Methodology for solving the task no. 8 of the unified state exam in informatics and ICT by a combinatory method and method of programming on python

2021 ◽  
pp. 56-62
Author(s):  
V. G. Ssmolnyakov
Keyword(s):  
Comparative Analysis ◽  
Programming Language ◽  
Gradual Increase ◽  
Programming Method ◽  
Successful Completion ◽  
Python Programming Language ◽  
Programming Tools ◽  
Interdisciplinary Nature ◽  
Python Programming ◽  
Basic Level

The article discusses the metodology for solving the task No. 8 of the Unified State Exam in informatics and ICT in two ways: by mathematical combinatorial calculation and writing a program in the Python programming language. The purpose of this methodology is the successful completion of task No. 8 (until 2021 — No. 10) in the Unified State Exam in informatics and ICT by graduates. The article is of an interdisciplinary nature, touches upon issues at the intersection of mathematics and informatics. The relevance of the work is due to the fact that tasks of this type are annually present in the Unified State Exam in informatics and ICT, but the success of this task is too low for tasks of the basic level of complexity. The use of programming tools in the Unified State Exam in informatics and ICT is available starting in 2021. The scientific novelty of the work lies in the use of the Python programming language to solve tasks of this type. The peculiarity of the metodology lies in the gradual increase in the complexity of the algorithms and the "modular" application of parts of the code, which allows using the "modules" of previous tasks to solve subsequent ones. Specific versions of the programs are proposed, a comparative analysis of methods for various prototypes of the corresponding tasks is given. As a result, it was determined that task No. 8 can be effectively solved by the programming method.

Practical Programming for NLP

2012 ◽  
pp. 122-156
Author(s):  
Patrick Jeuniaux ◽  
Andrew Olney ◽  
Sidney D’Mello
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Programming Language ◽  
Language Processing ◽  
Python Programming Language ◽  
Programming Tools ◽  
Python Programming

This chapter is aimed at students and researchers who are eager to learn about practical programmatic solutions to natural language processing (NLP) problems. In addition to introducing the readers to programming basics, programming tools, and complete programs, we also hope to pique their interest to actively explore the broad and fascinating field of automatic natural language processing. Part I introduces programming basics and the Python programming language. Part II takes a step by step approach in illustrating the development of a program to solve a NLP problem. Part III provides some hints to help readers initiate their own NLP programming projects.

A Python application to generate digital signals

2020 ◽  
Vol 65 (1) ◽  
pp. 96-104
Author(s):  
Tatian-Cristian Mălin
Keyword(s):  
Numerical Simulation ◽  
Programming Language ◽  
Digital Signals ◽  
Python Programming Language ◽  
Python Programming

We introduce in this paper an application developed in the Python programming language that can be used to generate digital signals with known frequencies and amplitudes. These digital signals, since have known parameters, can be used to create benchmarks for test and numerical simulation.

An Open Source Finance System for Stocks Backtesting Trade Strategies

2021 ◽  
Vol 12 (2) ◽  
pp. 52-65
Author(s):  
Eviatar Rosenberg ◽  
Dima Alberg
Keyword(s):  
User Interface ◽  
Open Source ◽  
Programming Language ◽  
Capital Market ◽  
Historical Data ◽  
Trading Strategies ◽  
Market Volatility ◽  
Finance System ◽  
Python Programming Language ◽  
Python Programming

A significant part of pension savings is in the capital market and exposed to market volatility. The COVID-19 pandemic crisis, like the previous crises, damaged the gains achieved in those funds. This paper presents a development of open-source finance system for stocks backtesting trade strategies. The development will be operated by the Python programming language and will implement application user interface. The system will import historical data of stocks from financial web and will produce charts for analysis of the trends in stocks price. Based on technical analysis, it will run trading strategies which will be defined by the user. The system will output the trade orders that should have been executed in retrospect and concluding charts to present the profit and loss that would occur to evaluate the performance of the strategy.

scholarly journals ATLaS: Assistant Software for Life Scientists to Use in Calculations of Buffer Solutions

2021 ◽  
Vol 15 (4) ◽  
pp. 541-545
Author(s):  
Ugur Comlekcioglu ◽  
Nazan Comlekcioglu
Keyword(s):  
Programming Language ◽  
Life Science ◽  
Source Code ◽  
Experimental Results ◽  
Acid Base ◽  
Buffer Solutions ◽  
Python Programming Language ◽  
Science Laboratories ◽  
Executable File ◽  
Python Programming

Many solutions such as percentage, molar and buffer solutions are used in all experiments conducted in life science laboratories. Although the preparation of the solutions is not difficult, miscalculations that can be made during intensive laboratory work negatively affect the experimental results. In order for the experiments to work correctly, the solutions must be prepared completely correctly. In this project, a software, ATLaS (Assistant Toolkit for Laboratory Solutions), has been developed to eliminate solution errors arising from calculations. Python programming language was used in the development of ATLaS. Tkinter and Pandas libraries were used in the program. ATLaS contains five main modules (1) Percent Solutions, (2) Molar Solutions, (3) Acid-Base Solutions, (4) Buffer Solutions and (5) Unit Converter. Main modules have sub-functions within themselves. With PyInstaller, the software was converted into a stand-alone executable file. The source code of ATLaS is available at https://github.com/cugur1978/ATLaS.

scholarly journals New implementation of OGC Web Processing Service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS

2016 ◽  
Vol XLI-B7 ◽  
pp. 927-930 ◽  
Author(s):  
Jáchym Čepický ◽  
Luís Moreira de Sousa
Keyword(s):  
Programming Language ◽  
Binary Data ◽  
Security Risks ◽  
Raster Data ◽  
Server Side ◽  
Python Programming Language ◽  
Technical Issues ◽  
Numeric Representation ◽  
Python Programming ◽  
Processing Service

The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server. <br><br> PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch. <br><br> The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.

scholarly journals Ak-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

2018 ◽  
Author(s):  
Erki Aun ◽  
Age Brauer ◽  
Veljo Kisand ◽  
Tanel Tenson ◽  
Maido Remm
Keyword(s):  
Statistical Model ◽  
Programming Language ◽  
Prediction Models ◽  
Genomic Sequence ◽  
Medical Diagnostics ◽  
Sequencing Data ◽  
Test Set ◽  
Phenotype Prediction ◽  
Python Programming Language ◽  
Python Programming

AbstractWe have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates ak-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167Klebsiella pneumoniaeisolates (virulence), 200Pseudomonas aeruginosaisolates (ciprofloxacin resistance) and 460Clostridium difficileisolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on theK. pneumoniaetest set, 88% on theP. aeruginosatest set and 96.5% on theC. difficiletest set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).SummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

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