File Name Research Articles

Study of storage devices properties for steganographic data hiding in cluster file systems

Methods for technical steganography have been developed in recent years. Hiding of information in such systems is achieved by using properties artificially created by human while constructing various technical means. An example of technical steganography is the application of the features of constructing clustered file systems. This makes it possible to hide information effectively by changing the alternation of individual clusters, the so-called сover files. The names of such files are the key information and it is extremely difficult to recover a hidden message without links (i.e. without names) of cover files. This work describes and analyzes various modern information storage technologies, namely HDD, Flash-USB, SSD. We have analyzed different indicators such as the number of implemented products, price, speed of reading and writing. The important indicators of storage media efficiency with regard to steganographic methods of hiding information in cluster file systems were also analyzed. For example, we have investigated the speed of sequential reading / writing and the speed of access to a random cluster that is similar to the speed of access to a fragmented file. For this, we used the test results from the UserBenchmark resource. Tests were performed using Sequential and Random4k methods. In conclusion, an assessment of information carriers is given and recommendations are given on using the method of hiding data by mixing clusters in the structure of the file system.

Evaluating a service-learning assignment in a doctor of chiropractic program public health course.

Despite the use of service learning in other health care education programs, little is published about its use in doctor of chiropractic programs. Since 2017, the public health course at our institution has included a service-learning assignment in which students volunteer for nonprofit organizations and write an essay about their experience. The objective of this study was to assess the effects of the assignment on students' self-reported public health knowledge and attitudes. Between April 2017 and June 2018, 56 essays were collected from students who volunteered at a nonprofit organization focused on 3 categories: youth, the environment, or poverty. Each essay was deidentified and assigned random 4-digit-number file names. Ten files were randomly selected from each of the 3 categories for qualitative thematic analysis using deductive and inductive coding. Student essays demonstrated competency in public health concepts, including organizational systems, levels of prevention, and the social ecological model. In addition, a majority of the students went beyond discussing knowledge gained from this assignment and described the impact of their experience on their personal growth. This study demonstrates that students respond favorably to a service-learning assignment that addresses public health competencies and may foster personal and professional development.

Speedup and Password Recovery for Encrypted WinRAR3 without Encrypting Filename on GPUs

The encryption mode of WinRAR3 which does not encrypt the file name uses encryption and compression, the password recovery complexity is high. The existing cracking systems crack on a single CPU or GPU platform. Because the decryption algorithm is slow on the CPU platform, while the decompression algorithm is slow on the GPU platform, the overall performance of the cracking algorithm is not high. This paper studies the mode of CPU and GPU collaborative computing, and proposes an efficient cracking method of encrypted WinRAR3 without encrypting filename. By using the CPU + GPU pipeline cooperation method, the waiting time in the calculation is reduced, and the performance of the algorithm is improved; by using the magic number matching method of compressed files, the decompression calculation can be effectively reduced. The experimental results show that the speed of the cracking algorithm proposed by this paper for 8-digit passwords is 24423/s, which is 2.3 times as fast as before.

Description of research data in laboratory notebooks: Challenges and opportunities

AbstractIn scientific research, laboratory (lab) notebooks have traditionally been used to record experiments and their results. Lab notebooks also act as an important record of the generation, processing and analysis of data over the research data lifecycle. As research data becomes increasingly digitized and voluminous, it becomes harder to record and describe research data, especially in paper lab notebooks (PLNs). This paper addresses the challenges of recording contemporary research data in lab notebooks and discusses the requirements in lab notebooks for describing research data. Two basic requirements of lab notebooks are the completeness of the data description and the ability to link the experiment records with their corresponding research datasets. Descriptions of research data should also document the provenance of the research data so that the original data can be retrieved. Guidelines for consistent file naming and systematic directory structures for saved research data can support efficient research data retrieval.

Python TensorFlow Big Data Analysis for the Security of Korean Nuclear Power Plants

The Republic of Korea also suffered direct and indirect damages from the Fukushima nuclear accident in Japan and realized the significance of security due to the cyber-threat to the Republic of Korea Hydro and Nuclear Power Co., Ltd. With such matters in mind, this study sought to suggest a measure for improving security in the nuclear power plant. Based on overseas cyber-attack cases and attacking scenario on the control facility of the nuclear power plant, the study designed and proposed a nuclear power plant control network traffic analysis system that satisfies the security requirements and in-depth defense strategy. To enhance the security of the nuclear power plant, the study collected data such as internet provided to the control facilities, network traffic of intranet, and security equipment events and compared and verified them with machine learning analysis. After measuring the accuracy and time, the study proposed the most suitable analysis algorithm for the power plant in order to realize power plant security that facilitates real-time detection and response in the event of a cyber-attack. In this paper, we learned how to apply data for multiple servers and apply various security information as data in the security application using logs, and match with regard to application of character data such as file names. We improved by applying gender, and we converted to continuous data by resetting based on the risk of non-continuous data, and two optimization algorithms were applied to solve the problem of overfitting. Therefore, we think that there will be a contribution in the connection experiment of the data decision part and the optimization algorithm to learn the security data.

Developing Lesson Plans for Teaching Spatial Data Management in Academic Libraries through a Lens of Popular Culture

ABSTRACT Understanding data management in spatial data science is fundamentally important when ensuring a project’s data is appropriately collected, analyzed, stored, and communicated. However, these concepts are often introduced to learners through compliance (i.e., we do this because we are required to or it is the right thing to do), and as a result can mean learning experiences around spatial data management are viewed less than enthusiastically. Academic libraries are places where engaging learning experiences around spatial data management can be explored and implemented through unique pedagogical techniques. For example, activities teaching spatial data management in academic libraries through a popular culture lens can make these topics more approachable and reduce learning barriers. In this paper, I present three lesson plans for academic librarians which approach teaching spatial data management through popular culture. The following concepts are represented: (1) understanding spatial metadata through Dungeons & Dragons; (2) learning to write good documentation through drawing Pokémon; and (3) developing good file naming habits with 1980s album covers. Alongside the lesson plans, I provide a narrative outlining why popular culture pedagogy is a useful technique to spatial data management education to increase the inclusivity and engagement around geospatial learning experiences in academic libraries.

A Simple Quantum Integro-Differential Solver (SQuIDS)

Simple Quantum Integro-Differential Solver (SQuIDS) is a C++ code designed to solve semi-analytically the evolution of a set of density matrices and scalar functions. This is done efficiently by expressing all operators in an SU(N) basis. SQuIDS provides a base class from which users can derive new classes to include new non-trivial terms from the right hand sides of density matrix equations. The code was designed in the context of solving neutrino oscillation problems, but can be applied to any problem that involves solving the quantum evolution of a collection of particles with Hilbert space of dimension up to six. New version program summaryProgram Title: SQuIDSCPC Library link to program files:http://dx.doi.org/10.17632/hnchbtnybs.1Developer’s respository link:https://github.com/jsalvado/SQuIDSLicensing provisions: GNU Lesser General Public License, version 3.Programming language: C++11.External routines: GNU Scientific Library (http://www.gnu.org/software/gsl/).Journal reference of previous version: Comput. Phys. Commun. 196 (2015) 569–591Does the new version supersede the previous version?: Yes.Nature of problem: Solve the evolution of open quantum systems of Hilbert space dimension N with self interactions and interaction with classical fields.Solution method: The algebra is implemented as a C++ object and is embedded into the GSL ordinary differential equation solver.Reasons for the new version: Added new features and improved runtime.Summary of revisions:•Library names have been moved into the ‘squids’ namespace.•Support for extraction of SU vectors and basis transformations to GSL matrices.•Implementation of matrix exponentiation for use cases like evolution with non-diagonalized Hamiltonians.•Implementation of a number of useful matrix operations on SU vectors, including applying arbitrary unitary transformations, extracting real and imaginary components, transposition, and generalized, weighted rotations.•Support for obtaining the eigensystem which diagonalizes an SU_vector.•Explicit disallowance of creating or manipulating SU(1) vectors, which are degenerate with scalars.•A fix to a severe issue in the use of the GSL ODE interface, now giving good output for much less stringent tolerance values.•Optimization of element-wise operations on SU vectors without the need to allocate temporary storage.•Redesign of SU vector operations for vectorizability, including optimized allocation of element storage with optimal alignment.•Implementation of a more efficient version of SU vector time evolution which can share initialization costs across multiple evolutions by the same operator over the same time interval.•A more precise definition of the meter.•Improvements to configuration and build system.•Clean up of capitalization for file and class names.•Rearrangement of project files to a more standard layout.•Elimination of memory leaks.•Limited support for the Intel and Portland Group compilers. These should not be considered guaranteed to work, but have been given minimal testing and appear compile the library correctly.•Added additional unit conversions to SQuIDS constants.Additional comments including restrictions and unusual features: The code is only implemented up to Hilbert spaces of dimension six, but a Mathematica notebook is provided in order to generate higher dimensional solutions. Furthermore, only ordinary differential equation solution methods that require only the first derivative can be used. Acknoledgements CAA is supported by NSF grant PHY-1912764. JS acknowledges financial support from EU Networks FP10ITN ELUSIVES (H2020-MSCA-ITN-2015-674896) and INVISIBLES-PLUS (H2020-MSCA-RISE2015-690575), by the MINECO grant FPA2016-76005-C2-1-P and by the Maria de Maeztu grant MDM-2014-0367 of ICCUB.

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Supervised Classification of Satellite Image Processing using Neural Networks

Now a day’s satellite image processing plays a major role. By using remote sensing technique, we can classify the satellite images like LISS (Linear image self-scanner), LANDSAT satellite image by using ERDAS imagine software. By using ERDAS imagine software, the classification of an satellite images will take more time. Rather than ERDAS imagine software we can use NEURAL NETWORKS in MATLAB software for classifying the satellite images by using the corresponding code with respect to the image by simply changing the file name. This paper includes the method like supervised and classification by using ERDAS imagine software and MATLAB code. The aim of this projects is to realize the image classification using NEURAL NETWORKS.

Technologies and software for the regional catalog of Russian spacecraft satellite data

The results of research and development devoted to the creation of technologies and software for the satellite data catalog from Russian spacecraft, formed at the Regional Center for Remote Sensing of the Earth in Krasnoyarsk, are examined. The created software package provides a full cycle of operations with satellite data, including their downloading from the Roscosmos Geoportal, pre-processing, archiving, conversion and visualization operations using a specialized geographic information web system. Practical experience with the data provider revealed a number of problems in the source information, which were taken into account when creating the software. In particular, some files were damaged (files are filled with zeros or have an incomplete size), some files in the data folder were missing, the names of files and directories did not meet the declared standards, and so on. Separately, we should pay attention to significant delays in data delivery, which reached six months or more. These problems were taken into account when creating the software.

Simulation Foundry: Automated and F.A.I.R. Molecular Modeling.

The Simulation Foundry (SF) is a modular workflow for the automated creation of molecular modeling (MM) data. MM allows for the reliable prediction of the microscopic and macroscopic properties of multicomponent systems from first principles. The SF makes MM repeatable, replicable, and findable, accessible, interoperable, and reusable (F.A.I.R.). The SF uses a standardized data structure and file naming convention, allowing for replication on different supercomputers and re-entrancy. We focus on keeping the SF simple by basing it on scripting languages that are widely used by the MM community (bash, Python) and making it reusable and re-editable. The SF was developed to assist expert users in performing parameter studies of multicomponent systems by high throughput molecular dynamics simulations. The usability of the SF is demonstrated by simulations of thermophysical properties of binary mixtures. A standardized data exchange format enables the integration of simulated data with data from experiments. The SF also provides a complete documentation of how the results were obtained, thus assigning provenance. Increasing computational power facilitates the intensification of the simulation process and requires automation and modularity. The SF provides a community platform on which to integrate new methods and create data that is reproducible and transparent (https://fairdomhub.org/studies/639/snapshots/1, https://fairdomhub.org/studies/639/snapshots/2).

An analysis of data paper templates and guidelines: types of contextual information described by data journals

Purpose: Data papers are a promising genre of scholarly communication, in which research data are described, shared, and published. Rich documentation of data, including adequate contextual information, enhances the potential of data reuse. This study investigated the extent to which the components of data papers specified by journals represented the types of contextual information necessary for data reuse.Methods: A content analysis of 15 data paper templates/guidelines from 24 data journals indexed by the Web of Science was performed. A coding scheme was developed based on previous studies, consisting of four categories: general data set properties, data production information, repository information, and reuse information.Results: Only a few types of contextual information were commonly requested by the journals. Except data format information and file names, general data set properties were specified less often than other categories of contextual information. Researchers were frequently asked to provide data production information, such as information on the data collection, data producer, and related project. Repository information focused on data identifiers, while information about repository reputation and curation practices was rarely requested. Reuse information mostly involved advice on the reuse of data and terms of use.Conclusion: These findings imply that data journals should provide a more standardized set of data paper components to inform reusers of relevant contextual information in a consistent manner. Information about repository reputation and curation could also be provided by data journals to complement the repository information provided by the authors of data papers and to help researchers evaluate the reusability of data.

Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions.

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.

EngMeta: metadata for computational engineering

Computational engineering generates knowledge through the analysis and interpretation of research data, which is produced by computer simulation. Supercomputers produce huge amounts of research data. To address a research question, a lot of simulations are run over a large parameter space. Therefore, handling this data and keeping an overview becomes a challenge. Data documentation is mostly handled by file and folder names in inflexible file systems, making it almost impossible for data to be findable, accessible, interopable and hence reusable. To enable and improve a structured documentation of research data from computational engineering, we developed EngMeta as a metadata model. We built this model by incorporating existing standards for general descriptive and technical information and adding metadata fields for disciplinespecific information like the components and parameters of the simulated target system and information about the research process like the used methods, software and computational environment. EngMeta functions, in practical use, as the descriptive core for an institutional repository. In order to reduce the burden of description on scientists, we have developed an approach for automatically extracting metadata information from the output and log files of computer simulations. Through a qualitative analysis, we show that EngMeta fulfills the criteria of a good metadata model. Through a quantitative survey, we can show that it meets the needs of engineering scientists.

Akurasi Sistem Penjadwalan Sholat Digital Menggunakan Arduino Sebagai Pengendali

This study aims to test the accuracy of the Digital Prayer scheduling system using the Arduino as a controller. The place of research is the scheduling of Digital Prayers at the Putra Putra University Mosque (UPI) of the Padang College of Computer Foundation (YPTK). Prayer schedule data is taken from the website of the Indonesian Ministry of Religion and scheduling routine lecturer activities every morning. Data is stored on a MicroSD card with a different data file name. Data is processed using the Arduino module. The data tested is the display of prayer schedules on Seven Segments and lecturer activity schedule displays on Running Text. The prayer times automatically work with the date and time display (hours, minutes, seconds) starting from the Fajr prayer to the Isha prayer at Seven Segments. Display the activity of lecturer routines automatically by displaying the names of lecturers with the specifications of their assignments, such as reading the Qur'an, reciting Asmaul Husna and praying. Testing is done by activating the equipment for a week (7 days) non stops. The system is tested in terms of the durability of components in operation, the timeliness in activating Buzzer when prayer time has entered and scheduling lecturer activities very well, precisely and accurate. Thus the system can be used and produced on a larger scale

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis.

SummaryThe development of the Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2016) gave the neuroscientific community a standard to organize and share data. BIDS prescribes file naming conventions and a folder structure to store data in a set of already existing file formats. Next to rules about organization of the data itself, BIDS provides standardized templates to store associated metadata in the form of Javascript Object Notation (JSON) and tab separated value (TSV) files. It thus facilitates data sharing, eases metadata querying, and enables automatic data analysis pipelines. BIDS is a rich system to curate, aggregate, and annotate neuroimaging databases.

Shortcuts for faster image creation in PyMOL.

PyMOL is often used to generate images of biomolecular structures. Hundreds of parameters in PyMOL provide precise control over the appearance of structures. We developed 241 Python functions-called "shortcuts"-that extend and ease the use of PyMOL. A user runs a shortcut by entering its name at the PyMOL prompt. We clustered the shortcuts by functionality into 25 groups for faster look-up. One set of shortcuts generates new styles of molecular representation. Another group saves files with time stamps in the file names; the unique filenames avoid overwriting files that have already been developed. A third group submits search terms in the user's web browser. The help function prints the function's documentation to the command history window. This documentation includes the PyMOL commands that the user can reuse by copying and pasting onto the command line or into a script file. The shortcuts should save the average PyMOL user many hours per year searching for code fragments in their computer or on-line. STATEMENT FOR LAY PUBLIC: Computer-generated images of protein structures are vital to the interpretation of and communication about the molecular structure of proteins. PyMOL is a popular computer program for generating such images. We made a large collection of macros or shortcuts that save time by executing complex operations with a few keystrokes.

Simulasi Analisis Bukti Digital Pada Layanan Cloud Computing Menggunakan Metode NIST 800-86

Ease and support for cloud-based data storage It has supported an increase in the number of cloud services. The increasing number of uses for cloud services also increases the number of digital-based criminal actions related to the addition of facilities to cloud services. The cloud service feature designed to store data to support the smooth running of business processes can be misused by criminal assistance to store crime data. Accurate digital evidence is one way to prove a digital crime, which can then be used as supporting evidence in the trial. This study discusses the analysis of digital evidence from a cloud service. The analysis process using the NIST 800-86 method is carried out on digital evidence from 5 previously prepared scenarios related to the use of cloud service features that use being misused. Data acquisition techniques use the method of direct acquisition and physical imaging to obtain digital evidence. The experimental results showed that after scenario 1 and scenario 3, information on the file name and directory of the paths downloaded by client 1 and client 2 were obtained with information on the IP address, mac address, user name, password and time stamp. After scenario 2, digital evidence has been obtained that contains information on the name and location of the folder on the cloud server. After scenario 4, information on the name of the file and the shared folder is successfully obtained, equipped with client information that has the right to access the files and folders. After scenario 5, information about the file name and directory of the file path is successfully obtained.

BAMixChecker: an automated checkup tool for matched sample pairs in NGS cohort.

SummaryMislabeling in the process of next generation sequencing is a frequent problem that can cause an entire genomic analysis to fail, and a regular cohort-level checkup is needed to ensure that it has not occurred. We developed a new, automated tool (BAMixChecker) that accurately detects sample mismatches from a given BAM file cohort with minimal user intervention. BAMixChecker uses a flexible, data-specific set of single-nucleotide polymorphisms and detects orphan (unpaired) and swapped (mispaired) samples based on genotype-concordance score and entropy-based file name analysis. BAMixChecker shows ∼100% accuracy in real WES, RNA-Seq and targeted sequencing data cohorts, even for small panels (<50 genes). BAMixChecker provides an HTML-style report that graphically outlines the sample matching status in tables and heatmaps, with which users can quickly inspect any mismatch events.Availability and implementationBAMixChecker is available at https://github.com/heinc1010/BAMixCheckerSupplementary information Supplementary data are available at Bioinformatics online.

Siamese neural network architecture for homoglyph attacks detection

Abstract Primarily an adversary uses homoglyph or spoofing attack approach to obfuscate domain name, file name or process names. This approach facilitates to create domain name, file name or process names which look visually homogeneous to legitimate domain name, file name or process names. This paper introduces Siamese neural network architecture which uses the application of recurrent structures with Keras character level embedding to learn the optimal features by considering an input in the form of raw strings. For comparative study, various recurrent structures are used. The performances obtained by recurrent structures are almost closer. However, the proposed method performed well in comparison to the existing methods such as Edit Distance, Visual Edit Distance and Siamese convolutional neural networks.