Final Examination & Defense Schedule
Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. Check back mid quarter for any new quarter defenses. View previous quarter schedules.
Select a master's program to navigate to candidates:
Master of Science in Computer Science & Software Engineering
SPRING 2023
Tuesday, May 2
AMITA RAJPUT
Chair: Dr. Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Checking Security Design Patterns In Source Code
A big challenge for software developers, engineers, and programmers is that the software they write may be subject to attacks by hostile actors. One way to address this problem is to use Security Design Patterns (SDP), but many software engineers are unaware of these patterns or do not have the proper understanding of them.
Currently, our research group has been working on finding existing SDPs in source code, to help software engineers determine if they are missing any SDPs. My project builds on this by not only finding additional SDPs in source code but also checking whether they are correctly implemented. During these studies, I will deep dive and find the bigger issue of whether software developers are unaware of the SDPs or they know about them but wrongly implement them. An improvement in this area of research will be helpful for programmers to identify errors in both existing and new programs quickly and fix the vulnerabilities faster and more efficiently. Hundreds of thousands of software engineers and programmers working at big tech companies such as Norton, Microsoft, Oracle, and Adobe, and writing thousands of lines of source codes every day will be highly benefited from my research. An automated process will help them save hundreds of their man hours every week and put them into more value-adding tasks. It brings higher productivity and efficiency to the organizations and also ensures a more robust firewall against outside attacks on the organization's proprietary data. This helps to safely keep the users’ private data which eventually helps the organizations retain their credibility and market share among their customers.
Tuesday, May 16
YIFEI YANG
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Agents Visualization and Web GUI Development in MASS Java
Multi-Agent Spatial Simulation (MASS) is an agent-based modeling (ABM) library that supports parallelized simulation over a distributed computing cluster. Places is a multi-dimensional array of elements, each called Place, which are dynamically allocated within the cluster. Agents is a set of execution instances that can reside on a Place and migrate to any other Place with global indices. MASS UI consists of two parts: InMASS and MASS-Cytoscape. InMASS allows users to execute commands line by line in an interactive way and provides users with additional features. MASS-Cytoscape enables users to visualize Places and Agents in Cytoscape. However, the current implementation of InMASS hacked MASS too much and became incompatible with the latest versions of MASS. The current visualization is limited to a single computing node and to agent existence. Moreover, the recent MASS does not have a web interface to simplify operations. To address these problems, the goals of this project are: (1) re-engineering the current implementation of InMASS; (2) developing place visualization of 2D continuous space, Binary Tree, and Quad Tree. Improve current Agent visualization; and (3) designing an all-in-one WEB GUI for InMASS design. We adopted the existing features to accomplish the first goal, re-implemented InMASS features, including dynamical loading, checkpointing/rollback, and agent history tracking; and optimized the current codebase. These modifications open the possibility of the future expansion of InMASS and allow InMASS to serve all MASS users. The project extended the current Places and Agents visualization for distributed settings and more descriptive Agents information, optimized the operation logic of MASS control panel. These additions and optimizations made it easy to use and analyze simulations. The implementation of the web interface enables users to monitor their clusters. And it provides a basic frame for future developers to add on more practical functions.
POOJA PAL
Chair: Mark Kochanski
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Enhancements in Teaching Tools: An Application to Simplify the Complexities in Course Management
Canvas is a web-based learning management system, or LMS, that allows institutions to manage digital learning, educators to create and present online learning materials and assess student learning, and students to engage in courses and receive feedback about skill development and learning achievement. Canvas features specifically designed to meet a variety of institutional, educational, and learning needs. However, Canvas can be improved with new features to increase the productivity of instructors and students using the system. This capstone project is part of a team effort developing a browser-based full stack application that supports new features by following software engineering principles, performing feature enhancements, and comparative analysis. The project’s focus is to build several independent features in the Teaching Tools application as a first step towards making it as a component to be embedded within Canvas to have a great digital learning experience at the University of Washington - Bothell along with practicing software engineering principles to ensure efficient system design and user experience.
Thursday, May 18
BRANDON VASSION
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Online
Thesis: Investigating Constrained Objects in AR for Validation of Real-life Models
Augmented Reality (AR) studies the approaches that enhance reality by integrating virtual content into the physical environment in real-time. In the simplest form, virtual objects in the physical environment are stationary, where AR applications serve as powerful tools for visualization. The support of interaction with objects in the environment brings the AR application from being passive for observing the augmented world to one where the user can actively explore. When the interactions follow intuitive physical world constraints, an AR application, or constraint-based AR, can immerse users in a realistic augmented world.
We categorize existing constraint-based AR by the relationship between and interaction of the objects being constrained: virtual objects constrained by virtual objects, physical by virtual, and virtual by physical. This straightforward classification provides insights into the types of and potentials for useful applications. For example, virtual by virtual can describe the pages of a virtual book being constrained where the corresponding interaction would be the flipping of the virtual pages. In contrast, physical by virtual would mean placing a physical coffee cup over the virtual book. Lastly, virtual by physical would be placing and pushing the virtual book on an actual physical desktop. The subtle and yet crucial differences are that in the first case, the objects and the interactions can also be carried out in a pure virtual 3D world, physical by virtual has practical implementation challenges, and that, virtual by physical presents an interesting opportunity for immersing and engaging users.
This project investigates using virtual by physical constraint-based AR to validate the functionality and visuals of real-life models. We observe and identify common and representative real-world interaction constraints to include: 1D sliding, 2D planar sliding, hinged rotation, and the potential for combining these constraints. The project examines the functionality, interactability, and integration of these constraints in practical applications, in this case, a home decoration setting. With the results from an initial technology investigation, aiming to achieve accuracy and reliability in interactions, we have chosen marker-based AR through Vuforia with Unity3D. We have derived a systematic workflow for creation and have demonstrated successful integration of virtual objects into the real world with relevant constraints by corresponding physical objects. Our prototype results are various versions of an augmented room with distinct decorative virtual objects that are constrained by relevant physical objects where the interactions are intuitive and integrations essentially seamless. These rooms support multiple constrained objects functioning in the same environment.
Our categorization points to a well-defined AR application domain, virtual by physical, for investigation. The success of the augmented rooms demonstrates the usefulness of this category of constraint-based AR applications in validating functionality and visuals. Lastly and significantly, our formulated workflow for constructing virtual by physical constraint-based AR applications serves as an efficient and effective template for future investigations into this domain.
Monday, May 22
IRENE LALIN WACHIRAWUTTHICHAI
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Multi-Stream, Multi-Modal Dataset Viewer That Supports the Navigational Work of Wide-Field Ethnography Research
Wide-Field Ethnography (WFE) refers to an approach of gathering and analyzing large datasets of videos, audio files, screen capture, photos, and other artifacts related to the intricate intermingling of human subjects with computer systems and the social relationship and collaborations among these entities. WFE datasets are high in volume, containing multiple types of data and multiple data sources capturing the same events or moments of interest. For instance, the BeamCoffer datasets has 6 terabytes of video and audio recordings of software developers at work, videos of their computer screens, and thousands of photographs. The sheer volume of data gathered and its modal diversity make it hard to navigate the dataset to find the moments that are meaningful to the research question, especially if one wants to simultaneously play more than one video or audio file to concurrently see and hear different perspectives of the action unfolding at a particular moment of time. There are currently no tools that offer a reasonable way to navigate a WFE dataset. This project describes a software system built to help researchers navigate through large multi-stream, multi-modal datasets effectively and efficiently: the WFE Navigator.
KOROSH MOOSAVI
Chair: Dr. Afra Mashhadi
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Online
Project: Domain-Specific NLP Recommendation System for Live Chat
Twitch.tv is one of the oldest and most popular livestreaming platforms in use today, where a unique culture of emote usage and niche language has developed. Emotes are custom-made images, or GIFs, used in chat with varying degrees of access. Emotes render standard forms of English NLP ineffective, even when using models trained on data from social media posts including traditional emoji. The largest prior study created a Word2Vec model of the 100 most popular emotes across Twitch for sentiment analysis. This project branches from this work by creating a chat recommender system with a model trained on more recent data. The system finds similar emotes in a new channel for users based on their available emotes, allowing for easier onboarding and moderation in the chat. Users are recommended new channels based on the usage of emotes in a channel they are already familiar with.
Tuesday, May 23
SAHANA PANDURANGI RAGHAVENDRA
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Agent-Based GIS Queries
A Geographical Information System (GIS) is a vital software tool used across numerous domains to help store, manage, analyze, and visualize geospatial data. One of the core functions of the GIS is its ability to query, enabling scientists and researchers to analyze and discover underlying patterns and associations among various data layers. However, it is extremely time and computationally intensive to process complex spatial GIS queries on a single standalone system sequentially. Therefore, in this capstone project we parallelize GIS queries using an agent-based parallelization framework, Multi-Agent Spatial Simulation (MASS), and further explore the idea of incorporating computational geometry algorithms such as closest pair of points, range search and minimum spanning tree to GIS queries using agent propagation.
The major motivation behind integrating MASS library and GIS queries stems from the results of previous research in comparing MASS with other popular big data streaming tools. This research observed that agent-based computation using MASS yielded competitive performance and intuitive parallelization when introduced into data structures such as graphs. To verify this hypothesis of agent’s superiority, we now would like to utilize MASS Agents in GIS queries where agents utilize computational geometry problems to find results of GIS queries through propagation over MASS Places spread across different computing nodes.
The significant contributions of this capstone project are to demonstrate GIS queries as a practical application of agent-based data analysis. Further, this project focuses on migrating the previous implementation of MASS-GIS system from Amazon Web Services (AWS) to the University of Washington Bothell computational clusters consisting of 24 computing nodes to achieve scalability and fine-grained partitioning of the GIS datasets suitable for agent-based parallel GIS queries. Sequential and parallel, attribute and spatial GIS queries are designed and implemented in this project using contextual query language (CQL) modules from GeoTools (open-source GIS package) and MASS. Additionally, we also extend and integrate the previous research on computational geometry algorithms using MASS to GIS queries. Algorithms such as the closest pair of points are incorporated into GIS queries to find the closest cities within a certain distance from a given city. Likewise range search is used to find all the cities in a given country given the range of geographical bounds of a country and minimum spanning tree is extended to find the shortest path between two points on a map. Lastly, we evaluate the performance of parallel agent-based GIS queries implemented using MASS. The results show that agent-based GIS queries using MASS-CQL and the closest pair of points algorithm are time efficient. Furthermore, MASS based GIS queries using computational geometry algorithms of the closest pair of points and range search provide 100% accuracy. However better optimization techniques need to be applied to improve the performance of agent-based GIS queries using the range search algorithm.
JASKIRAT KAUR
Chair: Dr. Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Improving the Accuracy of Mapping Vulnerabilities to Security Design Patterns
The increasing incidence of software vulnerabilities poses a significant threat to businesses and individuals worldwide. According to a threat report by Nuspire, 2022 was a record-breaking year for cyber threats, thus making mitigating vulnerabilities even more important. Identifying and mitigating vulnerabilities is challenging due to their complexity and the varied and increasing number of potential security threats that threaten the integrity of the software. Many researchers have proposed methods to identify vulnerabilities. Seyed Mohammad Ghaffarian and Hamid Reza Shahriari used data mining and machine learning techniques to discover vulnerabilities in their paper Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. Similar to their work, a substantial amount of work has been done on discovering vulnerabilities and doing analysis on them but not much has been done to predict security patterns to mitigate vulnerabilities.
To discover security design patterns for security vulnerabilities, Sayali Kudale developed a project for predicting security patterns by using keyword extraction and text similarity techniques. This capstone study extends her work. It proposes techniques and measures different similarity metrics, to improve precision by extending the Common Weakness Enumeration (CWE) dataset by including the Top Ten standards of the Open Worldwide Application Security Project (OWASP) data in each CWE vulnerability description. We have also manually verified the ground truth data using the mitigations described by LeBlanc et al. in the book “24 deadly sins of software security”. To draw comparisons we worked on 4 datasets: 1. The security design document; 2. The Common Weakness Enumeration (CWE) vulnerabilities; 3. The extended dataset includes both CWE and OWASP data; and 4. Ground truth data.
To implement this we have executed the keyword extraction technique, Rapid Automatic Keyword Extraction (RAKE) using which we extracted keywords from the security pattern and CWE description and mapped them to each other. After this, different similarity measures have been applied to calculate the similarity metrics of the mapping. We then used the ones that gave the best results and tested them again on the two datasets to compare precision. The evaluation results indicated that the extended dataset gave better precision and accuracy.
CONNOR BENJAMIN BROWNE
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Discovery Hall 464
Thesis: Evaluating the Effectiveness of Preprocessing Methods on Motor Imagery Classification Accuracy in EEG Data
Classification of motor imagery tasks is of significant interest in brain-computer interfacing today. Electroencephalograph data contains a large amount of noise obfuscating the signal associated with these motor imagery tasks. Various preprocessing techniques exist to increase the signal-to-noise ratio allowing for more accurate classifications. The effectiveness of these techniques varies between motor imagery tasks and in different environments. There is a need to evaluate these different techniques in many different environments and with different motor imagery tasks. This thesis investigates the effectiveness of several preprocessing techniques and classification models for classifying four different motor imagery tasks from EEG data. Specifically, Frequency Filtering, ICA, and CSP are evaluated using Naive Bayes, kNN, Linear SVM, RBF SVM, LDA, Random Forest, and a MLP Neural Network.
To control for the environment data was collected from student volunteers in short sessions designed to demonstrate either eye blinking, eye rolling, jaw clenching, or neck turning. Each task had its own procedure for the session. Motor imagery tasks in data were evaluated for frequency and amplitude commonalities using continuous wavelet transforms and Fourier transforms. Preprocessing Techniques were then iteratively applied to these datasets and evaluated using an ML model. The evaluation metrics used were Accuracy, F1, Precision, and Recall.
Results showed that the combination of Frequency Filtering, ICA, and CSP with the Naive Bayes model yielded the highest accuracy and F1 for all motor imagery tasks. These findings contribute to the field of EEG signal processing and could have potential applications in the development of brain-computer interfaces. It also directly contributes to a greater project in spatial neglect rehabilitation by providing novel insights to common artifacts in EEG data, as well as to the creation of a framework for data processing in real-time and offline.
Wednesday, May 24
ANIRUDH POTTURI
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall 464
Project: Programmability and Performance Analysis of Distributed and Agent-Based Frameworks
In big data, the importance shifts from raw text presentation of data to structure and space of the data. Computational geometry is an area of interest, particularly for the structure and distribution of data. Thus, we propose using Agent-Based Modelling (ABM) libraries for big data to leverage the benefits of parallelization and support the creation of complex data structures like graphs and trees. ABMs offer a unique and intuitive approach to solving problems by simulating the structural elements over an environment and using agents to break these problems down using swarming, propagation, collisions, and more. For this research, we introduce using Multi-agent Spatial Simulations (MASS) for big data. We compare the programmability and performance of MASS Java against Hadoop MapReduce and Apache Spark. We have chosen six different applications in computational geometry implemented using all three frameworks. We have conducted a formal analysis of the applications through a comprehensive set of tests. We have developed tools to perform code analysis to compute metrics like identifying the number of Lines of Code (LoC) and computing McCabe's cyclomatic complexity to analyze the programmability. From a quantitative perspective, in most cases, we found that MASS demanded less coding than MapReduce, while Spark required the least. While the cyclomatic complexity of MASS applications was higher in some cases, components of Spark and MapReduce applications were highly cohesive. From a qualitative viewpoint, MASS applications required fine-tuning resulting in significant improvements, while MapReduce and Spark offered very limited performance enhancement options. The performance of MASS directly correlates with the data, unlike MapReduce and Spark, whose performance is not affected by the distribution of data.
BALAJI RAM MOHAN CHALLAMALLA
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Online
Project: Yeast Analysis Tool
This paper presents an improvement of an application called the Yeast Analysis tool, which was developed by Dr. Lagesse to assist a group of biology researchers in their yeast analysis research project. The researchers aim to understand the cell division and chemical composition of yeast cells using fluorescent proteins. To achieve this, they need to examine the microscopic images of yeast cells and measure the distance between their nuclei. However, this is a tedious and error-prone task. They must segment the images manually, input the data, and check the accuracy. And even then, they are not sure about their outcomes. They require a better approach. That is why Dr. Lagesse created the Yeast Analysis tool, an automated image analysis method that can perform the task for them. It can segment yeast cells in images and measure the distance between their nuclei with high precision and speed. It uses deep learning techniques to learn from the data and enhance its performance. It is a valuable tool for researchers.
However, the Yeast Analysis tool is not perfect. It has large methods and most of the code is written in a single file, which makes it complicated and obscure. It has bugs that cause errors and crashes. It has some limitations and needs some refinements. That makes it hard to use and maintain. The paper focuses on re-architecting, refactoring, improving GUI, and resolving the bugs of the project. Followed best practices such as developing iteratively, managing requirements, and agile software development model to work on this project.
Proposed plugin-based architecture where an application can be created from the collection of different, reusable components that don't rely on one another but can still be assembled dynamically using these components. It helps to extend the functionality of the application without affecting the core structure of the application. Refactoring the code included making the methods modular, and removing the code duplicates. It helped increase the readability of code and increase in the performance by 7.4% of the application. Improving GUI and making the application bug free helps the user to use the application easy to use and increases user productivity. Performed a GUI survey where users said the new GUI is user-friendly and rated 4.3 out of 5. In conclusion, the Yeast Analysis tool is now more user-friendly, reliable, and efficient. It will help the researchers achieve their goals faster and easier. It will advance science and technology in various fields.
SIDHANT BANSAL
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Discovery Hall 464
Project: Refactoring Virtual Reality-Based Orthoptic Toolkit
According to the Centers for Disease Control (CDC) under the Vision Health Initiative, it is noted that approximately 6.8% of children under the age of eighteen years in the United States are diagnosed with vision problems. Vision problems can severely impact a child’s learning.
Strabismus (crossed eyes) is one of the most common eye conditions in children. If left untreated, it can lead to amblyopia, commonly known as lazy eye. To regain binocular vision, a person with strabismus requires training in five levels of fusion skills, each level indicating progression in ability and vision complexity. The existing toolkit uses virtual reality (VR) to provide an environment for individualized, supervised therapy for children suffering from strabismus to regain binocular vision. The toolkit has the following four applications that may be useful for improving vision: luster, simultaneous perception, sensory fusion, and motor fusion. Since each of these applications are a separate application right now, it doesn’t adhere to the non-functional requirements of the overall toolkit. This project aims to evaluate and provide an architecture that will support the nonfunctional requirements i.e., maintainability, portability, and extensibility.
Thursday, May 25
HARSHIT RAJVAIDYA
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: An Agent-based Graph Database
Graph databases are a type of NoSQL databases that use graph structures to store and represent data. Unlike traditional relational databases that use tables and rows to represent data, graph databases use nodes and edges to represent relationships between data items. This allows for more flexible and efficient querying of complex and connected data items. Graph databases provide us with functional capabilities of querying a large number of interconnected data schemas, such as social networks and biological networks. In this project, we aim to build a Graph database using the MASS (Muti-Agent Spatial Simulation) library that relies on Places and Agents as the core components. The MASS library has already supported graph data structure (GraphPlaces) which is distributed on a cluster of computing nodes. However, the current implementation worked on specific graph types. This project implements graph creation using CSV files as generic inputs as possible. We also implement a query-parsing engine that takes OpenCypher queries as inputs and parses it to method calls of MASS GraphPlaces. On top of that we have implemented four types of queries (including where clause, aggregate type, and multi relationship queries) in order to perform verification of the graph database and to perform query benchmarks. Each benchmark measures the query latency, graph creation times, and spatial scalability of all the queries. The performance measurements are performed on a cluster of eight computing nodes, and the spatial scalability is measured using a Twitch monthly dataset, which contains more than 7k nodes and more than 20k edges. The research presents significant improvements in query latency and spatial scalability as we increase the number of computing nodes.
VENKATA RAMANI SRILEKHA BANDARU
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Parallelization of Bio-inspired Computing Algorithms Using MASS JAVA
The exponential growth of big data has posed significant challenges for traditional optimization algorithms in effectively processing and extracting meaningful insights from large-scale datasets. In this context, bio-inspired computing has emerged as a promising approach, drawing inspiration from natural systems and phenomena. By
mimicking biological processes such as evolution, swarm behavior, and natural selection, bio-inspired algorithms offer innovative solutions for optimizing data processing, pattern recognition, classification, clustering, and other tasks related to big data analytics.
Parallelizing bio-inspired computing algorithms is crucial for achieving improved performance and scalability. This accelerates the optimization process and enhances the efficiency of solving challenging problems. Multi-Agent Spatial Simulation (MASS) is an agent-based modelling library that has been used in great extent to parallelize a variety of simulations and data analysis applications. Building on this foundation, the implementation of Bio-inspired Computing algorithms project is an exploration into the advantages of using MASS Java to parallelize computationally complex algorithms.
This project presents the applications of algorithm designs for agent-based versions of Swarm Based Computation, Evolutionary Computation and Ecological Computation Algorithms. In addition to the designs of the algorithms, we present an analysis of programmability and performance comparing MASS Java to another agent based modelling framework named Repast Simphony.
FIONA VICTORIA STANLEY JOTHIRAJ
Chair: Dr. Afra Mashhadi
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Discovery Hall 464
Thesis: Phoenix: Federated Learning for Generative Diffusion Model
Generative AI has made impressive strides in enabling users to create diverse and realistic visual content such as images, videos, and audio. However, training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. Federated learning is an approach that uses decentralized techniques to collaboratively train a shared deep learning model while retaining the training data on individual edge devices to preserve data privacy. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using federated learning techniques. Diffusion models, a newly emerging generative model, show promising results in achieving superior quality images than Generative Adversarial Networks (GANs). Our proposed method Phoenix is an unconditional diffusion model that leverages strategies to improve the data diversity of generated samples even when trained on data with statistical
heterogeneity (Non-IID data). We demonstrate how our approach outperforms the default diffusion model in a federated learning setting. These results are indicative that high-quality samples can be generated by maintaining data diversity, preserving privacy, and reducing communication between data sources, offering exciting new possibilities in the field of generative AI.
Friday, May 26
JEFFREY ALEXANDER KYLLO
Chair: Dr. Afra Mashhadi
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Online
Project: Inflorescence: A Framework for Evaluating Fairness with Clustered Federated Learning
Measuring and ensuring machine learning model fairness is especially difficult in federated learning (FL) settings where the model developer is not privy to client data. This project explores how the application of clustered FL strategies, which are designed to handle data distribution skew across federated clients, affects model fairness when the skew is correlated with privileged group labels. The study report presents empirical simulation results quantifying the extent to which clustered FL impacts various group and individual fairness metrics and introduces a Python package called Inflorescence ("a cluster of flowers") that extends Flower, an open-source FL framework, with several clustered FL strategies from the literature.
PRIANKA BANIK
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Online
Project: Automatic Fake News Detection
With the proliferation of fake news, automatic fake news detection has become an important research area in recent years. However, teaching computers to differentiate between fake and credible news is complex. One of the main challenges is to train computers with an abstract understanding of natural languages. This project introduces a web framework that is capable of classifying fake and real news, employing three different approaches. The first approach uses a TF-IDF vectorizer and a Multinomial Naive Bayes classifier to identify fake news based on the significance of words appearing in the text news. The second approach uses a count vectorizer in place of TF-IDF vectorizer which emphasizes the frequency of words occurring in the news article. As a third strategy, LSTM (long short-term memory networks) neural network is implemented along with the word embedding technique to improve classification accuracy. Experimental results compare these three models with some of the existing works and a comparative analysis of multiple fake news detection techniques is presented to justify the effectiveness of the proposed system.
Tuesday, May 30
SANJAY VARMA PENMETSA
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Collabotative Rhythm Analysis For Endangered Languages
Nearly 40% of 7000 languages in the world are expected to become extinct if no efforts are made to preserve them. To preserve the indigenous language and the heritage and culture associated with it, there is a significant need to analyze and document these languages. Blackfoot is an endangered language with approximately 3,000 native speakers left. It is used in the regions of Alberta in Canada, and Montana in the U.S.A. As a pitch accent language, meaning of Blackfoot words is dependent on the pitch patterns in addition to the spelling of the words. This makes it especially difficult to learn and teach the language. To address this need, MeTILDA (Melodic Transcription in Language Documentation and Application) was developed by Dr. Min Chen’s research group in collaboration with researchers at the University of Montana. It is a cloud-based platform to support the documentation and analysis of the Blackfoot language.
The primary goal of this capstone project is to enhance collaboration and data reuse on the MeTILDA platform. To achieve this goal, we have implemented several key features that are designed to improve user engagement and increase the overall efficiency of the platform. Firstly, to achieve improved collaboration, our project allows multiple users to work together for creating a Pitch Art, on the Create page. Secondly, we introduced enhancements to the file system, that includes the ability to share files with different levels of access, on the My Files page. Finally, to improve data reusability, we made significant improvements to the way Pitch Arts are saved to the Collections page. Specifically, allowing users to have the ability to modify the saved Pitch Art.
To ensure the quality of our implementation, we conducted extensive unit and load testing to identify any bugs or performance issues that could impact user experience. Additionally, we conducted a usability study with a diverse group of population to evaluate the effectiveness of the new features. The results of the study indicated that our improvements help in streamlining the workflow and improve the overall productivity on the MeTILDA platform. Furthermore, we published a paper at ACM ICMR 2023 with details to replicate and evaluate several main MeTILDA functions . Given the urgency in endangered language research, our ICMR paper helps share resources and knowledge among interested individuals in academic and local communities, and enables the operation, customization, and extension of our toolsets.
MEGANA REDDY BODDAM
Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering
3:30 PM.; Online
Project: Interpretation of a Residual Neural Network and an Inception Convolutional Neural Network Through Concept Whitening Layers
Deep Learning models are difficult to interpret because they are complex, non-linear, and high dimensional algorithms. This paper's goal is to contribute to interpreting one of these deep learning models: convolutional neural networks. Interpretive analysis is performed in the context of predicting Hepatocellular Carcinoma (HCC), the most common type of primary liver cancer, from liver tissue histopathology images. The convolutional neural network models analyzed are a 50 layer residual neural network and an inception convolutional network. The results from the predictive training and testing of the models show that the accuracy of models remains the same regardless of adding the interpretive training technique of concept whitening layers. Additionally, the results also show a greater interpretive power with concept whitening layers added to the model through post-hoc analysis methods, specifically inter-concept similarity rating, intra-concept similarity rating, concept importance rating, and feature vector displays.
TYLER CHOI
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Online
Project: Enhancing Search and Rescue Operations: A Pragmatic Application of User-Centered Development
This capstone paper investigates the development of a software solution tailored for search and rescue (SAR) operations, with a particular emphasis on evaluating the implementation and effectiveness of user-centered development (UCD) principles. Initially, the project aimed to create a Virtual Reality (VR) Interactive Topographical Mapping System. This phase resulted in the research and development of a sophisticated VR prototype, incorporating a comprehensive suite of features that facilitated live, interactive topographical mapping within a 3D virtual environment.
The objectives of UCD involve placing users' needs and requirements at the forefront of the design process, ensuring that solutions not only possess technical prowess but also deliver value and impact for the target audience. However, despite the numerous technical accomplishments of the VR project, end-user feedback from stakeholders, such as forest firefighters, revealed the necessity for a solution that better aligned with their real-world requirements. These users required direct observation of ground and vegetation conditions to make informed decisions about mission trajectories, a capability unattainable with the VR application. This insight led to a pivotal shift in our approach, redirecting the project towards the development of a targeted desktop application explicitly designed to address the operational needs of Search and Rescue (SAR) personnel.
The resulting product is a desktop application accessible through both a Graphical User Interface (GUI) and a Command Line Interface (CLI), with development centered on continuous end-user engagement and feedback. This solution offers two distinct interfaces catering to different end-users, prioritizing a concise UI and output while avoiding unnecessary complexity and irrelevant details.
In evaluating the implementation of UCD, the project demonstrates that adopting a user-centric approach can enhance the efficiency and effectiveness of SAR operations, emphasizing users' preference for utility over visual and graphical elements. Furthermore, the project's evolution from a cutting-edge VR system to a specialized desktop application provides insights into the broader fields of computer science and emergency response.
In future work, this report investigates potential enhancements, illustrating a sustained commitment to continuous improvement and alignment with user requirements. The accomplishments of the VR project, despite the pivot, attest to the importance of innovation and exploration in software development. Additionally, the project underscores the vital role of UCD in crafting solutions that combine technical utility with a focus on addressing real-world challenges.
Wednesday, May 31
RAGHAV NASWA
Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Conversational AI to Deliver Family Intervention Training for Mental Health Caregivers
Mental health issues are prevalent in the United States, affecting 22.8% of adults in 2022. Unfortunately, a significant proportion (55.6%) of these adults did not receive treatment. Effective communication characterized by empathy is essential for enhancing the well-being of individuals with mental health issues. Family intervention training can empower friends and family members to provide in-home treatment to mental health patients. However, many caregivers lack the necessary training to engage with patients in a compassionate and understanding manner. To address this issue, a conversational AI chatbot was developed to train caregivers in empathetic communication. The chatbot engages in interactive conversations with caregivers and offers guidance on compassionate and empathetic communication. The chatbot was designed to be interactive, user-friendly, and accessible to caregivers. Our study demonstrates that conversational AI can serve as a valuable tool for training caregivers, leading to improved patient outcomes through enhanced communication skills.
FAHMEEDHA APPARAWTHAR AZMATHULLAH
Chair: Dr. Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Design of Energy-Efficient Offloading Schemes for Opportunistic Vehicular Edge Computing
In edge computing, computation tasks are offloaded to the edge servers, which can be either stationary edge servers or mobile edge servers. Stationary Edge Servers (SES) are usually located at the edge of a network, such as a cellular tower. SES can provide computing resources for nearby users or devices with low communication latency. When more devices connect to the stationary edge servers they cannot easily scale up due to limited design capacity, resulting in degraded performance. In contrast, Vehicular Edge Servers (VES) are a type of mobile edge computing server that is usually deployed on vehicles. VES can provide low-latency computing services by bringing computing resources even closer to the users with higher flexibility. VES also overcomes the drawback of stationary edge servers by providing services to the areas where stationary edge servers may be unavailable to reach. These benefits ideally satisfy the performance requirement of latency-sensitive but computing-intensive mobile applications such as pervasive AI, augmented reality, and virtual reality. When designing computing offloading strategies for vehicular edge computing systems supported through opportunistic VES, one challenge is handling the tradeoff between the time-varying availability of VES resources and the limited energy of mobile devices. In this project, we formulated an optimization problem, which considers VES’s capacity constraints and mobile users’ energy constraints, for solving the offloading problems in opportunistic vehicular computing systems. To solve the formulated problem, we designed and implemented a solution using CVXPY, a convex optimization problem solver along with three heuristic approaches: greedy, round-robin, and moderate offloading methods. We conducted extensive simulations, and the obtained results demonstrated the effectiveness of the proposed algorithm in improving mobile users’ energy usage while maintaining an expected quality of computing tasks.
CAMERON KLINE-SHARPE
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Discovery Hall 464
Thesis: Technical and Clinical Approaches For Implementing a Vision Screening Tool
Detecting vision problems is a challenging task, especially in children and in underserved or rural communities. This is in part due to the difficulty of obtaining useful indications of vision problems, which may cause a child to be sent to an eye doctor. Modern vision screening approaches are either hard to scale, expensive, or limited in applicability. The aim of this thesis was to clinically test QuickCheck, a vision screening mobile application aimed to combat these limitations, and determine future development and testing plans based on the results of those tests. This was accomplished through continuing the development of QuickCheck from past work to a clinically testable state, completing several clinical tests of the application in different settings, analyzing the results of those trials, and determining what future work needed to be done on the application and in future clinical trials to get the application ready for distribution.
After four clinical tests across two different testing sites, QuickCheck's performance was measured using testing time, test accuracy, specificity, and sensitivity, and an analysis of error types and causes was also performed. While QuickCheck was able to detect most individuals who had vision problems, this work determined that further testing and development is needed to decrease the false negative error rate, improve testing time, and increase study sample size to ensure that QuickCheck is ready for deployment as a screening tool.
DIVYA KAMATH
Chair: Dr. Michael Stiber
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Online
Project: Migrating a Complex, Cpu-Gpu Based Simulator to Modern C++ Standards
Software engineering encompasses not just the act of writing code, but also the act of maintaining it. Maintainability can be improved in a number of ways; one such way involves updating the codebase to incorporate newer language features. This project focuses on updating Graphitti, a graph-based CPU-GPU simulator, to leverage modern C++ features and idioms. The objectives include enhancing reusability, reducing technical debt, and addressing serialization and deserialization limitations. All this while monitoring performance impact due to these changes.
The updated Graphitti codebase demonstrates improved memory management, enhanced reusability, and reduced technical debts, without sacrificing performance. This project has also paved the way for smoother integration of serialization and deserialization for all objects within Graphitti.
Thursday, June 1
NAYANA YESHLUR
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Thesis: Using Data Analysis to Detect Intracranial Hemorrhage Through Ultrasound Tissue Pulsatility Imaging
Traumatic Brain Injury (TBI) is a type of injury that affects how the brain functions. TBI can lead to short-term problems or more long-term severe problems including various types of intracranial hemorrhage, some of which can even result in death. For this reason, finding ways of detecting intracranial hemorrhages early in patients can help to provide faster and more appropriate care, potentially improving patient outcomes. While CT and MRI are more traditional methods of diagnosing intracranial hemorrhage, they have certain drawbacks which ultrasound imaging can overcome. This work utilizes data collected from experiments on TBI patients using an ultrasound technique known as Tissue Pulsatility Imaging (TPI), specifically data about brain and other tissues displacements over the cardiac cycle. The aim of this research is to use such data to understand the differences between healthy brain displacement and brain displacement of TBI patients (with dangerous bleeding in their brain). In addition, we explore if and how the identification of the points of maximum and minimum displacement can be used to further aid in the identification of intracranial hemorrhage. The identification of these displacement points has emerged as a significant objective in this study, as they hold the potential to uncover crucial distinctions between states of wellness and illness. Furthermore, their utility in future research lies in assessing the consistency of these discoveries when applied to a broader dataset.
KISHAN NAGENDRA
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Maya: An Open-Source Framework for Creating Educational Mobile Applications for Low-Tech Communities
This capstone project introduces "Maya," an open-source framework aimed at assisting content creators in developing mobile applications to disseminate educational and awareness-related information to members of low-tech and low-literacy communities with limited or no internet access. The framework automates the transformation of PowerPoint presentations into mobile applications. The framework currently consists of two stages. In the first stage, the content creator feeds a PowerPoint presentation file to a user-friendly executable software system that extracts relevant information from the presentation and generates a single extract folder that contains both: a) a JSON file containing the metadata from the PowerPoint file, and b) a sub-folder with all the media from the PowerPoint file. The metadata includes information such as text, fonts, hyperlinks to pages, and paths to the media in the sub-folder. In the second stage, this extracted folder serves as input to another application that uses that information to create a mobile application that replicates the layout, images, text, and features from the original PowerPoint presentation.
The design for the Maya framework is based on the specifications provided for the Luna mhealth project (Luna), an initiative by Eliana Socha and Jon Socha. Luna aims to develop and deploy a low-tech mobile application to raise awareness about prenatal and postnatal health among the indigenous tribes in the Comarca Ngäbe-Buglé region of Panama. The development of Maya was based on the insights gained during the design and development of a non-generic mobile app that implemented the functionality in the original PowerPoint mock-up provided by Eliana and Jon Socha. Developing the Luna mobile app motivated the creation of the generic Maya framework. By utilizing the Maya framework, educational content creators without knowledge of mobile development can create powerful educational mobile applications for underserved communities across the globe, without the need to write any code.
JASON CHEN
Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
11:30 A.M.; Online
Thesis: Protein Structure Refinement via DeepTracer and AlphaFold2
Understanding the structures of proteins has numerous applications, such as vaccine development. It is a slow and labor-intensive task to build protein structures from the experimental electron density maps through manual effort, therefore, machine learning approaches have been proposed to automate this process. However, most of the experimental maps are not atomic resolution, so the densities of side-chain residues are insufficient for computer vision-based machine learning methods to precisely determine the correct amino acid type when the sequence of the protein is not provided. On the other hand, methods that utilize evolutionary information from protein sequences to predict structures, like AlphaFold2, have recently achieved groundbreaking accuracy but often require manual effort to refine the results. We propose a method, DeepTracer-Refine, which automatically splits AlphaFold’s structure and aligns them to DeepTracer’s model to improve AlphaFold’s result. We tested our method on 39 multi-domain proteins and we increased the average residue coverage from 78.2% to 90.0% and average lDDT score from 0.67 to 0.71. We also compared DeepTracer-Refine against another method, Phenix’s AlphaFold refinement, to demonstrate that our method not only performs better when the initial AlphaFold model is less precise but also exceeds Phenix in run-time performance.
YIWEI TU
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Online
Project: Interactive Watercolor Painting
Watercolor, as a well-known style of artistic painting, is appealing due to the translucent patterns formed by the spreading of its coloring water-based solution. These translucent patterns are produced by two basic brushing techniques, wet-on-wet and wet-on-dry, and due the stochastic nature of liquid mixture motion, each watercolor painting is distinctive. For newcomers, challenges in creating watercolor paintings include soiled canvas and wrong brush strokes that cannot be corrected due to the limitations of the paper and inconvenient tools.
To offer more freedom to watercolor creation, simulation of watercolor painting has been extensively studied including physically-based methods. The physically-based approach presents watercolor patterns by emulating the physical dynamics of paint and water flow and rendering an image based on the simulated results. The Lattice-Boltzmann method (LBM) is favored by researchers in watercolor fluid dynamic simulation due to its computation efficiency and stability in dealing with complex boundaries, incorporation of microscopic interactions, and potentials for parallel implementation.
The project follows Chu and Tai's approach of modeling hydrodynamics with LBM and using the Kubelka-Munk (KM) reflectance model rendering method proposed by Curtis et al. Compared to other methods, this approach strikes a balance between accuracy of the simulation model and execution time. The LBE models fluid flow as a continuous propagation and collision process on a discrete lattice where the multiple lattices can be processed in parallel. By trading-off realism, LBE is capable of presenting relatively realistic watercolor results, while the parallel processing significantly reduces the simulation time.
The implementation of the physically-based simulation requires a platform that supports parallel processing at the per-pixel level, user's interactive drawing activities, and rendering of the simulation results. Unity3D, as a cross-platform game engine, is chosen for the implementation of the system because of its support for user-defined HLSH shaders that can process pixel operations in parallel, a well-designed editor that can accommodate complex parameter adjustments, and pre-built pipelines that can render simulation results.
The implemented simulation system consists of four components: fluid injection, fluid flow simulation, pigment movement, and pigment composition. The first component receives the water applied to the digital canvas from the brush, and updates the edges between the wet and dry canvas. The fluid flow simulation component then simulates the diffusion of the fluid with a semi-rebounding scheme of LBE. The pigment movement component calculates the movement of the paint. As the final step, the rendering component renders the resulting image based on the KM model with the transmittance and reflectance of the pigment layers as input parameters.
The provided watercolor simulation system allows for the manipulation of brush, paper on canvas, and simulation settings. The system can make images based on the two basic brush approaches with popular watercolor patterns such as edge darkening, purposeful backrun, and pigment granulation by adjusting the brush settings. The mixture of multiple pigments might result in new and distinct colors when the KM model is used. Paper and simulation parameters can be adjusted to allow painting on canvases that do not exist in the physical world.
A novice painter can use the features of our system to make simple watercolor paintings. The system proved that the KM model can render watercolor visuals with color blending on the canvas, and that LBM can reduce the computational load of fluid simulations while maintaining simulation realism. The results of this project lay a solid platform for additional, in-depth research into watercolor simulation.
JIAQI ZHAN
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Discovery Hall 464
Project: Refactoring of EYE Toolbox, a Web-Based Medical System
The EYE Toolbox is a web-based medical system offering services for testing, diagnose and therapy of patients’ learning related vision problem, and has been evolving for 10 years. It has been implemented and is currently being used in a single clinical setting, but now needs to be updated and prepared for use for wider distribution across multiple clinics. To prepare this software for more general use and proper maintenance, detailed analysis and review is required – and a new, refactored version needs to be developed to assure that all functional and non-functional requirements of this system may be maintained properly – and HIPAA-compliant and use modern-day cloud-based architectures.
After review of the existing code base, the importance of a focused refactoring effort was determined because during previous software development cycles (using code and fix processes/iterative development), the system did not appear to leverage standardized data structures and refined programming methods such as extendable classes mapping different types of users, and generalized methods covering multiple using cases. In addition, with limited focus on code maintenance, the system development over a long time span has problems of code redundancy and legacy code for deprecation. Facing the widening application environment the system supports with a greater number of users and new use demands, the system has a necessity to be reviewed and refactored to ensure it meets the required standards of adaptability, scalability, maintainability and performance to ensure its stable supports for the updating working environment.
This project determines at figuring out how to tailor a practical and suitable refactoring plan for the EYE Toolbox system, generating automatic tests for verifying the accuracy of refactored code based on its front-end and back-end effects, evaluating to what degree the generated refactoring plan could improve the system, and analyzing whether the refactored system could meet the requirement of system evolution and distribution in broader application environment or not. To design, implement, and evaluate the customized refactoring plan, this project applied a code review approach, used refactoring methods for PHP language to process back-end logic and HTML language to design web page, and measured the refactoring performance through derived metrics of adaptability ,scalability, maintainability and performance.
Based upon the evaluation result, the benefit and limitation of this refactoring plan was analyzed, and the further improvement direction including the further refactoring tasks and recommended evaluation metrics were discussed. This project enriches the exploration of the systematic refactoring approach for a medical system, and could inspire researchers and offer guidance for new team members in their future refactoring work of similar or related medical system.
Friday, June 2
POOJA NADAGOUDA
Chair: Dr. Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Online
Project: Scaling and Parallelizing LDA-GA (Latent Dirichlet Allocation - Genetic Algorithm) for Data Provenance Reconstruction
The task of ensuring the reliability of sources and accuracy of information is becoming increasingly challenging due to the Internet's ability to facilitate the creation, duplication, modification, and deletion of data with great ease. Hence the importance of Data Provenance Reconstruction, which attempts to create an estimated provenance of existing datasets when no provenance information has been previously recorded. The Provenance-Reconstruction approach proposed by the ”Provenance and Traceability Research Group”, is based on the Latent Dirichlet Allocation Genetic Algorithm (LDA-GA), which uses a Mallet library, was implemented in Java and achieved satisfactory results when applied to small datasets. As a result of the increase in datasets, performance degraded. To improve accuracy and performance, the GALDAR-C++, a Multi-Library Extensible Solution for Topic Modeling in C++, was developed. As compared to a Java implementation, this solution using WarpLDA offered satisfactory results. To improve the performance further, a parallel computing strategy, Message Passing Interface (MPI), was applied on both the serial Java and C++ versions of code by parallelizing the LDA calls in each generation of the LDA Genetic algorithm. Both parallel Java and C++ implementations gave extraordinary performance improvement compared to their respective serial implementations. But both the parallel solutions were limited to using 9 nodes in parallel as the Genetic algorithm supported 9 populations. In order to further scale the parallel solution, we implemented a scaled genetic algorithm to support 12 and 24 populations using 12 and 24 computing nodes for both Java and C++ versions. Also, the previous serial and parallel solutions did not provide much improvement in terms of accuracy. For bigger datasets of size 5K articles, the accuracy was as low as 8%. Hence we further extended our Scaled Parallel LDA-GA Java version to improve accuracy. We optimized the existing LDA-GA strategy by providing the genetic algorithm with initial LDA parameters (topic count and iteration count) proportional to the size of the dataset and applying a cosine filter to LDA-GA clusters. This strategy provides accuracy improvement of more than 3 times based on the dataset size in comparison to previous serial and parallel solutions and performance improvement of 4x to 8x based on the dataset size in comparison to the previous serial solution. The results obtained make this a viable solution for future studies on provenance reconstruction, especially for larger datasets.
SREJA BABU
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Online
Project: Gardening Application Powered By Large Language Model (LLM)
There are many research studies that show that gardening is a helpful hobby to improve physical and mental health in human beings. However, it is a well-known fact that gardening itself requires a lot of time, effort, and knowledge to be successful. While most of the information needed to be good at gardening is spread across various sources of plant databases and resources online, it needs a lot of effort from the gardeners to gather all in one place for the plants of their interest.
In this study, we research the impact of technology in facilitating effective gardening techniques, thereby offloading some of the human hard work to automation and technology, resulting in green gardens and happy people. We built an iOS application that is designed to assist gardeners in their gardening journey. The application allows users to identify plant species using digital image processing and machine learning techniques and to automatically add the plants into a “Virtual Garden.” We are able to identify the plant species with an accuracy of 91.6%, using the Support Vector Machine (SVM) model. The SVM model plugs directly into our backend REST server. The key advantage of our backend implementation is the possibility to swap our current classifier model with an improved model at any time, therefore, keeping the option to improve our accuracy with support to more plants. Currently, our application is capable of identifying 32 different plant species.
Once the user adds plants to the Virtual Garden, our application will be able to pull out detailed information about each plant. This information comes from our automatically populated plant database built on top of Generative Pre-trained Transformer, a Large Language Model (LLM). The GPT-3.5-Turbo LLM has been integrated with our backend application. We have a continuous process (DB Populator) that updates our internal plant database continuously. This uniquely enables our plant database to scale for new plants and species when more data becomes available later. We leverage the fine granular pruned gardening information with the help of GPTs to notify users when to water or replenish the soil and other useful information. These notifications will offload a lot of planning that a gardener typically has to perform and convert them to a set of simple instructions to follow easily.
Compared to most of the existing applications available on the market currently, our application provides the following unique features. To begin with, it leverages the GPT LLM models to automate the process of information gathering as opposed to relying heavily on user contributions, and plant experts like the existing applications. Lastly, it provides a “Community board” feature that helps gardeners to share and discuss their Virtual Gardens with their community. This social aspect of gardening fosters sharing of community experience and encourages them to stick to their hobby. We performed a “Usability study” for our application, to analyze the impact on our gardeners on three unique aspects. 1) Ease of Getting started with gardening 2) Accessibility to relevant information 3) Successful End goal of a healthy garden. For future work, we intend to add more social features and also measure the impact of the longevity of sticking with gardening with the app as compared to without using it. Overall, this application serves as a very useful tool that facilitates an enjoyable gardening experience for gardeners.
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Master of Science in Cybersecurity Engineering
SPRING 2023
Friday, May 26
NEIL PRAKASAM
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
8:45 A.M.; Online
Thesis: A System for Secure and Categorized Video-Sharing
Online video sharing is a phenomenon which continues to be increasingly utilized by the entire population. Preserving the privacy of videos shared online is of utmost importance, but there is one use case that hasn’t yet been covered by current mainstream video sharing platforms. This project aims to provide the ability to categorize whether multiple videos are of the same event, so that users can share them only amongst others who were also present at the event and have video evidence. The main method of categorization will be through DNA sequencing, where video files will be converted into literal DNA in order to be categorized into 4 categories. This includes those that are of the same event, space, activity, or are completely different videos. The research has shown promising results that can be further optimized to categorize videos between the 4 categories, let alone whether or not they are of the same event.
NIHARIKA JAIN
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
11:00 A.M.; Online
Project: FortifyML: Machine Learning Security Recommender for Everyone
Deep Neural Networks (DNN) models have achieved remarkable performance in various applications, ranging from language translation to autonomous vehicles. However, studies have shown that DNN models are vulnerable to adversarial attacks, wherein malicious inputs are carefully crafted to deceive the models. Adversarial attacks pose a significant threat to the reliability and security of DNN models, especially in critical applications such as robotics, finance, text-to-speech, healthcare, and even national security. The number of research publications focused on adversarial attacks and defenses, including the sophistication of approaches has grown immensely since the first such publication in 2013. Even with an abundance of research papers on adversarial attacks, there is a lack of tools or systems that can coherently and systematically align a researcher or user with the specific defenses that could strengthen their individual use case, more so for beginners in the machine learning domain. In this paper, we extended FortifyML, an existing machine learning security recommender for everyone. We have accomplished the following with this project: 1) Successfully extended the recommender system to support DNN models in the Natural Language Processing (NLP) domain, making it a valuable tool for researchers and practitioners in the field of machine learning. 2) Simplified the user interface to make the system accessible to everyone, including beginners. 3) Added suggested links to articles or academic papers to direct users to additional details regarding potential attacks or mitigation strategies. 4) Recommendations made by the system are based on real-world statistics collected by running actual attacks and defenses in contained environments and can act as guidelines out of the box. As a result, FortifyML will help guide machine learning engineers and practitioners secure the models and their applications without the need for explicit security knowledge.
Tuesday, May 30
SANGYOON JIN
Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering
11:00 A.M.; Online
Project: A Study on the Password Usage Behavior of Online Users
This project aims to measure online users' password usage behavior and examine the relationship between it and its antecedent factors based on Protection Motivation Theory (PMT). PMT is a representative theory that explains the process of changing protection motivation according to threat messages in the health science field and has been extended to the area of information security. This project uses PMT to explain how motivation for password usage behavior is formed against the threat of password compromise. While previous studies applying PMT have observed its relevance concerning protective behavioral intentions, such as the intention to comply with information security guidelines, this project focuses on the relevance concerning password usage behavior in terms of password security strength.
The Qualtrics survey platform and Amazon Mechanical Turk are used to create and distribute a survey. In addition, the survey uses the Rockyou.txt file, which contains password usage behaviors of past online users. The result suggests that different password usage behaviors are identified according to the characteristics of each user. In addition, multiple regression analysis derives some relationship between the PMT model and password usage behavior. At the same time, we found that the explanatory power of the antecedents can be enhanced in an extended PMT model that also considers the information security climate of the organization to which the online users belong. These findings suggest the need to consider new research models for future research in the field of password-based information security. Furthermore, these results can contribute to providing customized password policies to organizations that ultimately need to improve information security.
PAUL BEARD
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; Online
Project: Two Factor Message Authentication for Entry/Exit Operations During Autonomous Vehicle Platooning
As the use of autonomous vehicles becomes more prevalent, ensuring secure and reliable communication between the vehicles is crucial. One important aspect of this communication is message authentication during the docking and undocking process, which involves verifying the identity of the vehicles so that the origin of the message can be established.
An evolution of that autonomy involves vehicular operations know as platooning. This transit method involves multiple autonomous vehicles that are connected, either logically or physically, and behaving as a single unit. This capstone paper will discuss available methods to securely communicate during the entry and exit functions of those platooning operations. A vulnerable period during the platooning process occurs when a vehicle enters and exits the platoon.
The performance of various authentication methods has been analyzed based on security, computational complexity, and communication overhead. Additionally, the implementation feasibility of each method has been assessed for the docking/undocking process.
Overall, this paper will contribute to the body of knowledge on secure message authentication in autonomous vehicles and provide insights into the best practices for ensuring secure and trustworthy communication between autonomous vehicles during the docking and undocking process. Ultimately, this will help ensure the safety and security of autonomous vehicles and their passengers.
Thursday, June 1
ADITYA SIDDALINGASWAMY
Chair: Dr. Erika Parsons
Candidate: Master of Science in Cybersecurity Engineering
11:00 A.M.; Online
Project: Cybersecurity Framework for an Edge Computing Medical Application for Stroke Patient Rehabilitation
This project aims to design and develop a cybersecurity framework for an Edge Computing ecosystem used in a medical setting. In the scope of this project, the focus is a medical application for the rehabilitation of stroke patients. Such patients often experience spatial neglect, a condition that significantly affects their functional recovery and quality of life, making rehabilitation crucial. This work is part of a larger project to make use of the aforementioned Edge Computing ecosystem, which in the long term, is geared to make the rehabilitation experience more enjoyable for the patients while collecting data to help medical providers monitor patient progress and design individualized treatment plans. The design of the ecosystem is based on technologies such as Edge Computing, Cloud Computing, IoT, Kubernetes, and, from a medical standpoint, Electroencephalography. The use of all these technologies, used particularly in a medical environment, means that it is of the utmost importance to address cybersecurity risks to ensure patient data security and privacy. The project's goal is to create a strong cybersecurity framework that protects patient data from unauthorized access and prevents data breaches while promoting collaboration among healthcare providers and technology experts. The project focused on studying the current importance of cybersecurity in the medical industry and the potential applications of edge computing, the importance of collaboration, and teamwork in developing technological solutions. By achieving the cybersecurity work objectives, the project has the potential to enable current and future efforts to improve the quality of stroke patient rehabilitation methods.
Friday, June 2
MATT DIOSO
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; Online
Project: CSI: Channel State Investigation, A Device Localization System based on Physical Layer Properties
Hidden streaming devices are becoming a more widespread issue as these types of devices become smaller and more accessible. Existing methods for localizing these devices in an environment require a user to traverse the area of interest while monitoring network traffic to draw correlation between digital spikes and physical location. These methods state a localization time ranging from five to 30 minutes depending on the size of the environment and the number of reference points throughout the area.
This work presents a system that greatly reduces this localization time and removing the need for the user to traverse an entire area, thus enabling detection in a wider variety of locations and situations. The use of RGB images to determine environment information from depth images provides the bounds in which the streaming device can be located. Localization time is greatly reduced by leveraging Channel State Information (CSI), a physical layer characteristic of transmitted signals, which has been proven to be more temporally stable than the RSSI value and provides richer, fine-grained data to learn position from. The results from this work show the following:
- Localization precision within 1.9m of device’s true location
- 0.98 F1 score with 0.98 recall and precision
- Removed physical requirement for users to traverse an area for localization efforts
- Localization estimation time greatly reduced from 5 - 30 minutes down to 30 seconds
BALAPRASANTH RAMISETTY
Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; Online
Project: Developing individual Awareness on Phishing Towards Mitigating Increased Cases of Email Phishing
In today's digital landscape, safeguarding our online safety and security is of utmost importance. This capstone report delves into the issue of phishing attempts, specifically focusing on email phishing, and explores effective measures to prevent and mitigate their impact. To gather insights, a comprehensive questionnaire was administered using the user-friendly Qualtrics survey platform. Participants were engaged with an embedded awareness video and provided valuable feedback and perspectives. The findings highlight the widespread occurrence of phishing attempts and underscore the significance of understanding their characteristics, identifying suspicious elements in emails, and recognizing different types of phishing attacks. Education and awareness emerge as critical factors in empowering individuals to effectively combat phishing attempts. The research findings contribute to the existing body of knowledge on phishing prevention, offering practical recommendations for individuals and organizations to bolster their resilience. By leveraging the Qualtrics platform and incorporating an awareness video, the survey methodology comprehensively captures participants' perspectives, providing a deeper understanding of email phishing. This capstone report serves as a valuable resource for individuals, organizations, and security professionals seeking to tackle the persistent threat of phishing attacks. It presents insights, trends, strategies, and preventive measures to safeguard personal and sensitive information.
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Master of Science in Electrical Engineering
SPRING 2023
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