Zip File Research Articles

A Generator for Recursive Zip Files

This paper explores the concept of zip quines, which are zip files that contain themselves upon extraction, extending the idea of computational self-reference. While only two individuals, Russ Cox and Erling Ellingsen, have created such entities, this study focuses on Cox’s method to develop a generator for these files. Overcoming the initial limitations, the generator allows for the inclusion of additional files within the zip quine. Additionally, this research explores the concept of looped zip files, wherein a zip archive contains another archive. This archive then contains the initial zip file. By offering practical methodologies and insights, this study advances the understanding and application of quines in computer science.

Lucie: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository.

The University of California-Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the ucimlrepo package that is provided and recommended by the UCIMLR website. Instead, they are hosted as .zip files, containing nonstandard formats that are difficult to import without additional ad hoc processing. To address this issue, here we present lucie-load University California Irvine examples-a utility that automatically determines the data format and imports many of these previously non-importable datasets, while preserving as much of a tabular data structure as possible. lucie was designed using the top 100 most popular datasets and benchmarked on the next 130, where it resulted in a success rate of 95.4% vs. 73.1% for ucimlrepo. lucie is available as a Python package on PyPI with 98% code coverage.

Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.

Suicide stands as a global public health concern with a pronounced impact, especially in low- and middle-income countries, where it remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms such as Facebook (Meta Platforms Inc), offers the potential for detecting major depressive disorder and aiding individuals at risk of suicidal ideation. This study primarily focuses on India and Bangladesh, both South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features along with masked content from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. This quantitative research study will gather data through a web-based platform. Initially, participants will be asked a few demographic questions and to complete the 9-item Patient Health Questionnaire assessment. Eligible participants who provide consent will receive an email requesting them to upload a ZIP file of their Facebook data. The study will begin by determining whether Facebook is the primary application for the participants based on their active hours and Facebook use duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, and textual analysis features, such as captions, biographic information, group membership, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. The study obtained approval from the scientific review committee on October 2, 2023, and subsequently received institutional review committee ethical clearance on December 8, 2023. Our system is anticipated to automatically detect posts related to depression by analyzing the text and use pattern of the individual with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. This initiative aims to enhance engagement in suicidal ideation medical care in South Asia to improve health outcomes. It is set to be the first study to consider predicting participants' primary social application use before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions while maintaining quality through comprehensive Facebook feature analysis. DERR1-10.2196/55511.

Automated Google Scholar Crawling with a Web-Based Tool for Publication Data Management

Here’s the revised abstract in English: The rapid growth of academic publications requires efficient tools for publication data extraction and management, especially from widely used platforms like Google Scholar. To address this need, an automated web-based tool was developed, designed to simplify the processes of data crawling, extraction, and publication data management, allowing researchers to handle large volumes of academic publications more effectively. The tool supports both simple and detailed crawling modes, enabling users to input multiple Google Scholar URLs and neatly organize the extracted data into CSV files. For multiple URLs, the data is compiled into a ZIP file containing separate CSV files for each source, ensuring organized and accessible publication data management. The tool was tested with various dataset sizes. When processing 41 entries, the simple mode completed extraction in 9.054 seconds, while the detailed mode took 71.898 seconds. For smaller datasets of 5 entries, the simple mode executed in 3.283 seconds, while the detailed mode required 11.908 seconds. These results indicate that the tool is efficient and performs well with both small and large datasets. The differences in execution time between the simple and detailed modes offer users flexibility in balancing speed and depth of data extraction according to their research needs. This web-based tool not only automates the data extraction process from Google Scholar but also enhances the organization and accessibility of publication data, making it an asset for researchers and institutions in managing publication data.

PyDwCA: A Tool for Integrating Biodiversity Data

The Darwin Core Archive (DwC-A) format, based on the Darwin Core standard (Wieczorek et al. 2012), facilitates the exchange, management, and integration of biodiversity data from multiple sources. This ability to collate biodiversity data allows datasets to be aggregated at community-supported infrastructures, merged in different combinations, meta-analyzed and submitted to public repositories (Baker et al. 2014). Thus, the DwC-As serve as unifying archives in concatenated collective efforts, such as biodiversity inventories at different spatial and taxonomic scales. Here we describe PyDwCA*1, 2, a Python library implemented to handle the "star scheme" of DwC-A. This new library reads compressed zip files containing the expected meta.xml and uses it to assign the core component and its extensions. It also provides Python classes to define the core, the extensions, and the metadata file for creating an archive and writing it into a compressed zip file. PyDwCA also implements functionality to select, filter and merge DwC-A files. We present this new tool in the context of the construction of the Chilean National Biodiversity Inventory (Fig. 1), but PyDwCA serves as a versatile technical solution applicable to different contexts in the field of biodiversity informatics (e.g., integration of datasets from biological collection and sampling events). To exemplify how PyDwCA works, we present the step-by-step integration of the Chilean Catalogue of Vascular Plants (Rodriguez et al. 2018) on a matrix provided by the Catalogue of Life (Banki 2024), filtered with the species with occurrences recorded for Chile in the Global Biodiversity Information Facility (GBIF) (GBIF.Org 2023).

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein–protein interfaces

BackgroundProtein–protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein–protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.ResultsIn this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.ConclusionsCold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.research.google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb.

Abstract 7423: BATCH-FLEX: Feature-level equalization of x-batch in heterogeneous cancer data

Abstract Purpose: Develop a shiny application to help integrate cancer datasets and guide researchers in selecting an appropriate method of correction for their technical artifacts. Description: Integrative analysis of heterogeneous expression data remains challenging due to variations in platform, RNA quality, sample processing, and other unknown technical effects. As the field performs omics profiling of samples from cancer patients and murine models, there is the need for harmonizing, identifying, and correcting these technical effects to ensure robust analysis on the treatment and conditional effects of the underlying genetics or biological events. However, selecting and implementing different approaches for removing unwanted batch effects can be a time-consuming and tedious process, especially for more biologically focused investigators. In this project, we present Shiny BATCH-FLEX, a Shiny app to rapidly visualize batch correction by established batch correction methods such as ComBat, Mean Centering, ComBatSeq, and Limma RemoveBatchEffect. With BATCH-FLEX, users can visualize the contribution of variance of a factor before and after correction using principal component analysis, relative log expression plots, heatmaps, and explanatory variables. Users can also save all plots and matrices as a single ZIP file for further downstream analysis. Results: As a proof of concept, we assessed BATCH-FLEX using simulated data generated from a linear model framework introduced by Gagnon-Bartsch and Speed, which assumes that gene expression measurements can be distilled to a combination of the biological signal, systemic nose, and random noise. BATCH-FLEX was able to successfully identify and remove the introduced effect using each of the batch correction methods listed above. Next, we evaluated BATCH-FLEX using a comprehensive collection of bladder cancer data consisting of microarray data from 13 studies spanning 1452 samples. Following the cleaning of study-dependent noise, BATCH-FLEX was successful in revealing the heterogeneity among bladder cancer based on known sample type annotations. Conclusion: We have developed BATCH-FLEX, a tool for oncologic researchers to rapidly assess, select, and implement commonly used batch correction methods. This tool is available at https://github.com/shawlab-moffitt/BATCH-FLEX. Our integrative web portal of a Bladder Cancer Resource for Translational Science (BEACON) will also be shared at the meeting. Citation Format: Joshua Davis, Alyssa Obermayer, Thac Duong, Rebecca Hesterberg, Xuefeng Wang, Mingxiang Teng, G. Daniel Grass, Timothy Shaw. BATCH-FLEX: Feature-level equalization of x-batch in heterogeneous cancer data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7423.

Cyber Attack Against E-Albania and Its Social, Economic and Strategic Effects

Purpose: During last years, even because of pandemic situation caused by covid-19 virus, in Albania most of governmental public services for citizens, businesses and other customers were offered in an electronic way by creating a national database (e-Albania), offering more than 2200 services. As this electronic system was newly implemented, time after time it was attacked from hackers in different sectors of services, causing the interruption of service for hours, downloading all the confidential information and publishing them. After several partial attacks, in July 2022 came the general attack of the whole system, which black out the system and services for several days. Cyber actors - identifying as “HomeLand Justice” - launched a destructive cyber-attack against e-Albania which rendered websites and services unavailable. An investigation indicates cyber actors acquired initial access to the victim’s network approximately 14 months before launching the destructive cyber-attack, which included a ransomware-style file encryptor and disk wiping malware. The actors maintained continuous network access for approximately a year, periodically accessing and exfiltrating e-mail content. From late July to mid-August 2022, social media accounts associated with HomeLand Justice demonstrated a repeated pattern of advertising Albanian Government information for release, posting a poll asking respondents to select the government information to be released by HomeLand Justice, and then releasing that information - either in a .zip file or a video of a screen recording with the documents shown. This cyber-attack creates social problems, economical loss and influenced negatively in the reputation of e-Albania and damage as well strategically the country and development of this sector in the future. Methodology: We have monitored the system and the attack, and we continue to do this. We analyze and synthesis the data collected, to come to conclusions and recommendations needed for the future. All the data which we have used are open for public, and mostly are primary data. The research method combines both quantitative and qualitative methods, but it is closer with qualitative method, as far as there in not enough data for using e pure quantitative analysis. We have used mostly the descriptive method. Results/Findings: Improving essentially the cyber infrastructure to avoid in the future such attacks with high social, economic and strategical cost. Conclusions: In the institution there was not a team for Cyber Security Monitoring the system, so called SOC (Security Operation Center), who controls in the real time all the logins. It was missing as well so called “Identifying Behavior”. There was not e separation of active directory, in physic machines and virtual machines, they were altogether. As the administrator had Full Right Privilege, the hacker doesn’t need to create a Privilege Escalation Vertical, so he easily took all the right of Admin. Originality and Practical Implications: The paper is original; it has not been previously published and it is not under consideration by any other publisher. The originality of the method stands in the fact that it is the first case in the world in information age, that a country (a whole electronic system, e-Albania), face a such complex, well organized and hard cyber-attack, which collapse the system for several days. All the data are authentic ones.

The Catalogue of Life Data Package (CoLDP) and the Taxon Concept Standard (TCS): A Comparison

The Catalogue of Life Data Package (CoLDP; Catalogue of Life 2022) is the preferred mechanism for getting data in and out of the ChecklistBank. A CoLDP is a ZIP file containing files with delimiter-separated values, similar to the Darwin Core Archive (DwC-A; Global Biodiversity Information Facility 2023), but does not enforce the DwC-A star schema requiring a single Core file and zero or more Extension files. This makes it preferable for exchanging taxonomic data in tabular format as taxonomic data contains at least three different types of objects—taxa, names and references—that are all interlinked. TCS (currently still Taxon Concept Schema) is the TDWG (Biodiversity Information Standards) standard for dealing with taxonomic data specifically. An effort is currently underway (TCS 2 Task Group 2023) to free TCS from its XML schema and turn it into a TDWG vocabulary standard that is maintainable under the TDWG Vocabulary Maintenance Specification (VMS; Vocabulary Maintenance Specification Task Group 2017). For a domain standard like TCS, it is essential that it supports an important warehouse of taxonomic information like ChecklistBank and an exchange format like CoLDP. Therefore, as part of the Implementation Experience Report that has to accompany a major update to an existing TDWG standard, a comparison has been undertaken between TCS and the data model behind CoLDP. While some of the terms CoLDP uses are different from the ones used by TCS, the two specifications are completely compatible. This presentation will highlight some of the key similarities and differences. It is heartening to see that different groups of people independently chose to make the same departure from the existing specification. As TCS will be maintainable—and, in fact, still has to undergo public review—and CoLDP presumably can evolve as well, there are opportunities for TCS and CoLDP to come even closer together than they already are.

MASH Native: a unified solution for native top-down proteomics data processing.

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

Assignment Submission

Abstract: In every branch, teachers have to take submission of micro project. For that they message in students WhatsApp group, for that they need a specific student phone number and it take much time, in our web application teachers have to create an class folder and add students from college login details. After all process, teachers have to just deliver one message in created folder about micro project. This will reduce teacher’s time as well as they don’t have to add a many phone numbers of students. In our web application, teachers will also chat with students, if he/she gets confuse about student, if he/she student don’t give proper ppt/source code to teachers. With this teachers don’t have to share their phone numbers with anyone students. Firstly student will form the group and decide their title then they will submit it. After submitting, it is visible to the teacher. Once completing the project, student will upload the report and the zip file of their project. Then teacher can see and give the Mark's.

ANALISA EVALUASI KINERJA SOFTWARE PASSWORD ATTACKER PADA BERKAS FILE ZIP

As we know, in this era of rapid technological progress, we cannot avoid threats to cyber security, cyber security itself is an effort to protect computer systems and data from various threats. In Indonesia alone, as of September 13, 2022, there have been recorded as many as 12.74 million accounts that have experienced leaks, and this makes Indonesia the 3rd country with the most number of data leaks in the world. One solution that can be done to protect the data that is owned is to put it in an archive such as ZIP, ZIP functions to combine several rice into one and reduce the size. And to secure the data in a ZIP file, you can set a password. The password is a combination of letters, numbers and symbols, with this combination it will be difficult for hackers to carry out their actions.

Finding Patterns of Cyber-Attacks and Creating A Detection Model to Detect Cyber-Attacks Using Machine Learning

This research is a finding and detection type of research. That paper focuses on exploratory data analysis and YOLOv3, you only look once at version three, a real-time recognizing model. The hypothesis of this research are, a cyber-attack gives the same pattern in all the different, different IP addresses, and the research model, the YOLOv3 model can detect the cyber-attack by seeing its pattern. First, the researcher collects more than one hundred seventy-eight thousand data from the cyber department of some companies. Then, the researcher does exploratory data analysis of that data in the jupyter notebook. Then, the researcher finds all the important information about cyber-attacks. Then, the researcher finds the patterns of some cyber-attacks from the information of the data collection. Then, the researcher collects pictures of the patterns. Then, with pictures of those patterns, the researcher labeled those pictures by labellmg software and create a zip file of them. Then the researcher use Google colab and trained, "you only look once version three", the YOLOv3 model to detect the name of the pattern. The machine can detect the pattern and can tell us about what cyber-attack it is by only seeing the picture of the pattern. The researcher finds the patterns for eight IP addresses and in all the IP addresses, the attacks give the same patterns. So, the hypothesis is true in all that cases. By this research model, we can easily know about any kind of cyber-attack in detail and also can find the cyber-attacks by their patterns. So, the researcher use jupyter notebook, Google Colab, and pycharm environment. Pattern study always plays an important role to know about anything. By knowing and learning about anything’s pattern, we can easily understand anything. This research does that thing greatly. The researcher first finds the patterns of some cyber-attacks, then make a model which can detect the cyber-attack by just seeing the pattern.

Expansion of Content in the Phenx Toolkit for Sickle Cell Disease Pain

Background: Common measures and common data elements (CDEs) for sickle cell disease (SCD) are needed to help improve data quality and data comparability necessary for meta-analyses, guidelines development, comparative effectiveness research, and implementation science. In 2014, the National Heart, Lung, and Blood Institute (NHLBI) launched the PhenX (consensus measures for Phenotypes and eXposures) Measures for Sickle Cell Disease Research Project to establish a framework for data sharing across different SCD research projects. As a result of this project, a Core Collection and two Specialty Collections (Cardiovascular, Pulmonary, and Renal; Neurology, Quality of Life, and Health Services) of standard measure protocols were added to the PhenX Toolkit. The PhenX Toolkit (https://www.phenxtoolkit.org/) is a Web-based catalog of standard protocols and associated interoperability resources to help investigators improve quality of data collection and identify opportunities for collaborative research. In 2019, NHLBI provided additional funding to expand the collections of sickle cell disease measurement protocols in the PhenX Toolkit. Under the guidance of a Sickle Cell Disease Research and Scientific Panel (SRSP), six additional collections of protocols for use in SCD research will be added to the PhenX Toolkit. One of these collections of protocols relates to research involving SCD Pain. Methods: The SCD Pain Working Group (WG), consisting of 10 SCD experts, was convened to select high priority protocols for inclusion in the Toolkit. The SCD Pain WG selected representative protocols available in published literature using a consensus process which included input from the scientific community. Protocols were selected based on availability, protocol burden, and validity, with emphasis on measures being well established and broadly validated. Several included protocols had been previously validated in populations with SCD. For each measure, the Toolkit provides a description of the measure, the rationale for its inclusion, detailed protocol(s), and supporting documentation. The Toolkit also provides data collection worksheets and data dictionaries to help integrate PhenX measures into their study design. To support investigators who want to collect data via the Web, PhenX protocols are available as REDCap Instrument Zip files. Results: The SCD Pain collection of protocols was released into the Toolkit (https://www.phenxtoolkit.org/sub-collections/view/28) in May 2022. The SCD Pain WG identified a set of 23 protocols that encompass the burden of pain on an individual's day-to-day life, the magnitude of pain experienced by an individual, the location of the pain experienced, the extent to which the pain interferes with the person's daily life, the individual's overall physical mobility, the specific subjective sensations associated with pain, and the coping strategies employed by the individual (Table 1). For many of the measures, separate adult and child protocols were identified. These protocols, including the process, criteria, and rationale for their selection, will be presented. Implications: Consistent use of these protocols in future clinical and epidemiologic studies will allow clinicans and researchers to quantify how SCD impacts a patient's everyday life and develop more effective interventions. For researchers, adoption and use of PhenX protocols will facilitate cross-study analysis domestically and internationally, accelerate translational research, and lead to greater understanding of SCD pain phenotypes. For clinicians, using PhenX measures in routine clinical practice will help improve patient care and quality of life. Consistent use of these standard measures will establish a common currency to help better understand the etiology, progression, and treatment of SCD. Funding was provided by a Genomic Research Grant (U41HG007050) from NHGRI, with current or prior funding support from the National Institute on Drug Abuse (NIDA); Office of Behavioral and Social Sciences Research (OBSSR); National Institute of Mental Health (NIMH); National Heart, Lung, and Blood Institute (NHLBI); National Institute on Minority Health and Health Disparities (NIMHD); and Tobacco Regulatory Science Program (TRSP). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Quantitative Macroeconomics: Lessons Learned from Fourteen Replications

I replicate all tables and figures from fourteen papers in Quantitative Macroeconomics, with an emphasis on incomplete market heterogeneous agent models. I report three main findings: (i) all (non-welfare related) major findings of the papers replicate, (ii) welfare findings based on linear approximation methods—1st-order perturbation, linear and log-linearization around steady-state, and linear-quadratic methods—should be treated as quantitatively suspect, (iii) decisions around methods for discretizing exogenous shocks have a large and unappreciated influence on results and should be prominently discussed in papers. While some smaller aspects of the papers do not replicate exactly, rather than nitpick in the body of this paper I instead describe some lessons learnt that may be useful for practitioners working with Quantitative Macroeconomic models. The replications use global methods allowing for non-linearities and I argue that these are important and need to be more widely used. I provide a checklist that researchers can use when trying to check that their work will be more easily reproducible. Matlab codes implementing the replications using the VFI Toolkit are provided, and full results of all replications are given in the online appendix. I conclude with three core points for best practice: (i) codes be made directly available (e.g., on github, not only ’on request’, and not just inside a zip file), (ii) report not just baseline parameters but also hyperparameters, equilibrium values, non-baseline parameters and initial conditions, and (iii) replication means rewriting codes from scratch, not just re-running available codes.

NapierOne: A modern mixed file data set alternative to Govdocs1

It was found when reviewing the ransomware detection research literature that almost no proposal provided enough detail on how the test data set was created, or sufficient description of its actual content, to allow it to be recreated by other researchers interested in reconstructing their environment and validating the research results. A modern cybersecurity mixed file data set called NapierOne is presented, primarily aimed at, but not limited to, ransomware detection and forensic analysis research. NapierOne was designed to address this deficiency in reproducibility and improve consistency by facilitating research replication and repeatability. The methodology used in the creation of this data set is also described in detail. The data set was inspired by the Govdocs1 data set and it is intended that NapierOne be used as a complement to this original data set.An investigation was performed with the goal of determining the common files types currently in use. No specific research was found that explicitly provided this information, so an alternative consensus approach was employed. This involved combining the findings from multiple sources of file type usage into an overall ranked list. After which 5000 real-world example files were gathered, and a specific data subset created, for each of the common file types identified. In some circumstances, multiple data subsets were created for a specific file type, each subset representing a specific characteristic for that file type. For example, there are multiple data subsets for the ZIP file type with each subset containing examples of a specific compression method. Ransomware execution tends to produce files that have high entropy, so examples of file types that naturally have this attribute are also present. The resulting entire data set comprises of nearly 100 separate data subsets divided between 44 distinct file types, resulting in almost 500,000 unique files in total. A description of the techniques used to gather the files for each file type is provided together with the actions that were performed on the files to confirm that they were of the highest quality and provided an accurate representation of their specific file type. Details are also provided on the content of the entire data set as well as instructions on how researchers can gain free and unlimited access to the final data set.While the data set was initially created to aid research in ransomware detection, it is sufficiently broad and diverse enough to allow for its application in many other areas of research that require a varied mixture of common real-world file examples. The NapierOne data set is an ongoing project and researchers are strongly encouraged to leverage this data set in their own research.

Portfolio Builder and Career Recommender by Scraping Data Using Flask and Tensorflow

Abstract: In this day and age, the use of physical copies for portfolios and resumes has largely been replaced with the use of online documents to showcase the skills and experience of the individual in question. However, this too is beginning to give way to a newform of a portfolio- a website that communicates all of the above information in a document, but in an aesthetic and elegant way. While this was only usually required or recommended for a specific portion of the workforce, an increasing number of employers have begun to prefer this creative approach, making it more appealing to any aspiring applicants. In this project, we plan to build a website that will take data from users and build a custom portfolio using a variety of pre-developed templates and designs. The user just needs to provide their LinkedIn profile which will then be used to scrape the data of the user, usin Flask, Selenium, and BeautifulSoup. After the information and details of the user has been extracted, our website will provide various templates to choose from. The user will then have the option to build the website or download the zip file of the created website which could be used in future or could be hosted by the user. Hence, in this paper we aim to build a website that would help create a resume quickly and professionally without the hassle and struggle of figuring out how to format everything or what fonts and styles to use. We will help generate a dynamic resume that will highlight the skills, achievements and talents and also be able to showcase the user’s thoughts and personality. Moreover, using Naive Bayes, we will provide a career recommendation to the user, which will guide steer them towards the most suitable profession for their skills Keywords: Portfolio, Flask, Selenium, TensorFlow, Naive Bayes, Beautiful Soup, Machine Learning

File fingerprinting of the ZIP format for identifying and tracking provenance

While the overall structure of ZIP files is defined, their detailed structure differs depending on the operating system and application creating the file. These characteristics are also affected by the environment in which the file was first created or later modified. Conversely, analyzing the structure of ZIP files allows the determination of the environment it was created in, and this can be the basis for determining where the file was created through analyzing and comparing the user's PC. In addition, the creation, modification, and access time values of decompressed files are set differently according to the application used for decompression and the structure of the ZIP file. ZIP files reflect not only the environment in which they are created but also the one in which they were decompressed. Thus, the ZIP files' detailed structures and characteristics should be analyzed forensically. In this paper, it is suggested that the environment of file creation and modification can be inferred by analyzing the detailed structure of a single file by file fingerprints, and the characteristics of decompression can be compared with the applications installed on the system.

MONITORING ACTIVE FIRES IN THE LOWER PARANÁ RIVER FLOODPLAIN: ANALYSIS AND REPRODUCIBLE REPORTS ON SATELLITE THERMAL HOTSPOTS

Abstract. Floodplain wetlands play a key role in hydrological and biogeochemical cycles and comprise a large part of the world's biodiversity and resources. The exploitation of remote sensing data can substantially contribute to monitoring procedures at broad ecological scales. In 2020, the Lower Paraná River floodplain (also known as Paraná River Delta, Argentina) suffered from a severe drought, and extended areas were burned. To monitor the wildfire situation, satellite products provided by FIRMS-NASA were used. These thermal hotspots – associated with active fires – can be downloaded as zipped spatial objects (point shapefiles) and include recent and archive records from VIRRS and MODIS thermal infrared sensors. The main aim was to handle these data, analyze the number of hotspots during 2020, and compare the disaster with previous years' situation. Using a reproducible workflow was crucial to ingest the zip files and repeat the same series of plots and analyses when necessary. Obtaining updated reports allowed me to quickly respond to peers, technicians, and journalists about the evolving fire situation. A total of 39,821 VIIRS S-NPP thermal hotspots were detected, with August (winter) accounting for 39.8% of the whole year’s hotspots. MODIS hotspots have lower spatial resolution than VIIRS, so the cumulative MODIS hotspots recorded during 2020 were 8,673, the highest number of hotspots of the last 11 years. Scripts were written in R language and are shared under a CC BY 4.0 license. QGIS was also used to generate a high-quality animation. The workflow can be used in other study areas.