Thesis/Project Final Exam Schedule

 

Final Examination Schedule

PLEASE JOIN US AS THE FOLLOWING CANDIDATES PRESENT THEIR CULMINATING WORK.

Spring 2021

For spring quarter 2021, all Final Examination and Defenses will not be held in person due to public health guidelines. For a link to attend a candidate's online defense, please contact our office at stemgrad@uw.edu.

Monday, May 17

Avantika Agarwal

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

11:00 A.M.; Online
Project: Comparison of E2EE group chats provided by various communication platforms and implementing RBAC for E2EE group chats

Currently, there are quite a few communication platforms that people all over the world use such as WhatsApp, Signal, Zoom and Google Meet which provide End-to-End Encryption (E2EE) messaging solutions. Given the variety of communication platforms providing encrypted communication solutions, it is crucial to analyze the details of how E2EE works on these communication platforms especially group messaging. The author conducts a literature review and performs practical investigations of encryption strategies used by these communication platforms. The practical investigations are carried out by packet dissection and network analysis of encrypted group messaging traffic. This paper also deep dives into one specific aspect of group messaging – group management. Group messaging brings about interesting challenges with group management for large groups. As the number of people in such groups grow, it may not be easy for a limited set of administrators to carry out all administrative actions. To solve this challenge, this paper proposes a solution to perform group management through Role-based access control (RBAC) for E2EE groups. To demonstrate this protocol, the author has implemented it as an Android application based on top of Signal SDK. This paper also conducts a security and network analysis of the proposed solution.

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Thursday, May 20

Christopher Ijams

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

11:00 A.M.; Online
Project: Ethical Penetration Test for AAA Washington

Penetration testing is a type of ethical hacking in which an organization hires a skilled professional to find and exploit vulnerabilities on their network. With the continued rise of cyberattacks, modern best practices indicate that vulnerability scanning and penetration testing are essential for an organization to maintain a secure posture. To remain PCI-DSS compliant, organizations acting as a payment gateway must regularly execute penetration tests on their infrastructure. AAA Washington has expressed a need for an external penetration test on their internet-facing resources. This project sought to perform and document such a test for the organization while establishing a repeatable process for future work. The project identified and exploited vulnerabilities and weak configurations within assets owned by AAA Washington. A methodology tailored explicitly for external penetration testing was established during this process. The test documented here emphasizes interacting with hardened, internet-facing resources and a rigorous inspection of web applications. This project ends with a redacted six-chaptered penetration test report outlining all findings and recommendations for remediation. 

Princeton See

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

1:15 P.M.; Online
Project: Control Gap Analysis for AAA Washington

Every organization with data to protect needs to ensure that they have controls in place to mitigate or minimize cyber threats and risks. Due to the evolving nature of the cybersecurity threat landscape, a yearly risk assessment is crucial for keeping up to date with the latest attacks. As part of a larger risk assessment, the control gap analysis allows an organization to perform a detailed breakdown of how the controls in place measure up to commonplace standards. AAA Washington plans to migrate into the hybrid-cloud environment and has requested for a control gap analysis to be conducted on their organization. This project used the CIS Top 20 Control Standards as the base of the gap analysis and will also devise a theoretical risk model to assist in standardizing the current risks to the organization. The goal of the project is the creation of a risk assessment document that is accepted by AAA Washington and used as its reference for future years. The successful implementation of the theoretical risk model may see it adopted for use in yearly risk assessments.

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Friday, May 21

Sanjusha Cheemakurthi

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: Mobile application for sign language learning

Sign language plays a significant role in bridging the communication gap between the deaf, mute and general public. Though there are sign language learning applications available these days, there is a considerable deficiency in the area of real time testing. This research aims to provide a solution for this challenge by building a real time sign language learning application on mobile devices. This iOS application is used to learn alphabets and digits of American sign language (ASL) and it also helps users to evaluate their skills in real time. To facilitate real time evaluation, a machine learning model is trained using ASL image dataset. This dataset is a combination of static sign language gestures captured using an iPhone, dataset developed by Massey University and publicly available ASL alphabet from Kaggle. Preprocessing is performed to bring all the images in the dataset to a common size. Later, segmentation is performed to subtract the background using skin color detection. These processed images are used to train the model using Convolutional neural network. The model consists of multiple convolutional layers and filters which are helpful in extracting the features and training the model effectively. The proposed framework is successfully implemented on smart phone platform and performs with an average testing accuracy of 99.7% using 5-fold cross-validation and evaluation accuracy of 70%.

Satine Paronyan

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Agent-Based Computational Geometry

The Multi-Agent Spatial Simulation (MASS) library is a parallel programming library that uses agent-based modeling (ABM) parallelization approach over a distributed cluster. The MASS library contains several applications solving computational geometry problems using ABM algorithms. This research aims to build additional four ABM algorithm-based applications: (1) range search, (2) point location, (3) largest empty circle, and (4) Euclidean shortest path. This research presents ABM solutions implemented with MASS library as well as divide and conquer (D&C) solutions to four problems implemented with big data parallelization platforms MapReduce and Spark. In this paper, we discuss design approaches used in solutions for the four problems. We present ABM and D&C algorithms with MASS, MapReduce, and Spark platforms. We provide a detail analysis of programmability and execution performance metrics of ABM algorithm-based implementations with MASS against D&C algorithm-based versions with MapReduce and Spark. Results showed that MASS library provides an intuitive approach to developing parallel solutions to computational geometry problems. We observed that ABM MASS solutions produce competitive performance results when performing computations in-memory over distributed structured datasets.

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Monday, May 24

Sneha Manchukonda

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Classification of Customer Reviews

Fake review is a review written by someone who has not used the product or the service. Fake reviews are designed to give false impression to the customer during the time of purchasing. Emotion, competition, and intellectual laziness are some of the reasons for writing fake reviews. They effect the purchase decisions and result in the financial losses to the customers. Therefore, an effective solution is necessary to identify the fake reviews. Existing approaches of fake reviews detection consists of a machine learning models with features related to reviewer, review, and social. The problem with the existing approaches is that the model and the features to the model are website dependent. Different websites like Yelp, Amazon, Trip Advisor consist of different levels of user meta data information for the reviews. Some websites might be containing rating, name, verified purchase in the reviews section while others might not. To address this issue, we develop two uber machine learning models, that can be plugged into any website, which takes text of the review. The textual features obtained from reviews are fed to the machine learning models to predict the classification of review. The average accuracy of fake reviews detection among different websites is 80%. In the future, any website can extend the current machine learning model, by adding more reviewer features pertaining to that website, for greater accuracy.

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Wednesday, May 26

Robert Laurenson

Chair: Dr. Clark Olson
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Thesis: Method of Adding Color Information to Spatially-Enhanced, Bag-of-Visual-Words Models

This thesis provides a late-fusing method, based on the HoNC (Histogram of Normalized Colors) descriptor, for combining color with shape in spatially-enhanced-BOVW models to improve predictive accuracy for image classification. The HoNC descriptor is a pure color descriptor that has several useful properties, including the ability to differentiate achromatic colors (e.g., white, grey, black), which are prevalent in real-world images, and to provide illumination intensity invariance. The method is distinguishable from prior late-fusing methods that utilize alternative descriptors, e.g., hue and color names descriptors, that are lacking with respect to one or both of these properties. The method is shown to boost the predictive accuracy by between about 1.9% - 3.2% for three different spatially-enhanced BOVW model types, selected for their suitability for real-time use cases, when tested against two datasets (i.e., Caltechl0l, Caltech256), across a range of vocabulary sizes. The method adds between about 150 - 190 mS to the model's total inference time.

Victoria Salvatore

Chair: Dr. Michael Stiber
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Thesis: Demonstrating Software Reusability: Simulating Emergency Response Network Agility with a Graph-Based Neural Simulator

This research validates the re-engineering of a neural network simulator to implement other graph-based scenarios. Most components were abstracted to increase reusability and maintainability through strategic refactoring decisions. This paper demonstrates how the simulator, developed at the University of Washington Bothell, can be applied to another graph-based problem: the resilience of the US’s Next-Generation 911 system in the face of a crisis. This research focuses on separating the neurospecific components from the architecture of the simulator and verifying its functionality as reusable software. It also includes first-person interviews, literature reviews, data analyses, and NG-911 system research to establish the system requirements for the NG-911 testbed. Initial results demonstrate that the NG-911 testbed reroutes calls after a crisis destroys emergency response infrastructure. This will support future work that will investigate the patterns that emerge from the interconnected events of a regional emergency response network. By applying previous research findings on the self-organizing behavior observed in both neural networks and emergency response networks during catastrophic events, this research will also contribute to the demonstration of self-organized criticality in complex networks. The NG-911 implementation of the simulator intends to model the resilience of emergency response infrastructure at varying levels of network connectivity.   

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Thursday, May 27

Zican Li

Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: Network Motif Detection: Motif-centric approach, and DIRECT method

Network motifs, with their statistical significance, are frequent subgraph patterns in a network. There are various kinds of algorithms for network motif detection, where the most popular is the EXPLICIT method, which includes generation of random graphs (normally 1000) and computation of P-value and Z-score. Here, we investigated alternative algorithms that do not require such a large number of random graph generation. We implemented the motif-centric algorithm and established an online website for it. Then, we investigated DIRECT method, which speeds up the network motif detection process by omitting the random graph generation step. Although DIRECT method has been proposed for a while, it has never been adapted to the detection of motifs in practice. Therefore, besides implementing it, we also applied a statistical measurement on it for motif detection. Experiment results support that for subgraphs in small sizes, DIRECT method is a feasible alternative for EXPLICIT, since they have consistent results, but with superior performance. In the end, we added those two algorithms as an extension to an open-source library, which originally contains EXPLICIT approach.

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Friday, May 28

Iswarya Hariharan

Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Associate-Degree-Plan scheduling and recommendation system improvement for Virtual Academic Advisor system

Community college students come from diverse backgrounds and experience levels.  They begin their education path pursuing a degree in a major of their choice. Most students aim to get transferred to certain universities, an academic path that demands to fulfill specific requirements, which makes students eligible for the transfer. Academic advisors at community colleges help students in creating academic plans trying their best to incorporate students’ interests, life constraints, and background. Being a heavily manual process that demands experience and familiarity with the process, there is a clear need to automate this process. The Virtual Academic Advisor (VAA) system aims to address the problem of automating academic plan creation for community colleges. The VAA is a research project paired with the development of an interactive software system that supports creating and displaying academic plans based on the needs and preferences of students. Work previously done by various students, focused on automated recommendation of core courses for targeted majors. However, no research or development has been done to incorporate selection of elective-course choices when generating an academic plan, nor a clear strategy on how to integrate elective-recommendation with the VAA system has been outlined.  Incorporating electives opens up a whole new research aspect of automated scheduling.  Furthermore, elective-course selection is crucial for scheduling associate degrees plans.  Associate degrees are offered by community colleges and students can earn such a degree before/without getting transferred to a university.  In this capstone project, we incorporate the logic and functionality of scheduling elective courses along with the core courses to generate associate degree schedules for the intended major and university of the student. We gather and collect the necessary data for the elective courses and test our scheduler for the associate degree schedules. This project also addresses the research and implementation necessary to generate alternative-schedule recommendations and its integration with the VAA system using APIs. This will assist students in exploring alternate academic paths.

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