SELECTION OF A SYSTEM OF COMPUTER SCIENCE TEACHING METHODS USING A SOFTWARE PACKAGE

2018 ◽  
pp. 50-58
Author(s):  
Tatyana Yurevna KITAEVSKAYA ◽  
Vyacheslav Petrovich TIGROV
Keyword(s):  
Computer Science ◽  
Science Teaching ◽  
Teaching Methods ◽  
Software Package ◽  
Large Data ◽  
Academic Disciplines ◽  
External Information ◽  
Data Sets ◽  
Factors Affecting ◽  
Selection Of

We discuss the problem of designing a system of teaching methods in the context of the dynamics of the external information environment. The necessity of attracting technology that makes it possible to quickly select the methods of organizing the educational and cognitive activities of students by automating the processing of a database containing large data sets of available general, special and particular didactic methods and their functional suitability is substantiated. The problem is solved using formalized methods. The factors affecting the selection of a system of teaching methods are identified. An algorithm for designing a system of teaching methods is developed in accordance with the necessary restrictions reflecting a system of factors determining a complex of effective methods for studying didactic units and modules of the content of the discipline. The developed algorithm is implemented as a software package. This complex is a means of supporting the decision making of choosing a system of computer science teaching methods and allows the teacher to make an informed selection of the most effective teaching methods in automatic or automated modes, taking into account personal preferences. The universality of the complex consists in the possibility of modifying the database in accordance with the specifics of specific academic disciplines, which makes it possible to use it in the education system.

KNOWLEDGE-BASED APPROACH TO ADAPTIVE SELECTION OF EQUIPMENT FOR TEACHING ROBOTICS

2019 ◽  
Author(s):  
Tetiana Mazurok ◽  
◽  
Volodymyr Chernykh ◽  
Keyword(s):  
Decision Making ◽  
Teaching Methods ◽  
Academic Disciplines ◽  
Administrative Staff ◽  
Adaptive Selection ◽  
Knowledge Based ◽  
Technical Base ◽  
Decision Making System ◽  
Oriented Approach ◽  
Selection Of

Innovations and progress in teaching, introduction of new academic disciplines in the curricula, changes in the paradigm of school education in Ukraine, the search for innovative tools, techniques and teaching methods, especially teaching STEAM create a good basis for teaching robotics. However, the organization of classes in robotics requires the creation of a special educational ecosystem, which important element is the technical base (equipment). It is not a secret, that administrative staff who sometimes even do not have required experience in technology are often engaged in the procurement of equipment and its selection. The current study was conducted to solve this problem, as well as to create a universal recommendation for creating an appropriate ecosystem for teaching robotics As part of the study, the task was to develop a prototype of an expert decision-making system for selecting of an appropriate equipment and zoning of a classroom (ICR) for conducting classes in robotics. Knowledge-oriented approach was used to create the prototype. Keywords: knowledge-based approach, robotics, adaptive selection, teaching robotics.

scholarly journals Toward Psychoinformatics: Computer Science Meets Psychology

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Christian Montag ◽  
Éilish Duke ◽  
Alexander Markowetz
Keyword(s):  
Computer Science ◽  
Online Social Network ◽  
Large Data ◽  
Research Field ◽  
Large Data Sets ◽  
Future Research ◽  
Data Sets ◽  
Psychological Traits ◽  
Scientific Methods ◽  
Insight Into

The present paper provides insight into an emerging research discipline calledPsychoinformatics. In the context ofPsychoinformatics, we emphasize the cooperation between the disciplines of psychology and computer science in handling large data sets derived from heavily used devices, such as smartphones or online social network sites, in order to shed light on a large number of psychological traits, including personality and mood. New challenges await psychologists in light of the resulting “Big Data” sets, because classic psychological methods will only in part be able to analyze this data derived from ubiquitous mobile devices, as well as other everyday technologies. As a consequence, psychologists must enrich their scientific methods through the inclusion of methods from informatics. The paper provides a brief review of one area of this research field, dealing mainly with social networks and smartphones. Moreover, we highlight how data derived fromPsychoinformaticscan be combined in a meaningful way with data from human neuroscience. We close the paper with some observations of areas for future research and problems that require consideration within this new discipline.

Alpha diversity of Phanerozoic marine communities positively correlates with longevity of genera

Paleobiology ◽  
2009 ◽  
Vol 35 (2) ◽  
pp. 231-250 ◽  
Author(s):  
Alexander V. Markov
Keyword(s):  
Alpha Diversity ◽  
Large Data ◽  
Global Scale ◽  
Large Data Sets ◽  
Data Sets ◽  
Factors Affecting ◽  
Marine Communities ◽  
Genus Richness ◽  
Average Longevity ◽  
Major Factors

Several lines of theoretical and empirical evidence suggest that there can be a positive correlation between alpha diversity (genus richness) of marine communities (D) and average longevity of marine genera included in these communities (L). One possible reason for such a correlation is that diversity can be expected to give rise to ecosystem stability, which, in turn, may slow down the extinction of taxa. However, this hypothesis has not been verified on the global scale. The analysis of two large data sets (Sepkoski's compendium of fossil marine genera and the Paleobiology Database) shows that the correlation (1) actually exists and (2) is robust to some possible sources of errors in L and D estimation. Further analysis reveals that the correlation is not a secondary pattern caused by any of the following factors: (1) encounter probability of taxa, which is greatly influenced by differential incompleteness of the fossil record; (2) degree of sediment lithification, which is one of the major factors affecting the preservation of fossils; (3) onshore-offshore gradient; (4) parallel growth of both L and D through the Phanerozoic; (5) paleolatitudinal gradient. Although there may be other factors that influence both L and D in a similar way, the results generally confirm the hypothesis that higher alpha diversity enhances longevity of genera.

scholarly journals Large-scale sequence comparisons with sourmash

2019 ◽  
Author(s):  
N. Tessa Pierce ◽  
Luiz Irber ◽  
Taylor Reiter ◽  
Phillip Brooks ◽  
C. Titus Brown
Keyword(s):  
Software Package ◽  
Large Scale ◽  
Sequence Similarity ◽  
Protein Sequences ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Sequence Comparisons ◽  
Large Databases ◽  
Scale Sequence

The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

scholarly journals Integral methods for automatic quantification of fast-scan-cyclic-voltammetry detected neurotransmitters

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254594
Author(s):  
Leonardo X. Espín ◽  
Anders J. Asp ◽  
James K. Trevathan ◽  
Kip A. Ludwig ◽  
J. Luis Lujan
Keyword(s):  
Ad Hoc ◽  
Time Integration ◽  
Large Data ◽  
Data Sets ◽  
Oxidation Reactions ◽  
Fast Scan Cyclic Voltammetry ◽  
Integral Methods ◽  
Selection Of

Modern techniques for estimating basal levels of electroactive neurotransmitters rely on the measurement of oxidative charges. This requires time integration of oxidation currents at certain intervals. Unfortunately, the selection of integration intervals relies on ad-hoc visual identification of peaks on the oxidation currents, which introduces sources of error and precludes the development of automated procedures necessary for analysis and quantification of neurotransmitter levels in large data sets. In an effort to improve charge quantification techniques, here we present novel methods for automatic selection of integration boundaries. Our results show that these methods allow quantification of oxidation reactions both in vitro and in vivo and of multiple analytes in vitro.

scholarly journals Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART

2020 ◽  
Vol 13 (11) ◽  
pp. 5277-5310
Author(s):  
Anne Tipka ◽  
Leopold Haimberger ◽  
Petra Seibert
Keyword(s):  
Software Package ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Particle Dispersion ◽  
Data Sets ◽  
Weather Forecasts ◽  
Online Documentation ◽  
Open Source Software Package ◽  
The Right ◽  
Selection Of

Abstract. Flex_extract is an open-source software package to efficiently retrieve and prepare meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) as input for the widely used Lagrangian particle dispersion model FLEXPART and the related trajectory model FLEXTRA. ECMWF provides a variety of data sets which differ in a number of parameters (available fields, spatial and temporal resolution, forecast start times, level types etc.). Therefore, the selection of the right data for a specific application and the settings needed to obtain them are not trivial. Consequently, the data sets which can be retrieved through flex_extract by both member-state users and public users as well as their properties are explained. Flex_extract 7.1.2 is a substantially revised version with completely restructured code, mainly written in Python 3, which is introduced with all its input and output files and an explanation of the four application modes. Software dependencies and the methods for calculating the native vertical velocity η˙, the handling of flux data and the preparation of the final FLEXPART input files are documented. Considerations for applications give guidance with respect to the selection of data sets, caveats related to the land–sea mask and orography, etc. Formal software quality-assurance methods have been applied to flex_extract. A set of unit and regression tests as well as code metric data are also supplied. A short description of the installation and usage of flex_extract is provided in the Appendix. The paper points also to an online documentation which will be kept up to date with respect to future versions.

scholarly journals Large-scale sequence comparisons with sourmash

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1006 ◽  
Author(s):  
N. Tessa Pierce ◽  
Luiz Irber ◽  
Taylor Reiter ◽  
Phillip Brooks ◽  
C. Titus Brown
Keyword(s):  
Software Package ◽  
Large Scale ◽  
Sequence Similarity ◽  
Protein Sequences ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Sequence Comparisons ◽  
Large Databases ◽  
Scale Sequence

The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

Web Tools for Molecular Biological Data Analysis

2005 ◽  
pp. 3068-3073
Author(s):  
Denise Fukumi Tsunoda ◽  
Heitor Silvério Lopes ◽  
Ana Tereza Vasconcelos
Keyword(s):  
Computer Science ◽  
Protein Structures ◽  
Large Data ◽  
Biological Data ◽  
Amino Acid Sequences ◽  
Data Sets ◽  
Efficient Access ◽  
Biological Data Analysis ◽  
Types Of Information ◽  
New Algorithms

Bioinformatics means solving problems arising from biology using methods from computer science. The National Center for Biotechnology Information (www.ncbi.nih.gov) defines bioinformatics as: “…the field of science in which biology, computer science, and information technology merge into a single discipline...There are three important sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to access relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information.”

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