDF/A, txt, xml) so that contents are not locked into proprietary formats. It will also discuss options for backing up items and associated metadata, including simple backups, off-site storage of files, Lots of Copies Keep Stuff Safe (LOCKSS), Private LOCKSS Networks, and Portico. The article offers suggestions for how your library might best preserve local content.

A New Digital Look for JDSDE

Some exciting changes are coming to the Journal of Deaf Studies and Deaf Education (JDSDE) that should make the journal even more useful to readers, authors, and libraries. With electronic innovations happening now at Oxford University Press, new options and opportunities are being put into place. Perhaps most importantly for many readers and authors, papers accepted for publication will no longer have to wait until an entire issue is published in order to be available to the public. As soon as a manuscript is accepted by the editors, it will be typeset and posted on the JDSDE Web site. Known as Publish Ahead of Print and available via the Advance Access system, electronic versions of manuscripts are regarded as published and assigned a digital object identifier. The article then can be printed, cited, and accessed from HighWire, PubMed, or directly from the JDSDE Web site, http://www.deafed.oxfordjournals.org (soon to be remodeled and made available at http://www. jdsde.org). Another exciting option is that authors will be able to post ‘‘supplementary data’’ relevant to their research on the JDSDE Web site. Such data might involve appendixes that go beyond what would normally appear in print—perhaps scoring procedures, questionnaires, or illustrative transcripts. What makes this particularly important for JDSDE is that supplementary postings also can include either video or audio files. Investigators working with signed or spoken language, play behaviors, or mother–child (and other social) interactions will now have the opportunity to show readers the kinds of behaviors or procedures that underpin research findings. This option will be an important advance in a field that often cannot capture fine nuances of communication in print. (Imagine seeing video clips that explain these tough-to-understand sign language transcriptions!) Supplementary data must be submitted at the time of initial manuscript submission, and investigators will need permission from recognizable individuals appearing in the video footage—and do not forget clearance from ethical review panels, if necessary. Decisions concerning acceptance of a manuscript, however, will be independent of whether supplementary data are supplied or accepted for posting. Oxford Journals’ new Open Access feature, known as Oxford Open, offers JDSDE authors the option to pay for their articles to be made freely available online immediately upon publication. To learn more, read the Oxford Journals press release at http://www3. oup.co.uk/jnls/press/2005/05/04/index.html and register to receive information and updates about this initiative. Visitors to our Web site and readers of electronic full text may have already noticed a variety of other JDSDE upgrades. Portable document formats (PDFs) are now ‘‘framed,’’ meaning that information about the article being viewed and about the journal is available even if readers have not accessed the article through the Web site (e.g., if it has been e-mailed to them). PDFs also now will be ‘‘extended,’’ showing where they came from, when they were downloaded, and