Computer Sciences Commons^™

Spatial Semantics For Better Interoperability And Analysis: Challenges And Experiences In Building Semantically Rich Applications In Web 3.0, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher

Systems Science Friday Noon Seminar Series

Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …

Efficacy Of The United States Safe Harbor Framework, Margaret M. Beck

Regis University Student Publications (comprehensive collection)

This study assessed the efficacy of the United States Safe Harbor Framework by approximating the size of a population of large United States organizations (50,000 - 500,000 employees) that were eligible to participate in the Safe Harbor that did not participate. The Safe Harbor Framework, administered by the United States Department of Commerce, is a voluntary program that assists United States organizations in complying with the European Union Data Privacy Directive. Out of a population of 337 large organizations, 168 were potentially eligible to participate in the Safe Harbor Framework. One hundred and ten, or 66%, of these organizations did …

Analysis Of A Database Insider Threat Model, Andrea Samuel

Computer Science and Computer Engineering Undergraduate Honors Theses

According to Silicon.com's CIO Insight - Beware the Insider Security Threat, insiders are bigger threats to corporate security than external threats such as denial of service attacks or malware. Statistics show that 70% of fraud is perpetrated by staff and that the main data security threat comes from poorly trained or disgruntled employees who are authorized to have access to data and file stores [4]. This research project focuses specifically on the problem of insider threat in relational database systems. The project involves simulating research conducted in Qussai Yaseen and Brajendra Panda's research paper, Predicting and Preventing Insider Threat in …

Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki

Masters Theses & Specialist Projects

Stages in Drosophila development denote the time after fertilization at which certain specific events occur in the developmental cycle. Stage information of a host embryo, as well as spatial information of a gene expression region is indispensable input for the discovery of the pattern of gene-gene interaction. Manual labeling of stages is becoming a bottleneck under the circumstance of high throughput embryo images. Automatic recognition based on the appearances of embryos is becoming a more desirable scheme. This problem, however, is very challenging due to severe variations of illumination and gene expressions. In this research thesis, we propose an appearance …

Sysml-Based Domain-Specific Executable Workflows, Vikas Patel

All Theses

The Systems Modeling Language (SysML) is a general-purpose graphical modeling language for specifying, analyzing, designing, and verifying complex systems. This thesis presents a tool called SysFlow Workflow Engine (SWE) that is being developed to execute a domain workflow defined using SysML's Activity Diagram. The thesis also describes extensions added to the SysML semantics to make them SWE executable. SWE focuses on grid computing, cyberinfrastructure and related domains; however, support for other domains can be easily added. SWE aims to provide a common interface to grid, cyberinfrastructure and other domain-specific software by abstracting their complexity and idiosyncrasies. To create a workflow, …

The R Journal (December 2010) 2(2): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

Solving Differential Equations in R, Karline Soetaert, Thomas Petzoldt and R. Woodrow Setzer

Source References, Duncan Murdoch

hglm: A Package for Fitting Hierarchical Generalized Linear Models, Lars Rönnegård, Xia Shen and Moudud Alam

dclone: Data Cloning in R, Péter Sólymos

stringr: Modern, Consistent String Processing, Hadley Wickham

Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations, David Ardia and Lennart F. Hoogerheide

cudaBayesreg: Bayesian Computation in CUDA, Adelino Ferreira da Silva

binGroup: A Package for Group Testing, Christopher R. Bilder, Boan Zhang, Frank Schaarschmidt, and Joshua M. Tebbs

The RecordLinkage Package: Detecting Errors in Data, Murat Sariyar …

Legal Research In An Electronic Age: Electronic Data Discovery, A Litigation Albatross Of Gigantic Proportions, Ahunanya Anga

The University of New Hampshire Law Review

[Excerpt] “The increase in e-discovery, e-discovery‘s impact on litigation, and the courts‘ unavoidable role in defining the limits of discovery led to the author‘s decision to develop this article. The availability, accessibility, and the ease of requesting electronic data, resulting in increased e-discovery under the Federal Rules of Civil Procedure, is an important issue that will affect the legal profession and its constituents in many ways for years to come. Part II of this article is an overview of Federal Rule of Civil Procedure 26(f). This part stresses that in recognizing the herculean task involved in e-discovery, courts expect that …

Considerations On Unsupervised Spectral Data Unmixing And Complexity Pursuit, Stefan Robila

Department of Computer Science Faculty Scholarship and Creative Works

Hyperspectral sensors carry the distinctive advantage of recording hundreds of contiguous spectral images for the same scene providing an extraordinary amount of information that leads to precise differentiation of materials present in the scene even when such materials contribute only to few pixels [1]. With the advent of more and more powerful sensing platforms, coupled with reduction in manufacturing costs and diversification of technologies, hyperspectral imaging has become a powerful approach in remote sensing with applications spanning all traditional fields (such as agriculture, mining, military, resource management, etc.) as well as new ones (manufacturing quality control, pollution detection, health and …

Parallel Unmixing Of Hyperspectral Data Using Complexity Pursuit, Stefan Robila, Martin Butler

Department of Computer Science Faculty Scholarship and Creative Works

Accurate and fast data unmixing is key to most applications employing hyperspectral data. Among the large number unmixing approaches, Blind Source Separation (BSS) has been employed successfully through a variety of techniques, yet most of these approaches continue to be computationally expensive due to their iterative nature. In this context, it is imperative to seek efficient approaches that leverage the accuracy of the algorithms and the availability of off-the-shelf computationally performant systems such as multi-cpu and multi core. In this paper we tackle the spatial complexity based unmixing, a new technique shown to outperform many BSS solutions. We develop a …

Sweeping Minimum Perimeter Enclosing Parallelograms: Optimal Crumb Cleanup, Yonit Bousany, Mary Leah Karker, Joseph O'Rourke, Leona Sparaco

Computer Science: Faculty Publications

We examine the problem of pushing all the points of a planar region into one point using parallel sweeps of an infinite line, minimizing the sum of the lengths of the sweep vectors. We characterize the optimal 2-sweeps of triangles, and provide a linear-time algorithm for convex polygons.

Best-First Heuristic Search For Multicore Machines, Ethan Burns, Sofia N. Lemons, Wheeler Ruml, Rong Zhou

Computer Science

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS …

Batch Testing, Adaptive Algorithms, And Heuristic Applications For Stable Marriage Problems, John Dabney

All Dissertations

In this dissertation we focus on different variations of the stable matching (marriage) problem, initially posed by Gale and Shapley in 1962. In this problem, preference lists are used to match n men with n women in such a way that no (man, woman) pair exists that would both prefer each other over their current partners. These two would be considered a blocking pair, preventing a matching from being considered stable. In our research, we study three different versions of this problem.
First, we consider batch testing of stable marriage solutions. Gusfield and Irving presented an open problem in their …

The Recordlinkage Package: Detecting Errors In Data, Murat Sariyar, Andreas Borg

The R Journal

Record linkage deals with detecting homonyms and mainly synonyms in data. The package RecordLinkage provides means to per form and evaluate different record linkage methods. A stochastic framework is implemented which calculates weights through an EM algorithm. The determination of the necessary thresholds in this model can be achieved by tools of extreme value theory. Furthermore, machine learning methods are utilized, including decision trees (rpart), bootstrap aggregating (bagging), ada boost (ada), neural nets (nnet) and support vector machines (svm). The generation of record pairs and comparison patterns from single data …

Bingroup: A Package For Group Testing, Christopher R. Bilder, Boan Zhang, Frank Schaarschmidt, Joshua M. Tebbs

The R Journal

When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this set ting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be …

Stringr: Modern, Consistent String Processing, Hadley Wickham

The R Journal

String processing is not glamorous, but it is frequently used in data cleaning and preparation. The existing string functions in R are powerful, but not friendly. To remedy this, the stringr package provides string functions that are simpler and more consistent, and also fixes some functionality that R is missing compared to other programming languages.

Source References, Duncan Murdoch

The R Journal

Since version 2.10.0, R includes expanded support for source references in R code and ‘.Rd’ files. This paper describes the origin and purposes of source references, and current and future support for them.

Mapping And Measuring Country Shapes, Nils B. Weidmann, Kristian Skrede Gleditsch

The R Journal

The article introduces the cshapes R package, which includes our CShapes dataset of contemporary and historical country boundaries, as well as computational tools for computing geographical measures from these maps. We provide an overview of the need for considering spatial dependence in comparative re search, how this requires appropriate historical maps, and detail how the cshapes associated R package cshapes can contribute to these ends. We illustrate the use of the package for drawing maps, computing spatial variables for countries, and generating weights matrices for spatial statistics.

Dclone: Data Cloning In R, Péter Sólymos

The R Journal

The dclone R package contains low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov Chain Monte Carlo methods with support for JAGS, WinBUGS and OpenBUGS.

Solving Differential Equations In R, Karline Soetaert, Thomas Petzoldt, R. Woodrow Setzer

The R Journal

Although R is still predominantly applied for statistical analysis and graphical representation, it is rapidly becoming more suitable for mathematical computing. One of the fields where considerable progress has been made recently is the solution of differential equations. Here we give a brief overview of differential equations that can now be solved by R.

Spikeslab: Prediction And Variable Selection Using Spike And Slab Regression, Hemant Ishwaran, Udaya B. Kogalur, J. Sunil Rao

The R Journal

Weighted generalized ridge regression offers unique advantages in correlated high dimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we de scribe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.

Hglm: A Package For Fitting Hierarchical Generalized Linear Models, Lars Rönnegård, Xia Shen, Moudud Alam

The R Journal

We present the hglm package for fit ting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Bayesian Estimation Of The Garch(1,1) Model With Student-T Innovations, David Ardia, Lennart F. Hoogerheide

The R Journal

This note presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning an MCMC sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns

Online Reproducible Research: An Application To Multivariate Analysis Of Bacterial Dna Fingerprint Data, Jean Thioulouse, Claire Valiente-Moro, Lionel Zenner

The R Journal

This paper presents an example of online reproducible multivariate data analysis. This example is based on a web page providing an online computing facility on a server. HTML forms contain editable R code snippets that can be executed in any web browser thanks to the Rweb software. The example is based on the multivariate analysis of DNA fingerprints of the internal bacterial flora of the poultry red mite Dermanyssus gallinae. Several multivariate data analysis methods from the ade4 package are used to compare the fingerprints of mite pools coming from various poultry farms. All the computations and graphical displays …

Cudabayesreg: Bayesian Computation In Cuda, Adelino Ferreira Da Silva

The R Journal

Graphical processing units are rapidly gaining maturity as powerful general parallel computing devices. The package cudaBayesreg uses GPU–oriented procedures to improve the performance of Bayesian computations. The paper motivates the need for devising high performance computing strategies in the con text of fMRI data analysis. Some features of the package for Bayesian analysis of brain fMRI data are illustrated. Comparative computing performance figures between sequential and parallel implementations are presented as well.

Evaluating An Edms : A Case Study Of An Engineering Design Company., Nik Nazaruddin Nik Osman, Anisah Ali

Student Works (2010-2019)

Electronic Document Management System (EDMS) is a system that is commonly found in organizations that deal with a lot of documents. It is the application of technology to reduce paper utilization, expedite communications and increases organization’s performance and productivity. Leveraging information technology to manage document is one of the most important challenges for organizations since most of the valuable information in the organizations is in the form of letters, contracts, agreement, memos, reports, notes of discussion, meeting minutes, policy statements etc. The manual processes usually required to be re-engineered for computerization purposes. The availability of technologies provides more and better …

A Test-Bed For Secure Hierarchical Data Aggregation In Wireless Sensor Networks, Vimal Kumar, Joshua Mccarville-Schueths, Sanjay Madria

Computer Science Faculty Research & Creative Works

Data aggregation is a technique used to conserve battery power in wireless sensor networks (WSN). When securing such a network, it is important that we minimize the number of computationally expensive security operations without compromising on the security. This paper deals with the test-bed implementation of our end-to-end secure data aggregation algorithm. Unlike previous algorithms which required separate phases for secure aggregation and integrity verification, ours does not require an additional phase for verification. This saves energy by avoiding additional transmissions and computation overhead on the sensor nodes. We have implemented our secure data aggregation algorithms on mica2 motes. ©2010 …

A Multi-User Steganographic File System On Untrusted Shared Storage, Jin Han, Meng Pan, Debin Gao, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Existing steganographic file systems enable a user to hide the existence of his secret data by claiming that they are (static) dummy data created during disk initialization. Such a claim is plausible if the adversary only sees the disk content at the point of attack. In a multi-user computing environment that employs untrusted shared storage, however, the adversary could have taken multiple snapshots of the disk content over time. Since the dummy data are static, the differences across snapshots thus disclose the locations of user data, and could even reveal the user passwords. In this paper, we introduce a Dummy-Relocatable …

Sequence Alignment Based Analysis Of Player Behavior In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following …

Functional Reactive Musical Performers, Justin M. Phillips

Master's Theses

Computers have been assisting in recording, sound synthesis and other fields of music production for quite some time. The actual performance of music continues to be an area in which human players are chosen over computer performers. Musical performance is an area in which personalization is more important than consistency. Human players play with each other, reacting to phrases and ideas created by the players that they are playing with. Computer performers lack the ability to react to the changes in the performance that humans perceive naturally, giving the human players an advantage over the computer performers.

This thesis creates …