Computer Sciences Commons^™

A Fast Method For Computing Volume Potentials In The Galerkin Boundary Element Method In 3d Geometries, Sasan Mohyaddin

Mathematics Theses and Dissertations

We discuss how the Fast Multipole Method (FMM) applied to a boundary concentrated mesh can be used to evaluate volume potentials that arise in the boundary element method. If $h$ is the meshwidth near the boundary, then the algorithm can compute the potential in nearly $\Ord(h^{-2})$ operations while maintaining an $\Ord(h^p)$ convergence of the error. The effectiveness of the algorithms are demonstrated by solving boundary integral equations of the Poisson equation.

Proximal Policy Optimization For Radiation Source Search, Philippe Erol Proctor

Dissertations and Theses

Rapid localization and search for lost nuclear sources in a given area of interest is an important task for the safety of society and the reduction of human harm. Detection, localization and identification are based upon the measured gamma radiation spectrum from a radiation detector. The nonlinear relationship of electromagnetic wave propagation paired with the probabilistic nature of gamma ray emission and background radiation from the environment leads to ambiguity in the estimation of a source's location. In the case of a single mobile detector, there are numerous challenges to overcome such as weak source activity, multiple sources, or the …

Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed

All Works

The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific …

Information Security Maturity Model For Healthcare Organizations In The United States, Bridget Joan Barnes Page

Dissertations and Theses

This research provides a maturity model for information security for healthcare organizations in the United States. Healthcare organizations are faced with increasing threats to the security of their information systems. The maturity model identifies specific performance metrics, with relative importance measures, that can be used to enhance information security at healthcare organizations allowing them to focus scarce resources on mitigating the most important information security threat vectors. This generalizable, hierarchical decision model uses both qualitative and quantitative metrics based on objective goals. This model may be used as a baseline by which to measure individual organizational performance, to measure performance …

Computer Science Principles With Python, Seth D. Bergmann

Open Educational Resources

This textbook is intended to be used for a first course in computer science, such as the College Board’s Advanced Placement course known as AP Computer Science Principles (CSP). This book includes all the topics on the CSP exam, plus some additional topics. It takes a breadth-first approach, with an emphasis on the principles which form the foundation for hardware and software. No prior experience with programming should be required to use this book. This version of the book uses the Python programming language.

Power-Over-Tether Unmanned Aerial System Leveraged For Trajectory Influenced Atmospheric Sensing, Daniel Rico

School of Computing: Dissertations, Theses, and Student Research

The use of unmanned aerial systems (UASs) in agriculture has risen in the past decade and is helping to modernize agriculture. UASs collect and elucidate data previously difficult to obtain and are used to help increase agricultural efficiency and production. Typical commercial off-the-shelf (COTS) UASs are limited by small payloads and short flight times. Such limits inhibit their ability to provide abundant data at multiple spatiotemporal scales. In this thesis, we describe the design and construction of the tethered aircraft unmanned system (TAUS), which is a novel power-over-tether UAS configured for long-term, high throughput atmospheric monitoring with an array of …

Secure Self-Checkout Kiosks Using Alma Api With Two-Factor Authentication, Ron Bulaon

Research Collection Library

Self-checkout kiosks have become a staple feature of many modern and digitized libraries. These devices are used by library patrons for self-service item loans. Most implementations are not new, in fact many of these systems are simple, straight forward and work as intended. But behind this useful technology, there is a security concern on authentication that has to be addressed.

In my proposed presentation, I will discuss the risk factors of self-checkout kiosks and propose a solution using Alma APIs. I will address the technical shortcomings of the current implementations, compared to the proposed solution, and where the weakest link …

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem

Computational and Data Sciences (MS) Theses

Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …

Ensemble Data Fitting For Bathymetric Models Informed By Nominal Data, Samantha Zambo

Dissertations

Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension.

In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge …

Predicting (Economic) Trends: Why Signature Method In Machine Learning, Vladik Kreinovich, Chon Van Le

Departmental Technical Reports (CS)

In many practical situations, we can predict the trend -- i.e., how the system will change -- but we cannot predict the exact timing of this change: this timing may depend on many unpredictable factors. For example, we may be sure that the economy will recover, but how fast it will recover may depend on the status of the pandemic, on the weather-affected agriculture input, etc. In such trend predictions, one of the most efficient methods is signature method, which is based on applying machine learning techniques to several special characteristics of the corresponding time series. In this paper, we …

How To Work? How To Study? Shall We Cram For The Exams? And How Is This Related To Life On Earth?, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

If we follow the same activity for a long time, our productivity decreases. To increase productivity, a natural idea is therefore to switch to a different activity, and then to switch back and resume the current task. On the other hand, after each switch, we need some time to get back to the original productivity. As a result, too frequent switches are also counterproductive. Natural questions are: shall we switch? if yes, when? In this paper, we use a simple model to provide approximate answers to these questions.

Correcting Interval-Valued Expert Estimates: Empirical Formulas Explained, Laura A. Berrout Ramos, Vladik Kreinovich, Kittawit Autchariyapanitkul

Departmental Technical Reports (CS)

Experts' estimates are approximate. To make decisions based on these estimates, we need to know how accurate these estimate are. Sometimes, experts themselves estimate the accuracy of their estimates -- by providing the interval of possible values instead of a single number. In other cases, we can gauge the accuracy of the experts' estimates by asking several experts to estimates the same quantity and using the interval range of these values. In both situations, sometimes the interval is too narrow -- e.g., if an expert is overconfident. Sometimes, the interval is too wide -- if the expert is too cautious. …

Why Moving Fast And Breaking Things Makes Sense?, Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

In the traditional approach to engineering system design, engineers usually come up with several possible designs, each improving on the previous ones. In coming up with these designs, they try their best to make sure that their designs stay within the safety and other constraints, to avoid potential catastrophic crashes. The need for these safety constraints makes this design process reasonably slow. Software engineering at first followed the same pattern, but then realized that since in most cases, failure of a software test does not lead to a catastrophe, it is much faster to first ignore constraints and then adjust …

Why 70/100 Is Satisfactory? Why Five Letter Grades? Why Other Academic Conventions?, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Why 70/100 is usually a threshold for a student's satisfactory performance? Why there are usually only five letter grades? Why the usual arrangement of research, teaching, and service is 40-40-20? We show that all these arrangements -- and other similar academic arrangements -- can be explained by two ideas: the Laplace Indeterminacy Principle and the seven plus minus two law.

Blessings, God, Sacrifices: Possible Rational Explanations Of Biblical Ideas, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that many seemingly irrational Biblical ideas can actually be rationally interpreted: that God is everywhere, that we can only say what God is not, that God's name is holy, why cannot you bless as many people as you want, etc. We do not insist on our interpretations, there probably are many others, our sole objective was to show that many Biblical ideas can be rationally explained.

Narrow Band Active Contour Attention Model For Medical Segmentation, Ngan Le, Toan Bui, Viet-Khao Vo-Ho, Kashu Yamazaki, Khoa Luu

Computer Science and Computer Engineering Faculty Publications and Presentations

Medical image segmentation is one of the most challenging tasks in medical image analysis and widely developed for many clinical applications. While deep learning-based approaches have achieved impressive performance in semantic segmentation, they are limited to pixel-wise settings with imbalanced-class data problems and weak boundary object segmentation in medical images. In this paper, we tackle those limitations by developing a new two-branch deep network architecture which takes both higher level features and lower level features into account. The first branch extracts higher level feature as region information by a common encoder-decoder network structure such as Unet and FCN, whereas the …

The Affect Of Globalization On Terrorism, Philip R. Passante

Master's Theses

This thesis proposal will dive into the concept of terrorism and how it is an act of force and has proven to be detrimental to the modern world. In addition, this thesis will analyze the concept of terrorism as well as the rationale behind it. It is important to understand and study this as terrorism is a complex entity made up of different themes. The concentration of this thesis will highlight how globalization has affected the phenomena of terrorism in the past, present, and ultimately the future. Globalization and terrorism have a relationship that many scholars and researchers have noticed. …

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

Mining Bitcoin To Avoid Sanctions, Tyler C. Lubin

Master's Theses

Though the world’s first cryptocurrency, Bitcoin, was introduced over a decade ago, it was not until recently that it became a mainstream subject. While cryptocurrencies offer many advantages, a potential downside for governments, is that no central bank controls the monetary policy and new coins can be mined by anyone anywhere in the world. Governments have always been deeply involved with how a their countries’ currency is ran and the policies they create are meant to keep a currencies’ value stable and make sure other factors like inflation is under control. Even though as of 2021, there were well over …

How To Gauge Students' Ability To Collaborate?, Christian Servin, Olga Kosheleva, Shahnaz Shahbazova, Vladik Kreinovich

Departmental Technical Reports (CS)

Usually, we mostly gauge individual students' skills. However, in the modern world, problems are rarely solved by individuals, it is usually a group effort. So, to make sure that students are successful, we also need to gauge their ability to collaborate. In this paper, we describe when it is possible to gauge the students' ability to collaborate; in situations when such a determination is possible, we explain how exactly we can estimate these abilities.

Representation And Strategy Learning For Variable-Size Tree Transformation Using Reinforcement Learning, Shirin Hosseini Shirvani

Computer Science and Engineering Dissertations - Archive

Trees as acyclic graphs are ubiquitous in representing different context where they encode connectivity patterns at all scales of organization, from biological systems to social networks. Trees are powerful resources which have been used many times for the exploration and discovery of interactions and properties in different context. Tree data structure representation approaches have led to remarkable discoveries in different real-world applications. In the last decades, extensive research and algorithms have been developed on tree or acyclic graph data structures with deep theoretical properties. The cost of solving these various problems ranges from simple linear time algorithms, to more complex …

On The Efficacy Of Knowledge Graph Completion Methods, Accuracy Measures And Evaluation Protocols, Farahnaz Akrami

Computer Science and Engineering Dissertations - Archive

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used some specific benchmark datasets to evaluate such models. Most triples in those datasets belong to reverse and duplicate relations, which exhibit high data redundancy due to semantic duplication, correlation, or data incompleteness. This is a case of excessive data leakage—a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product …

Decoupling-Based Approach To Centrality Detection In Heterogeneous Multilayer Networks, Kiran Mukunda

Computer Science and Engineering Theses - Archive

Graph analysis is one of the techniques widely used for data analysis. It is used extensively on single graphs. Its ability to capture entities and relationships makes it an attractive data model. Search on graphs, such as finding triangles, cliques, shortest paths, etc., and aggregate analysis, such as communities, substructure, or centrality measures have well-defined algorithms for single graphs. The centrality measure, which is the focus of this thesis, identifies the most important nodes in a graph or network. While there are many centrality measures, the most commonly used ones are degree and betweenness centrality. Algorithms for analyzing these measures …

Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain

Computer Science and Engineering Dissertations - Archive

Machine learning-based decision support systems bring relief to the decision-makers in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the embedded patterns. However,these decisions can also absorb and amplify bias embedded in the data. An increasing number of applications of machine learning-based decision sup-port systems in a growing number of domains has directed the attention of stake-holders to the accuracy, transparency, interpretability, cost effectiveness, and fairness encompassed in the ensuing decisions. In this dissertation, we have focused on fairness and accuracy …

Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran

Computer Science and Engineering Dissertations - Archive

Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled data. With the limited availability of labeled data, it is challenging to obtain great performance. Moreover, in many real-world problems, transfer learning has been applied to cope with limited labeled training data. Transfer learning is a machine learning paradigm where pre-trained models on one task can be reused for another task. This dissertation investigates transfer learning and related machine learning techniques such as domain adaptation on visual categorization applications. At …

Modeling Factual Claims With Semantic Frames: Definitions, Datasets, Tools, And Fact-Checking Applications, Fatma Arslan

Computer Science and Engineering Dissertations - Archive

As social media sites have become major channels for the quick dissemination of news, misinformation has become a significant challenge for our society to tackle. Today fact-checking rests primarily on the shoulders of human fact-checkers who laboriously sift through various trustworthy sources, interview subject experts, and check references before reaching a verdict regarding the degree of truthfulness of a factual claim. Compounded with the speed and scale at which misinformation spreads, the demanding process may leave many harmful factual claims unchecked. In the fight to curb the spread of misinformation, researchers from various disciplines have come forward to assist fact-checkers …

Transitioning From Vue 2 To Vue 3, Adele Kanley

Theses/Capstones/Creative Projects

Frontend development is a field that is constantly changing because of the vast amounts of tools that are made available each year. One of the most popular frameworks being utilized to create fluid user experience is the Vue framework. Branching from the well-known Angular.js, Vue.js is an independent open-source project that is making its mark in the user interface community.

Regardless of the popularity of a framework, updates are inevitable to keep up with the innovations required by the IT Field. To ensure that UNO IS&T students are being offered opportunities to learn and develop in the most update to …

Credit Assignment In Multiagent Reinforcement Learning For Large Agent Population, Arambam James Singh

Dissertations and Theses Collection (Open Access)

In the current age, rapid growth in sectors like finance, transportation etc., involve fast digitization of industrial processes. This creates a huge opportunity for next-generation artificial intelligence system with multiple agents operating at scale. Multiagent reinforcement learning (MARL) is the field of study that addresses problems in the multiagent systems. In this thesis, we develop and evaluate novel MARL methodologies that address the challenges in large scale multiagent system with cooperative setting. One of the key challenge in cooperative MARL is the problem of credit assignment. Many of the previous approaches to the problem relies on agent's individual trajectory which …

Teaching Students How To Code Qualitative Data: An Experiential Activity Sequence For Training Novice Educational Researchers, Jennifer E. Lineback

University of South Florida (USF) M3 Publishing

Coursework on qualitative research methods is common in many collegiate departments, including psychology, nursing, sociology, and education. Instructors for these courses must identify meaningful activities to support their students’ learning of the domain. This paper presents the components of an experiential activity sequence centered on coding and coding scheme development. Each of the three component activities of this sequence is elaborated, as are the students’ experiences during their participation in the activities. Additionally, the issues concerning coding and coding scheme development that typically emerge from students’ participation in these activities are discussed. Results from implementations of both in-person (face-to-face) and …