scholarly journals Leisure Studies on Program Output With Delay Relaxation.

Author(s):  
Frank Appiah

Interactive computing environments consisting of screen and keyboard provides a means to relax and enjoy the program output. Leisurely, ways to slow and relax program execution is delved with system calls like delay execution, synthesis execution and file management execution. The leisure time can be the exact delay time used in slowly the chances of output activity.

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 159-165
Author(s):  
Robert Hoyt ◽  
Victoria Wangia-Anderson

Abstract Objective To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. Methods and Materials The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. Results Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics Discussion Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. Conclusion Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.


2020 ◽  
Vol 55 (5) ◽  
pp. 1184-1189 ◽  
Author(s):  
Danielle C. Gomes ◽  
Ingrid G. Azevedo ◽  
Ana G. Figueiredo Araújo ◽  
Lenice D. Costa Lopes ◽  
Danilo A. P. Nagem ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Bao Rong Chang ◽  
Hsiu-Fen Tsai ◽  
Po-Wen Su

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance.


2015 ◽  
Vol 22 (1) ◽  
pp. 154
Author(s):  
Thiago Teixeira Santos

In research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV’s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks.


2019 ◽  
Author(s):  
Nathan Sheffield

Reproducible computing environments are required for reproducible analysis and are also useful for interactive exploratory analysis. In the past, scientific computing environments have been managed with package managers or with virtual machines. More recently, modern workflow managers have incorporated linux container technology to improve reproducibility. These existing solutions solve some of the challenges of managing reproducible computing environments, but they remain limited: System-wide environments and native package managers lack the portability, efficiency, and robustness of modern containers, while container-aware workflows are specialized and incapable of interactive computing. Here, I introduce *bulker*, an approach that combines the advantages of virtual machines, native package managers, and container-aware workflow managers. Bulker creates and distributes complete environments, like virtual machines, but with re-usable modular components, like a native package manager. It uses individually containerized tools like a container-aware workflow manager, but also allows these environments to be activated interactively and distributed independently of a particular workflow. Bulker is thus a more general approach to portable and reproducible computing environments.


Author(s):  
Jason Williams

AbstractPosing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling).


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