Digital Commons Network^™

Personalized Medical Predictions, Bhargavi Chevva

Master's Projects

Over the past few years, personalized medicine has gained traction due to its ability to solve medical issues efficiently for a person based on their personal characteristics. This project aims to design a machine-learning model that can generate predictions of lab test scores in the future based on past medical history. The model is trained using the MIMIC-4 (Medical Information Mart for Intensive Care) dataset that consists of medical records of over 40,000 patients. The proposed model, MOE-BEHRT, consists of Bidirectional Encoder Representations from Transformers on Electronic Health Records (BEHRT) and Mixture of Experts (MOE). The BEHRT model was originally …

Reinforcement Learning-Based Dynamic Pricing For Revenue Maximization With Elastic Network Slicing, Jovian Anthony Jaison

Master's Projects

Network slicing is a key enabler of next-generation networking that supports a diverse array of network applications with different service requirements. In particular, elastic network slicing that dynamically scales the bandwidth reserved for each network slice would benefit both slice users and providers through cost-effective resource utilization. However, the elasticity poses a complex problem of managing the dynamics of fluctuating network slices. It is necessary for a slice provider to maintain the balance of different types of slice requests, so it can accommodate more requests while satisfying the service requirements for each slice type. Dynamic pricing of slice resources is …

Hindi Image Captioning Using Indictrans2 And Encoder-Decoder Architecture, Anahita Vayalombrone Dinesh

Master's Projects

One of the most prominent tasks that lie on the conjunction of Natural Language Processing (NLP) and computer vision, is image captioning. Image captioning is the generative task of achieving textual descriptions from images. Its application finds use in many real-world scenarios like aiding the visually impaired, editing applications, recommendation systems, and medical imaging. This research focus lies in Hindi image captioning, the official language of India, as it has not been explored as far as its need. Several challenges such as the lack of substantial Hindi text data for training models, the need for human annotators to verify the …

A Rule-Based Hybrid Translation Model For Context-Aware Speech To Indian Sign Language Tasks, Dania Jaison

Master's Projects

In India, one of the most significant tools to communicate with the Deaf and Hard of Hearing (DHH) communities is Indian Sign Language(ISL). The issue of a scarcity of computational resources for ISL is even more pronounced when it comes to the translation of written or spoken English into ISL. This paper proposes a rule-based model for translation where the spoken english sentences are translated into ISL by focusing on the specific syntactic and grammatical differences between these two languages. ISL uses a simplified syntax unlike the nuanced sentence structures in English — we omit most of the auxiliary verbs …

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

Improving The Diversity And Fairness In Job Recommendations Using The Stable Matching Algorithm, Jatin Unecha

Master's Projects

In today’s competitive job market, recommendation systems are essential for con- necting job seekers with suitable opportunities. However, traditional recommendation

models, such as collaborative and content-based filtering face challenges in ensuring diversity and fairness. This results in low diversity and high congestion around popular job listings which refers to the problem where a few jobs are recommended to large numbers of users as well as imbalanced job exposure. These drawbacks lower the satisfaction for job applicants and reduce the employers’ ability to reach a broader pool of candidates. In this research we address these challenges by introducing the Gale-Shapley algorithm …

The Effect Of A Neonatal Palliative Care Education Program In Increasing Knowledge And Communication Self-Efficacy Among Nicu Nurses, Shiou-Huei Joy Wang

Doctoral Projects

Background: Neonatal palliative care is crucial to providing comprehensive, high-quality care to neonates with life-limiting or life-threatening health conditions and their families. One significant barrier to the inconsistent provision of neonatal palliative care is the lack of education. This project aimed to deliver an education training program to increase neonatal palliative care knowledge and self-efficacy in NICU nurses. Method: A pre-post-test study design was used to examine the efficacy of an educational training program on NICU nurses’ knowledge and communication self-efficacy in neonatal palliative care. A post-test was administered immediately after the training and again four weeks later to evaluate …

Posttraumatic Symptoms As Predictors Of Mobile App Engagement: An Evaluation Of Usage Data From Public Users Of An App For Military Sexual Trauma, Shilpa Hampole

Master's Theses

Veterans who have experienced military sexual trauma (MST) often face barriers to help-seeking. Mental health mobile applications (apps) such as Beyond MST, developed by the National Center for PTSD, can expand access to evidence-based coping tools, psychoeducation, and resources. However, user engagement is crucial for these apps to be effective. Researchers are exploring factors impacting app engagement, with emerging evidence suggesting that symptom severity may play a key role. This study examined whether the severity of posttraumatic stress disorder (PTSD) symptoms and posttraumatic negative cognitions, as well as level of well-being, are associated with app engagement. Anonymous app usage data …

Navigating Enterprise Constraints: Building A Hybrid Multi-Modal Mobile Intelligent App, Ketan Malempati, Akshat Gupta, Kaikai Liu

Faculty Research, Scholarly, and Creative Activity

The promise of artificial intelligence (AI) has prompted various industries to explore how it can enhance their businesses and improve customer experiences. However, the practical deployment of AI services faces challenges due to internal infrastructure limitations, data policies, and network restrictions. In this paper, we present our current project - a solution designed to empower mental health centers with AI tools while adhering to stringent data and network constraints. Our approach involves deploying AI services and data within internal enterprise networks, while the customer-facing mobile app resides in the public cloud. We introduce a custom AI agent system that monitors …

Rescaling: Change Agency And The Emerging Geography Of Economic Relationships, Markus Grillitsch, Björn Asheim, Nichola Lowe, Sophie Kelmenson, Lea Fünfschilling, Karl Johan Lundquist, Yahia Mahmoud, Mikhail Martynovich, Pauline Mattsson, Magnus Nilsson, Johan Miörner, Torben Schubert

Faculty Research, Scholarly, and Creative Activity

In this paper, we propose a theoretical and conceptual framework for articulating and studying changes in the global economic landscape and illustrate this framework in the current context. The framework pivots around the notion of rescaling, which is interpreted from a human agency perspective. We disentangle three theoretically distinct dimensions of rescaling and show how they play together using concrete examples. The theoretical and conceptual framework links the intended and unintended consequences of globalisation to current rescaling efforts, and allows us to investigate when, why, and how changes to the geography of economic relationships occur.

Assessment Of Digital Collaboration Skills, Esperanza Huerta, Ana Lidia Franzoni Velázquez, Scott Jensen

Faculty Research, Scholarly, and Creative Activity

This exploratory study proposes a methodology for assessing digital collaboration skills based on the students' actual behavior. Students' comments using a digital collaboration tool during a one-month long project were manually coded. This methodology is not context specific and can be used across different domains. This assessment contrasts with self-reported measures in which students rate themselves as already possessing collaboration skills. Finally, the study explores the use of generative AI to automatically code student's comments to alleviate the labor-intensive process that coding requires and to enable scalability in coding data.

Chapter 17. Approaches To Inclusive Recruitment: Practical And Hopeful, Adriana Poo, Anamika Megwalu, Nick Szydlowski, Kathryn Blackmer Reyes, Peggy Cabrera, Ann Agee

Faculty Research, Scholarly, and Creative Activity

San José State University (SJSU) is located in downtown San José, a city of over one million residents in California’s San Francisco Bay Area. Established in 1857, SJSU is the oldest public university on the west coast of the United States. It is also the founding campus of the California State University (CSU) system that comprises twenty-three campuses, making it the largest four-year public university system in the country. SJSU has nine colleges and sixty-seven departments, which offer bachelor’s, master’s, and a growing number of doctoral degree programs. It enrolls over 36,000 students and employs more than 2,100 faculty members. …

Corrigendum: A Live Fuel Moisture Climatology In California (Front. For. Glob. Change, (2023), 6, 1203536, 10.3389/Ffgc.2023.1203536), Jack R. Drucker, Angel Farguell, Craig B. Clements, Adam K. Kochanski

Faculty Research, Scholarly, and Creative Activity

In the published article, there was an error in Figures 4, 5 as published. The plots originally appearing in Figure 4 should be interchanged with those in Figure 5, and vice versa. The corrected Figure 4 and its caption “Figure 4. The general (annual average) (black dashed line), maximum (green dashed line), and minimum (red dashed line) LFMC trends for the (A) Bay Area, (B) Central Coast, and (C) South Coast regions. The differences in the time ranges stem from the length of the data record which varies across the regions” and the corrected Figure 5 along with its caption …

Erratum To: Reconfigurations In Brain Networks Upon Awakening From Slow Wave Sleep: Interventions And Implications In Neural Communication (Network Neuroscience), Cassie J. Hilditch, Kanika Bansal, Ravi Chachad, Lily R. Wong, Nicholas G. Bathurst, Nathan H. Feick, Amanda Santamaria, Nita L. Shattuck, Javier O. Garcia, Erin E. Flynn-Evans

Faculty Research, Scholarly, and Creative Activity

After publication of our paper Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication, we noticed an error in our condition labels that required some revision of figures and text and some new interpretations throughout the manuscript. The error was a consequence of the double-blind nature of the original experimentation which contained two interventions: (1) a blue-enriched light intervention, as originally mentioned; and (2) an olfactory odorant condition. In review of previous analyses and presentations of the data, there was a time when non-descriptive labels (e.g., “A”, “B”) were accidentally swapped, indicating the …

A Comparison Of Adversarial Malware Generators, Pavla Louthánová, Matouš Kozák, Martin Jureček, Mark Stamp, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Machine learning has proven to be a valuable tool for automated malware detection, but machine learning systems have also been shown to be subject to adversarial attacks. This paper summarizes and compares related work on generating adversarial malware samples, specifically malicious Windows Portable Executable files. In contrast with previous research, we not only compare generators of adversarial malware examples theoretically, but we also provide an experimental comparison and evaluation for practical usability. We use gradient-based, evolutionary-based, and reinforcement-based approaches to create adversarial samples, which we test against selected antivirus products. The results show that applying optimized modifications to previously detected …

Anti-Trafficking And Humanitarian Operations: Transferring Learnings For A Better World, Kezban Yagci Sokat, Maria Besiou

Faculty Research, Scholarly, and Creative Activity

Purpose: The purpose of this study is twofold: first, to draw insights from the rich literature on humanitarian operations efforts to combat human trafficking; second, to inspire humanitarian operations researchers to work more on human anti-trafficking. Design/methodology/approach: This is a conceptual paper inspired by recent relevant reports, the academic literature and the authors’ years of involvement in both humanitarian operations and anti-trafficking. Findings: Humanitarian supply chains and human trafficking supply chains very often operate in the same environments and hence face similar challenges. The paper highlights the overlaps between the two domains and demonstrates how two decades of learnings from …

Oculometric Biomarkers Of Visuomotor Deficits In Clinically Asymptomatic Patients With Systemic Lupus Erythematosus Undergoing Long-Term Hydroxychloroquine Treatment, Andrew R. Berneshawi, Kimia Seyedmadani, Rahul Goel, Mark R. Anderson, Terence L. Tyson, Yasmin M. Akay, Metin Akay, Loh Shan B. Leung, Leland S. Stone

Faculty Research, Scholarly, and Creative Activity

Introduction: This study examines a set of oculomotor measurements, or “oculometric” biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. Methods: We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical …

Opening The Black Box: Carbon-Footprint Calculators, Meat Consumption, And The “Wicked Problem” Of Metric Governance, E. Melanie Dupuis, Dustin Mulvaney

Faculty Research, Scholarly, and Creative Activity

Carbon-footprint calculators are widely used as communication and education tools, designed to make people aware of the ways their personal actions contribute to greenhouse-gas emissions. However, they have come under criticism for emphasizing individual behavior versus structural change. We extend this critique to argue that carbon-footprint calculators are a form of “metric governance” that not only disciplines users but also constrains their abilities to engage with the complexities of climate change and social action. We focus on a particularly wicked problem of carbon-metric governance: livestock greenhouse-gas emissions. We show that carbon-footprint calculators black box and fix the complex politics and …

Chronic Pelvic Pain Among Transgender Men And Gender Diverse Adults Assigned Female At Birth, Diana M. Tordoff, Mitchell R. Lunn, Annesa Flentje, Diana Atashroo, Bertha Chen, Zubin Dastur, Micah E. Lubensky, Matthew Capriotti, Juno Obedin-Maliver

Faculty Research, Scholarly, and Creative Activity

Background: There are limited data on pelvic pain among transgender men and gender diverse people, and the impact of testosterone on pelvic pain is poorly understood. Objective: Characterize the prevalence and correlates of chronic pelvic pain (CPP) among transgender men and gender diverse people and examine the association between testosterone use and CPP. Materials and methods: We used 2020–2022 data from The Population Research in Identity and Disparities for Equality (PRIDE) Study, an online prospective cohort study of sexual and gender minority adults in the United States, to conduct complementary cross-sectional and longitudinal analyses. Our primary outcome was self-reported CPP …

Research On Teachers Of Color And Indigenous Teachers: Developing And Fortifying Policies That Diversify The Educator Workforce, Conra Gist, Travis J. Bristol, Saili Kulkarni

Faculty Research, Scholarly, and Creative Activity

This special issue expands upon the Handbook of Research on Teachers of Color and Indigenous Teachers (Gist & Bristol, 2022a) and the practitioner-focused Phi Delta Kappan special issue, “Learning from the Voices of Black, Indigenous and People of Color Educators: Charting New Directions for Research, Policy, and Practice” (Gist & Bristol, 2021). The co-created articles in this special issue highlight the combined expertise of policy makers and education researchers. While the Handbook featured empirical research, and the Phi Delta Kappan special issue featured research briefs and teacher testimonies, the policy-driven articles in this special issue explore how research can be …

The Role Of Intersectionality In Research On Teachers Of Color And Indigenous Teachers, Saili S. Kulkarni, Tanja Burkhard, David J. Johns

Faculty Research, Scholarly, and Creative Activity

This paper emphasizes the complexity and criticality of intersectionality in understanding the research on Teachers of Color and Indigenous Teachers (TOCIT). Cho and colleagues (2013) identified three dimensions of intersectional dialogues: how intersectionality has been used in research and teaching, how it has been used as a theory and methodology, and how it has been used in community organizations. As Cho and colleagues noted, what intersectionality does in theory is equally as important as what it does in practice. Therefore, we drew from the recently published Handbook of Research on Teachers of Color and Indigenous Teachers to highlight how intersectionality …

Emergency And Disaster Preparedness For Individuals Who Use Augmentative And Alternative Communication: A Pilot Study On Supported Planning Using A Toolkit, Andrea Barton-Hulsey, Miriam C. Boesch, Yoosun Chung, Tina Caswell, Amy Miller Sonntag, Wendy Quach

Faculty Research, Scholarly, and Creative Activity

Purpose: When emergencies or disasters arise, individuals who use augmenta-tive and alternative communication (AAC) are particularly vulnerable. It is critical that individuals who use AAC are supported to make decisions that impact their own lives and are able to communicate during such a crisis. Preparedness efforts that include a plan around communication needs and supports are necessary for ensuring that individuals who use AAC are able to understand options that ensure personal safety and express their needs during a time of crisis. Method: Qualitative methods were used to identify and describe the experi-ences of two young adults who use AAC …

Leading From The Middle: Arts Administrators' Beliefs About The Impact Of Arts Education On High-Needs Students, Sofia Fojas

Dissertations

Research on arts education notes the positive impact of arts education on educational outcomes for students, particularly students of color, students in poverty, and students needing additional academic support. Students living in low-income communities receive different educational opportunities than those in high-wealth districts. This disparity is especially true for highly mobile and high-needs students of color. Arts education has made positive differences in student outcomes. This qualitative study examined the beliefs of county arts leads, leaders in the middle, situated between the California Department of Education and their local school districts, and how they can be empowered to be change …

Whose Vote Is It Anyway? A Geospatial Analysis Of The California Voter's Choice Act Ballot Drop Box Criteria In Santa Cruz County, Judith Heher

Master's Projects

The way Californians vote is changing. While some of the laws surrounding these changes are close to a decade old, the events of 2020 surrounding the November Presidential Election and the COVID-19 pandemic both accelerated and expanded the adoption of contactless voting approaches such as vote-by-mail and the ballot drop box. Neither tool is new to California voters. Vote-by-mail was introduced to the state in 1962 (CA Secretary of State, unknown) and ballot drop boxes were first used in 2014 (Sherman, 2020). In 2016 California Senate Bill 450 (SB-450, 2016) was signed into law. Also known as the Voters Choice …

Fake Malware Generation Using Gans As Api Calls, Saieswar Reddy Vaka

Master's Projects

With malware threats on the rise, they have also grown more complicated and subtle. Consequently, incorporating cutting-edge machine learning into cybersecurity defenses has never been more crucial. Nevertheless, building resilient machine-learning models is a significant challenge due to the need for existing diversified and complete malware datasets. This project will relieve this difficulty by employing a Generative

Adversarial Network (GAN) to develop artificial malware samples featuring an Appli- cation Programming Interface (API) call series. While traditional generative modeling

has primarily been limited to image-based fields, we offer an “outside the box” domain – malware signature generation – as an API …

Base Station Selection Based On The Predicted Dwell Time In 5g-V2x Handover, Anushree Jayesh Shah

Master's Projects

Vehicle-to-Everything (V2X) networks have facilitated smooth communication in vehicles and their surroundings via 5G technology. As vehicles move into different coverage areas, they tend to switch between cellular base stations in order to talk to these "things". Handovers are crucial to maintaining these networks’ effectiveness. To ensure uninterrupted connectivity and seamless handovers, it is extremely important to select a base station that is most appropriate. A vehicle’s dwell time is the duration it stays connected to a base station before handoff. This research focuses on predicting dwell time using regression techniques to proactively choose the most suitable base station. We …

Ai-Driven Credit Card Fraud Detection With Enhanced User Interaction, Toshi Bhat

Master's Projects

This thesis describes the development and testing of a unique system for detecting credit card fraud. The system employs graph neural networks (GNNs) and a real-time user interaction platform. The primary goal of this study is to use advanced machine learning methods and interactive technologies to improve fraud detection accuracy and the speed with which users can receive assistance. GraphSAGE, a type of GNN, was trained on a simulated set of credit card transactions, allowing the system to detect and predict fraud very accurately. Simulating a real-world transaction scenario is an important aspect of the project. In this case, the …

Nft Price Prediction Using Machine Learning, Akhil Patil Bagili

Master's Projects

In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Detecting Fake Reviews Using Aspect Based Sentiment Analysis And Graph Convolutional Networks, Prathana Phukon

Master's Projects

Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as …