Programming Languages and Compilers Commons^™

Algorithmic Music Generation For Pedagogy Of Sight Reading, Ryan Stephen Davis

Electronic Theses and Dissertations

Autodeus is the name of the program that has been developed and was designed to aid guitar students in the attainment and betterment of musical notation sight reading skills. Its primary goal is to provide a very flexible tool that has the ability to generate virtually endless types of sight reading exercises at many various skill levels.

A complimentary 2 year-long comprehensive guitar sight-reading course syllabus can be implemented via Autodeus as it is capable of generating all the necessary exercises. It is able to generate these exercises quickly and efficiently through the use of a back tracking algorithm that …

C++ Risk Simulation To Generate Ai, David Sky Nunez

A with Honors Projects

For this A with Honors project, the student set out to create a way to run thousands of simulations for the board game, RISK, supplying information for the game player to be able to make more strategic decisions when playing the game.

Modeling Programming Language Trends Using Markov Processes, Cody Clymer, Adrian Alberto, Mike Merrill

Math 365 Class Projects

Which Languages Are Worth Investing In? One of the many issues in the computer science industry is knowing which technologies to invest in. Programming languages are a prime example of these technologies, so investors and innovators need to know which programming languages will grow in use over time so that new businesses can grow alongside them.

Slade: A Smart Large-Scale Task Decomposer In Crowdsourcing, Yongxin Tong, Lei Chen, Zimu Zhou, H. V. Jagadish, Lidan Shou

Research Collection School Of Computing and Information Systems

Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at …

Elephant 2000: A Programming Language For Remembering The Past And Building On It, Kerry J. Holmes

Williams Honors College, Honors Research Projects

Elephant 2000 is a programming language to specify programs that accept user speech as text inputs and outputs speech text. The inputs and outputs are based on Dialogue Act theory which describes several forms of speech outputs, such as requests, questions, and answers. The language also relies on Named Entity Recognition to determine what types of objects a user references. These entities include persons, locations, times and so on. Using these attributes of user speech, a program is able to perform simple rule matching and pattern recognition to respond to input. The result is a programming language with English like …

Modeling Engagement Of Programming Students Using Unsupervised Machine Learning Technique, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Engagement is instrumental to students’ learning and academic achievements. In this study, we model the engagement states of students who are working on programming exercises in an intelligent tutoring system. Head pose, keystrokes and action logs of students automatically captured within the tutoring system are fed into a Hidden Markov Model for inferring the engagement states of students. With the modeling of students’ engagement on a moment by moment basis, intervention measures can be initiated automatically by the system when necessary to optimize the students’ learning. This study is also one of the few studies that bypass the need for …

Pip: An Abstract Dataplane And Virtual Machine, Samuel Goodrick

Williams Honors College, Honors Research Projects

We present an abstract machine and S-expression-based programming language to describe OpenFlow-style software-defined networking. The implemented Pip virtual machine and language provide facilities for packet decoding, safely writing and setting bitfields within packets, and switching based on packet contents. We have outlined an abstract syntax and structural operational semantics for Pip, thus allowing Pip programs to have predictable and provable properties. Pip allows for easy and safe access and writing to packet fields, as well as a programmable packet pipeline that will rarely stall.

Integrated Reward Scheme And Surge Pricing In A Ride Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye

Research Collection School Of Computing and Information Systems

Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. In this paper, to address the …

A Restful Framework For Writing, Running, And Evaluating Code In Multiple Academic Settings, Christopher Ban

MS in Computer Science Project Reports

In academia, students and professors want a well-structured and implemented framework for writing and running code in both testing and learning environments. The current limitations of the paper and pencil medium have led to the creation of many different online grading systems. However, no known system provides all of the essential features our client is interested in. Our system, developed in conjunction with Doctor Halterman, offers the ability to build modules from flat files, allow code to be compiled and run in the browser, provide users with immediate feedback, support multiple languages, and offer a module designed specifically for an …

R Teaching Column, Matthias Gehrke, Reed Davis, Norman Matloff, Paul Thompson, Tiffany Chen, Emily Watkins, Laurel Beckett

The R Journal

The revisit package, developed as a collaborative tool for scientists, also serves as a tool for teaching statistics, in a manner that can be highly motivating for students. Using either the included case studies or datasets/code provided by the instructor, students can explore several alternate paths of analysis, such as the effects of including/excluding certain variables, employing different types of statistical methodology and so on. The package includes features that help students follow modern statistical standards and avoid various statistical errors, such as “p-hacking” and lack of attention to outlier data.

Forwards Column, Stella Bollmann, Dianne Cook, Jasmine Dumas, John Fox, Julie Josse, Oliver Keyes, Carolin Strobl, Heather Turner, Rudolf Debelak

The R Journal

Forwards is a task force that was set up by the R Foundation in 2015 to address the under representation of women that has since widened its scope to encompass other under represented groups. The task force is organised as a core team comprising leaders from a number of sub-teams that focus on particular aspects:

An Introduction To Rocker: Docker Containers For R, Carl Boettiger, Dirk Eddelbuettel

The R Journal

We describe the Rocker project, which provides a widely-used suite of Docker images with customized R environments for particular tasks. We discuss how this suite is organized, and how these tools can increase portability, scaling, reproducibility, and convenience of R users and developers.

Openebgm: An R Implementation Of The Gamma-Poisson Shrinker Data Mining Model, Travis Canida, John Ihrie

The R Journal

We introduce the R package openEBGM, an implementation of the Gamma-Poisson Shrinker (GPS) model for identifying unexpected counts in large contingency tables using an empirical Bayes approach. The Empirical Bayes Geometric Mean (EBGM) and quantile scores are obtained from the GPS model estimates. openEBGM provides for the evaluation of counts using a number of different methods, including the model-based disproportionality scores, the relative reporting ratio (RR), and the proportional reporting ratio (PRR). Data squashing for computational efficiency and stratification for confounding variable adjustment are included. Application to adverse event detection is discussed.

The R Journal (December 2017) 9(2): Complete Issue, The R Foundation

The R Journal

Editorial, Roger Bivand

Contributed Research Articles

anchoredDistr: A Package for the Bayesian Inversion of Geostatistical Parameters with Multi-type and Multi-scale Data, Heather Savoy, Falk Heße, and Yoram Rubin

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data, Nicolae Teodor Melita and Stefan Holban

Allele Imputation and Haplotype Determination from Databases Composed of Nuclear Families, Nathan Medina-Rodríguez and Ángelo Santana

Visualization of Regression Models Using visreg, Patrick Breheny and Woodrow Burchett

fourierin: An R package to compute Fourier integrals, Guillermo Basulto-Elias, Alicia Carriquiry, Kris De Brabanter, and Daniel J. Nordman

Discrete Time Markov Chains with …

The Welchadf Package For Robust Hypothesis Testing In Unbalanced Multivariate Mixed Models With Heteroscedastic And Non-Normal Data, Pablo J. Villacorta

The R Journal

A new R package is presented for dealing with non-normality and variance heterogeneity of sample data when conducting hypothesis tests of main effects and interactions in mixed models. The proposal departs from an existing SAS program which implements Johansen’s general formulation of Welch-James’s statistic with approximate degrees of freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Improved type I error rate control is obtained using bootstrapping for calculating an empirical critical value, whereas robustness against non-normality is achieved through trimmed …

Mle.Tools: An R Package For Maximum Likelihood Bias Correction, Josmar Mazucheli, André Felipe B. Menezes, Saralees Nadarajah

The R Journal

Recently, Mazucheli (2017) uploaded the package mle.tools to CRAN. It can be used for bias corrections of maximum likelihood estimates through the methodology proposed by Cox and Snell (1968). The main function of the package, coxsnell.bc(), computes the bias corrected maximum likelihood estimates. Although in general, the bias corrected estimators may be expected to have better sampling properties than the uncorrected estimators, analytical expressions from the formula proposed by Cox and Snell (1968) are either tedious or impossible to obtain. The purpose of this paper is twofolded: to introduce the mle.tools package, especially the coxsnell.bc() function; secondly, to compare, for …

Carx: An R Package To Estimate Censored Autoregressive Time Series With Exogenous Covariates, Chao Wang, Kung-Sik Chan

The R Journal

We implement in the R package carx a novel and computationally efficient quasi-likelihood method for estimating a censored autoregressive model with exogenous covariates. The proposed quasi-likelihood method reduces to maximum likelihood estimation in absence of censoring. The carx package contains many useful functions for practical data analysis with censored stochastic regression, including functions for outlier detection, model diagnostics, and prediction with censored time series data. We illustrate the capabilities of the carx package with simulations and an elaborate real data analysis.

Ctmcd: An R Package For Estimating The Parameters Of A Continuous-Time Markov Chain From Discrete-Time Data, Marius Pfeuffer

The R Journal

This article introduces the R package ctmcd, which provides an implementation of methods for the estimation of the parameters of a continuous-time Markov chain given that data are only available on a discrete-time basis. This data consists of partial observations of the state of the chain, which are made without error at discrete times, an issue also known as the embedding problem for Markovchains. The functions provided comprise matrix logarithm based approximations as described in Israel et al. (2001), as well as Kreinin and Sidelnikova (2001), an expectation-maximization algorithm and a Gibbs sampling approach, both introduced by Bladt and …

Discrete Time Markov Chains With R, Giorgio Alfredo Spedicato

The R Journal

The markovchain package aims to provide S4 classes and methods to easily handle Discrete Time Markov Chains (DTMCs), filling the gap with what is currently available in the CRAN repository. In this work, I provide an exhaustive description of the main functions included in the package, as well as hands-on examples.

Visualization Of Regression Models Using Visreg, Patrick Breheny, Woodrow Burchett

The R Journal

Regression models allow one to isolate the relationship between the outcome and an explanatory variable while the other variables are held constant. Here, we introduce an R package, visreg, for the convenient visualization of this relationship via short, simple function calls. In addition to estimates of this relationship, the package also provides pointwise confidence bands and partial residuals to allow assessment of variability as well as outliers and other deviations from modeling assumptions. The package provides several options for visualizing models with interactions, including lattice plots, contour plots, and both static and interactive perspective plots. The implementation of the …