Programming Languages and Compilers Commons^™

Law, Technology, And Pedagogy: Teaching Coding To Build A “Future-Proof” Lawyer, Alfredo Contreras, Joe Mcgrath

Minnesota Journal of Law, Science & Technology

No abstract provided.

Helion’S Snapshot Module, Nii-Kwartei Quartey

Cybersecurity Undergraduate Research Showcase

During my undergraduate research, I spent my time working with a home automation program known as Helion, specifically, its Snapshot module. I was tasked with learning new material and completing part of the webpage that were unfinished. I also had to get a little creative when working on a design that users could find appealing. There were times I found working on Helion difficult but overall, working with Helion’s Snapshot Module is something that will help me improve with my undergraduate studies.

Functional Programming For Systems Software: Implementing Baremetal Programs In Habit, Donovan Ellison

University Honors Theses

Programming in a baremetal environment, directly on top of hardware with very little to help manage memory or ensure safety, can be dangerous even for experienced programmers. Programming languages can ease the burden on developers and sometimes take care of entire sets of errors. This is not the case for a language like C that will do almost anything you want, for better or worse. To operate in a baremetal environment often requires direct control over memory, but it would be nice to have that capability without sacrificing safety guarantees. Rust is a new language that aims to fit this …

Automated Synthesis Of Local Time Requirement For Service Composition, Étienne André, Tian Huat Tan, Manman Chen, Shuang Liu, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial, especially in time-critical business environments, which is often stated as a clause in service-level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language that aims at specifying Web services. We extend it …

What Was Written Vs. Who Read It: News Media Profiling Using Text Analysis And Social Media Context, Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav. Nakov

Research Collection School Of Computing and Information Systems

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim, either manually or automatically. Thus, it has been proposed to profile entire news outlets and to look for those that are likely to publish fake or biased content. This makes it possible to detect likely “fake news” the moment they are published, by simply checking the reliability of their …

Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns. We propose a framework by extending GPT-2 models to tackle these challenges by formulating video-grounded dialogue tasks as a sequence-to-sequence task, combining both …

Ari: The Automated R Instructor, Sean Kross, Jeffrey T. Leek, John Muschelli

The R Journal

We present the ari package for automatically generating technology-focused educational videos. The goal of the package is to create reproducible videos, with the ability to change and update video content seamlessly. We present several examples of generating videos including using R Markdown slide decks, PowerPoint slides, or simple images as source material. We also discuss how ari can help instructors reach new audiences through programmatically translating materials into other languages.

The R Journal (June 2020) 12(1): Complete Issue, The R Foundation

The R Journal

Editorial, Michael J. Kane

Contributed Research Articles

gk: An R Package for the g-and-k and Generalised g-and-h Distributions, Dennis Prangle

NlinTS: An R Package for Causality Detection in Time Series, Youssef Hmamouche

Mapping Smoothed Spatial Effect Estimates from Individual-Level Data: MapGAM, Lu Bai, Daniel L. Gillen, Scott M. Bartell, and Verónica M. Vieira

mudfold: An R Package for Nonparametric IRT Modelling of Unfolding Processes, Spyros E. Balafas, Wim P. Krijnen, Wendy J. Post, and Ernst C. Wit

tsmp: An R Package for Time Series with Matrix Profile, Francisco Bischoff and Pedro Pereira Rodrigues

Individual-Level Modelling of Infectious Disease Data: EpiILM, …

Provenance Of R’S Gradient Optimizers, John C. Nash

The R Journal

Gradient optimization methods (function minimizers) are well-represented in both the base and package universe of R (R Core Team, 2019). However, some of the methods and the codes developed from them were published before standards for hardware and software were established, in particular the IEEE arithmetic (IEEE, 1985). There have been cases of unexpected behaviour or outright errors, and these are the focus of the histoRicalg project. A summary history of some of the tools in R for gradient optimization methods is presented to give perspective on such methods and the occasions where they could be used effectively.

S, R, And Data Science, John M. Chambers

The R Journal

Data science is increasingly important and challenging. It requires computational tools and programming environments that handle big data and difficult computations, while supporting creative, high-quality analysis. The R language and related software play a major role in computing for data science. R is featured in most programs for training in the field. R packages provide tools for a wide range of purposes and users. The description of a new technique, particularly from research in statistics, is frequently accompanied by an R package, greatly increasing the usefulness of the description.

The history of R makes clear its connection to data science. …

The Rockerverse: Packages And Applications For Containerisation With R, Daniel Nüst, Dirk Eddelbuettel, Dom Bennett, Robrecht Cannoodt, Dav Clark, Gergely Daróczi, Mark Edmondson, Colin Fay, Ellis Hughes, Lars Kjeldgaard, Sean Lopp, Ben Marwick, Heather Nolis, Jacqueline Nolis, Hong Ooi, Kathik Ram, Noam Ross, Lori Shepard, Péter Sólymos, Tyson Lee Swetnam, Nitesh Turaga, Charlotte Van Petegem, Jason Williams, Craig Willis, Nan Xiao

The R Journal

The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We …

Linear Fractional Stable Motion With The Rlfsm R Package, Stepan Mazur, Dmitry Otryakhin

The R Journal

Linear fractional stable motion is a type of a stochastic integral driven by symmetric alpha-stable Lévy motion. The integral could be considered as a non-Gaussian analogue of the fractional Brownian motion. The present paper discusses R package rlfsm created for numerical procedures with the linear fractional stable motion. It is a set of tools for simulation of these processes as well as performing statistical inference and simulation studies on them. We introduce: tools that we developed to work with that type of motions as well as methods and ideas underlying them. Also we perform numerical experiments to show finite-sample behavior …

Bayesmallows: An R Package For The Bayesian Mallows Model, Øystein Sørensen, Marta Crispino, Qinghua Liu, Valeria Vitelli

The R Journal

BayesMallows is an R package for analyzing preference data in the form of rankings with the Mallows rank model, and its finite mixture extension, in a Bayesian framework. The model is grounded on the idea that the probability density of an observed ranking decreases exponentially with the distance to the location parameter. It is the first Bayesian implementation that allows wide choices of distances, and it works well with a large amount of items to be ranked. BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns. The Bayesian paradigm allows coherent quantification of …

Rcosmo: R Package For Analysis Of Spherical, Healpix And Cosmological Data, Daniel Fryer, Ming Li, Andriy Olenko

The R Journal

The analysis of spatial observations on a sphere is important in areas such as geosciences, physics and embryo research, just to name a few. The purpose of the package rcosmo is to conduct efficient information processing, visualisation, manipulation and spatial statistical analysis of Cosmic Microwave Background (CMB) radiation and other spherical data. The package was developed for spherical data stored in the Hierarchical Equal Area isoLatitude Pixelation (Healpix) representation. rcosmo has more than 100 different functions. Most of them initially were developed for CMB, but also can be used for other spherical data as rcosmo contains tools for transforming spherical …

Lspartition: Partitioning-Based Least Squares Regression, Matias D. Cattaneo, Max H. Farrell, Yingjie Feng

The R Journal

Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise polynomials. This article discusses the main methodological and numerical features of the R software package lspartition, which implements results for partitioning-based least squares (series) regression estimation and inference from Cattaneo and Farrell (2013) and Cattaneo, Farrell, and Feng (2020). These results cover the multivariate regression function as well as its derivatives. First, the package provides data-driven methods to choose the number of partition knots optimally, according to integrated mean squared error, yielding optimal point estimation. Second, robust …

Mudfold: An R Package For Nonparametric Irt Modelling Of Unfolding Processes, Spyros E. Balafas, Wim P. Krijnen, Wendy J. Post, Ernst C. Wit

The R Journal

Item response theory (IRT) models for unfolding processes use the responses of individuals to attitudinal tests or questionnaires in order to infer item and person parameters located on a latent continuum. Parametric models in this class use parametric functions to model the response process, which in practice can be restrictive. MUDFOLD (Multiple UniDimensional unFOLDing) can be used to obtain estimates of person and item ranks without imposing strict parametric assumptions on the item response functions (IRFs). This paper describes the implementation of the MUDFOLD method for binary preferential-choice data in the R package mudfold. The latter incorporates estimation, visualization, …

Mapping Smoothed Spatial Effect Estimates From Individual-Level Data: Mapgam, Lu Bai, Daniel L. Gillen, Scott M. Bartell, Verónica M. Vieira

The R Journal

We introduce and illustrate the utility of MapGAM, a user-friendly R package that provides a unified framework for estimating, predicting and drawing inference on covariate-adjusted spatial effects using individual-level data. The package also facilitates visualization of spatial effects via automated mapping procedures. MapGAM estimates covariate-adjusted spatial associations with a univariate or survival outcome using generalized additive models that include a non-parametric bivariate smooth term of geolocation parameters. Estimation and mapping methods are implemented for continuous, discrete, and right-censored survival data. In the current manuscript, we summarize the methodology implemented in MapGAM and illustrate the package using two example simulated …

Nlints: An R Package For Causality Detection In Time Series, Youssef Hmamouche

The R Journal

The causality is an important concept that is widely studied in the literature, and has several applications, especially when modelling dependencies within complex data, such as multivariate time series. In this article, we present a theoretical description of methods from the NlinTS package, and we focus on causality measures. The package contains the classical Granger causality test. To handle non-linear time series, we propose an extension of this test using an artificial neural network. The package includes an implementation of the Transfer entropy, which is also considered as a non linear causality measure based on information theory. For discrete variables, …

Editorial, Michael J. Kane

The R Journal

Onbehalf of the editorial board, I am pleased to present Volume 12, Issue 1 of the R Journal and mysecond issue as the Editor in Chief. Since the last issue Simon Urbanek has joined the editorial board and we have made a few structural changes. First, the R Foundation has approved the R Journal having Associate Editors. This change will allow us to address the increase in submission volume. The addition of the new AE positions should help alleviate some of the workload the editors have been dealing with and will result in shorter turn-around times for submissions. Second, complete …

Gk: An R Package For The G-And-K And Generalised G-And-H Distributions, Dennis Prangle

The R Journal

The g-and-k and (generalised) g-and-h distributions are flexible univariate distributions which can model highly skewed or heavy tailed data through only four parameters: location and scale, and two shape parameters influencing the skewness and kurtosis. These distributions have the unusual property that they are defined through their quantile function (inverse cumulative distribution function) and their density is unavailable in closed form, which makes parameter inference complicated. This paper presents the gk R package to work with these distributions. It provides the usual distribution functions and several algorithms for inference of independent identically distributed data, including the finite difference stochastic approximation …