Computer Sciences Commons^™

Unmanned Aerial Vehicles In Opportunistic Networks, Salih Safa Bacanli

Electronic Theses and Dissertations, 2020-2023

This dissertation presents novel algorithms for utilizing unmanned aerial vehicles (UAVs) through various scenarios within opportunistic networks. The opportunistic networks are considered challenging due to the intermittent and unreliable communication between nodes. UAVs can be used for delivering packets within opportunistic networks that can alleviate communication issues. We start examining the UAV usage in opportunistic networks by first investigating their effectiveness and proposing a UAV scanning approach. To validate the usage of UAVs, we evaluated the performance of an opportunistic network with and without using UAVs. The scanning techniques we investigated were random scan, meander scan, and our proposed approach …

Exploring Relationships Between Ground And Aerial Views By Synthesis And Matching, Krishna Regmi

Electronic Theses and Dissertations, 2020-2023

Cross-view images, referring to the images taken from aerial and street views, contain drastically differing representations of the same scene of a given location. Due to the differences in the camera viewpoints of ground and aerial images the same semantic concepts in the two viewpoints look very different. Therefore the problem of relating them is very challenging. Thus, it becomes crucial to explore the cross-view relations and learn appropriate representations such that images from these two domains can be associated. In this dissertation we explore the relationship between ground and aerial views by synthesis and matching. First, we explore supervised …

Test Overfitting In Automated Program Repair: Measurements And Approaches Using Formal Methods, Amirfarhad Nilizadeh

Electronic Theses and Dissertations, 2020-2023

Bugs exist in software systems; unfortunately, manually finding bugs and repairing them is complex, time-consuming, and expensive. Automated Program Repair (APR) techniques have promising results to make the debugging process automatic and dramatically decreasing the cost of developing a software system. Almost all developed APR tools use test suites to bug localization and evaluate generated candidate patches' correctness; thus, it is named dynamic APR. Test overfitting is one of the main challenges of dynamic APR tools, which is evident from several recent studies. Test overfitting means the repaired program is not correct based on the program's expected behavior while the …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Cyber Insurance Effects On Cyber Hygiene: Does The Homeostatic Effect Apply?, Wendi M. Kappers, Aaron Glassman, Michael S. Wills

Publications

A theoretical framework and research strategy is proposed to gain insight into perceptions and decisions as to how SMBs make decisions regarding cybersecurity hygiene measures, which could lead to betterinformed decisions regarding insurance as part of an ISA program, as well as have a bearing on policy structures and pricing for such insurance. This is because the definition of “cybersecurity hygiene habits”(CHH) as a task appears to vary within the industry and makes the practice hard to measure and evaluate. Research suggests that there may be a poorly understood connection between CHHs undertaken by organizations and their perceptions and/or adoption …

A Tiling Algorithm-Based String Similarity Measure, Peter Revesz

School of Computing: Faculty Publications

This paper describes a similarity measure for strings based on a tiling algorithm. The algorithm is applied to a pair of proteins that are described by their respective amino acid sequences. The paper also describes how the algorithm can be used to find highly conserved amino acid sequences and examples of horizontal gene transfer between different species.

An Empirical Analysis Of Collaborative Recommender Systems Robustness To Shilling Attacks, Anu Shrestha, Francesca Spezzano, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Recommender systems play an essential role in our digital society as they suggest products to purchase, restaurants to visit, and even resources to support education. Recommender systems based on collaborative filtering are the most popular among the ones used in e-commerce platforms to improve user experience. Given the collaborative environment, these recommenders are more vulnerable to shilling attacks, i.e., malicious users creating fake profiles to provide fraudulent reviews, which are deliberately written to sound authentic and aim to manipulate the recommender system to promote or demote target products or simply to sabotage the system. Therefore, understanding the effects of shilling …

Enriching Language Models With Visually-Grounded Word Vectors And The Lancaster Sensorimotor Norms, Casey Kennington

Computer Science Faculty Publications and Presentations

Language models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical entities and embodied states. To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers …

An Analysis On Pixel Redundancy Structure In Equirectangular Images, I. Vazquez, S. Cutchin

Computer Science Faculty Publications and Presentations

360° photogrammetry captures the surrounding light from a central point. To process and transmit these types of images over the network to the end user, the most common approach is to project them onto a 2D image using the equirectangular projection to generate a 360° image. However, this projection introduces redundancy into the image, increasing storage and transmission requirements. To address this problem, the standard approach is to use compression algorithms, such as JPEG or PNG, but they do not take full advantage of the visual redundancy produced by the equirectangular projection. In this study of the 360SP dataset (a …

Automated Waterloo Rubric For Concept Map Grading, Shresht Bhatia, Sajal Bhatia, Irfan Ahmed

School of Computer Science & Engineering Faculty Publications

Concept mapping is a well-known pedagogical tool to help students organize, represent, and develop an understanding of a topic. The grading of concept maps is typically manual, time-consuming, and tedious, especially for a large class. Existing research mostly focuses on topological scoring based-on structural features of concept maps. However, the scoring does not achieve comparable accuracy to well-defined rubrics for manual analysis on the quality of content in a concept map. This paper presents Kastor, a new method to automate the Waterloo Rubric of scoring concept maps by quantifying the rubric’s quality assessment parameters. The evaluation is performed on a …

Fast And Memory-Efficient Tfidf Calculation For Text Analysis Of Large Datasets, Samah Senbel

School of Computer Science & Engineering Faculty Publications

Term frequency – Inverse Document Frequency (TFIDF) is a vital first step in text analytics for information retrieval and machine learning applications. It is a memory-intensive and complex task due to the need to create and process a large sparse matrix of term frequencies, with the documents as rows and the term as columns and populate it with the term frequency of each word in each document.

The standard method of storing the sparse array is the “Compressed Sparse Row” (CSR), which stores the sparse array as three one-dimensional arrays for the row id, column id, and term frequencies. We …

Cybersecurity Analysis Of Load Frequency Control In Power Systems: A Survey, Sahaj Saxena, Sajal Bhatia, Rahul Gupta

School of Computer Science & Engineering Faculty Publications

Today, power systems have transformed considerably and taken a new shape of geographically distributed systems from the locally centralized systems thereby leading to a new infrastructure in the framework of networked control cyber-physical system (CPS). Among the different important operations to be performed for smooth generation, transmission, and distribution of power, maintaining the scheduled frequency, against any perturbations, is an important one. The load frequency control (LFC) operation actually governs this frequency regulation activity after the primary control. Due to CPS nature, the LFC operation is vulnerable to attacks, both from physical and cyber standpoints. The cyber-attack strategies ranges from …

Occam Software (And Manual) For Reconstructability Analysis, Martin Zwick, Kenneth Willett, Joe Fusion, Heather Alexander

Complex Systems Faculty Publications and Presentations

OCCAM is a Discrete Multivariate Modeling (DMM) tool based on the methodology of Reconstructability Analysis (RA). As an acronym it stands for Organizational Complexity Computation and Modeling, and the name is also a reference (with a non-standard spelling) to Ockham’s Razor. The principal programmers of its current version have been Kenneth Willett, Joe Fusion, and Heather Alexander. Ken Willett totally rewrote earlier versions of OCCAM. His version was originally called “OCCAM3” to distinguish it from these earlier OCCAM incarnations; the “3” has finally been dropped.

OCCAM’s typical use is to analyze data involving a large number of discrete variables, but …

Neural Representations Of Concepts And Texts For Biomedical Information Retrieval, Jiho Noh

Theses and Dissertations--Computer Science

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user's query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the …

Computational Utilities For The Game Of Simplicial Nim, Nelson Penn

Theses and Dissertations--Computer Science

Simplicial nim games, a class of impartial games, have very interesting mathematical properties. Winning strategies on a simplicial nim game can be determined by the set of positions in the game whose Sprague-Grundy values are zero (also zero positions). In this work, I provide two major contributions to the study of simplicial nim games. First, I provide a modern and efficient implementation of the Sprague-Grundy function for an arbitrary simplicial complex, and discuss its performance and scope of viability. Secondly, I provide a method to find a simple mathematical expression to model that function if it exists. I show the …

Novel Hedonic Games And Stability Notions, Jacob Schlueter

Theses and Dissertations--Computer Science

We present here work on matching problems, namely hedonic games, also known as coalition formation games. We introduce two classes of hedonic games, Super Altruistic Hedonic Games (SAHGs) and Anchored Team Formation Games (ATFGs), and investigate the computational complexity of finding optimal partitions of agents into coalitions, or finding - or determining the existence of - stable coalition structures. We introduce a new stability notion for hedonic games and examine its relation to core and Nash stability for several classes of hedonic games.

Representing And Learning Preferences Over Combinatorial Domains, Michael Huelsman

Theses and Dissertations--Computer Science

Agents make decisions based on their preferences. Thus, to predict their decisions one has to learn the agent's preferences. A key step in the learning process is selecting a model to represent those preferences. We studied this problem by borrowing techniques from the algorithm selection problem to analyze preference example sets and select the most appropriate preference representation for learning. We approached this problem in multiple steps.

First, we determined which representations to consider. For this problem we developed the notion of preference representation language subsumption, which compares representations based on their expressive power. Subsumption creates a hierarchy of preference …

Personality And Emotion For Virtual Characters In Strong-Story Narrative Planning, Alireza Shirvani

Theses and Dissertations--Computer Science

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. The interaction of rich virtual characters can produce interesting narratives and enhance user experience in virtual environments. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. I demonstrate the value of the strong-story properties of the model by generating story conflicts intelligently. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts …

Revisiting Absolute Pose Regression, Hunter Blanton

Theses and Dissertations--Computer Science

Images provide direct evidence for the position and orientation of the camera in space, known as camera pose. Traditionally, the problem of estimating the camera pose requires reference data for determining image correspondence and leveraging geometric relationships between features in the image. Recent advances in deep learning have led to a new class of methods that regress the pose directly from a single image.

This thesis proposes methods for absolute camera pose regression. Absolute pose regression estimates the pose of a camera from a single image as the output of a fixed computation pipeline. These methods have many practical benefits …

Expanding Social Network Modeling Software And Agent Models For Diffusion Processes, Patrick Vaden Shepherd

Theses and Dissertations--Computer Science

In an increasingly digitally interconnected world, the study of social networks and their dynamics is burgeoning. Anthropologically, the ubiquity of online social networks has had striking implications for the condition of large portions of humanity. This technology has facilitated content creation of virtually all sorts, information sharing on an unprecedented scale, and connections and communities among people with similar interests and skills. The first part of my research is a social network evolution and visualization engine. Built on top of existing technologies, my software is designed to provide abstractions from the underlying libraries, drive real-time network evolution based on user-defined …

Developing A Deterministic Polymorphic Circuit Generator Using Random Boolean Logic Expansion, Trinity Stroud

Honors Theses

Securing applications on untrusted platforms can involve protection against legitimate endusers who act in the role of malicious reverse engineers and hackers. Such adversaries have access to the full execution environment of programs, whether the program comes in the form of software or hardware. In this thesis, we consider the nature of obfuscating algorithms that perform iterative, stepwise transformation of programs into more complex forms that are intended to increase the complexity (time, resources) for malicious reverse engineers.

We consider simple Boolean logic programs as the domain of interest and examine a specific transformation technique known as Iterative Selection and …

Quantum Computing For The Quantum Curious, Ciaran Hughes, Joshua Isaacson, Anastasia Perry, Ranbel F. Sun, Jessica Turner

Open Access Books and Manuals

This open access book makes quantum computing more accessible than ever before. A fast-growing field at the intersection of physics and computer science, quantum computing promises to have revolutionary capabilities far surpassing “classical” computation. Getting a grip on the science behind the hype can be tough: at its heart lies quantum mechanics, whose enigmatic concepts can be imposing for the novice.

This classroom-tested textbook uses simple language, minimal math, and plenty of examples to explain the three key principles behind quantum computers: superposition, quantum measurement, and entanglement. It then goes on to explain how this quantum world opens up a …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …

An Evaluation On The Performance Of Code Generated With Webassembly Compilers, Raymond Phelan

Dissertations

WebAssembly is a new technology that is revolutionizing the web. Essentially it is a low-level binary instruction set that can be run on browsers, servers or stand-alone environments. Many programming languages either currently have, or are working on, compilers that will compile the language into WebAssembly. This means that applications written in languages like C++ or Rust can now be run on the web, directly in a browser or other environment. However, as we will highlight in this research, the quality of code generated by the different WebAssembly compilers varies and causes performance issues. This research paper aims to evaluate …

The Design And Evaluation Of An Educational Software Development Process For First Year Computing Undergraduates, Catherine Higgins

Doctoral

First year, undergraduate computing students experience a series of well-known challenges when learning how to design and develop software solutions. These challenges, which include a failure to engage effectively with planning solutions prior to implementation ultimately impact upon the students’ competency and their retention beyond the first year of their studies. In the software industry, software development processes systematically guide the development of software solutions through iterations of analysis, design, implementation and testing. Industry-standard processes are, however, unsuitable for novice programmers as they require prior programming knowledge. This study investigates how a researcher-designed educational software development process could be created …

Requirements Engineering Education Slr Data Set 1988-2020, Marian Daun, Alicia M. Grubb, Bastian Tenbergen

Data

Requirements Engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or exceeding budgets of software development projects. Therefore, it is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. However, to date there exists no central dataset for RE Education articles. To lay the foundation for this important mission, we conducted a systematic literature review. In this dataset, we present 152 articles from the Requirements Engineering …

"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

Publications

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to …

A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine

Dissertations

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock …

Identifying Significant Features For Player Evaluation In Nfl Comparing Anns And Traditional Models, Ronan Walsh

Dissertations

The evaluation of player performance in sports is popular and important in modern sports, enabling teams to use real data in the construction of their rosters. This dissertation proposes to apply machine learning algorithms to predicting the player evaluations from a leading NFL analytics company who use a combination of statistics and expert evaluation. In addition, it will investigate what features are significant in the evaluation of a position. Data for the dissertation is obtained from multiple online sources - Pro Football Reference and Pro Football Focus (the the NFL analytics company). These data sets are combined and analysed before …

Identifying Roles Of Software Developers From Their Answers On Stack Overflow, Dean Power

Dissertations

Stack Overflow is the world’s largest community of software developers. Users ask and answer questions on various tagged topics of software development. The set of questions a site user answers is representative of their knowledge base, or “wheelhouse”. It is proposed that clustering users by their wheelhouse yields communities of similar software developers by skill-set. These communities represent the different roles within software development and could be used as the basis to define roles at any point in time in an ever-evolving landscape of software development. A network graph of site users, linked if they answered questions on the same …