Programming Languages and Compilers Commons^™

Loopster: Static Loop Termination Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Shang-Wei Lin, Yang Liu, Xiaohong Li

Research Collection School Of Computing and Information Systems

Loop termination is an important problem for proving the correctness of a system and ensuring that the system always reacts. Existing loop termination analysis techniques mainly depend on the synthesis of ranking functions, which is often expensive. In this paper, we present a novel approach, named Loopster, which performs an efficient static analysis to decide the termination for loops based on path termination analysis and path dependency reasoning. Loopster adopts a divide-and-conquer approach: (1) we extract individual paths from a target multi-path loop and analyze the termination of each path, (2) analyze the dependencies between each two paths, and then …

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao

Research Collection School Of Computing and Information Systems

In this paper, we study how to improve thedomain adaptability of a deletion-basedLong Short-Term Memory (LSTM) neuralnetwork model for sentence compression.We hypothesize that syntactic informationhelps in making such modelsmore robust across domains. We proposetwo major changes to the model: usingexplicit syntactic features and introducingsyntactic constraints through Integer LinearProgramming (ILP). Our evaluationshows that the proposed model works betterthan the original model as well as a traditionalnon-neural-network-based modelin a cross-domain setting.

Testing And Debugging: A Reality Check, Pavneet Singh Kochhar

Dissertations and Theses Collection

Testing and debugging are important activities during software development and maintenance. Testing is performed to check if the code contains errors whereas debugging is done to locate and fix these errors. Testing can be manual or automated and can be of different types such as unit, integration, system, stress etc. Debugging can also be manual or automated. These two activities have drawn attention of researchers in the recent years. Past studies have proposed many testing techniques such as automated test generation, test minimization, test case selection etc. Studies related to debugging have proposed new techniques to find bugs using various …

Real-Time Bursty Topic Detection And Virality Forecasting In Microblogs, Wei Xie

Dissertations and Theses Collection

Microblogs such as Twitter have become the largest social platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in microblogs is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage microblogs for early detection and further impact analysis of bursty topics has, therefore, become an important research problem with immense practical value.

Auditing Anti-Malware Tools By Evolving Android Malware And Dynamic Loading Technique, Yinxing Xue, Guozhu Meng, Yang Liu, Tian Huat Tan, Hongxu Chen, Jun Sun, Jie Zhang

Research Collection School Of Computing and Information Systems

Although a previous paper shows that existing antimalware tools (AMTs) may have high detection rate, the report is based on existing malware and thus it does not imply that AMTs can effectively deal with future malware. It is desirable to have an alternative way of auditing AMTs. In our previous paper, we use malware samples from android malware collection GENOME to summarize a malware meta-model for modularizing the common attack behaviors and evasion techniques in reusable features. We then combine different features with an evolutionary algorithm, in which way we evolve malware for variants. Previous results have shown that the …

Iupdater: Low Cost Rss Fingerprints Updating For Device-Free Localization, Liqiong Chang, Jie Xiong, Yu Wang, Xiaojiang Chen, Junhao Hu, Dingyi Fang

Research Collection School Of Computing and Information Systems

While most existing indoor localization techniques are device-based, many emerging applications such as intruder detection and elderly monitoring drive the needs of device-free localization, in which the target can be localized without any device attached. Among the diverse techniques, received signal strength (RSS) fingerprint-based methods are popular because of the wide availability of RSS readings in most commodity hardware. However, current fingerprint-based systems suffer from high human labor cost to update the fingerprint database and low accuracy due to the large degree of RSS variations. In this paper, we propose a fingerprint-based device-free localization system named iUpdater to significantly reduce …

Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Lipyeow Lim, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

In this paper, we reduce the energy overheads of continuous mobile sensing, specifically for the case of context-aware applications that are interested in collective context or events, i.e., events expressed as a set of complex predicates over sensor data from multiple smartphones. We propose a cloud-based query management and optimization framework, called CloQue, that can support thousands of such concurrent queries, executing over a large number of individual smartphones. Our central insight is that the context of different individuals & groups often have significant correlation, and that this correlation can be learned through standard association rule mining on historical data. …

Convergence Technologies For Sensor Systems In The Next Generation Networks, Conor Gildea, Declan Barber

The ITB Journal

This paper describes an approach to the internetworking of sensory nodes in a converged network environment. This preliminary investigation of sensory network creation is driven by a joint applied research project which seeks to establish the feasibility of the real-time remote monitoring of animal welfare while in transit between Ireland, Europe and the Middle East. This paper examines the use of Java to create sensor services in converging architectures which leverage the Internetworking protocols and describes our implementation of such a system.

Levity Polymorphism, Richard A. Eisenberg, Simon Peyton Jones

Computer Science Faculty Research and Scholarship

Parametric polymorphism is one of the linchpins of modern typed programming, but it comes with a real performance penalty. We describe this penalty; offer a principled way to reason about it (kinds as calling conventions); and propose levity polymorphism. This new form of polymorphism allows abstractions over calling conventions; we detail and verify restrictions that are necessary in order to compile levity-polymorphic functions. Levity polymorphism has created new opportunities in Haskell, including the ability to generalize nearly half of the type classes in GHC's standard library.

Fusing Mobile, Wearable And Infrastructure Sensing For Immersive Daily Lifestyle Analytics, Sougata Sen

Dissertations and Theses Collection

With the prevalence of sensors in public infrastructure as well as in personal devices, exploitation of data from these sensors to monitor and profile basic activities (e.g., locomotive states such as walking, and gestural actions such as smoking) has gained popularity. Basic activities identified by these sensors will drive the next generation of lifestyle monitoring applications and services. To provide more advanced and personalized services, these next-generation systems will need to capture and understand increasingly finer-grained details of various common daily life activities. In this dissertation, I demonstrate the possibility of building systems using offthe- shelf devices, that not only …

The R Journal (June 2017) 9(1): Complete Issue, The R Foundation

The R Journal

Editorial, Roger Bivand

Contributed Research Articles

iotools: High-Performance I/O Tools for R, Taylor Arnold, Michael J. Kane, and Simon Urbanek

IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R, Martin Otava, Rudradev Sengupta, Ziv Shkedy, Dan Lin, Setia Pramana, Tobias Verbeke, Philippe Haldermans, Ludwig A. Hothorn, Daniel Gerhard, Rebecca M. Kuiper, Florian Klinglmueller, and Adetayo Kasim

Network Visualization with ggplot2, Sam Tyner, François Briatte, and Heike Hofmann

OrthoPanels: An R Package for Estimating a Dynamic Panel Model with Fixed Effects Using the Orthogonal Reparameterization Approach, Mark Pickup, Paul Gustafson, Davor Cubranic, and Geoffrey Evans

The mosaic Package: Helping …

The Noisefiltersr Package: Label Noise Preprocessing In R, Pablo Morales, Julián Luengo, Luís P.F. Garcia, Ana C. Lorena, André C.P.L.F. De Carvalho

The R Journal

In Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms …

Hosting Data Packages Via Drat: A Case Study With Hurricane Exposure Data, G Brooke Anderson, Dirk Eddelbuettel

The R Journal

Data-only packages offer a way to provide extended functionality for other R users. However, such packages can be large enough to exceed the package size limit (5 megabytes) for the Comprehensive R Archive Network (CRAN). As an alternative, large data packages can be posted to additional repostiories beyond CRAN itself in a way that allows smaller code packages on CRAN to access and use the data. The drat package facilitates creation and use of such alternative repositories and makes it particularly simple to host them via GitHub. CRAN packages can draw on packages posted to drat repositories through the use …

Milr: Multiple-Instance Logistic Regression With Lasso Penalty, Ping-Yang Chen, Ching-Chuan Chen, Chun-Hao Yang, Sheng-Mao Chang, Kuo-Jung Lee

The R Journal

The purpose of the milr package is to analyze multiple-instance data. Ordinary multiple instance data consists of many independent bags, and each bag is composed of several instances. The statuses of bags and instances are binary. Moreover, the statuses of instances are not observed, whereas the statuses of bags are observed. The functions in this package are applicable for analyzing multiple-instance data, simulating data via logistic regression, and selecting important covariates in the regression model. To this end, maximum likelihood estimation with an expectation-maximization algorithm is implemented for model estimation, and a lasso penalty added to the likelihood function is …

Flan: An R Package For Inference On Mutation Models, Adrien Mazoyer, Rémy Drouilhet, Stéphane Despréaux, Bernard Ycart

The R Journal

This paper describes flan, a package providing tools for fluctuation analysis of mutant cell counts. It includes functions dedicated to the distribution of final numbers of mutant cells. Parametric estimation and hypothesis testing are also implemented, enabling inference on different sorts of data with several possible methods. An overview of the subject is proposed. The general form of mutation models is described, including the classical models as particular cases. Estimating from a model, when the data have been generated by another, induces different possible biases, which are identified and discussed. The three estimation methods available in the package are …

Psf: Introduction To R Package For Pattern Sequence Based Forecasting Algorithm, Neeraj Bokde, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Kishore Kulat

The R Journal

This paper introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF …

Market Area Analysis For Retail And Service Locations With Mci, Thomas Wieland

The R Journal

In retail location analysis, marketing research and spatial planning, the market areas of stores and/or locations are a frequent subject. Market area analyses consist of empirical observations and modeling via theoretical and/or econometric models such as the Huff Model or the Multiplicative Competitive Interaction Model. The authors’ package MCI implements the steps of market area analysis into R with a focus on fitting the models and data preparation and processing.

Update Of The Nlme Package To Allow A Fixed Standard Deviation Of The Residual Error, Simon H. Heisterkamp, Engelbertus Van Willigen, Paul-Matthias Diderichsen, John Maringwa

The R Journal

The use of linear and non-linear mixed models in the life sciences and pharmacometrics is common practice. Estimation of the parameters of models not involving a system of differential equations is often done by the R or S-Plus software with the nonlinear mixed effects nlme package. The estimated residual error may be used for diagnosis of the fitted model, but not whether the model correctly describes the relation between response and included variables including the true covariance structure. The latter is only true if the residual error is known in advance. Therefore, it maybe necessary or more appropriate to fix …

Autoimage: Multiple Heat Maps For Projected Coordinates, Joshua P. French

The R Journal

Heat maps are commonly used to display the spatial distribution of a response observed on a two-dimensional grid. The autoimage package provides convenient functions for constructing multiple heat maps in unified, seamless way, particularly when working with projected coordinates. The autoimage package natively supports: 1. automatic inclusion of a color scale with the plotted image, 2. construction of heat maps for responses observed on regular or irregular grids, as well as non-gridded data, 3. construction of a matrix of heat maps with a common color scale, 4. construction of a matrix of heat maps with individual color scales, 5. projecting …

Imputets: Time Series Missing Value Imputation In R, Steffen Moritz, Thomas Bartz-Beielstein

The R Journal

The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact, that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a …

On Some Extensions To Ga Package: Hybrid Optimisation, Parallelisation And Islands Evolution, Luca Scrucca

The R Journal

Genetic algorithms are stochastic iterative algorithms in which a population of individuals evolve by emulating the process of biological evolution and natural selection. The R package GA provides a collection of general purpose functions for optimisation using genetic algorithms. This paper describes some enhancements recently introduced in version 3 of the package. In particular, hybrid GAs have been implemented by including the option to perform local searches during the evolution. This allows to combine the power of genetic algorithms with the speed of a local optimiser. Another major improvement is the provision of facilities for parallel computing. Parallelisation has been …

Mdplot: Visualise Molecular Dynamics, Christian Margreitter, Chris Oostenbrink

The R Journal

The MDplot package provides plotting functions to allow for automated visualisation of molecular dynamics simulation output. It is especially useful in cases where the plot generation is rather tedious due to complex file formats or when a large number of plots are generated. The graphs that are supported range from those which are standard, such as RMSD/RMSF (root-mean-square deviation and root-mean-square fluctuation, respectively) to less standard, such as thermodynamic integration analysis and hydrogen bond monitoring over time. All told, they address many commonly used analyses. In this article, we set out the MDplot package’s functions, give examples of the function …

Working With Daily Climate Model Output Data In R And The Futureheatwaves Package, G Brooke Anderson, Colin Eason, Elizabeth A. Barnes

The R Journal

Research on climate change impacts can require extensive processing of climate model output, especially when using ensemble techniques to incorporate output from multiple climate models and multiple simulations of each model. This processing can be particularly extensive when identifying and characterizing multi-day extreme events like heat waves and frost day spells, as these must be processed from model output with daily time steps. Further, climate model output is in a format and follows standards that may be unfamiliar to most R users. Here, we provide an overview of working with daily climate model output data in R. We then present …

Network Visualization With Ggplot2, Sam Tyner, François Briatte, Heike Hofmann

The R Journal

This paper explores three different approaches to visualize networks by building on the grammar of graphics framework implemented in the ggplot2 package. The goal of each approach is to provide the user with the ability to apply the flexibility of ggplot2 to the visualization of network data, including through the mapping of network attributes to specific plot aesthetics. By incorporating networks in the ggplot2 framework, these approaches (1) allow users to enhance networks with additional information on edges and nodes, (2) give access to the strengths of ggplot2, such as layers and facets, and (3) convert network data objects …

Iotools: High-Performance I/O Tools For R, Taylor Arnold, Michael J. Kane, Simon Urbanek

The R Journal

The iotools package provides a set of tools for input and output intensive data processing in R. The functions chunk.apply and read.chunk are supplied to allow for iteratively loading contiguous blocks of data into memory as raw vectors. These raw vectors can then be efficiently converted into matrices and data frames with the iotools functions mstrsplit and dstrsplit. These functions minimize copying of data and avoid the use of intermediate strings in order to drastically improve performance. Finally, we also provide read.csv.raw to allow users to read an entire dataset into memory with the same efficient parsing code. In this …

Dgaselid: An R Package For Selecting A Variable Number Of Features In High Dimensional Data, Nicolae Teodor Melita, Stefan Holban

The R Journal

The dGAselID package proposes an original approach to feature selection in high dimensional data. The method is built upon a diploid genetic algorithm. The genotype to phenotype mapping is modeled after the Incomplete Dominance Inheritance, over passing the necessity to define a dominance scheme. The fitness evaluation is done by user selectable supervised classifiers, from a broad range of options. Cross validation options are also accessible. A new approach to crossover, inspired from the random assortment of chromosomes during meiosis is included. Several mutation operators, inspired from genetics, are also proposed. The package is fully compatible with the data formats …

Orthopanels: An R Package For Estimating A Dynamic Panel Model With Fixed Effects Using The Orthogonal Reparameterization Approach, Mark Pickup, Paul Gustafson, Davor Cubranic, Geoffrey Evans

The R Journal

This article describes the R package OrthoPanels, which includes the function opm(). This function implements the orthogonal reparameterization approach recommended by Lancaster(2002) to estimate dynamic panel models with fixed effects(and optionally: wave specific intercepts). This article provides a statistical description of the orthogonal reparameterization approach, a demonstration of the package using real-world data, and simulations comparing the estimator to the known-to-be-biased OLSestimator and the commonly used GMM estimator.

Editorial, Roger Bivand

The R Journal

This new issue, Volume 9, Issue 1, of the R Journal contains 33 contributed research articles, like the second issue of 2016. Most of the articles present R packages, and cover a very wide range of uses of R. Our journal continues to be critically dependent on its readers, authors, reviewers and editors. Annual submission numbers have grown markedly, but the rate of growth is less than that of the number of CRAN packages. Table 1 shows the outcomes of submitted contributed articles by year of submission. The proportion of submissions reaching publication has been roughly half since 2012.

Minval: An R Package For Minimal Validation Of Stoichiometric Reactions, Daniel Osorio, Janneth González, Andrés Pinzón

The R Journal

A genome-scale metabolic reconstruction is a compilation of all stoichiometric reactions that can describe the entire cellular metabolism of an organism, and they have become an indispensable tool for our understanding of biological phenomena, covering fields that range from systems biology to bioengineering. Interrogation of metabolic reconstructions are generally carried through Flux Balance Analysis, an optimization method in which the biological sense of the optimal solution is highly sensitive to thermodynamic unbalance caused by the presence of stoichiometric reactions whose compounds are not produced or consumed in any other reaction (orphan metabolites) and by mass unbalance. The minval package was …