Other Computer Engineering Commons^™

Malware Classification Using Opcode N-Grams And Word Embeddings, Siddhita Joshi

Master's Projects

Malware is a serious risk to any software application whether it is standalone or over the network. In order to protect computer systems, it is essential to detect and classify malware effectively. Modern malware classification research focuses on Machine Learning and Deep Learning techniques to identify advanced malicious software. This project explores malware classification by combining two robust methods: n-grams and word embedding. By extracting opcode n-grams, we make use of sequential nature of malware execution to identify any local patterns within the malware executable.

We use word embedding methods such as Word2Vec, Doc2Vec, and FastText to produce dense vector …

Evaluation Of The Effect Of Walnut Extract On Sp1-Related Pathways, Jihan Yehia

Master's Projects

Walnut extract (WE) has shown promising anti-cancer effects, such as inducing apoptosis and moderating cell cycle progression. A previous study by Dr. Brandon White’s Lab at San Jose State University hypothesizes that WE can downregulate the expression of the pro-tumoral specificity protein 1 (Sp1) in triple negative breast cancer (TNBC). This project builds an RNA-seq pipeline that runs differential gene expression (DGE) analysis to study the effect of WE on TNBC, thereby offering a wider perspective on genes that may be affected by this treatment. The data used in this project originated from Illumina and Nanopore sequencing methods, and DGE …

Evalsql - Automated Assessment Of Database Queries, Damanpreet Kaur

Master's Projects

In computer science programs, database is a fundamental subject taught through several undergraduate courses. These courses develop theoretical and practical concepts of databases. Building queries is a key aspect of this learning process, and students are assessed through assignments and quizzes. However, grading these assignments can be time-consuming for professors, and students usually receive feedback only after the deadlines have passed. As a result, students may miss the opportunity to improve their work and achieve better grades. To address this issue, it would be beneficial to provide students with immediate feedback on their submissions. EvalSQL is an automated system that …

Gesture Recognition Of Sign Language Alphabet Using Machine Learning Techniques, Gursimran Singh

Master's Projects

With the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of realworld settings, prompting a shift toward more adaptable vision-based recognition systems. Distinct from previous studies, this work pioneers the use of ensemble methods with advanced filtering techniques on the Sign Language MNIST dataset, offering a novel perspective on sign language recognition. This research delves into the intersection of machine learning and image processing to develop a robust framework for sign language recognition. A range of filters, including …

Multi-Agent Learning For Game-Theoretical Problems, Kshitija Taywade

Theses and Dissertations--Computer Science

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Bifurcation Levels Of The Integral Manifolds Of The Newtonian N-Body Problem, Hannah G. Havel

CURE Proceedings

The N-body problem, first proposed by Isaac Newton, is a field of study in mathematics and physics that involves predicting the motion of particles moving under their mutual gravitational attraction. It has significance to many areas of science, including physics and computer science, and is crucial in understanding how the universe works. In fact, it was a primary motivation for Newton's development of calculus. An important application of the N-body problem is within celestial mechanics and involves how planets and other celestial bodies move with mutual gravitational attraction. It is important in developing how satellites behave in space using complicated …

Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose

Theses and Dissertations

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

Image-Based Classification Of Malware Using T-Sne Images, Vincent Stowbunenko

Master's Projects

This Master’s project proposes a novel technique for classifying malware using image-based methods. The approach involves generating t-SNE images from the EMBER dataset, which contains one million samples of both malware and benign files, each represented by over 2,000 features. The t-SNE technique is well-suited for capturing intricate patterns in complex datasets because it effectively maintains the local structure. These t-SNE images are then used as inputs to train two lightweight image classification models, SqueezeNet and MobileNet. Additionally, to provide a benchmark for comparison, a non-image classification model using LightGBM is also explored.

As part of the investigation, the project …

A Natural Language Processing Approach To Malware Classification, Ritik Mehta

Master's Projects

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a …

Metagenomic Survey Of Marine 16s Bacterial Communities Off Palmer Station In Antarctica, Daniel Salter

Master's Projects

This project surveys the metagenomic bacterial community composition in marine surface waters off Palmer Station, Western Antarctic Peninsula and correlates findings with temperature and salinity data. Marine bacterial communities play a vital role in nutrient cycling, but data on surface waters in this region are limited. Analyzing fifteen samples of 16S sequencing data from three austral summers, consistent dominance was observed by the classes Alphaproteobacteria, Gammaproteobacteria, and Flavobacteria. Correlation analysis confirmed significant relationships between taxa and environmental conditions. The observed trends suggest varying abilities of phyla to resist and adapt to changing environmental conditions. Notably, Alphaproteobacteria demonstrated adaptability to favorable …

Efficient Video Qoe Prediction In Intelligent O-Rans, Aditya Kulkarni

Master's Projects

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into opensource elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user’s subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program …

Implicit Racial Bias: A Human Computer Interaction Study Using Eye Tracker, Wenfan Zhang

Master's Projects

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater …

Enhancing Driver Distraction Detection Through The Synergy Of Deep And Traditional Machine Learning, Gowtham Chandrasekaran

Master's Projects

Distracted driving is a major contributor to motor vehicle accidents, causing injury and loss of life. It is one of the major factors that affect the overall driving behavior of a person. Insurance companies take into consideration factors like gender, age, etc. to set insurance premiums for their customers. Today, machine learning and artificial intelligence can eradicate this bias. A machine learning model can analyze driving behavior, such as the frequency and severity of accidents, the speed at which they drive, and their habits such as distracted driving. Based on this information, the model can then determine the risk of …

Unlearning Hidden Bias Between Refugees : An Initial Empirical Investigation, Akshay Sunil Gurnaney

Master's Projects

The challenges that refugees in various regions encounter are common knowledge. One such challenge is a bias among refugees on ethnocentric grounds. In particular, there are various articles that have pointed out the struggles faced by Syrian refugees in countries like Europe as a result of implicit bias. In fact, the media coverage of Syrian crises and the government responses to the same shed negative light on the refugees themselves. On the contrary, the media coverage of Ukrainian crises is very different with lesser restrictions from the governments.

This research attempts to identify the extent of implicit bias between Ukrainian …

Poriferal Vision: Using Mobilenet To Classify Sponge Spicules Through Transfer Learning, Brian Tran

Master's Projects

Global warming is an ongoing issue where the Earth is rapidly warming up. It negatively affects the growth of coral through ocean warming and ocean acidification. Many coral communities, home to a large variety of marine life, are expected to be severely impacted by these effects. Past evidence suggests that sponges will take over as the primary reef builders since many species of sponges have skeletons made of silica or glass which is not affected by ocean acidification. More research is needed to determine which kinds of sponge will most likely be able to thrive in today’s climate.

This can …