UNIX Research Articles

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

<span>This paper explores using cameras aimed at the accelerator and brake pedals during sudden unintended acceleration in cars, removing noise from captured images to determine driver incompetence. A car model was constructed using Raspberry Pi to simulate brake malfunction using random functions, increasing the revolutions per minute (RPM) to simulate sudden acceleration. By employing a DC encoder motor to measure the motor's rotational speed through waveform counts, the RPM was calculated. The study recognized sudden acceleration when the brake malfunctioned through the DC encoder motor, causing an abnormal RPM increase, allowing camera capture toward the accelerator and brake during sudden acceleration events. Precautions were taken for problems arising from noise in captured images. The Unix operating system was utilized to apply Gaussian filter image processing techniques for noise removal. While using an average value filter led to abrupt changes by replacing with the average of surrounding signals, resulting in an unsmooth image, a Gaussian filter was used in this study to decrease weights as distance from the center increased, mitigating these issues.</span>

RI−Calc: A user friendly software and web server for refractive index calculation

The refractive index of an optical medium is essential for studying a variety of physical phenomena. One useful method for determining the refractive index of scalar materials (i.e., materials which are characterized by a scalar dielectric function) is to employ the Kramers−Kronig (K−K) relations. The K−K method is particularly useful in cases where ellipsometric measurements are unavailable, a situation that frequently occurs in many laboratories. Although some packages can perform this calculation, they usually lack a graphical interface and are complex to implement and use. Those deficiencies inhibit their utilization by a plethora of researchers unfamiliar with programming languages. To address the aforementioned gap, we have developed the Refractive Index Calculator (RI−Calc) program that provides an intuitive and user−friendly interface. The RI−Calc program allows users to input the absorption coefficient spectrum and then easily calculate the complex refractive index and the complex relative permittivity of a broad range of thin films, including of molecules, polymers, blends, and perovskites. The program has been thoroughly tested, taking into account the Lorentz oscillator model and experimental data from a materials' refractive index database, demonstrating consistent outcomes. It is compatible with Windows, Unix, and macOS operating systems. You can download the RI−Calc binaries from our GitHub repository or conveniently access the program through our dedicated web server at nanocalc.org.

IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.

In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).

Gmx_qk: An Automated Protein/Protein-Ligand Complex Simulation Workflow Bridged to MM/PBSA, Based on Gromacs and Zenity-Dependent GUI for Beginners in MD Simulation Study.

Open-source MD simulation tools provide academics and low-income countries with the ability to compete in drug discovery advancements. Gromacs is a well-known and established MD simulation tool, among others. Although command-line tools offer full flexibility to users, they require expertise and familiarity with the UNIX operating system. In this context, we have developed an automated bash workflow that enables users with minimal knowledge of UNIX or command-line tools to run protein/protein-ligand complex simulations bridged to MM/PBSA calculations. The workflow provides information to the user using Zenity widgets and requires minimal intervention, such as energy minimization, simulation duration, and output file naming. It initiates MD simulations within a few seconds (energy minimization, NVT, NPT, and MD) after taking input files and parameters, which takes 20-30 min in a command-line-based protocol. The single workflow also helps users to produce reproducible research results with fewer errors. The workflow is available at the GitHub repository: https://github.com/harry-maan/gmx_qk.

PyHMMER: a Python library binding to HMMER for efficient sequence analysis.

PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows the annotation of protein sequences with profile HMMs and building new ones directly with Python. PyHMMER increases flexibility of use, allowing creating queries directly from Python code, launching searches, and obtaining results without I/O, or accessing previously unavailable statistics like uncorrected P-values. A new parallelization model greatly improves performance when running multithreaded searches, while producing the exact same results as HMMER. PyHMMER supports all modern Python versions (Python 3.6+) and similar platforms as HMMER (x86 or PowerPC UNIX systems). Pre-compiled packages are released via PyPI (https://pypi.org/project/pyhmmer/) and Bioconda (https://anaconda.org/bioconda/pyhmmer). The PyHMMER source code is available under the terms of the open-source MIT licence and hosted on GitHub (https://github.com/althonos/pyhmmer); its documentation is available on ReadTheDocs (https://pyhmmer.readthedocs.io).

LDmat: efficiently queryable compression of linkage disequilibrium matrices.

Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. Supplementary data are available at Bioinformatics online.

DEVELOPMENT OF A TEXT EDITOR FOR UNIX OPERATING SYSTEM

The task of developing and implementing a software application for creating, editing, displaying on the screen, as well as saving in the form of files of various formats, which in turn will be used in programming in languages such as C, C++, Python, and the Linux system language – Bash, is being considered. The developed software application is designed to enable the user to interact with files of various extensions, edit and save changes, work with the content of files for further development of software code in languages such as C, C++, Python. The software application can be used both as a console application and with the use of a graphical interface on UNIX and MS Windows operating systems. During development, the UNIX operating system was used, namely the Linux – Ubuntu distribution, which made it possible to use the created text editor on such operating systems as Ubuntu, Linux Mint, Kali Linux, Raspberry Pi Ubuntu. A free cross–platform multimedia package of libraries – SFML was also used, which made it possible to use a text editor on the MS Windows platform. The source code of the SFML library is provided under the ZLIB/PNG Free Software Distribution License. During development, a method of processing input information, as well as saving it in a file of a separate format, was defined and implemented. The following programming languages were used: C, C++11, a convenient software application architecture was developed, which allows you to easily maintain and improve the program in the future. The use of a virtual machine was an integral part of the project. By using a virtual machine, many computer resources are utilized, such as: RAM, number of cores, data storage and discrete desktop acceleration in the desired operating system. A friendly graphical interface for user interaction has been implemented.

Semantic analysis and classification of malware for UNIX-likeoperating systems with the use of machine learning methods

The paper focuses on malware classification, based on semantic analysis of disassembled binaries sections’ opcodes with the use of n-grams, TF-IDF indicator and machine learning algorithms. The purpose of the research is to improve and extend the variety of methods for identifying malware developed for UNIX-likeoperating systems. The taskof the research is to create an algorithm, which can identify the types of threats in malicious binary files using n-grams, TF-IDF indicator and machine learning algorithms. Malware classification process can be based either on static or dynamic signatures. Static signatures can be represented as byte-code sequences, binary-assembled instructions, or importedlibraries. Dynamic signatures can be represented as the sequence of actions made by malware. We will use a static signatures strategy for semantic analysis and classification of malware. In this paper,we will work with binary ELF files, which is the mostcommon executable file type for UNIX-likeoperating systems. For the purpose of this research we gathered 2999 malwareELFfiles, using data from VirusShare and VirusTotal sites, and 959 non malware program files from /usr/bin directory in Linux operatingsystem. Each malware file represents one of 3 malware families: Gafgyt, Mirai, and Lightaidra, which are popular and harmful threats to UNIX systems. Each ELF file in dataset was labelled according to its type. The proposed classification algorithm consists of several preparation steps: disassembly of every ELF binary file from the dataset and semantically processing and vectorizing assembly instructions in each file section. For the settingclassification threshold, the Multinomial Naive Bayes model is used. Using the classification threshold, we define the sizefor n-grams and the section of the file, which will give the best classification results. For obtaining the best score, multiple machine learning models, along with hyperparameter optimization, will be used. As a metric of the accuracy of the designed algorithm, mean accuracy and weighted F1 score are used. Stochastic gradient descent for SVM model was selected as the best performing ML model, based on the obtained experimental results.Developed algorithm was experimentally proved to be effective for classifying malware for UNIX operating systems. Results were analyzed and used for making conclusions and suggestions for future work.

Secure HPC: A workflow providing a secure partition on an HPC system

Driven by the progress of data and compute-intensive methods in various scientific domains, there is an increasing demand from researchers working with highly sensitive data to have access to the necessary computational resources to be able to adapt those methods in their respective fields. To satisfy the computing needs of those researchers cost-effectively, it is an open quest to integrate reliable security measures on existing High Performance Computing (HPC) clusters. The fundamental problem with securely working with sensitive data is, that HPC systems are shared systems that are typically trimmed for the highest performance—not for high security. For instance, there are commonly no additional virtualization techniques employed, thus, users typically have access to the host operating system. Since new vulnerabilities are being continuously discovered, solely relying on the traditional Unix permissions is not secure enough. In this paper, we discuss Secure HPC, a workflow allowing users to transfer, store and analyze data with the highest privacy requirements. Our contributions are the design of a multi-node secure workflow with parallel I/O, a strict security model enforced by the system and network features, and lastly the demonstration of a medical use case. In our experiments, we see an advantage in the asynchronous execution of IO requests in dm_crypt, while reaching 80% of the ideal performance. When comparing eCryptFS with GoCryptFS as two representative filesystem-level encryption stacks, eCryptFS was twice as fast. In a real use case, we observed on average 97% of the native performance.

Enhanced system for ns2 trace file analysis with network performance evaluation

One of the critical challenges facing operators around the world is how to ensure that everything is running smoothly as well as how to analyze the performance of the network. Nonetheless, the analytic system must be precise, user-friendly, and quick enough to depict network performance in real time. Network performance is essential for ensuring service quality. In this light, the Network Simulator NS-2 is generally employed for network research on the widely-used UNIX and Windows systems. Next, the network scenarios are generated using network simulation scripts, and upon completion of the simulation, trace files that record network events are generated. Information that might be utilized in a performance study could be recorded in trace files. This could include the total number of packets sent, their arrival times, any delays or losses, and so on. This trace file analysis problem arises mostly because NS-2 does not provide any visualization options for analyzing simulation results (trace files). A system is proposed to analyze the NS2 trace file and facilitate data extraction and evaluation. The main feature of this system is the Java language for network performance evaluation. And to prove the validity of the system, the system was compared with the Trace graph, where the speed of the new system was very high.

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments.

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students’ hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.

C Programming Language–Still Ruling the World

C Programming Language, developed by Dennis Ritchie in 1972, is an evergreen programming language. It is the backbone of the Unix operating system and paved the way for the development of object-oriented programming. C has been around for 30 years, and there are many source codes available which means there is a lot to learn and a lot to use. C programming language is that which sits close to the operating system. This feature makes it an efficient language because system-level resources, such as memory, can be accessed easily. The features like fast, flexible, portable, and structured programming language with a rich library makes the C programming language not to have an expiry date. C programming language closeness to the hardware and great portability make it ideal for low-level development such as operating systems, kernels, and embedded systems. Its versatility, efficiency, and good performance make it an excellent choice for high complexity data manipulation software, like databases or 3D animation. The fact is that many programming languages today are better than C for their intended use, which does not mean that they beat C in all areas. C is still unsurpassed when performance is the priority.

A systematic approach to understanding MACB timestamps on Unix-like systems

File timestamps are used by forensics practitioners as a fundamental artifact. For example, the creation of user files can show traces of user activity, while system files, like configuration and log files, typically reveal when a program was run. Despite timestamps being ubiquitous, the understanding of their exact meaning is mostly overlooked in favor of fully-automated, correlation-based approaches. Existing work for practitioners aims at understanding Windows and is not directly applicable to Unix-like systems. In this paper, we review how each layer of the software stack (kernel, file system, libraries, application) influences MACB timestamps on Unix systems such as Linux, OpenBSD, FreeBSD and macOS. We examine how POSIX specifies the timestamp behavior and propose a framework for automatically profiling OS kernels, user mode libraries and applications, including compliance checks against POSIX. Our implementation covers four different operating systems, the GIO and Qt library, as well as several user mode applications and is released as open-source. Based on 187 compliance tests and automated profiling covering common file operations, we found multiple unexpected and non-compliant behaviors, both on common operations and in edge cases. Furthermore, we provide tables summarizing timestamp behavior aimed to be used by practitioners as a quick-reference.

Novel Framework-Based Routing for Task-Adaptive Mobile Networks of Unmanned Aerial Vehicular

Many practical mobile ad hoc networks (MANET) have certain tasks instead of just randomly changing each node’s positions. We call such a mission-driven network task-adaptive MANET. A typical example is the flying ad hoc network (FANET) that consist of unmanned aerial vehicles (UAVs), which may change its network topology based on different task requirements. Each node moves to new locations based on the targeted network shape. To maintain a smooth topology transformation and minimize the position changes, during shape change, a MANET typically keeps the core-area nodes more stable and allows the nodes in the outer area of the network to move more drastically. This means the entire network has an approximate framework that reflects the relatively stable nodes located in the core area. This research proposes a new routing scheme to quickly identify the optimal end-to-end path using the network framework extraction result. The proposed routing scheme ensures that the packets flow along the more stable network regions (thus with a lower packet loss rate). The framework extraction scheme is based on network shape geometry analysis for the median axis recognition. Our work has contributions to three aspects of realistic network protocol applications: (1) Provides a network multi-center election and member control methodology with detailed protocol design. (2) Creates a stable and reliable MANET framework extraction algorithm which aids in routing table generation. (3) Real-time Unix system protocol implementation and emulation based on Common Open Research Emulator (CORE) + Extendable Mobile Ad-hoc Network Emulator (EMANE). Simulation results indicate that our framework-based routing scheme outperforms a popularly used mobility-adaptive MANET routing scheme—OLSR (optimized link state routing)—in terms of throughput and delay.

Proposed L-Shape Pattern on UFS ACM For Risk Analysis

At this cloud age, there is tremendous growth in business, services, resources, and cloud technology. This growth comes with a risk of unsafe, unordered, and uncertainty due to unauthorized access and theft of confidential propriety data. Our objective is to model around Read, Write and Execute to resolve these unordered, unsafe, and uncertain issues. We will develop a L-Shape pattern model matching UFS ACM to minimize the accessibilities based on RIGHT & ROLE of the resources and maximize the quality of services for safety and high availability. The preventive, detective, corrective (PDC) services are the major roles for all levels of management to coordinate, control the multiple technologies and resources which are working simultaneously. It will be more ordered, accountable, and actionable on real-time access control mechanism for scalabilities, reliability, performance, and high availability of computational services. We have to make safer, certain, unified, and step-by-step normalization by applying this UFS ACM mechanism on UNIX operating system. This proposed research paper covers a wide range of areas covering optimization, normalization, Fuzzy Low, and Risk assessment.

Structural features of accounting automation application

The article discusses the behavior of the “1C platform: Enterprise 8” in different launch environments on devices running Unix, Windows, and Android operating systems. The load testing of the platform was carried out on the example of an application solution for automating the distribution of labor tasks of an organization to determine the optimal hardware and software part of user devices.

Inhibiting crypto‐ransomware on windows platforms through a honeyfile‐based approach with R‐Locker

After several years, crypto-ransomware attacks still constitute a principal threat for individuals and organisations worldwide. Despite the fact that a number of solutions are deployed to fight against this plague, one main challenge is that of early reaction, as merely detecting its occurrence can be useless to avoid the pernicious effects of the malware. With this aim, the authors introduced in a previous work a novel anti-ransomware tool for Unix platforms named R-Locker. The proposal is supported on a honeyfile-based approach, where ‘infinite’ trap files are disseminated around the target filesystem for early detection and to effectively block the ransomware action. The authors extend here the tool with three main new contributions. First, R-Locker is migrated to Windows platforms, where specific differences exist regarding FIFO handling. Second, the global management of the honeyfiles around the target filesystem is now improved to maximise protection. Finally, blocking suspicious ransomware is (semi)automated through the dynamic use of white-/black-lists. As in the original work for Unix systems, the new Windows version of R-Locker shows high effectivity and efficiency in thwarting ransomware action.

JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping.

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

Corporation Bell Labs is a factory of scientific innovations

The results of scientific research and development have become the basis for the development of the economy, the most important factor in increasing its competitiveness, the foundation of modern technologies. Therefore, the problem of effective organization of scientific research and the effectiveness of their financing is extremely urgent. The study of the evolution of organizational forms of science allows us to conclude that in firms that do not have government funding, scientific research at the Nobel level is often carried out. The story of the creation of one of these organizations, the world famous Bell Labs corporation, which is a real incubator of progressive scientific and technical ideas, is told. Over the years, Bell Labs employees have made a number of grandiose discoveries: the detection of cosmic radio emission, the invention of a point-contact transistor, quartz clocks, charge-coupled devices, the creation of information theory, the UNIX operating system, programming languages C, C ++, etc.Considerable attention is paid to describing the scientific and technical results of Bell Labs employees who have become Nobel, Turing, Emmy and Grammy laureates, as well as holders of the US National Medal of Technology and Innovation and the IEEE Medal of Honor. In conclusion, some other scientific achievements of Bell Labs employees that have not received the above awards are discussed. The experience of studying the history of the creation and functioning of this company can be useful when organizing innovative research centers in our country.

A Critical Guide to Unix

This Critical Guide in the Introduction to Bioinformatics series briefly introduces the Unix Operating System, and provides a subset of some of the most helpful and commonly used commands, including those that allow various types of search, navigation and file manipulation. Several keystroke short-cuts are also explained, which help to make the routine use of Unix commands more efficient. Specifically, this Guide showcases some of the simplest, most frequently used commands to help new users to understand and gain confidence in using the Unix Operating System. On reading the Guide, users will be able to exploit a range of commands to: manipulate files, directories and processes; navigate directory structures and explore their contents; search for files, and search and compare file contents; direct command outputs into files or into other commands; and explain what many simple commands mean and how they’re used.