Semiparametric Generalized Linear Models With The Gldrm Package,
2018
University of Wisconsin–Madison
Semiparametric Generalized Linear Models With The Gldrm Package, Michael J. Wurm, Paul J. Rathouz
The R Journal
This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood. The new algorithm incorporates several computational stability and efficiency improvements over the algorithm originally proposed. In particular, the new algorithm performs well for either small or large support for the nonparametric response distribution. The …
Editorial,
2018
R Project
Editorial, John Verzani
The R Journal
On behalf of the Editorial Board, I am pleased to present Volume 10, Issue 1 of the R Journal. This issue contains 36 contributed articles. The majority of which cover new or newly enhanced packages on CRAN.
Nonparametric Independence Tests And K-Sample Tests For Large Sample Sizes Using Package Hhg,
2018
Tel-Aviv University
Nonparametric Independence Tests And K-Sample Tests For Large Sample Sizes Using Package Hhg, Barak Brill, Yair Heller, Ruth Heller
The R Journal
Nonparametric tests of independence and k-sample tests are ubiquitous in modern applications, but they are typically computationally expensive. We present a family of nonparametric tests that are computationally efficient and powerful for detecting any type of dependence between a pair of univariate random variables. The computational complexity of the suggested tests is sub-quadratic in sample size, allowing calculation of test statistics for millions of observations. We survey both algorithms and the HHG package in which they are implemented, with usage examples showing the implementation of the proposed tests for both the independence case and the k-sample problem. The tests are …
Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel,
2018
Universidad de Castilla-La Mancha
Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel, Gonzalo Garcia-Donato, Anabel Forte
The R Journal
In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.
R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series,
2018
USEPA National Health and Environmental Effects Research Laboratory
R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series, Marcus W. Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat
The R Journal
Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are …
Onewaytests: An R Package For One-Way Tests In Independent Groups Designs,
2018
Hacettepe University Faculty of Medicine
Onewaytests: An R Package For One-Way Tests In Independent Groups Designs, Osman Dag, Anil Dolgun, Naime Meric Konar
The R Journal
One-way tests in independent groups designs are the most commonly utilized statistical methods with applications on the experiments in medical sciences, pharmaceutical research, agriculture, biology, engineering, social sciences and so on. In this paper, we present the one-way tests package to investigate treatment effects on the dependent variable. The package offers the one-way tests in independent groups designs, which include ANOVA, Welch’s heteroscedastic F test, Welch’s heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander Govern test, James second order test and Kruskal-Wallis test. The package also provides pairwise comparisons, graphical approaches, and assesses variance homogeneity and …
A System For An Accountable Data Analysis Process In R,
2018
UT Health San Antonio
A System For An Accountable Data Analysis Process In R, Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov
The R Journal
Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the ’verifiable …
Stackelberg Security Games: Looking Beyond A Decade Of Success,
2018
Singapore Management University
Stackelberg Security Games: Looking Beyond A Decade Of Success, Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, Milind Tambe
Research Collection School Of Computing and Information Systems
The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.
Static Analysis Of Context Leaks In Android Applications,
2018
Singapore Management University
Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa
Research Collection School Of Computing and Information Systems
Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of key resources, such as activities and services (contexts). Programmers are not supposed to interfere with such lifecycle events. Otherwise contexts might be leaked, i.e. they will never be deallocated from memory, or be deallocated too late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and subsequently leads to context leakage.In this work, we present …
Overfitting In Automated Program Repair: Challenges And Solutions,
2018
Singapore Management University
Overfitting In Automated Program Repair: Challenges And Solutions, Dinh Xuan Bach Le
Dissertations and Theses Collection (Open Access)
This chapter discusses the main problem and motivation of this dissertation. It also discusses a quantification of various research issues directly related to the dissertation. A summary of works done will also be presented along with the structure of the dissertation.
Funqual: User-Defined, Statically-Checked Call Graph Constraints In C++,
2018
California Polytechnic State University, San Luis Obispo
Funqual: User-Defined, Statically-Checked Call Graph Constraints In C++, Andrew P. Nelson
Master's Theses
Static analysis tools can aid programmers by reporting potential programming mistakes prior to the execution of a program. Funqual is a static analysis tool that reads C++17 code ``in the wild'' and checks that the function call graph follows a set of rules which can be defined by the user. This sort of analysis can help the programmer to avoid errors such as accidentally calling blocking functions in time-sensitive contexts or accidentally allocating memory in heap-sensitive environments. To accomplish this, we create a type system whereby functions can be given user-defined type qualifiers and where users can define their own …
Compiler Optimization Effects On Register Collisions,
2018
California Polytechnic State University, San Luis Obispo
Compiler Optimization Effects On Register Collisions, Jonathan S. Tan
Master's Theses
We often want a compiler to generate executable code that runs as fast as possible. One consideration toward this goal is to keep values in fast registers to limit the number of slower memory accesses that occur. When there are not enough physical registers available for use, values are ``spilled'' to the runtime stack. The need for spills is discovered during register allocation wherein values in use are mapped to physical registers. One factor in the efficacy of register allocation is the number of values in use at one time (register collisions). Register collision is affected by compiler optimizations that …
An Empirical Study Of Alias Analysis Techniques,
2018
California Polytechnic State University, San Luis Obispo
An Empirical Study Of Alias Analysis Techniques, Andrew T. Tran
Master's Theses
As software projects become larger and more complex, software optimization at that scale is only feasible through automated means. One such component of software optimization is alias analysis, which attempts to determine which variables in a program refer to the same area in memory, and is used to relocate instructions to improve performance without interfering with program execution. Several alias analyses have been proposed over the past few decades, with varying degrees of precision and time and space complexity, but few studies have been conducted to compare these techniques with one another, nor to measure with program data to confirm …
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams,
2018
CUNY Graduate Center
Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed
Publications and Research
The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. However, using streams correctly and efficiently may involve subtle considerations. In this poster, we present our ongoing work and preliminary results towards an automated refactoring approach that assists developers in writing optimal stream code. The approach, based on ordering and typestate analysis, determines when it is safe and advantageous to convert streams to parallel and optimize a parallel streams.
Less-Java, More Learning: Language Design For Introductory Programming,
2018
James Madison University
Less-Java, More Learning: Language Design For Introductory Programming, Zamua Nasrawt
Senior Honors Projects, 2010-2019
Less-Java is a new procedural programming language with static, strong, and inferred typing, native unit testing, and support for basic object-oriented constructs. These features make programming in Less-Java more intuitive than traditional introductory languages, which will allow professors to dedicate more class time to overarching computer science concepts and less to syntax and language-specific quirks.
Designing Smart Applications Using Ar (Augmented Reality),
2018
CUNY New York City College of Technology
Designing Smart Applications Using Ar (Augmented Reality), Kimberly A. De La Santa
Publications and Research
Augmented Reality is rapidly developing in popularity because it brings elements of the virtual world, into our real world. Augmented Reality (AR) is a variation of Virtual Reality (VR). VR technologies immerses a user inside an imaginary environment. While immersed, the user cannot see the real world around them. In contrast, AR allows the user to see the real world, with virtual objects and information intertwined. Therefore, AR supplements reality and enhances the things we see, hear, and feel. This research project will implement a Web page that gives the user the opportunity to experiment with AR.
Tamscript - High Level Programming Interface For The Abstract Tile Assembly Model,
2018
University of Arkansas, Fayetteville
Tamscript - High Level Programming Interface For The Abstract Tile Assembly Model, Perry Mills
Computer Science and Computer Engineering Undergraduate Honors Theses
This paper describes a programming interface, TAMScript, for use with the PyTAS simulator. The interface allows for the dynamic generation of tile types as the simulation progresses, with the goal of reducing complexity for researchers. This paper begins with an introduction to the PyTAS software and a description of the 3D model which it simulates. Next, the changes made to support a dynamic generation scheme are detailed, and some of the potential benefits of this scheme are outlined. Then several of the example scripts which have been written using the TAMScript interface are reviewed. Finally, the potential for future research …
Comparative Study Of Deep Learning Models For Network Intrusion Detection,
2018
Southern Methodist University
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …
Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods,
2018
CUNY Hunter College
Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara
Publications and Research
Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …
Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods,
2018
CUNY Hunter College
Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara
Publications and Research
Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …
