Computer Sciences Commons^™

Abmr: An R Package For Agent-Based Model Analysis Of Large-Scale Movements Across Taxa, Benjamin Gochanour, Javier Fernández-López, Andrea Contina

School of Integrative Biological & Chemical Sciences Faculty Publications

1. Agent-based modelling (ABM) shows promise for animal movement studies. However, a robust, open-source and spatially explicit ABM coding platform is currently lacking.
2. We present abmR, an R package for conducting continental-scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process.
3. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations.
4. Potential future additions to this open-source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with …

S-400s, Disinformation, And Anti-American Sentiment In Turkey, Russell "Alex" Korb, Saltuk Karahan, Gowri Prathap, Ekrem Kaya, Luke Palmieri, Hamdi Kavak, Richard L. Wilson (Ed.), Major Brendan Curran (Ed.)

Political Science & Geography Faculty Publications

As social and political discourse in most countries becomes more polarized, anti-Americanism has risen not only in the Middle East and Latin America but also among the U.S. allies in Europe. Social media is one platform used to disseminate anti-American views in NATO countries, and its effectiveness can be magnified when mass media, public officials, and popular figures adopt these views. Disinformation, in particular, has gained recognition as a cybersecurity issue from 2016 onward, but disinformation can be manufactured domestically in addition to being part of a foreign influence campaign. In this paper, we analyze Turkish tweets using sentiment analysis …

The Mceliece Cryptosystem As A Solution To The Post-Quantum Cryptographic Problem, Isaac Hanna

Senior Honors Theses

The ability to communicate securely across the internet is owing to the security of the RSA cryptosystem, among others. This cryptosystem relies on the difficulty of integer factorization to provide secure communication. Peter Shor’s quantum integer factorization algorithm threatens to upend this. A special case of the hidden subgroup problem, the algorithm provides an exponential speedup in the integer factorization problem, destroying RSA’s security. Robert McEliece’s cryptosystem has been proposed as an alternative. Based upon binary Goppa codes instead of integer factorization, his cryptosystem uses code scrambling and error introduction to hinder decrypting a message without the private key. This …

The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku

Graduate Research Theses & Dissertations

Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …

An Empirical Analysis Of Algorithms For Simple Stochastic Games, Cody William Klingler

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents the findings of a computational study on algorithms for Simple Stochastic Games (SSG). Simple Stochastic Games are a restriction of the Shapley stochastic model motivated by their applications in AI planning, logic synthesis, and theoretical computer science. This thesis seeks to empirically assess the performance of these algorithms to compensate for their lack of strong complexity results. Where applicable, we include both variations of algorithms where stable strategies are computed by a linear-programming and naive approach. These algorithms are evaluated on random inputs, in addition to specific difficult cases that were identified experimentally. We are interested in …

A Longitudinal Study Of Factors That Affect User Interactions With Social Media And Email Spam, Wojciech M. Mazurek

Graduate Theses, Dissertations, and Problem Reports (ETD)

Given the rapid growth of social media and the increasing prevalence of spam, it is crucial to understand users’ interactions with unsolicited content to develop effective countermeasures against spam. This thesis focuses on exploring the factors that influence users’ decisions to interact with spam on social media and email. It builds upon prior work, which serves as a foundation for further research and conducting a longitudinal analysis. Our results are based on the analysis of 221 responses collected through an online survey. The survey not only gathered demographic information such as age, gender, and race but also collected data on …

Scene Representation And Matching For Visual Localization In Hybrid Camera Scenarios, Marcela A. Mera Trujillo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Scene representation and matching are crucial steps in a variety of tasks ranging from 3D reconstruction to virtual/augmented/mixed reality applications, to robotics, and others. While approaches exist that tackle these tasks, they mostly overlook the issue of efficiency in the scene representation, which is fundamental in resource-constrained systems and for increasing computing speed. Also, they normally assume the use of projective cameras, while performance on systems based on other camera geometries remains suboptimal. This dissertation contributes with a new efficient scene representation method that dramatically reduces the number of 3D points. The approach sets up an optimization problem for the …

An Empirical Analysis Of Approximation Algorithms For The Unweighted Tree Augmentation Problem, Jacob Thomas Restanio

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this thesis, we perform an experimental study of approximation algorithms for the tree augmentation problem (TAP). TAP is a fundamental problem in network design. The goal of TAP is to add the minimum number of edges from a given edge set to a tree so that it becomes 2-edge connected. Formally, given a tree T = (V, E), where V denotes the set of vertices and E denotes the set of edges in the tree, and a set of edges (or links) L ⊆ V × V disjoint from E, the objective is to find a set of edges …

Mentoring Deep Learning Models For Mass Screening With Limited Data, Suprim Nakarmi

Dissertations and Theses

Deep Learning (DL) has an extensively rich state-of-the-art literature in medical imaging analysis. However, it requires large amount of data to begin training. This limits its usage in tackling future epidemics, as one might need to wait for months and even years to collect fully annotated data, raising a fundamental question: is it possible to deploy AI-driven tool earlier in epidemics to mass screen the infected cases? For such a context, human/Expert in the loop Machine Learning (ML), or Active Learning (AL), becomes imperative enabling machines to commence learning from the first day with minimum available labeled dataset. In an …

A Survey Of Wearable Devices Pairing Based On Biometric Signals, Jafar Pourbemany, Ye Zhu, Riccardo Bettati

Electrical and Computer Engineering Faculty Publications

With the rapid growth of wearable devices, more applications require direct communication between wearable devices. To secure the communication between wearable devices, various pairing protocols have been proposed to generate common keys for encrypting the communication. Since the wearable devices are attached to the same body, the devices can generate common keys based on the same context by utilizing onboard sensors to capture a common biometric signal such as body motion, gait, heartbeat, respiration, and EMG signals. The context-based pairing does not need prior information to generate common keys. As context-based pairing does not need any human involvement in the …

Network Intrusion Detection With Two-Phased Hybrid Ensemble Learning And Automatic Feature Selection, Asanka Kavinda Mananayaka, Sunnie S. Chung

Electrical and Computer Engineering Faculty Publications

The use of network connected devices has grown exponentially in recent years revolutionizing our daily lives. However, it has also attracted the attention of cybercriminals making the attacks targeted towards these devices increase not only in numbers but also in sophistication. To detect such attacks, a Network Intrusion Detection System (NIDS) has become a vital component in network applications. However, network devices produce large scale high-dimensional data which makes it difficult to accurately detect various known and unknown attacks. Moreover, the complex nature of network data makes the feature selection process of a NIDS a challenging task. In this study, …

Comments Of The Cordell Institute On Ai Accountability, Neil M. Richards, Woodrow Hartzog, Jordan Francis

Scholarship@WashULaw

These comments are a response to the National Telecommunications and Information Administration's 2023 request for comment on AI accountability (AI Accountability RFC, NTIA–2023–0005).

Responding to NTIA’s recent inquiry into AI assurance and accountability, we offer two main arguments regarding the importance of substantive legal protections. First, a myopic focus on concepts of transparency, bias mitigation, and ethics (for which procedural compliance efforts such as audits, assessments, and certifications are proxies) is insufficient when it comes to the design and implementation of accountable AI systems. We call rules built around transparency and bias mitigation “AI half-measures,” because they provide the appearance …

Investigating Collaborative Explainable Ai (Cxai)/Social Forum As An Explainable Ai (Xai) Method In Autonomous Driving (Ad), Tauseef Ibne Mamun

Dissertations, Master's Theses and Master's Reports

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system's behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don't encounter or provide information that users do not seek.

In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers …

Natural Language Processing In The Legal Domain, Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Ii Bommarito

Research Collection Yong Pung How School Of Law

In this paper, we summarize the current state of the field of NLP and Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a corpus of more than six hundred NLP and Law related papers published over the past decade. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Slowly but surely, Legal NLP is …

Sequence Checking And Deduplication For Existing Fingerprint Databases, Tahsin Islam Sakif

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biometric technology is a rapidly evolving field with applications that range from access to devices to border crossing and entry/exit processes. Large-scale applications to collect biometric data, such as border crossings result in multimodal biometric databases containing thousands of identities. However, due to human operator error, these databases often contain many instances of image labeling and classification; this is due to the lack of training and throughput pressure that comes with human error. Multiple entries from the same individual may be assigned to a different identity. Rolled fingerprints may be labeled as flat images, a face image entered into a …

Visualizing Dynamic Multivariate Networks, Bharat Kumar Kale

Graduate Research Theses & Dissertations

Many real-world networks are multivariate in nature and also evolve over time. Such a network whose nodes and/or edges along with their associated attributes, evolve over time is called A dynamic multivariate network (DMVN). For instance, in the scholarly community, researchers often collaborate with others. These collaborations have various attributes, such as scientific domain and number of publications, as well as they change over time. At any given point in time, the collaborations among researchers can be modeled as a network resulting in a collaboration network. When the changes in collaborations and associated attributes are also included in the network, …

Analysis Of The Effect Of Surface Roughness In Additive Manufacturing On Thermo-Hydraulic Performance By Ml Models Supported By Physics-Based Modeling, Kaylen A. Platt

Graduate Research Theses & Dissertations

Heat exchangers (HX) are crucial components of thermal control systems which facilitate proper transfer of heat in systems such as nuclear reactors. This research focuses on the heat transfer-enhancing effect associated with turbulence in fluid flowing conduits. Turbulence can be induced at low flow rates when surface roughness is present on channel walls. Rough surfaces are typically regarded as a flawed product of the Additive Manufacturing (AM) process during the construction of Heat Exchangers. Machining surface finishes onto AM parts is a typical post-processing method used to remove the roughness. Thorough investigations have not yet been published on the effects …

Machine Learning Techniques For Credit Card Fraud Detection, Amal Alamri

Theses, Dissertations and Culminating Projects

Credit card fraudulent transactions are becoming an ever-growing problem in the financial market. There has been a rapid increase in the rate of fraudulent transactional activities in recent years producing considerable financial loss to many companies, organizations, and government agencies. These numbers are anticipated to increase in the near future and many scholars in this field are focused on detecting fraudulent transactions early on, using advanced Machine Learning techniques. However, credit card fraud transaction detection is not easy for two reasons: (I) fraudulent methods usually vary for each attempt, and (II) the dataset is extremely imbalanced with many more normal …

Measuring The Impact Of Youth Research In The Us Policy Documents, Miftahul Jannat Mokarrama

Graduate Research Theses & Dissertations

With the growing demand for aligning research with society, in recent years, researchers and policymakers have been unequivocal in increasing research use in the policy-making process. Integrating research into policy formulation not only results in more effective policies but also allows researchers to extend the reach and influence of their work to a broaderand more diverse audience. In the US, as youth comprise a substantial portion of the population, their perspectives, needs, and aspirations must be understood and addressed in policymaking. Therefore, in this thesis, we assessed the impact of youth-focused research on US policy documents using state-of-the-art pre-trained Large …

Deep Learning Approaches For Automatic Colorization, Super-Resolution, And Representation Of Volumetric Data, Sudarshan Devkota

Graduate Thesis and Dissertation 2023-2024

This dissertation includes a collection of studies that aim to improve the way we represent and visualize volume data. The advancement of medical imaging has revolutionized healthcare, providing crucial anatomical insights for accurate diagnosis and treatment planning. Our first study introduces an innovative technique to enhance the utility of medical images, transitioning from monochromatic scans to vivid 3D representations. It presents a framework for reference-based automatic color transfer, establishing deep semantic correspondences between a colored reference image and grayscale medical scans. This methodology extends to volumetric rendering, eliminating the need for manual intervention in parameter tuning. Next, it delves into …

A Class Of Regression Models For Pairwise Comparisons Of Forensic Handwriting Comparison Systems, Cami M. Fuglsby

Electronic Theses and Dissertations

Handwriting analysis is a complex field largely living in forensic science and the legal realm. One task of a forensic document examiner (FDE) may be to determine the writer(s) of handwritten documents. Automated identification systems (AIS) were built to aid FDEs in their examinations. Part of the uses of these AIS (such as FISH[5] [7],WANDA [6], CEDAR-FOX [17], and FLASHID®2) are tomeasure features about a handwriting sample and to provide the user with a numeric value of the evidence. These systems use their own algorithms and definitions of features to quantify the writing and can be considered a black-box. The …

Assessing The Impact Of Parallel Burnout Fires On Flank Rate Of Spread, Erik Borke

Graduate Student Theses, Dissertations, & Professional Papers

The effects of flank-parallel suppression fires on the local rate of spread (ROS) of freely burning headfires through fully cured homogeneous grass fuels are assessed. Data sets include: one thermal image stack of a prescribed burn recorded by drone, and a suite of simulation experiments carried out in Wildland Urban Interface Fire Dynamics Simulator (WFDS). A new approach to computing ROS, curvature proxy driven normals to convex polylines, was developed to carry out this analysis. ROS time series depicting flank acceleration of the prescribed burn and simulation experiments, observable under coarse and fine directional classification schemes respectively, are the primary …

A Sociotechnical Systems View Of Computer Self-Efficacy And Usability Determinants Of Technical Readiness, Stefani L. Tucker

Walden Dissertations and Doctoral Studies

The specific research problem was that it is unknown whether computer self-efficacy and usability determine technical readiness in hourly and exempt information technology support employees in the United States. The purpose of this correlational study was to examine the relationship between computer self-efficacy and technical readiness, usability and technical readiness, and computer self-efficacy, usability, and technical readiness in hourly and exempt information technology support employees in the United States. Sociotechnical system theory suggests that every transaction has a human and technical aspect; thus, the theoretical framework. The convenience sample included 136 information technology support employees aged 18-70. The regression results …

Technology Manufacturing Leaders’ Innovation Strategies To Improve Users’ Choice Capabilities In A Fast-Changing Markets, Magnus Ekwunife

Walden Dissertations and Doctoral Studies

Some leaders of technology manufacturing organizations lack strategies to educate their users on how to make the optimal cloud technology selection decisions for their organizations during rapidly evolving innovation, resulting in significant risk of wrong choices and loss of customer loyalty. Grounded in resource-based view theory, the purpose of this qualitative single case study was to explore strategies technology manufacturing leaders use to educate users on how to make optimal cloud technology selection decisions for their organizations. The participants were six executive-level leaders of the strategic sales division of a multinational technology organization based in the western United States who …

Generational Information Security Awareness And The Role Of Big Five Personality Traits, Gloria Mccue

Walden Dissertations and Doctoral Studies

AbstractTechnological change drives organizations to safeguard information systems. However, such safeguards are dependent upon people to follow security rules. This study examined generational cohorts and personality traits and their impact on information security awareness. Participants in this study were 137 volunteers who completed an anonymous survey online. Two tools were utilized to collect data from the participants: the Human Aspects of Information Security Questionnaire and the Big Five Inventory, which captured behaviors and personality traits, respectively. The three main generational cohorts represented in the study, Baby Boomers, Generation X, and Generation Y, were in today’s workforce. The results of the …

Diversification Strategies Business Managers Use To Improve Profitability, Kayode Itiola

Walden Dissertations and Doctoral Studies

A lack of diversification strategies can negatively impact business profitability. Business managers of small and medium-sized enterprises who implement appropriate diversification strategies can improve business profitability, ensuring the firm’s sustainability. Grounded in the modern portfolio theory, the purpose of this qualitative single case study was to explore diversification strategies business managers use to improve profitability. The participants were five business managers who successfully used diversification strategies to enhance business profitability. Sources for data collection were semi-structured interviews, company archival documents, and field notes. Data were analyzed using thematic analysis. Four themes emerged: concentric diversification strategy, horizontal diversification strategy, customer needs …

Effective Strategies For Using Telecommuting By Owners Of Small Businesses, Thomas Law

Walden Dissertations and Doctoral Studies

Small business owners who lack effective strategies to incorporate telecommuting may be unable to retain teleworking employees, create a flexible working environment, or improve workforce morale, negatively impacting company productivity and profitability. Grounded in transformational leadership theory and sociotechnical systems theory, the purpose of this qualitative multiple case study was to explore strategies small business owners use to incorporate telecommuting to retain teleworking employees. Data were collected from five small business owners in Texas with at least 1 year of management experience and created and maintained remote working strategies. Data collection included semistructured interviews and company documents. Three themes emerged …

Knowledge-Sharing Practice As A Tool In Organizational Development In Nigerian Higher Educational Institutions, Emmanuel Ajiri Ojo

Walden Dissertations and Doctoral Studies

AbstractUnderstanding knowledge management is key to understanding organizational development and innovations. Inadequate knowledge-sharing practices in Nigerian educational institutions has impeded innovation and management development. The purpose of this qualitative modified Delphi study was to seek consensus among administrators from Nigerian educational institutions and scholars from Nigerian universities regarding knowledge-sharing practices that nourish innovation in Nigerian higher educational institutions. The organizational development framework was used to guide the study. Data collection included a nonprobability purposive sampling of 25 participants and three rounds of surveys administered online. A consensus was reached on eight factors after coding and thematic analysis: setting knowledge-sharing expectations, …

Analyzing Small Business Strategies To Prevent External Cybersecurity Threats, Dr. Kevin E. Moore

Walden Dissertations and Doctoral Studies

Some small businesses’ cybersecurity analysts lack strategies to prevent their organizations from compromising personally identifiable information (PII) via external cybersecurity threats. Small business leaders are concerned, as they are the most targeted critical infrastructures in the United States and are a vital part of the economic system as data breaches threaten the viability of these organizations. Grounded in routine activity theory, the purpose of this pragmatic qualitative inquiry was to explore strategies small business organizations utilize to prevent external cybersecurity threats. The participants were nine cybersecurity analysts who utilized strategies to defend small businesses from external threats. Data were collected …

Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad

All Works

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and …