Social Media Translational Action

Internet application advancement has enabled Korean pop culture (K-Pop) to rapidly spread worldwide. However, technology alone is insufficient in delivering k-pop content to K-Pop fans because of language barriers. Hence, the translator's role is pivotal in decoding these data. Realising this crucial need, fans have acted as translators in interpreting enormous data file that have been improperly translated or unavailable in the original file. This research examined the translation process occurring in Twitter microblogging environment which is rarely analysed among linguistic scholars. the translation style of fan translators was identified, and the translational action involved discussed. K-Pop group, Bangtan Sonyeondan's (BTS) twitter account was selected as the main data source and Korean-English fan translation of the content distributed in the account was collected. The microblogging interface is equipped with the latest technology that supports multimedia data form, resulting in more dynamic translation work which needs to be highlighted in translation studies.

Keamanan Data Pasien dengan Algoritma Blowfish pada HOTSPODT

At HOTSPODT (Hospital Ship for Covid Disaster) there are no stages regarding the application of the use of information technology systems, especially for securing patient data which includes personal data and patient medical records. Confidential patient data collected during the current pandemic, including the patient’s name, address, diagnosis, family history and medical records without the patient’s consent, may pose a risk to the individual concerned. The concept of patient data security is adjusted to the user’s position on the importance of data. Access to patient data authorization is one of the security gaps that the security system needs to pay attention to and guard against. So, in this case applied a data security algorithm in the form of cryptography. The algorithm used is the Blowfish Algorithm. The test results of the scenario in the application prove that it can be successfully processed from the encrypted file to ciphertext until it is returned as the original file.

PHYSICAL CONTENT OF COMPUTER STEGANOGRAPHY

Introduction. The development of computer technology has given a new impetus to the use of computer steganography. However, it is important to understand the physical content of this type of steganography.Purpose. The work aims to describe the practical use and physical content of the phenomenon of computer steganography, the results of the study on the hiding of files in the stegocontainer.Results. Describes the main ns currently computer steganography methods are actively used to solve the following tasks: Protection of confidential information from unauthorized access, overcoming monitoring and management of net-work resources, software camouflage, copyright protection, which is manifested in the use of digital watermarks, is one of the most promising areas of computer steganography. Among the methods of hiding information in images, the most common is the category of algorithms using the lower bits of the image data. They are considered in this paper. These algorithms are based on the fact that in some file formats, the lower bits of the values, although present in the file, but do not affect a person's perception of sound or image. The steganographic software S-Tools was chosen for the study. We created two test monotonous images with the size of 50 × 50 pixels in 24-bit bmp format to analyze the peculiarities of the placement of stego-data in container files. We chose black and white images for the study. A text file was hidden in each of the images, after which the reverse action was performed - extracting the file. As a result of hiding, two stego files were obtained. The paper compared the binary content of the original images and files containing private data. For comparison, the binary content of the black square image and the contents of the stegocontainer with a latent text file are given. Note that the contents of the container and the stego file are only partially listed, but the addresses of the memory cells have selected accordingly. The right column shows the contents of the memory cells in hexadecimal format. The bytes that display the colour of the square are set to "00" because the original image contains only black. We noted that the contents of the cells responsible for the image changed after hiding additional data (this reflected by cells with values of "01"). The paper also describes the procedure for hiding a group of different types of files. During the study, we found that the image file (1920 × 1080 pixels) with a volume of 6,220,854 bytes can hide 777,584 bytes of information.Conclusion. When using steganography, the program uses some algorithms that hide confidential data among the contents of the container: bits of the hidden file replace the bits of the original file at random positions. Thus, the size of the source file and the container file (containing the attached information) is the same, even if you hide a different number of files or different amounts of data.

Application of Apache Big Data technologies for the problems of climate monitoring

Описан эксперимент по использованию технологий Apache Big Data в исследованиях климатических систем. В ходе эксперимента реализовано четыре варианта решения тестовой задачи. Ускорение расчетов с помощью технологий Apache Big Data вполне достижимо, и наиболее эффективный способ для этого найден в четвертом варианте решения тестовой задачи. Суть найденного решения сводится к преобразованию исходных наборов данных к формату, подходящему для хранения в распределенной файловой системе и применения технологи Spark SQL из стека Apache Big Data для параллельной обработки данных на вычислительных кластерах. The core of the Apache Big Data stack consists of two technologies: Apache Hadoop for organizing distributed file storages of unlimited capacity and Apache Spark for organizing parallel computing on computing clusters. The combination of Apache Spark and Apache Hadoop is fully applicable for creating big data processing systems. The main idea implemented by Spark is dividing data into separate parts (partitions) and processing these parts in memory of many computers connected within a network. Data is sent only when needed, and Spark automatically detects when the exchange will take place. For testing, we chose the problem of calculating the monthly, annual, and seasonal trends in the temperature of the atmosphere of our planet for the period from 1960 to 2010 according to the NCEP/NCAR and JRA-55 reanalysis data. During the experiment, four variants of solving the test problem were implemented. The first variant represents the simplest implementation without parallelism. The second implementation variant assumes parallel reading of data from the local file system, aggregation, and calculation of trends. The third variant was the calculation of a test problem on a two-node cluster. NCEP and JRA-55 reanalysis files were placed in their original format in the Hadoop storage (HDFS), which combines the disk subsystems of two computers. The disadvantage of this variant is loading all reanalysis files completely into the random access memory of the workflow. The solution proposed in the fourth variant is to pre-convert the original file format to a form when reading from HDFS is selective, based on the specified parameters.

The method of primary processing of poorly structured medical data

The article deals with the approach to the primary processing of poorly structured medical protocol textual data stored and disseminated as pdf files. The relevance of this work is due to the lack of a universal structure for the presentation of medical protocols and methods of their processing. In the course of the work, the problem of primary processing of clinical protocol data was solved by the example of a unified clinical protocol of primary, secondary (specialized) and tertiary (highly specialized) medical care. The method of primary data processing was developed to create a clear structure of the symptoms of the disease. The first step in structuring clinical protocol data is to divide the protocol information into four basic parts, which allows it to be quickly converted to other formats. This process is implemented using an algorithm developed in C # programming language. The proposed algorithm parses the information from a pdf file and converts it to a txt file. After that, the received information is processed, which consists in the syntactic analysis of the text of the protocol and selection of the structural parts of the protocol corresponding to the headings of the sections: title page; introduction; a list of abbreviations used in the protocol; the main part of the protocol; list of literary sources. The identification of the disease name in the medical protocol is performed by comparing the protocol data and the list of disease names, presented in the world classification MKH-10. The headings “Introduction”, “List of abbreviations used in the protocol” and the main part of the protocol were analyzed and the algorithm for removing uninformed sections from the beginning of the protocol, for example, literature sources, was proposed. An algorithm for finding information in the main part of the medical protocol by processing input data by: tables, diagrams, headings, words, phrases and special symbols are also proposed. As a result of the clinical protocol processing algorithms, a new clinical protocol file is generated, which is three times smaller than the original file. It contains only meaningful information from clinical protocols that will speed up further work on this file, namely its use in medical decision support. The disease card based on a medical protocol in JSON format is presented.

Bedshift: perturbation of genomic interval sets

Results of functional genomics experiments such as ChIP-Seq or ATAC-Seq produce data summarized as a region set. Many tools have been developed to analyze region sets, including computing similarity metrics to compare them. However, there is no way to objectively evaluate the effectiveness of region set similarity metrics. In this paper we present bedshift, a command-line tool and Python API to generate new BED files by making random perturbations to an original BED file. Perturbed files have known similarity to the original file and are therefore useful to benchmark similarity metrics. To demonstrate, we used bedshift to create an evaluation dataset of 3,600 perturbed files generated by shifting, adding, and dropping regions from a reference BED file. Then, we compared four similarity metrics: Jaccard score, coverage score, Euclidean distance, and cosine similarity. The results show that the Jaccard score is most sensitive to detecting adding and dropping regions, while the coverage score is more sensitive to shifted regions.AvailabilityBSD2-licensed source code and documentation can be found at https://bedshift.databio.org.

Investigasi Bukti Digital Optical Drive Menggunakan Metode National Institute of Standard and Technology (NIST)

DVD-R is a type of optical drive that can store data in one burning process. However, there is a feature that allows erasing data in a read-only type, namely multisession. The research was conducted to implement the data acquisition process which was deleted from a DVD-R using Autopsy forensic tools and FTK Imager. The National Institute of Standards and Technology (NIST) is a method commonly used in digital forensics in scope storage with stages, namely collection, examination, analysis, and reporting. The acquisition results from Autopsy and FTK-Imager show the same results as the original file before being deleted, validated by matching the hash value. Based on the results obtained from the analysis and presentation stages, it can be concluded from the ten files resulting from data acquisition using the FTK Imager and Autopsy tools on DVD-R. FTK Imager detects two file systems, namely ISO9660 and Joliet, while the Autopsy tool only has one file system, namely UDF. The findings on the FTK Imager tool successfully acquired ten files with matching hash values and Autopsy Tools detected seven files with did not find three files with extensions, *.MOV, *.exe, *.rar. Based on the results of the comparative analysis of the performance test carried out on the FTK Imager, it got a value of 100% because it managed to find all deleted files and Autopsy got a value of 70% because 3 files were not detected because 3 files were not detected and the hash values ​​were empty with the extensions * .exe, * .rar and *.MOV. This is because the Autopsy tool cannot detect the three file extensions.

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

Urgensi Autentikasi dan Legalisasi Arsip Pertanahan Hasil Digitalisasi

Land archive is the living archive of that must be fully maintained. As things goes, Sleman Land Office does digitalization archives to Gambar Ukur, Surat Ukur, Buku Tanah and Warkah by using a web-based application system which is called the Laris application. The research is aimed at finding out the reason why the office is doing digitalization with Laris application and finding out wheter this digitalized archive has been able to replace the original file in the form of paper. This research was conducted with a qualitative method with a descriptive approach Based on research results, obtained information that the digitalization of land records carried out in the Sleman’s Land office which has been done since year 2014 not solely done to change the format of analog files to digital files, but is expected to able provide more comprehensive benefits related to the acceleration of land services so that in practice it uses the Laris application. However, the existence of this digitalized archive still not able to replace the position of analog archive in manner so that the analog archive is still maintained. Therefore, its important to authentication and legalization so the archive of digitalization result legally strong.