Computer Sciences Commons^™

Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe

Research Collection School Of Computing and Information Systems

Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for …

A Tool For Optimizing Java 8 Stream Software Via Automated Refactoring, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed

Publications and Research

Streaming APIs are pervasive in mainstream Object-Oriented languages. For example, the Java 8 Stream API allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we describe the engineering aspects of an open source automated refactoring tool called Optimize Streams that assists developers in writing optimal stream software in …

Break The Dead End Of Dynamic Slicing: Localizing Data And Control Omission Bug, Yun Lin, Jun Sun, Lyly Tran, Guangdong Bai, Haijun Wang, Jin Song Dong

Research Collection School Of Computing and Information Systems

Dynamic slicing is a common way of identifying the root cause when a program fault is revealed. With the dynamic slicing technique, the programmers can follow data and control flow along the program execution trace to the root cause. However, the technique usually fails to work on omission bugs, i.e., the faults which are caused by missing executing some code. In many cases, dynamic slicing over-skips the root cause when an omission bug happens, leading the debugging process to a dead end. In this work, we conduct an empirical study on the omission bugs in the Defects4J bug repository. Our …

The Thoralf Plugin: For Your Fancy Type Needs, Divesh Otwani, Richard A. Eisenberg

Computer Science Faculty Research and Scholarship

Many fancy types (e.g., generalized algebraic data types, type families) require a type checker plugin. These fancy types have a type index (e.g., type level natural numbers) with an equality relation that is difficult or impossible to represent using GHC’s built-in type equality. The most practical way to represent these equality relations is through a plugin that asserts equality constraints. However, such plugins are difficult to write and reason about. In this paper, we (1) present a formal theory of reasoning about the correctness of type checker plugins for type indices, and, (2) apply this theory in creating Thoralf, a …

Type Variables In Patterns, Richard A. Eisenberg, Joachim Breitner, Simon Peyton Jones

Computer Science Faculty Research and Scholarship

For many years, GHC has implemented an extension to Haskell that allows type variables to be bound in type signatures and patterns, and to scope over terms. This extension was never properly specified. We rectify that oversight here. With the formal specification in hand, the otherwise-labyrinthine path toward a design for binding type variables in patterns becomes blindingly clear. We thus extend ScopedTypeVariables to bind type variables explicitly, obviating the Proxy workaround to the dustbin of history.

Rule-Based Specification Mining Leveraging Learning To Rank, Zherui Cao, Yuan Tian, Bui Tien Duy Le, David Lo

Research Collection School Of Computing and Information Systems

Software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analyze execution traces of a system to infer the rules that characterize the protocols, typically of a library, that its clients must obey. Rule-based specification mining approaches work by exploring the search space of all possible rules and use interestingness measures to differentiate specifications from false positives. Previous rule-based specification mining approaches often rely on one or two interestingness measures, while the potential benefit of combining multiple available interestingness measures is not …

Teaching Basic Programming To Pre-University Students Through Blended Learning Pedagogy: A Descriptive Study, Vandana Ramachandra Rao, Ngee Mok Heng

Research Collection School Of Computing and Information Systems

Students enrolling for undergraduate programmes in Singapore would have either finished their polytechnic diploma or completed Junior College (JC) studies. Most pre-university students coming through the JC pathway are not exposed to programming as computing is offered as a subject in a very few JCs. The authors of this paper conducted four runs of an introductory programing course between 2016 and 2017 for a research project funded by the Ministry of Education, Singapore. The project named “Let’s Code!” was intended to introduce fundamental programming concepts to students and guide them to consider taking a computer-science related degree for their university …

Computational Thinking And Literacy, Sharin Rawhiya Jacob, Mark Warschauer

Journal of Computer Science Integration

Today’s students will enter a workforce that is powerfully shaped by computing. To be successful in a changing economy, students must learn to think algorithmically and computationally, to solve problems with varying levels of abstraction. These computational thinking skills have become so integrated into social function as to represent fundamental literacies. However, computer science has not been widely taught in K-12 schools. Efforts to create computer science standards and frameworks have yet to make their way into mandated course requirements. Despite a plethora of research on digital literacies, research on the role of computational thinking in the literature is sparse. …

Mass Spectrometry Image Creator (Msic): Ion Mobility / Mass Spectrometry Imaging Workflow In Python, Stephen Creger, Julia Laskin, Daniela Mesa Sanchez

The Summer Undergraduate Research Fellowship (SURF) Symposium

Mass spectrometry (MS) is a powerful characterization technique that enables identification of compounds in complex mixtures. Acquiring mass spectra in a spatially-resolved manner (i.e. over a grid), allows the data to be used to generate images that show the spatial distribution and relative intensities of every compound in a sample. These images can be used to monitor and identify biomarkers, explore the metabolism of compounds within tissues, and much more. However, the limitations of mass spectrometry can result in ambiguous compound identifications. Another characterization tool, ion mobility spectrometry (IM) can be integrated into existing MS routines to address this problem; …

A Divide-And-Conquer Approach To Syntax-Guided Synthesis, Peiyuan Shen, Xiaokang Qiu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Program synthesis aims to generate programs automatically from user-provided specifications. One critical research thrust is called Syntax-Guideds Synthesis. In addition to semantic specifications, the user should also provide a syntactic template of the desired program, which helps the synthesizer reduce the search space. The traditional symbolic approaches, such as CounterExample-Guided Inductive Synthesis (CEGIS) framework, does not scale to large search spaces. The goal of this project is to explore a compositional, divide-n-conquer approach that heuristically divides the synthesis task into subtasks and solves them separately. The idea is to decompose the function to be synthesized by creating a set of …

Densely Connected Bidirectional Lstm With Applications To Sentence Classification, Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, Jian Yang

Student Publications

Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space. However, since most deep architectures like stacked RNNs tend to suffer from the vanishing-gradient and overfitting problems, their effects are still understudied in many NLP tasks. Inspired by this, we propose a novel multi-layer RNN model called densely connected bidirectional long short-term memory (DCBi-LSTM) in this paper, which essentially represents each layer by the concatenation of its hidden state and all preceding layers’ hidden states, followed by recursively passing each layer’s …

Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor

Electronic Theses and Dissertations

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …

Lightweight Call-Graph Construction For Multilingual Software Analysis, Anne-Marie Bogar, Damian Lyons, David Baird

Faculty Publications

Analysis of multilingual codebases is a topic of increasing importance. In prior work, we have proposed the MLSA (MultiLingual Software Analysis) architecture, an approach to the lightweight analysis of multilingual codebases, and have shown how it can be used to address the challenge of constructing a single call graph from multilingual software with mutual calls. This paper addresses the challenge of constructing monolingual call graphs in a lightweight manner (consistent with the objective of MLSA) which nonetheless yields sufficient information for resolving language interoperability calls. A novel approach is proposed which leverages information from …

Lightweight Multilingual Software Analysis, Damian Lyons, Anne Marie Bogar, David Baird

Faculty Publications

Large software systems can often be multilingual – that is, software systems are written in more than one language. However, many popular software engineering tools are monolingual by nature. Nonetheless, companies are faced with the need to manage their large, multilingual codebases to address issues with security, efficiency, and quality metrics. This paper presents a novel lightweight approach to multilingual software analysis – MLSA. The approach is modular and focused on efficient static analysis computation for large codebases. One topic is addressed in detail – the generation of multilingual call graphs to identify language boundary problems in multilingual code. The …

Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis

SMU Data Science Review

A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.

A New Functional-Logic Compiler For Curry: Sprite, Sergio Antoy, Andy Jost

Computer Science Faculty Publications and Presentations

We introduce a new native code compiler for Curry codenamed Sprite. Sprite is based on the Fair Scheme, a compilation strategy that provides instructions for transforming declarative, non-deterministic programs of a certain class into imperative, deterministic code. We outline salient features of Sprite, discuss its implementation of Curry programs, and present benchmarking results. Sprite is the first-to-date operationally complete implementation of Curry. Preliminary results show that ensuring this property does not incur a significant penalty.

Lightweight Multilingual Software Analysis, Damian Lyons, Anne Marie Bogar, David Baird

Faculty Publications

Developer preferences, language capabilities and the persistence of older languages contribute to the trend that large software codebases are often multilingual – that is, written in more than one computer language. While developers can leverage monolingual software development tools to build software components, companies are faced with the problem of managing the resultant large, multilingual codebases to address issues with security, efficiency, and quality metrics. The key challenge is to address the opaque nature of the language interoperability interface: one language calling procedures in a second (which may call a third, or even back to the first), resulting in a …

Lp Algorithms For Portfolio Optimization: The Portfoliooptim Package, Andrzej Palczewski

The R Journal

The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Haneveld using Benders de composition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li. The algorithms are implemented …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.0 patched

CHANGES IN R 3.5.0

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 7 months, 1178 new packages were added to the CRAN package repository. 18 packages were unarchived, 493 archived and none removed. The following shows the growth of the number of active packages in the CRAN package repository:

R Day Report, Fernando P. Mayer, Walmes M. Zeviani, Wagner H. Bonat, Elias T. Krainski, Paulo J. Ribeiro Jr.

The R Journal

R Day¹- National Meeting of R Users, took place on May, 22, 2018 at Federal University of Paraná (UFPR), Curitiba, Brazil. It was the first event in Brazil endorsed by The R Foundation.

Setmethods: An Add-On R Package For Advanced Qca, Ioana-Elena Oana, Carsten Q. Scheider

The R Journal

This article presents the functionalities of the R package SetMethods, aimed at performing advanced set-theoretic analyses. This includes functions for performing set-theoretic multi-method research, set-theoretic theory evaluation, Enhanced Standard Analysis, diagnosing the impact of temporal, spatial, or substantive clusterings of the data on the results obtained via Qualitative Comparative Analysis (QCA), indirect calibration, and visualising QCA results via XY plots or radar charts. Each functionality is presented in turn, the conceptual idea and the logic behind the procedure being first summarized, and afterwards illustrated with data from Schneider et al. (2010).

Simple Features For R: Standardized Support For Spatial Vector Data, Edzer Pebesma

The R Journal

Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use.

Epistemic Game Theory: Putting Algorithms To Work, Bilge BaşEr, Nalan Cinemre

The R Journal

The aim of this study is to construct an epistemic model in which each rational choice under common belief in rationality is supplemented by a type which expresses such a belief. In practice, the finding of type depends on manual solution approach with some mathematical operations in scope of the theory. This approach becomes less convenient with the growth of the size of the game. To solve this difficulty, a linear programming model is constructed for two-player, static and non-cooperative games to find the type that is supporting that player’s rational choice is optimal under common belief in rationality and …

Grpstring: An R Package For Analysis Of Groups Of Strings, Hui Tang, Elizabeth L. Day, Molly B. Atkinson, Norbert J. Pienta

The R Journal

The R package GrpString was developed as a comprehensive toolkit for quantitatively analyzing and comparing groups of strings. It offers functions for researchers and data analysts to prepare strings from event sequences, extract common patterns from strings, and compare patterns be tween string vectors. The package also finds transition matrices and complexity of strings, determines clusters in a string vector, and examines the statistical difference between two groups of strings.

Lba: An R Package For Latent Budget Analysis, Enio G. Jelihovschi, Ivan Bezerra Allaman

The R Journal

The latent budget model is a mixture model for compositional data sets in which the entries, a contingency table, may be either realizations from a product multinomial distribution or distribution free. Based on this model, the latent budget analysis considers the interactions of two variables; the explanatory (row) and the response (column) variables. The package lba uses expectation-maximization and active constraints method (ACM) to carry out, respectively, the maximum likelihood and the least squares estimation of the model parameters. It contains three main functions, lba which performs the analysis, goodnessfit for model selection and goodness of fit and the plotting …

Icsoutlier: Unsupervised Outlier Detection For Low-Dimensional Contamination Authors: Structure, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen

The R Journal

Detecting outliers in a multivariate and unsupervised context is an important and ongoing problem notably for quality control. Many statistical methods are already implemented in R and are briefly surveyed in the present paper. But only a few lead to the accurate identification of potential outliers in the case of a small level of contamination. In this particular context, the Invariant Coordinate Selection (ICS) method shows remarkable properties for identifying outliers that lie on a low-dimensional subspace in its first invariant components. It is implemented in the ICSOutlier package. The main function of the package, ics.outlier, offers the possibility of …

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 …

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.

Tackling Uncertainties Of Species Distribution Model Projections With Package Mopa, M. Iturbide, J. Bedia, J.M. Gutiérrez

The R Journal

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM projections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R …