Thesis/Project Final Exam Schedule


Final Examination Schedule


Summer 2020

Monday, June 22

Nasser Alghamdi

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

11:00 A.M.; Online
Supporting Interactive Computing Features for MASS Library: Rollback and Monitoring System

Multi-Agent Spatial Simulation (MASS) library provides parallel execution over multiple computing-nodes for a wide range of agent-based simulations and data science applications. It conceals the complexity of parallel execution, dynamic and static entities allocation, and management process behind a set of APIs. Developing MASS applications for non-computing specialists or novices is a challenging and time-consuming task. Applications that depend on the library have to be compiled, distributed, executed for every change that is introduced by the  user. Additionally, the user has to use distributed log les or additional library calls for probing the application state. Thus, the user spends more time when experimenting on re-compiling, re-distributing, re-executing the application executable, and gathering information from distributed logs or results of additional calls. Though the library provides an intuitive programming model, its rigidity and the lack of convenient inspection tools can draw users away from using the library. In this project, we introduce InMASS (Interactive computing feature for MASS library) with two supporting features, namely: monitoring tool and rollback. We design computer experiment to emulate user-changes and we found that interactive version performs 9.2 times faster than non-interactive version when experimenting in ABM settings. Also, We compare and demonstrate how the  rollback and monitoring tool adds exibility and observability, respectively, to ABM systems by comparing InMASS against Repast Simphony, a well-known ABM framework.

Tuesday, July 28

Savitha Kumari

Chair: Dr. Brent Lagesse
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Intuitive GUI based Context Instrumentation Framework

Crowdsensing is a technique where a large group of individuals having mobile devices capable of sensing and computing (such as smartphones, tablet computers, wearables) collectively share data and extract information to measure, map, analyze, estimate or infer (predict) any processes of common interest. AWARE is an open platform for context-aware mobile based crowdsensing research. This designed to capture, analyze and generate context aware data on mobile devices. The smart phones possess abundant sensors in recent times and can be used in multiple studies. AWARE is a solution to those researchers who are lacking the tools for mobile crowdsensing studies and are not capable to build one on their own. The toolkit abstracts the implementation details to the researcher and the participant. This allows them to focus more on sensor data collection and retrieval. This is enhanced with features like incentive mechanism for improving user participation, improved location privacy through obfuscation and data trustworthiness trackers based on user’s past contributions for better researcher satisfaction. Further the application is tested based on several functional tests and evaluated accordingly.

Wednesday, August 5

Jonas Pfab

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

11:00 A.M.; Online
Deciphering Protein Complex Structures from Cryo-electron Microscopy Maps using Deep Learning

Information about the macromolecular structure of protein complexes such as SARS-CoV-2, and the related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automatic deep learning-based method for de novo multi-chain protein complex structure determination from high-resolution cryo-electron microscopy (cryo-EM) density maps. We applied DeepTracer on a previously published set of 476 raw experimental density maps and compared the results with a current state of the art method. The residue coverage increased by over 30% (45.65% to 76.93%) using DeepTracer and the RMSD value improved from 1.29Å to 1.18Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related density maps, among them 10 with no deposited structure available in the database. We observed an average residue match of 84% with the deposited structures and an average RMSD of 0.93Å. Furthermore, we found that the efficient structure modeling procedure of DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only two hours. The web service is globally accessible at

Thursday, August 6

Meng Yang

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

1:15 P.M.; Online
MoodPlay: An AI-Empowered Music Recommendation iOS App

Personalized and automatic music recommendation improves user experience, hence increases customer engagement and conversion. Representative music streaming service providers such as Spotify, Pandora, and Youtube Music all use various recommendation algorithms, often of a hybrid of Content-Based Recommendation and Collaborative Filtering, which study user’s listening behavior as well as the characteristics of the listened music. Though there exists work on the mood of music and human beings [18, 23, 38, 46, 48, 51, 56], application that recommends music based on user’s mood and activity is limited. The goal of this project is to create an iOS mobile application, called MoodPlay, which recommends music according to user’s mood and activity, estimated from real-time data collected by Apple Watch and iPhone. MoodPlay utilizes Machine Learning techniques to find the pattern between user’s mood and activity as well as user’s mood and a playing song’s tempo. Based on the discovered patterns, MoodPlay can recommend suitable songs to the user and further adjust its model in real-time according to user’s preference. By evaluating MoodPlay based on user reviews in App Store and the performance results of Machine Learning models and mobile application metrics, MoodPlay proves its high acceptance and usability.

Archit Gupta

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

5:45 P.M.; Online
Document Clustering Using a Fact Graph Approach

Words in human language are given meaning by their context and its structure. Sentences can vary in meaning depending on where punctuation is placed, while homonyms need additional information in order to be disambiguated. Bodies of text in human language are ordered and structured; sentences encode ideas, paragraphs group sentences, and chapters group paragraphs. However, it is often the case in the analysis of documents that we treat them as a bag-of words, i. e., a multiset of the contained words. In doing so, the meaning of the text derived from its structural properties is lost. We can attempt to preserve more of the documents' meaning by instead treating each of them as a graph of words, where the vertices are the words in the document, and the edges approximately represent relations between these words.  The relations are derived from the properties of the document's structure. In particular, we focus on using graph-encoded documents for clustering. The main motivation is to find an an alternative approach to the clustering algorithm LDA-GA. Data provenance reconstruction, which is concerned with approximating origins of documents within a dataset, uses LDA-GA for topic modeling, or discovering groupings of related documents [1, 2]. The original implementation of LDA-GA requires a large amount of system memory and resources, and results in significantly long execution times as well.  For these reasons, better performance is desired. In this capstone work, we apply graph-based clustering in order to group similar documents, and with the aim for better performance in terms of execution time and resource utilization. 

[1] Ailifan Aierken, Delmar B. Davis, Qi Zhang, Kriti Gupta, Alex Wong, and Hazeline U. Asuncion. A multi-level funneling approach to data provenance reconstruction. In e-Science Workshop of Works in Progress, October 2014

[2] Subha Vasudevan, William Pfeffer, Delmar Davis, and Hazeline Asuncion. Improving data provenance reconstruction via multi-level funneling approach. In the 12th International Conference on eScience, October 2016.

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