Portable Document Format Files Research Articles

Processing 2D barcode data with metaheuristic based CNN models and detection of malicious PDF files

Portable Document Format (PDF) is a file format created to create portable and printable documents across platforms. PDF files are one of the most widely used application types in computer-based systems. Thanks to the functionality that PDF files provide, they are used by many users around the world. Malware developers can exploit PDF files due to various factors. Malware can integrate embedded files, JavaScript, PDF files, etc. As a result, PDFs are susceptible to security vulnerabilities in computer-based systems. In this study, we utilised the CIC-Evasive-PDFMal2022 dataset, made accessible by the Canadian Cybersecurity Institute in 2022, that includes two categories, namely benign and malicious. In the preprocessing step, the proposed model transformed text-based PDF parameter data into the 2D PDF417 barcode. 2D Convolutional Neural Network (CNN) models (MobileNetV2, ResNet18, and ShuffleNet) are trained using the dataset generated by the preprocessing step. CNN is a type of artificial neural network used in image recognition, processing, and classification. Type/class based feature sets were then obtained by each CNN model. In the last step, the metaheuristic optimization method (Honey Badger Algorithm) was used. Thanks to this method, the best performing feature set was determined among the feature sets of the types extracted from each CNN model. It was then classified by the softmax method, and an overall accuracy of 99.73% was achieved. The proposed approach has successfully trained 1D data with 2D CNNs. In addition, with the barcode imaging technique, direct understanding of the data by the users is prevented.

Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature

The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing.

Automatic Data Extraction Utilizing Structural Similarity From A Set of Portable Document Format (PDF) Files

Instead of storing data in databases, common computer-aided office workers often choose to keep data related to their work in the form of document or report files that they can conveniently and comfortably access with popular off-the-shelf softwares, such as in Portable Document Format (PDF) format files. Their workplaces may actually use databases but they usually do not possess the privilege nor the proficiency to fully utilize them. Said workplaces likely have front-end systems such as Management Information System (MIS) from where workers get their data containing reports or documents.These documents are meant for immediate or presentational uses but workers often keep these files for the data inside which may come to be useful later on. This way, they can manipulate and combine data from one or more report files to suit their work needs, on the occasions that their MIS were not able to fulfill such needs. To do this, workers need to extract data from the report files. However, the files also contain formatting and other contents such as organization banners, signature placeholders, and so on. Extracting data from these files is not easy and workers are often forced to use repeated copy and paste actions to get the data they want. This is not only tedious but also time-consuming and prone to errors. Automatic data extraction is not new, many existing solutions are available but they typically require human guidance to help the data extraction before it can become truly automatic. They may also require certain expertise which can make workers hesitant to use them in the first place. A particular function of an MIS can produce many report files, each containing distinct data, but still structurally similar. If we target all PDF files that come from such same source, in this paper we demonstrated that by exploiting the similarity it is possible to create a fully automatic data extraction system that requires no human guidance. First, a model is generated by analyzing a small sample of PDFs and then the model is used to extract data from all PDF files in the set. Our experiments show that the system can quickly achieve 100% accuracy rate with very few sample files. Though there are occasions where data inside all the PDFs are not sufficiently distinct from each other resulting in lower than 100% accuracy, this can be easily detected and fixed with slight human intervention. In these cases, total no human intervention may not be possible but the amount needed can be significantly reduced.

Robust PDF Watermarking against Print-Scan Attack.

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print-scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print-scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print-scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.

Extraction and Digitization of ECG Signals from Standard Clinical Portable Document Format Files for the Principal Component Analysis of T-wave Morphology.

T-wave analysis from standard electrocardiogram (ECG) remains one of the most available clinical and research methods for evaluating myocardial repolarization. T-wave morphology was recently evaluated to aid with diagnosis and characterization of diastolic dysfunction. Unfortunately, PDF stored ECG datasets limit additional numerical post-processing of ECG waveforms. In this study, we apply a simple custom process pipeline to extract and re-digitize T-wave signals and subject them to principal component analysis (PCA) to define primary T-wave shape variations. We propose simple pre-processing and digitization algorithms programmable as a MATLAB tool using standard thresholding functions without the need for advanced signal analysis. To validate digitized datasets, we compared clinically standard measurements in 20 different ECGs with the original ECG machine interpreted values as a gold standard. Afterwards, we analyzed 212 individual ECGs for T-wave shape analysis using PCA. The re-digitized signal was shown to preserve the original information as evidenced by excellent agreement between original - machine interpreted and re-digitized clinical variables including heart rate: bias ~ 1bpm (95% CI: -1.0 to 3.5), QT interval: bias ~ 0.000 ms (95% CI: -0.012 to 0.012), PR interval: bias = -0.015 ms (95% CI: -0.015 to 0.003), and QRS duration: bias = -0.001 ms (95% CI: -0.007 to 0.006). PCA revealed that the first principal component universally modulates the T-wave height or amount of repolarization voltage regardless of the investigated ECG lead. The second and third principal components described variation in the T-wave peak onset and the T-wave peak morphology, respectively. This study presents a straightforward method for re-digitizing ECGs stored in the PDF format utilized in many academic electronic medical record systems. This process can yield re-digitized lead specific signals which can be retrospectively analyzed using advanced custom post-processing numerical analysis independent of commercially available platforms.

PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)

The number of cyber-attacks is increasing daily, and attackers are coming up with new ways to harm their target by disseminating viruses and other malware. With new inventions and technologies appearing daily, there is a chance that a system might be attacked and its weaknesses taken advantage of. Malware is distributed through Portable Document Format (PDF) files, among other methods. These files’ adaptability makes them a prime target for attackers who can quickly insert malware into PDF files. This study proposes a model based on the Fuzzy Unordered Rule Induction Algorithm (FURIA) to detect PDF malware. The proposed model outperforms currently used methods in terms of reducing error rates and increasing accuracy. Other models, such as Naïve Bayes (NB), Decision Tree (J48), Hoeffding Tree (HT), and Quadratic Discriminant Analysis (QDA), were compared to the proposed model. The accuracy achieved by the proposed model is 99.81%, with an error rate of 0.0022.

Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files

The Portable Document Format (PDF) is considered one of the most popular formats due to its flexibility and portability across platforms. Although people have used machine learning techniques to detect malware in PDF files, the problem with these models is their weak resistance against evasion attacks, which constitutes a major security threat. The goal of this study is to introduce three machine learning-based systems that enhance malware detection in the presence of evasion attacks by substantially relying on evasive data to train malware and evasion detection models. To evaluate the robustness of the proposed systems, we used two testing datasets, a real dataset containing around 100,000 PDF samples and an evasive dataset containing 500,000 samples that we generated. We compared the results of the proposed systems to a baseline model that was not adversarially trained. When tested against the evasive dataset, the proposed systems provided an increase of around 80% in the f1-score compared to the baseline. This proves the value of the proposed approaches towards the ability to deal with evasive attacks.

Application of deep reinforcement learning in attacking and protecting structural features-based malicious PDF detector

Portable Document Format (PDF) is a file format used for document exchange and supports cross-platform use. Therefore, most PDF files with embedded malicious scripts can be widely distributed and executed. Many researchers use machine learning models to analyze structural features extracted from PDF files. However, most detection models are vulnerable to adversarial samples, so attackers can evade detection through Adversarial Machine Learning (AML). Although several methods already exist for generating adversarial PDF files, many of these cannot precisely control the process of generating samples, and neglect the number of requests issued to the target detector, which is a potential limitation in real scenarios. In this paper, we propose a deep reinforcement learning (DRL)-based attack framework to evade malicious PDF files detection. Motivated by the reward mechanism, the reinforcement learning agent will construct adversarial samples by continuously manipulating malicious PDF files, and eventually automate the process of evading detection under the given constraints. Different from adversarial learning which is used in most studies, a DRL-based defense strategy is proposed to confront this attack. This strategy collects adversarial samples to train a new detector, which in turn is used by defenders to guide a new agent to recover the original feature vectors of malicious PDF files. Experimental results show that the attack framework is of great significance for hiding malicious features, and the defense strategy can effectively mitigate the threat posed by this type of attack.

PDF Malware Detection Based on Optimizable Decision Trees

Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.

Design and Analysis of Machine Learning Based Technique for Malware Identification and Classification of Portable Document Format Files

Modern day antivirus software, which is available commercially, is incapable of providing the protection from the malicious portable document format (PDF) files and thus considered as a threat to system security. In order to mitigate the same to some extent, a new PDF malware classification system based on machine learning (ML) is introduced in this paper. The novelty of this system is that it will be inspecting the given PDF file both statistically and dynamically, which in turn will increase the accuracy of finding the correct nature of the document. This method is nonsignature-based and hence can possibly distinguish obscure and zero-day malware. The experiment is carried out for this system by deploying five different classifier algorithms to find out the best fit for the system. The best fit approach is analyzed by calculating the true positive rate (TPR), precision, false positive rate (FPR), false negative rate (FNR), and F1-score for each of these classifier algorithms. Comparison of this work is carried out with previously existing PDF classification systems. A malicious attack on to the proposed system is also implemented, which will in turn obfuscate the malicious code inside the PDF file by making it hidden during the parsing phase by the PDF parser. It has been inferred that the proposed approach achieved F1-measure of 0.986 by using the random forest (RF) classifier in comparison to state-of-the-art where F1-measure was 0.978. Thus, our approach is quite effective in the identification of the malwares when embedded in the PDF file in comparison to the existing systems.

Current situation of electronic medical records in ophthalmological departments in Germany

Electronic medical records are required in ophthalmology clinics to be integrated into digital care networks and efficient clinical registry databases. To assess the prevalence and methods of electronic medical recordkeeping in inpatient ophthalmological care in Germany. An online questionnaire was sent to all German university eye hospitals and ophthalmology departments in June 2021. It included13 open and closed option questions concerning current practices of digital recordkeeping, including the structure of data storage and the recording of billing-relevant codes in the departments. A total of 44 (44%) out of 100 clinics responded. Patient documentation was completely digital in15 (34%) clinics and partly digital and paper-based in the remaining29 (66%). A total of 16different constellations of documentation programs were specified. The most frequently used programs were Orbis (27%) (Dedalus HealthCare, Bonn, Germany), FIDUS (18%) (Arztservice Wente, Darmstadt, Germany), and SAP/i.s.h.med (16%) (SAP Deutschland, Walldorf, Germany; Cerner Deutschland, Berlin, Germany) and 3 clinics indicated primary use of paper records. Structured documentation of findings was performed in 61% of the departments, while 23% used asemistructured manner and 15% used anonstructured format. Electronic documents are stored as DICOM (Digital Imaging and Communications in Medicine) documents 20% of the clinics and as PDF (Portable Document Format) files in 34% of the clinics while 23% store scanned printouts. Methods of medical record keeping in German eye clinics are heterogeneous, with paper-based documentation continuing to play an important role. This, as well as the high number of different electronic medical record software pose important challenges in terms of interoperability and secondary use of clinical data.

PDFDataExtractor: A Tool for Reading Scientific Text and Interpreting Metadata from the Typeset Literature in the Portable Document Format.

The layout of portable document format (PDF) files is constant to any screen, and the metadata therein are latent, compared to mark-up languages such as HTML and XML. No semantic tags are usually provided, and a PDF file is not designed to be edited or its data interpreted by software. However, data held in PDF files need to be extracted in order to comply with open-source data requirements that are now government-regulated. In the chemical domain, related chemical and property data also need to be found, and their correlations need to be exploited to enable data science in areas such as data-driven materials discovery. Such relationships may be realized using text-mining software such as the “chemistry-aware” natural-language-processing tool, ChemDataExtractor; however, this tool has limited data-extraction capabilities from PDF files. This study presents the PDFDataExtractor tool, which can act as a plug-in to ChemDataExtractor. It outperforms other PDF-extraction tools for the chemical literature by coupling its functionalities to the chemical-named entity-recognition capabilities of ChemDataExtractor. The intrinsic PDF-reading abilities of ChemDataExtractor are much improved. The system features a template-based architecture. This enables semantic information to be extracted from the PDF files of scientific articles in order to reconstruct the logical structure of articles. While other existing PDF-extracting tools focus on quantity mining, this template-based system is more focused on quality mining on different layouts. PDFDataExtractor outputs information in JSON and plain text, including the metadata of a PDF file, such as paper title, authors, affiliation, email, abstract, keywords, journal, year, document object identifier (DOI), reference, and issue number. With a self-created evaluation article set, PDFDataExtractor achieved promising precision for all key assessed metadata areas of the document text.

EXFOR-NSR PDF database: a system for nuclear knowledge preservation and data curation

Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition, to actual data. Experimental nuclear reaction data (EXFOR) and Nuclear Science References (NSR) databases contain compilations based on primary (journals) and secondary (conference proceedings, theses, preprints, etc.) publications, and data received from authors via private communications. The secondary library materials and private communications often represent a bottleneck for nuclear data verification, compilation, evaluation, and dissemination activities. To address this issue, bibliographic materials were scanned into PDF (Portable Document Format) files and uploaded in a relational database.The traditional scope of nuclear databases that includes meta-data and numbers derived from data in specialized formats was broadened to accommodate the large volumes of original nuclear data publications. The complete PDF publication files were stored in a relational database as Binary Large OBjects (BLOB). This unique collection of nuclear data compilations and supporting publications generate many opportunities for machine learning applications.The Web interfaces for authorized and public access to the EXFOR-NSR nuclear publications database were implemented at the U.S. National Nuclear Data Center, https://www.nndc.bnl.gov/ and IAEA Nuclear Data Section, https://www-nds.iaea.org/. The current system is complementary to major nuclear libraries and narrowly focused on nuclear data compilation and evaluation procedures. The contents of the PDF database, details of implementation, and Web interface are described. New capabilities for data curation, knowledge preservation, worldwide dissemination, and natural language processing (NLP) applications are given.

Introduction to teaching science with three-dimensional images of dinosaur footprints from Cristo Rey, New Mexico

Abstract. In this paper we discuss the use of three-dimensional (3-D) imagery and virtual field trips to teach pre-university and non-major university geoscience courses. In particular, 3-D PDF (Portable Document Format) files can be used to either prepare students for or completely replace a field trip when logistical problems make the actual trip too difficult to be effective or when some students need an alternative accommodation. Three-dimensional images can replace or supplement classroom activities, such as the identification of rocks and minerals from hand samples or the identification of geologic structures from 2-D photographs and limited field observations. Students can also become involved in data collection and processing to further their understanding of photogrammetry and visualization. The use of 3-D imagery can make additional time available to instructors to cover more advanced topics and teach students more about the role of science in geologic research. We use an example from Cristo Rey, New Mexico, where dinosaur footprints and tracks are present but difficult to see in many cases, and they are often in places that are hard to access for many people. At this site, approximately 10 000 photographs were collected and processed as 3-D images to show one approximately 72 m2 area of known footprints. However, we also conducted some very simple digital manipulations of the images that allowed us to identify new footprints and tracks that were not apparent when viewed in the field. The photographs and 3-D images have been donated to the Insights El Paso Science Center (denoted Insights Museum herein) that owns the fossil site, and they are now being used to develop educational materials and lessons for the nearby communities.

Interoperable genetic lab test reports: mapping key data elements to HL7 FHIR specifications and professional reporting guidelines.

In many cases, genetic testing labs provide their test reports as portable document format files or scanned images, which limits the availability of the contained information to advanced informatics solutions, such as automated clinical decision support systems. One of the promising standards that aims to address this limitation is Health Level Seven International (HL7) Fast Healthcare Interoperability Resources Clinical Genomics Implementation Guide-Release 1 (FHIR CG IG STU1). This study aims to identify various data content of some genetic lab test reports and map them to FHIR CG IG specification to assess its coverage and to provide some suggestions for standard development and implementation. We analyzed sample reports of 4 genetic tests and relevant professional reporting guidelines to identify their key data elements (KDEs) that were then mapped to FHIR CG IG. We identified 36 common KDEs among the analyzed genetic test reports, in addition to other unique KDEs for each genetic test. Relevant suggestions were made to guide the standard implementation and development. The FHIR CG IG covers the majority of the identified KDEs. However, we suggested some FHIR extensions that might better represent some KDEs. These extensions may be relevant to FHIR implementations or future FHIR updates.The FHIR CG IG is an excellent step toward the interoperability of genetic lab test reports. However, it is a work-in-progress that needs informative and continuous input from the clinical genetics' community, specifically professional organizations, systems implementers, and genetic knowledgebase providers.

Learn2Evade: Learning-Based Generative Model for Evading PDF Malware Classifiers

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial examples</i> . Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware classifiers. We deem that our problem is more challenging than those against ML models for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generative adversarial networks</i> that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yet maintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost’13, Hidost’16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-the-art PDF malware classifiers and raises a serious security concern in the presence of adversaries. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact statement</i>—PDF has been one of the most popular media to conceal adversarial contents for many years. The reason being that adversaries can exploit the complex structure of PDF in their favor by hiding malicious content. In 2019, more than 73k PDF-based attacks were reported in one month, which accounts for 17% of newly detected threats. With increasing popularity, many ML-based techniques have been proposed for PDF malware classifiers. Such defense mechanisms include support vector machine and random forest classification models trained with a structural map of PDF (Hidost’13 and Hidost’16). Furthermore, ensemble training has been applied with metadata collected from PDF as the training data (PDFrate-v2). In recent studies, many researchers have attempted and succeeded in generating evasive PDF malware (adversarial examples) that bypass such defense techniques. However, the current method heavily relies on a random mutation algorithm resulting in repeated computation for a significant period of time. To resolve this, we propose a novel solution by employing a variant of generative adversarial networks, which is trained to identify intrinsic properties of PDF and to generate evasive PDF malware with the minimum perturbation to the original PDF. Our solution successfully generated evasive PDF malware with a maximum number of 12 manipulation operations and found effective against ML-based classifiers and AntiVirus engines provided by VirusTotal.

Embedding interactive, three-dimensional content in portable document format to deliver gross anatomy information and knowledge.

The Portable Document Format (PDF) is likely the most widely used digital file format for scholarly and scientific electronic publishing. Since format specification version 1.6, three-dimensional (3D) models in Universal 3D (U3D) format can be embedded into PDF files. The present study demonstrates a repertoire of graphic strategies and modes of presentation that exploit the potentials of 3D models embedded in PDF to deliver anatomical information and knowledge. Three-dimensional models and scenes representing anatomical structures generated by 3D surface scanning or by segmentation from either clinical imaging data or cadaver sectional images were converted into U3D format and then embedded into PDF files using both freely and commercially available software. The relevant steps and required software tools are described. Built-in tools in Adobe Acrobat and JavaScript scripting both were used to pre-configure user interaction with 3D contents. Eight successive proof-of-concept examples of increasing complexity are presented and provided as supplementary material, including both unannotated and annotated 3D specimens, use of bitmap-textures, guided navigation through predetermined 3D scenes, 3D animation, and interactive navigation through tri-planar sectional human cadaver images. Three-dimensional contents embedded in PDF files are generally comparable to multimedia and dedicated 3D software in terms of quality, flexibility, and convenience, and offer new unprecedented opportunities to deliver anatomical information and knowledge.

Robotic Process Automation for the Extraction of Audit Information: A Use Case

SUMMARYThe reconciliation of audit evidence to the audit subject matter is a key and recurring audit procedure. Before reconciling information, data needs to be extracted from the audit subject matter, which is often in a Portable Document Format (PDF). Reconciliations are a recurring task for every new version of the audit subject matter. Large audit firms typically “offshore” simple and repetitive audit tasks such as reconciliations to shared service centers. Offshoring however comes at the expense of coordination costs, delays in the process, and challenges regarding the liability risk to the auditor. This paper presents an open-source algorithm to extract data from (draft) annual reports (PDF files) using Python to automate, rather than outsource, the data extraction for reconciliations. The algorithm resulted in a significant time saving for the audit of a large Dutch asset management firm. Researchers apply the algorithm to minimize hand-collection of financial statement data.Data Availability: The algorithm this paper presents is open-source and publicly available.JEL Classifications: M42; G23; G29.

Detection of Malicious PDF Files Using a Two‐Stage Machine Learning Algorithm

Portable document format (PDF) files are increasingly used to launch cyberattacks due to their popularity and increasing number of vulnerabilities. Many solutions have been developed to detect malicious files, but their accuracy decreases rapidly in face of new evasion techniques. We explore how to improve the robustness of classifiers for detecting adversarial attacks in PDF files. Content replacement and the n-gram are implemented to extract robust features using proposed guiding principles. In the two-stage machine learning model, the objects are divided based on their types, and the anomaly detection model is first trained for each type individually. The former detection results are organized into tree-like information structure and treated as inputs to convolutional neural network. Experimental results show that the accuracy of our classifier is nearly 100% and the robustness against evasive samples is excellent. The object features also enable the identification of different vulnerabilities exploited in malicious PDF files.

The Standards behind the Scenes: Explaining data from the Plazi workflow

As part of the CETAF COVID19 task force, Plazi liberated taxonomic treatments, figures, observation records, biotic interactions, taxonomic names, and collection and specimen codes involving bats and viruses from scholarly publications with the intention to create open access, findable, accessible, interoperable and reusable data (FAIR). The data is accessible via TreatmentBank and the Biodiversity Literature Repository (BLR) and it is continually harvested and reused by the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI). This data was processed, enhanced and liberated by the Plazi workflow, which involves a dedicated infrastructure including a desktop application (GoldenGate Imagine) that converts portable document format files (PDF) to a dedicated open compressed file format (Image Markup File (IMF)) that is responsible for the data enhancement. To enhance the data contained in the publications, including the biological interactions, a series of standards and vocabularies are used. To the exception of TaxPub, which is a taxonomic specific extension of the U.S. National Center for Biotechnology Information's (NCBI) Journal Article Tag Suite (JATS), all other used vocabulary were previously proposed. This goes along with Plazi’s mission to reuse standards unless they are not available. The following standards of vocabularies are used: Metadata Object Description Schema (MODS) to model article metadata information on Plazi’s XMLs; Darwin Core for taxonomic ranks and materials citation related data; Open Biological and Biomedical Ontology (OBO); Relations Ontology for biological interactions between organisms. The latter two are also used in the custom metadata in the Biodiversity Literature Repository at Zenodo. In this presentation we will provide an overview of the different types of data followed by the standards or vocabularies applied for every and each one of them and their parts. The goal is to provide the context on how the data liberated by Plazi is described, which is extensively reused by third-party applications such as GBIF or GloBI. The use of the standards allows fully automated, daily data ingests by GBIF.