Computational Performance of MATLAB and Python for Electromagnetic Applications

MATLAB and Python are two commonly used scripting languages for prototyping electromagnetic problems today. Each of these languages provides access to computationally efficient functions allowing a user to easily run many math heavy problems with minimal programming. In this paper we will discuss the usage of MATLAB and a variety of libraries in Python capable of running these efficient computations. Tests will be run in both languages to compare both CPU and GPU computations. The runtimes of a variety of problems using each of these platforms will also be compared for a variety of mathematical operations typically used in electromagnetic problems. Finally, a simple angle of arrival calculation using conventional beamforming will be performed to show these speeds on a realistic problem.

Automated server-side model for recognition of security vulnerabilities in scripting languages

With the increase of global accessibility of web applications, maintaining a reasonable security level for both user data and server resources has become an extremely challenging issue. Therefore, static code analysis systems can help web developers to reduce time and cost. In this paper, a new static analysis model is proposed. This model is designed to discover the security problems in scripting languages. The proposed model is implemented in a prototype SCAT, which is a static code analysis Tool. SCAT applies the phases of the proposed model to catch security vulnerabilities in PHP 5.3. Empirical results attest that the proposed prototype is feasible and is able to contribute to the security of real-world web applications. SCAT managed to detect 94% of security vulnerabilities found in the testing benchmarks; this clearly indicates that the proposed model is able to provide an effective solution to complicated web systems by offering benefits of securing private data for users and maintaining web application stability for web applications providers.

Interfaces to Scripting Languages in Visual Analytics Applications

The need to use data visualization and visual analysis in various fields has led to the development of feature-rich standalone applications such as Tableau and MS Power BI. These applications provide ready-to-use functionality for loading, analyzing and visualizing data, even for users who are not familiar with programming and scripting. Meanwhile, data scientists have to combine many different tools and techniques in their daily work, since no standalone application can yet cover the entire workflow. As a result, a rich landscape of open source libraries is available today, covering various tasks from data analysis to modeling and visualization. To combine the best of two worlds, interfaces for scripting languages have been integrated into standalone applications in recent years. We analyzed which interfaces to six common scripting languages are offered. The interfaces offer different levels of integration and therefore support different steps of the data science workflow. In this paper we investigated the integration levels of script languages in standalone applications and divided them into four groups. We used this classification to evaluate 13 standalone visual analysis applications currently available on the market. We then analyzed which groups of applications best support which steps in the data science workflow. We found that a tight integration of scripting languages can especially support the explorative analysis and modeling phase of the data science workflow. We also discuss our results in the light of visual analysis research and give suggestions for future research directions.

Experience in the Use of Scripting Languages in Distance Learning: Advantages and Disadvantages

The article is devoted to the consideration of scripting languages (scripts) used in distance learning. The aim of the research is to assess the satisfaction of distance learning (DL) participants and identify the advantages and disadvantages of the main platforms using scripting languages. Research methods. In the article, based on 3 years of experience in using tools for DL, the positive and negative aspects of the use of various scripting languages (scripts) for organizing distance learning and teleconferences are reviewed. The sample size is represented by a three-year period, during which 5,712 teleconferences were held. The questionnaire responses of 2500 participants were analyzed. The sections of the assessment dealt with the satisfaction of the DL participants and the functionality of the script tools. Results. The majority of participants were satisfied with the use of BigBlue Point, Jitsi, Zoom systems. The interviewees indicated the need for registration (49%) and the mandatory download of the application (42%) as the main shortcomings. It is concluded that absolutely reliable criteria for choosing a platform, which is associated with a large number of specific factors and tasks, have not been identified. It is emphasized that creation of domestic products seems to be a promising area of activity for developers.

hts-nim: scripting high-performance genomic analyses

Motivation Extracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets. Results We present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance. Availability and implementation hts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT license.

hts-nim: scripting high-performance genomic analyses

MotivationExtracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets.ResultsWe present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance.Availabilityhts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT [email protected] informationSupplementary data are available at Bioinformatics online.