Physical Sciences and Mathematics Commons^™

On Improving The Memorability Of System-Assigned Recognition-Based Passwords, Mahdi Nasrullah Al-Ameen, Sonali T. Marne, Kanis Fatema, Matthew Wright, Shannon Scielzo

Computer Science Faculty and Staff Publications

User-chosen passwords reflecting common strategies and patterns ease memorization but offer uncertain and often weak security, while system-assigned passwords provide higher security guarantee but suffer from poor memorability. We thus examine the technique to enhance password memorability that incorporates a scientific understanding of long-term memory. In particular, we examine the efficacy of providing users with verbal cues—real-life facts corresponding to system-assigned keywords. We also explore the usability gain of including images related to the keywords along with verbal cues. In our multi-session lab study with 52 participants, textual recognition-based scheme offering verbal cues had a significantly higher login success …

Exact Generalized Voronoi Diagram Computation Using A Sweepline Algorithm, Daniel Marsden

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Voronoi Diagrams can provide useful spatial information. Little work has been done on computing exact Voronoi Diagrams when the sites are more complex than a point. We introduce a technique that measures the exact Generalized Voronoi Diagram from points, line segments and, connected lines including lines that connect to form simple polygons. Our technique is an extension of Fortune’s method. Our approach treats connected lines (or polygons) as a single site.

Automation Of Feature Selection And Generation Of Optimal Feature Subsets For Beehive Audio Sample Classification, Aditya Bhouraskar

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The last couple of decades have witnessed an abnormal phenomenon of reduction in the bee population, this is a serious matter of concern as three out of four crops available globally have honey bee as their sole pollinator causing significant economic losses and an unbalance in the ecosystem. There have been many theories about the cause of bee colony collapses such as parasites, pesticides and poor nutrition however conclusive evidence of this phenomenon is yet to be identified.

Human inspection of beehives requires precision. It takes an experienced beekeeper to determine the health of a hive by the sounds generated …

Micro Grid Control Optimization With Load And Solar Prediction, Shaju Saha

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Using renewable energy can save money and keep the environment cleaner. Installing a solar PV system is a one-time cost but it can generate energy for a lifetime. Solar PV does not generate carbon emissions while producing power. This thesis evaluates the value of being able to make accurate predictions in the use of solar energy. It uses predicted solar power and load for a system and a battery to store the energy for future use and calculates the operating cost or profit in several designed conditions. Various factors like a different place, tuning the capacity of sources, changing buy/sell …

Acquisition, Processing, And Analysis Of Video, Audio And Meteorological Data In Multi-Sensor Electronic Beehive Monitoring, Sarbajit Mukherjee

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures.

Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top …

Job Satisfaction And Employee Turnover Determinants In Fortune 50 Companies: Insights From Employee Reviews From Indeed.Com, Bishal Sainju

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We explored 682176 employee reviews of Fortune 50 companies from Indeed.com using topic discovery techniques like Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) to identify salient aspects in employee reviews and automatically infer latent topics that tend to drive employee satisfaction. We also studied how various satisfaction factors could be related to employee turnover. We discovered important topics in the reviews, including Management and Leadership, Advancement Opportunity, Pay and Benefits, Work-Life Balance, and Culture, which we compare to the five Job Descriptive Index (JDI) facets. Both LDA and STM discovered well-separated and distinguishable topics. We also …

Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Developing a strategy for stock trading is a vital task for investors. However, it is challenging to obtain an optimal strategy, given the complex and dynamic nature of the stock market. This thesis aims to explore the applications of Reinforcement Learning with the goal of maximizing returns from market investment, keeping in mind the human aspect of trading by utilizing stock prices represented as candlestick graphs. Furthermore, the algorithm studies public interest patterns in form of graphs extracted from Google Trends to make predictions. Deep Q learning has been used to train an agent based on fused images of stock …

Visual Saliency Estimation And Its Applications, Fei Xu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The human visual system can automatically emphasize some parts of the image and ignore the other parts when seeing an image or a scene. Visual Saliency Estimation (VSE) aims to imitate this functionality of the human visual system to estimate the degree of human attention attracted by different image regions and locate the salient object. The study of VSE will help us explore the way human visual systems extract objects from an image. It has wide applications, such as robot navigation, video surveillance, object tracking, self-driving, etc.

The current VSE approaches on natural images models generic visual stimuli based on …

An Analysis Of Syntax Exercises On The Performance Of Cs1 Students, Shelsey B. Sullivan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Students in introductory programming classes (CS1) generally have a difficult time learning the rules of programming. Although the general concepts of programming are relatively easy to learn, it can be difficult to learn what exactly can be typed in what order, which is known as syntax. To attempt to help students overcome this barrier, a study was conducted that introduced exercises into a CS1 class which taught the programming syntax in simple steps. The results of this study were obtained by analyzing the keys the students pressed, the errors of their code, their midterm exam scores, and their responses to …

Automating And Analyzing Whole-Farm Carbon Models, Aditi Maheshwari

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The goal of this research is to learn about whole farm carbon models. A whole farm carbon model estimates the emissions of greenhouse gasses (GHGs) based on information for a farm. We analyzed two models, HOLOS whole-farm and COMET-Farm, by running the models on random inputs and building classifiers from the runs. HOLOS estimates GHG emissions for a particular year based on crop and animal agriculture input, while COMET-farm adds past and future farm management practices. Users of the models must manually enter farm data through a graphical user interface (GUI), which is a good method for a single farm, …

Facial Expression Recognition In The Wild Using Convolutional Neural Networks, Amir Hossein Farzaneh

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Facial Expression Recognition (FER) is the task of predicting a specific facial expression given a facial image. FER has demonstrated remarkable progress due to the advancement of deep learning. Generally, a FER system as a prediction model is built using two sub-modules: 1. Facial image representation model that learns a mapping from the input 2D facial image to a compact feature representation in the embedding space, and 2. A classifier module that maps the learned features to the label space comprising seven labels of neutral, happy, sad, surprise, anger, fear, or disgust. Ultimately, …

Solar Irradiance Prediction Using Xg-Boost With The Numerical Weather Forecast, Pratyusha Sai Kamarouthu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

To defeat global warming, the world expects to look at renewable energy sources. Solar energy is one of the best renewable energy sources which causes no harm to the environment. As solar energy changes with atmospheric parameters like temperature, relative humidity, cloud coverage, dewpoint, sun position, day of the year, etc. It is difficult to understand its nature by science. Predicting solar irradiance which is directly proportional to solar energy using atmospheric parameters is the main goal of this work. Powerful artificial intelligence algorithms that won many coding competitions have been used to predict it. Using these methods and numerical …

Development And Identification Of Metrics To Predict The Impact Of Dimension Reduction Techniques On Classical Machine Learning Algorithms For Still Highway Images, Wasim Akram Khan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We are witnessing an influx of data - images, texts, video, etc. Their high dimensionality and large volume make it challenging to apply machine learning to obtain actionable insight. This thesis explores several aspects pertaining to dimensional reduction: dimension reduction methods, metrics to measure distortion, image preprocessing, etc. Faster training and inference time on reduced data and smaller models which can be deployed on commodity hardware are a critical advantage of dimension reduction. For this study, classical machine learning methods were explored owing to their solid mathematical foundation and interpretability.

The dataset used is a time series of images from …

Machine Learning Enhanced Free-Space And Underwater Oam Optical Communications, Patrick L. Neary

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Communications, bandwidth, security, and hardware simplicity are principles of interest to society at large. Recent advances in optics and in understanding properties of light, such as orbital angular momentum (OAM), have provided new potential mediums for communication.

Machine learning has wound its way into a broad range of fascinating areas. An emerging field of research is the use of a unique property of lasers called orbital angular momentum (OAM). With the proper hardware, a laser can go from a Gaussian shaped distribution to a doughnut shaped pattern, where the radius can be changed. Multiple OAM patterns, or modes, can be …

Load Forecasting Analysis Using Contextual Data And Integration With Microgrids Used For Off Grid Ev Charging Stations, Ashit Neema

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Electricity is an essential component of the smooth working of every sector. If a successful prediction of how much electricity will be required for say the next 24 hours or 48 hours can be made, it will not only help in efficiently planning the activities and operations but also help in minimizing the cost incurred. In this thesis the same is being attempted, first, a model is created that can predict the energy consumption of households using various tools available. To achieve this, historical data of the past 5 years that has been recorded in London has been used. Secondly, …

Satc: Core: Medium: Collaborative: Baitbuster 2.0: Keeping Users Away From Clickbait, Mahdi Nasrullah Al-Ameen

Funded Research Records

No abstract provided.

Developing Open Source Software Using Version Control Systems: An Introduction To The Git Language For Documenting Your Computational Research, Jared D. Smith, Jonathan D. Herman

All ECSTATIC Materials

Version control systems track the history of code as it is committed (saved) by any number of developers. Have you made a coding error and cannot debug it? Version control systems allow for resetting code back to when it worked, and show what code has changed since previous commits.

The contents of this lecture provide an introduction to the git version control language, GitHub for cloud hosting open source code repositories, and tutorials that demonstrate common and useful git and GitHub practices. This lecture is intended to be coupled with a discussion on creating reproducible computational research.

The zipped folder …

Machine Learning-Based Signal Degradation Models For Attenuated Underwater Optical Communication Oam Beams, Patrick L. Neary, Abbie T. Watnik, K. Peter Judd, James R. Lindle, Nicholas S. Flann

Computer Science Faculty and Staff Publications

Signal attenuation in underwater communications is a problem that degrades classification performance. Several novel CNN-based (SMART) models are developed to capture the physics of the attenuation process. One model is built and trained using automatic differentiation and another uses the radon cumulative distribution transform. These models are inserted in the classifier training pipeline. It is shown that including these attenuation models in classifier training significantly improves classification performance when the trained model is tested with environmentally attenuated images. The improved classification accuracy will be important in future OAM underwater optical communication applications.

A Robust Structured Tracker Using Local Deep Features, Mohammadreza Javanmardi, Amir Hossein Farzaneh, Xiaojun Qi

Computer Science Faculty and Staff Publications

Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization …

Teaching Distributed Application Design Using Drones, Cameron Frandsen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis provides a new platform that instructors can use to create a learning environment for students to learn how to design and implement distributed applications. One possible way to use the platform uses milestones to test the student’s understanding of different concepts. Milestones contain pretests that students can use to test their code. Each milestone focuses on a different concept, including interprocess communication, reliability, and security. A monitor process can be created by the students to provide away for the instructor to see how well the students are learning the concepts by using a monitor process. The platform gives …

Predicting The Impact Of Weather On Rural Travel Times Using Now-Cast Weather Forecast Data, Manish Meshram

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In the states which record extreme weather conditions and high snow in winters, the travel time to drive between cities can get highly affected due to these bad weather conditions. The present solutions to tackle this problem are largely flow or time related and do not take weather conditions into account while making the predictions about travel time. Also these solutions can mostly be used for real time travel and not the future travel. In addition to that, the studies that have been done in this space are mostly for urban travel times but most parts of the interstate highways …

Retention Of Women In Computer Science: Why Women Persist In Their Computer Science Majors, Katarina Pantic

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Retention of women through graduation in Computer Science (CS) majors is one of the biggest challenges for CS education. Most research in this area focuses on factors influencing attrition rather than why and how women remain committed. The goal of this research study is to understand retention from the perspective of women who persisted in their CS major. Using the theoretical lens of legitimate peripheral participation in communities of practice, I designed and conducted a study that involved focus groups, interviews, journey maps, and experience sampling methods. I found that retention of women in this study was influenced by four …

Using Imagery To Improve Program Quality In Computer Programming, Joseph S. Ditton

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A common practice in sports is to review film footage of a game. This is done in the hope that individuals on the team or the a team as a whole might learning something about technique, form, process, or strategy that they either did well on or need to improve on. We believe that the benefits of doing this could extend to computer science, and more specifically, the process of writing a computer program. This has never been done in the past, as such this research is exploratory in nature. A new tool called Phanon can record someone writing a …

High Dimensional Event Exploration Over Multiple Simulations, Steven Deron Scott

Undergraduate Honors Capstone Projects

In this project, we introduce a visualization technique to analyze event simulation data. In particular, we allow the user to discover families of events based on the topological evolution of discrete events across simulations. Discovering how events behave across runs of a simulation has applications in financial market analysis, military simulations, physical mechanics, and other settings. Our approach is to use established methods to produce a linearized tour through parameter space of arbitrary dimension and visualize events of interest in two dimensions, where the first dimension is the tour ordering and the second dimension is usually time. This paper presents …

The Two Types Of Society: Computationally Revealing Recurrent Social Formations And Their Evolutionary Trajectories, Lux Miranda

Undergraduate Honors Capstone Projects

Comparative social science has a long history of attempts to classify societies and cultures in terms of shared characteristics. However, only recently has it become feasible to conduct quantitative analysis of large historical datasets to mathematically approach the study of social complexity and classify shared societal characteristics. Such methods have the potential to identify recurrent social formations in human societies and contribute to social evolutionary theory. However, in order to achieve this potential, repeated studies are needed to assess the robustness of results to changing methods and data sets. Using an improved derivative of the Seshat: Global History Databank, we …

Understanding Personal Data In The World Of Social Media, Nicholas Scott Rodgers

Undergraduate Honors Capstone Projects

Personal data is behind many of the online interactions that people have through social media and other online sites and services. This data allows sites to understand their users, which in turn allows them to provide better content for their users. This data is also used to determine user interests, which these online services use to target more relevant advertising to their users, and share the information that they collect about their users with third parties. It is only recently that this personal data is being regulated by lawmakers, the businesses running these sites are held accountable for managing the …

Infrared And Visible Image Fusion Based On Oversampled Graph Filter Banks, Chunyan Song, Xueying Gao, Yu-Long Qiao, Kaige Zhang

Computer Science Student Research

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multiscale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is …

Application Of Digital Particle Image Velocimetry To Insect Motion: Measurement Of Incoming, Outgoing, And Lateral Honeybee Traffic, Sarbajit Mukherjee, Vladimir Kulyukin

Computer Science Faculty and Staff Publications

The well-being of a honeybee (Apis mellifera) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed …