Thesis/Project Final Exam Schedule

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Final Examination Schedule

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

Summer 2022

Thursday, July 14

Christian Dunham

Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Cybersecurity Engineering

11:00 A.M.; Online
Thesis: Adversarial Trained Deep Learning Poisoning Defense: SpaceTime

Smart homes, hospitals, and industrial complexes are increasingly reliant on the Internet of Things (IoT) technology to unlock doors, regulate insulin pumps, or operate critical national infrastructure. While these technologies have made tremendous improvements that were not achievable before IoT, the increased the adoption of IoT has also expanded the attack surface and increased the security risks in these landscapes. Diverse IoT protocols and networks have proliferated allowing these tiny sensors with limited resources to both create new edge
networks and deploy at depth into conventional internet stacks. The diverse nature of the IoT devices and their networks has disrupted traditional security solutions.

Intrusion Detection Systems (IDS) are one security mechanism that must adopt a new paradigm to provide measurable security in this technological evolution. The diverse resource limitations of IoT devices and their enhanced need for data privacy complicates centralized machine learning models used by modern IDS for IoT environments. Federated Learning (FL) has drawn recent interest adapting solutions to meet the requirements of the unevenly distributed nodes in IoT environments. A federated anomaly-based IDS for IoT adapts to the computational restraints, privacy needs, and heterogeneous nature of IoT networks.

However, many recent studies have demonstrated that federated models are vulnerable to poisoning attacks. The goal of this research is to harden the security of federated learning models in IoT environments to poisoning attacks. To the best of our knowledge poisoning defenses do not exist for IoT. Existing solutions to defend against poisoning attacks in other domains commonly utilize different spatial similarity measurements from Euclidean Distance (ED), cosine similarity (CS), and other pairwise measurements to identify poison attacks.

Poisoning attack methodologies have also adapted to IoT causing an evolution that defeats these existing defensive solutions. Poisoning evolution creates a need to develop new defensive methodologies. In this we develop SpaceTime a deep learning recurrent neural network that uses a four-dimensional spacetime manifold to distinguish federated participants. SpaceTime is built upon a time series regression many-to-one architecture to provide an adversarial trained defense for federated learning models. Simulation results shows that SpaceTime exceeds the previous solutions for Byzantine and Sybil label flipping, backdoor,   and distributed backdoor attacks in an IoT environment.

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Monday, August 1

Junjie Liu

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

11:00 A.M.; Online
Project: COVID-19 Fake News Detector

COVID-19, caused by a coronavirus called SARS-Cov-2, has triggered a pandemic impacting people’s everyday life for more than two years. With the fast spreading of online communication and social media platforms, the number of fake news related to COVID-19 is in rapid growth and propagates misleading information to the public. To tackle this challenge and stop the spreading of fake news regarding COVID-19, this project proposes to build an online software detector specifically for COVID-19 news to classify whether the news is trustworthy. Specifically, the intellectual contributions for this project are summarized below:

  1. This project specifically focuses on fake news detection for COVID-19 related news. In general, it is difficult to train a generic model for all domains, the general practice is to fine-tune a base model to adapt the specific domain context.
  2. A data collection mechanism to obtain fresh COVID-19 fake news data and to keep the model fresh.
  3. Performance comparisons between different models: traditional machine learning models, ensemble machine learning models, and state-of-the art models – Transfer models.
  4. From engineering perspective, the project will be the first online fake news detection website to focus on COVID-19 related fake news. 

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Andrew Nelson

Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering

5:45 P.M.; Online
Project: Real-time Caustic Illumination 

Caustic illumination is the natural phenomenon that occurs when light rays bend as they pass through transparent objects and focus onto receiver objects. One might notice this effect on the ocean floor as light rays pass through the water and focus on the floor. Rendering this effect in a simulated environment would provide an extra touch of realism in applications that are meant to fully immerse a user in the experience. Traditionally, caustic illumination is simulated with offline ray tracing solutions that simulate the physical phenomenon of transporting photon particles through refraction and depositing the results on the receiving object. While this approach can yield accurate results, it is computationally intensive, and these ray tracing solutions can only be rendered in batches. To support caustics in real time, the calculations must simulate the natural phenomenon of photons traveling through transparent objects in every rendering frame without slowing down the application. This project focuses on rendering caustics in real time using a multi-pass rendering solution developed by Shah et al. Their approach constructs a caustic map in every frame which is used by subsequent rendering frames to create the final effect. The goal of this project was to develop an application that renders caustics and supports user interaction in real time. Our implementation uses the Unity game engine to successfully create the desired effect while maintaining a minimum frame rate of thirty frames per second.

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Thursday, August 4

Kaluad Abdulbaset Sanyour

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

11:00 A.M.; Online
Project: The Role of Machine Learning Algorithms in Editing Genes of Rare Diseases

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), is an adaptive immunity mechanism in prokaryotes. Scientists have discovered that it is a programmable system that could be used to edit the genes of various species, which allows us to edit genes causing some rare diseases. CRISPR is associated with Cas9 protein causing double-stranded breaks in DNA. Cas9 binds to a gRNA that guides the Cas9 to a specific site that can be edited. Although gRNA is versatile and easy to design, it lacks accuracy in determining the editable sites. This can misguide Cas9 to a wrong location, causing changes in different genes. Hence, CRISPR process needs to find the ideal gRNA that can guide Cas9 to on-target, and avoid off-target. Various machine learning (ML) algorithms can play an important role in evaluating gRNAs for the CRISPR mechanism, and recently many computational tools have been developed to predict the cleavage efficiency of gRNA design process. Here, the project aims to provide an overview and comparative analysis of various machine and deep learning (MDL)-based methods that are effective in predicting CRISPR gRNA on-target activities. Comparison results show that hybrid approach combining deep learning and other ML algorithms presented excellent results.

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Monday, August 8

Syed Abdullah

Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Remote Onboarding for Software Engineers:  From “Forming” to “Performing”

Onboarding is defined as the process when a new employee joins, learns about, integrates into and becomes a contributing member of a team. A successful onboarding is essential for moving a team from Forming to Performing stage. It helps increase the new hire’s job satisfaction, improve the team’s performance, and reduce turnovers (which bring the team back to the forming stage). With remote work being the new norm in software engineering, remote onboarding brings a unique set of challenges.

In this project, I aim to identify the main challenges faced during remote onboarding for software engineers, specifically for role-specific onboarding that happens in the team domain, and provide recommendations on improving this onboarding process. To achieve these aims, I conducted a qualitative interview study and activity exercise with software engineers who have gone through remote onboarding. Nine interviews were conducted with software engineers ranging from junior software engineers to senior software engineers and software engineering managers. I analyzed these interviews to gain insights into factors affecting onboarding. From the interviews, I identified a hierarchy of needs, in which I classified the needs of the new hire into basic needs and needs required for excellence. Needs such as access to tools, clarity of tasks and knowledge were categorized as basic needs to do the work, whereas mentorship, relationship building, and collaboration transform the onboarding into an excellent experience. I then further linked these needs to 5 main themes that emerged during the interviews for having an effective onboarding: (i) having an effective onboarding buddy; (ii) the ability to create relationships with team members and other stakeholders; (iii) being provided with up to date and organized documentation and onboarding plan; (iv) the manager's ability to listen and adapt to remote needs; and (v) a team culture which enables team members to communicate effectively and get unblocked quickly. Based on the interviews’ analysis together with insights from the literature, I developed checklists for recommended best practices for effective onboarding. A checklist was developed for each of the main onboarding stakeholders i.e., manager, onboarding buddy and new hire, along with a template of an onboarding plan. Using these checklists will help improve the effectiveness and consistency of remote onboarding for software engineering new hires.

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Tuesday, August 9

Ashwini Arun Rudrawar

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

1:15 P.M.; Online
Thesis: Evaluating the impact of GPU API evolution on application performance and software quality

Researchers and engineers have started to build parallel computing systems using sequential CPU + parallel GPU programs. In recent years, there has been an increasing number of hardware GPU devices available in the market along with a number of software solutions that support these hardware devices. A substantial amount of work is required in identifying the best combination of hardware and software for building heterogeneous solutions. One of the combinations developers use is NVIDIA GPUs and CUDA APIs. With the rapid architectural changes in GPU hardware, the related functioning of APIs also changes. There is considerable regression in the development of applications built using prior versions of APIs due to backward compatibility limitations. This thesis evaluates the evolution of NVIDIA GPU and CUDA APIs with the help of Graphitti, a graph based heterogeneous CUDA/C++ simulator.  This thesis identifies the advantages, limitations, and underlying functioning of a subset of APIs. This research explores these APIs in the context of performance, compatibility, ease of development, and code readability. It discusses how this process helped to implement a software change compatible with the simulator. This thesis documents the implementation of two APIs, ‘separate compilation’ and ‘link time optimization’ on the simulator, and how the implementation will help users to write modular code in Graphitti.  It also shows there is almost no performance overhead over one of the largest neural network simulations in Graphitti. The implementation offers flexibility and scope to enhance the heterogeneous nature of Graphitti which will help to simulate much larger networks.

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Wednesday, August 10

Andrew Hitoshi Nakamura

Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Thesis: Macromolecular Modeling: Integrating DNA/RNA into DeepTracer's Prediction Pipeline

DeepTracer is a fully automatic deep learning-based method for fast de novo multi-chain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) density maps. The macromolecular pipeline extends DeepTracer’s functions by including a segmentation step and pipeline steps to predict nucleic acids from the density. Segmentation uses a Convolutional Neural Network (CNN) to separate the densities of the two types of macromolecules, amino acids and nucleotides. Two U-Nets are trained to predict amino acid and nucleotide atoms in order to predict the structure from the density data. The nucleotide U-Net was trained with a map sample size of 163 cryo-EM maps containing nucleotide density, and identifies phosphate, sugar carbon 4 and sugar carbon 1 atom positions. When compared to Phenix’s pipeline, amino acids show favorable RMSD metrics, and nucleotide show comparable phosphate and nucleotide correlation coefficient (CC) metrics. The trained nucleotide U-Net model primarily focuses on double stranded DNA/RNA. Future work involves utilizing more density map data in training the nucleotide U-Net to detect single stranded DNA/RNA and removing phosphate outliers in postprocessing to improve the nucleic acid prediction.

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Alex Xie

Chair: Dr. Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering

1:15 P.M.; Online
Project: Improving the Quality of Inference during Edge Server Switch for Applications using Chained DNN Models

Recent advances in deep neural networks (DNN) have substantially benefited intelligent applications, for example, real-time video analytics. More complex DNN models typically require a more robust computing capacity. Unfortunately, the considerable computation resource requirements of DNNs make the inference on resource-constrained mobile devices challenging. Edge intelligence is a paradigm solving this issue by offloading DNN inference tasks from mobile devices to more powerful edge servers. Due to user mobility, however, one challenge for such mobile intelligent services is maintaining the quality of service during the handover between edge servers. To address this problem, we propose in this report a solution to help improve the quality of inference for real-time video analytics applications that use chained DNN models. The scheme comprises two sub-schemes: (1) a non-handover scheme that determines the optimal offloading decisions with the shortest end-to-end inference latency, and (2) a handover scheme that improves the inference quality by maximizing the usage of mobile devices for the most useful inference outcomes. We evaluated the proposed scheme using a DNN-based real-time traffic monitoring application via testbed and simulation experiments. The results show that our solution can improve the inference quality by 57% during handovers compared to a greedy algorithm-based solution.

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Thursday, August 11

Michael J. Waite

Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Mobile-Ready Expression Analysis

The field of computerized facial expression analysis has grown fast in recent years, with multiple commercial solutions and a plethora of research being produced. However, there has not been much focus on this technology's use in disability assistance. Studies have shown that an inability to read facial expressions can have a drastic negative impact on a person's life, presenting a clear need for tools to help those impacted. Most work in this field focuses on analytic performance over computational performance. This project aims to create an application that can be used by the disabled to read facial expressions in situations where they cannot, with a focus on computational performance to allow for real-time analysis. By utilizing a simplified methodology inspired by classic object detection such as SIFT and SURF, we found that our emotional analysis can achieve a computational performance of 100 milliseconds per image while retaining an overall accuracy of 64% when evaluated on the CK+ database. We hope that in the future our system can be further developed to produce greater accuracy with minimal loss in computational performance using machine learning.

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Questions: Please email cssgrad@uw.edu