Computer Sciences Commons^™

Understanding Eye Gaze Patterns In Code Comprehension, Jonathan Saddler

School of Computing: Dissertations, Theses, and Student Research

Program comprehension is a sub-field of software engineering that seeks to understand how developers understand programs. Comprehension acts as a starting point for many software engineering tasks such as bug fixing, refactoring, and feature creation. The dissertation presents a series of empirical studies to understand how developers comprehend software in realistic settings. The unique aspect of this work is the use of eye tracking equipment to gather fine-grained detailed information of what developers look at in software artifacts while they perform realistic tasks in an environment familiar to them, namely a context including both the Integrated Development Environment (Eclipse or …

Emotional Awareness During Bug Fixes: A Pilot Study, Jada O. Loro, Abigail L. Schneff, Sarah J. Oran, Bonita Sharif

School of Computing: Dissertations, Theses, and Student Research

This study examines the effects of a programmer's emotional awareness on progress while fixing bugs. The goal of the study is to capitalize on emotional awareness to ultimately increase progress made during software development. This process could result in improved software maintenance.

An Eye Tracking Replication Study Of A Randomized Controlled Trial On The Effects Of Embedded Computer Language Switching, Cole Peterson

School of Computing: Dissertations, Theses, and Student Research

The use of multiple programming languages (polyglot programming) during software development is common practice in modern software development. However, not much is known about how the use of these different languages affects developer productivity. The study presented in this thesis replicates a randomized controlled trial that investigates the use of multiple languages in the context of database programming tasks. Participants in our study were given coding tasks written in Java and one of three SQL-like embedded languages: plain SQL in strings, Java methods only, a hybrid embedded language that was more similar to Java. In addition to recording the online …

An Algorithm For Building Language Superfamilies Using Swadesh Lists, Bill Mutabazi

School of Computing: Dissertations, Theses, and Student Research

The main contributions of this thesis are the following: i. Developing an algorithm to generate language families and superfamilies given for each input language a Swadesh list represented using the international phonetic alphabet (IPA) notation. ii. The algorithm is novel in using the Levenshtein distance metric on the IPA representation and in the way it measures overall distance between pairs of Swadesh lists. iii. Building a Swadesh list for the author's native Kinyarwanda language because a Swadesh list could not be found even after an extensive search for it.

Advisor: Peter Z. Revesz

A Memory Usage Comparison Between Jitana And Soot, Yuanjiu Hu

School of Computing: Dissertations, Theses, and Student Research

There are several factors that make analyzing Android apps to address dependability and security concerns challenging. These factors include (i) resource efficiency as analysts need to be able to analyze large code-bases to look for issues that can exist in the application code and underlying platform code; (ii) scalability as today’s cybercriminals deploy attacks that may involve many participating apps; and (iii) in many cases, security analysts often rely on dynamic or hybrid analysis techniques to detect and identify the sources of issues.

The underlying principle governing the design of existing program analysis engines is the main cause that prevents …

Open Dynamic Interaction Network: A Cell-Phone Based Platform For Responsive Ema, Gisela Font Sayeras

School of Computing: Dissertations, Theses, and Student Research

The study of social networks is central to advancing our understanding of a wide range of phenomena in human societies. Social networks co-evolve concurrently alongside the individuals within them. Selection processes cause network structure to change in response to emerging similarities/differences between individuals. At the same time, diffusion processes occur as individuals influence one another when they interact across network links. Indeed, each network link is a logical abstraction that aggregates many short-lived pairwise interactions of interest that are being studied. Traditionally, network co-evolution is studied by periodically taking static snapshots of social networks using surveys. Unfortunately, participation incentives …

Advanced Techniques To Detect Complex Android Malware, Zhiqiang Li

School of Computing: Dissertations, Theses, and Student Research

Android is currently the most popular operating system for mobile devices in the world. However, its openness is the main reason for the majority of malware to be targeting Android devices. Various approaches have been developed to detect malware.

Unfortunately, new breeds of malware utilize sophisticated techniques to defeat malware detectors. For example, to defeat signature-based detectors, malware authors change the malware’s signatures to avoid detection. As such, a more effective approach to detect malware is by leveraging malware’s behavioral characteristics. However, if a behavior-based detector is based on static analysis, its reported results may contain a large number of …

Engagement And Computational Thinking Through Creative Coding, Dana Hoppe

Honors Program: Senior Projects (Public)

Rising enrollments in Computer Science pose an opportunity to engage students from diverse backgrounds and interests; and a challenge to deliver on positive learning outcomes. While student engagement is the driving factor for increased learning performance and retention, it has been declining to new lows for Computer Science students in recent years. In order to further explore the potential of contextualized computing as a tool for increasing engagement in computing and developing Computational Thinking aptitude in students, we have developed an introductory computing course contextualized with Art and Design with modules centered around guiding pedagogical principles and aimed at middle …

An Anns Based Failure Detection Method For Onos Sdon Controller, Shideh Yavary Mehr

School of Computing: Dissertations, Theses, and Student Research

Network reachability is an important factor of an optical telecommunication network. In a wavelength-division-muliplexing (WDM) optical network, any failure can cause a large amount of loss and disruptions in network. Failures can occur in network elements, link, and component inside a node or etc. Since major network disruptions can caused network performance degradations, it is necessary that operators have solutions to prevent such those failures. This work examines a prediction model in optical networks and propose a protection plan using a Machine Learning (ML) algorithm called Artificial Neural Networks (ANN) using Mininet emulator. ANN is one of the best method …

Explainable Deep Learning For Medical Image Analysis, Brennan Rhoadarmer

UCARE: Research Products

Explainable Deep Learning for Medical Image Analysis is a project focused on improving the ability for deep learning models to explain the reasoning behind their classification in order to improve their viability in the medical field, where explanations of decisions is critical for the care of patients. In order to explore this topic, we work to implement GradCAM, which is a new method of determining the cause classification in models by tracing back through the model layers to the input.

Communicating Computing Limitations Through Kinesthetic Pedagogy, Michael Mason

Honors Program: Senior Projects (Public)

Abstract concepts, such as those in advanced Computer Science and Mathematics, can be extremely difficult to understand fundamentally without an existing background in a similar subject. Recent research has shown that raw visualizations without learner interaction are not particularly effective at communicating complex information because they allow the learner to ignore the example (Lauer 2006, Naps 2002). Forcing somebody to interact with an example ensures that they can grasp the visualization. This paper describes a six step technique to demonstrate the limitations of computing through kinesthetic pedagogy, then offers an example exercise utilizing the method. The six proposed steps are: …

Scraping Bepress: Downloading Dissertations For Preservation, Stephen Zweibel

Copyright, Fair Use, Scholarly Communication, etc.

This article will describe our process developing a script to automate downloading of documents and secondary materials from our library’s BePress repository. Our objective was to collect the full archive of dissertations and associated files from our repository into a local disk for potential future applications and to build out a preservation system.

Unlike at some institutions, our students submit directly into BePress, so we did not have a separate repository of the files; and the backup of BePress content that we had access to was not in an ideal format (for example, it included “withdrawn” items and did not …

Algorithms Of Oppression [Uno Pa Theory Proseminar Presentation], Sue Ann Gardner

University of Nebraska-Lincoln Libraries: Presentations

Slides of two classes taught in the Theory Proseminar in the School of Public Administration at the University of Nebraska at Omaha by Sue Ann Gardner on February 11 and 18, 2020.

Connects information theory to applicable knowledge frameworks in public administration. Includes an in-depth discussion of the concepts addressed in Samiya Umoja Noble's book Algorithms of Oppression (published by New York University Press, New York, New York, United States, 2018) in the context of public administration and public academic libraries.

Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Presentations

This presentation to Library of Congress staff, delivered onsite on January 10, 2020, presents a tour through the demonstration project pursued by the Aida digital libraries research team with the Library of Congress in 2019-2020. In addition to providing an overview and analysis of the specific machine learning projects scoped and explored, this presentation includes a number of high-level take-aways and recommendations designed to influence and inform the Library of Congress's machine learning efforts going forward.

Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Faculty Publications

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our …

Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola

School of Computing: Conference and Workshop Papers

The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …

Svat4: A Computer Program For Visualization And Analysis Of Crystal Structures, Xingzhong Li

Nebraska Center for Materials and Nanoscience: Faculty Publications

SVAT4 is a computer program for interactive visualization of three-dimensional crystal structures, including chemical bonds and magnetic moments. A wide range of functions, e.g. revealing atomic layers and polyhedral clusters, are available for further structural analysis. Atomic sizes, colors, appearance, view directions and view modes (orthographic or perspective views) are adjustable. Customized work for the visualization and analysis can be saved and then reloaded. SVAT4 provides a template to simplify the process of preparation of a new data file. SVAT4 can generate high-quality images for publication and animations for presentations. The usability of SVAT4 is broadened by a software suite …

Designing Shared Control Strategies For Teleoperated Robots Across Intrinsic User Qualities, Nancy Pham

School of Computing: Dissertations, Theses, and Student Research

Accounting for variance in human behavior is an integral part of interacting with robotic systems that share control between users and robots in order to reduce errors, improve performance, and maintain safety. In this work we focus on the shared control of a telepresence robot and how individual user traits may affect a person's performance while navigating the robot. This requires understanding which user qualities impact performance and cause conflicts -- with the ultimate goal of building shared controllers that adapt to those qualities. Toward this goal, we develop novel adaptive shared controllers and integrate the study of intrinsic user …

Repositories For Taxonomic Data: Where We Are And What Is Missing, Aurélian Miralles, Teddy Bruy, Katherine Wolcott, Mark D. Scherz, Dominik Begerow, Bank Beszteri, Michael Bonkowski, Janine Felden, Birgit Gemeinholzer, Frank Glaw, Frank Oliver Glöckner, Oliver Hawlitschek, Ivaylo Kostadinov, Tim W. Nattkemper, Christian Printzen, Jasmin Renz, Nataliya Rybalka, Marc Stadler, Tanja Weibulat, Thomas Wilke, Susanne S. Renner, Miguel Vences

Harold W. Manter Laboratory of Parasitology: Library Materials

Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4,113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use …

Comparative Evaluation Of Machine Learning Models For Groundwater Quality Assessment, Shine Bedi, Ashok Samal, Chittaranjan Ray, Daniel D. Snow

School of Computing: Faculty Publications

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration …

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

The Trust Principles For Digital Repositories, Dawei Lin, Jonathan Crabtree, Ingrid Dillo, Robert R. Downs, Rorie Edmunds, David Giaretta, Marisa De Giusti, Hervé L'Hours, Wim Hugo, Reyna Jenkyns, Varsha Khodiyar, Maryann E. Martone, Mustapha Mokrane, Vivek Navale, Jonathan Petters, Barbara Sierman, Dina V. Sokolova, Martina Stockhause, John Westbrook

Copyright, Fair Use, Scholarly Communication, etc.

As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

Following a year-long public discussion and building on existing community consensus , several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency, Responsibility, User focus, …

Advanced Security Analysis For Emergent Software Platforms, Mohannad Alhanahnah

School of Computing: Dissertations, Theses, and Student Research

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.

This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the …

The R Journal (December 2019) 11(2): Complete Issue, The R Foundation

The R Journal

Editorial, Michael J. Kane

Contributed Research Articles

Using Web Services to Work with Geodata in R, Jan-Philipp Kolb

orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization, Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, and Xin Zhang

coxed: An R Package for Computing Duration-Based Quantities from the Cox Proportional Hazards Model, Jonathan Kropko and Jeffrey J. Harden

Modeling Regimes with Extremes: The Bayesdfa Package for Identifying and Forecasting Common Trends and Anomalies in Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, and Michael A. Litzow

Fitting Tails by the Empirical Residual …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2019-09-05 and 2020-02-24.

Conference: Report Conectar 2019, Marcela Alfaro Córdoba, Frans Van Dunné, Agustín Gómez Meléndez, Jacob Van Etten

The R Journal

ConectaR 2019: Encuentro de Usuarios R en Latinoamérica, took place during January 24-26, 2019 at the University of Costa Rica, in San José, Costa Rica. It was the first event in Central America endorsed by The R Foundation, and it was held completely in Spanish. The majority of the attendants were from Costa Rica (85%), but we had participants from 12 countries: Costa Rica, Guatemala, Peru, Colombia, Mexico, Argentina, Uruguay, Chile, Spain, the Netherlands, France and the USA. The three-day event consisted of talks, workshops, and poster sessions.

Lpirfs: An R Package To Estimate Impulse Response Functions By Local Projections, Philipp Adämmer

The R Journal

Impulse response analysis is a cornerstone in applied (macro-)econometrics. Estimating impulse response functions using local projections (LPs) has become an appealing alternative to the traditional structural vector autoregressive (SVAR) approach. Despite its growing popularity and applications, however, no R package yet exists that makes this method available. In this paper, I introduce lpirfs, a fast and flexible R package that provides a broad framework to compute and visualize impulse response functions using LPs for a variety of data sets.

Resampling-Based Analysis Of Multivariate Data And Repeated Measures Designs With The R Package Manova.Rm, Sarah Friedrich, Frank Konietschke, Markus Pauly

The R Journal

Nonparametric statistical inference methods for a modern and robust analysis of longitudinal and multivariate data in factorial experiments are essential for research. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or equal covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p value and confidence interval estimates. Therefore, newly developed nonparametric statistical methods based on bootstrap- and permutation-approaches, which neither assume multivariate normality nor specific covariance matrices, have been …

The Landscape Of R Packages For Automated Exploratory Data Analysis, Mateusz Staniak, Przemysław Biecek

The R Journal

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering

There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). …

Roahd Package: Robust Analysis Of High Dimensional Data, Francesca Ieva, Anna Maria Paganoni, Juan Romo, Nicholas Tarabelloni

The R Journal

The focus of this paper is on the open-source R package roahd (RObust Analysis of High dimensional Data), see Tarabelloni et al. (2017). roahd has been developed to gather recently proposed statistical methods that deal with the robust inferential analysis of univariate and multivariate functional data. In particular, efficient methods for outlier detection and related graphical tools, methods to represent and simulate functional data, as well as inferential tools for testing differences and dependency among families of curves will be discussed, and the associated functions of the package will be described in details.