Digital Commons Network^™

The Methodological Problems Of Incorporating Indigenous Philosophy In Collaborative Research, Luthonliu Gangmei, P. Milan Khangamcha

Comparative Philosophy

This paper addresses the methodological challenges inherent in collaborative research endeavours focused on the development of Indigenous philosophy. It argues that these challenges persist due to a lack of recognition of the autonomy and intrinsic philosophical value of ethno-philosophy or Indigenous philosophy. Despite the United Nations acknowledging the significance of indigenous worldviews and their philosophical underpinnings, they are still yet to be fully integrated into the global knowledge domain. The paper highlights the tendency to view indigenous philosophy solely as a cultural asset of indigenous peoples rather than recognizing its broader philosophical relevance. It identifies this as a critical interdisciplinary …

Vol 16 No 1 Content Page

Comparative Philosophy

No abstract provided.

Vol 16 No 1 Information Page

Comparative Philosophy

No abstract provided.

Vol 16 No 1 Cover Page

Comparative Philosophy

No abstract provided.

Computing Education In African Countries: A Literature Review And Contextualised Learning Materials, Sally Hamouda, Linda Marshall, Kate Sanders, Ethel Tshukudu, Oluwatoyin Adelakun-Adeyemo, Brett A. Becker, Emma R. Dodoo, G. Ayorkor Korsah, Sandani Luvhengo, Oluwakemi Ola, Jack Parkinson, Ismaila Temitayo Sanusi

Faculty Research, Scholarly, and Creative Activity

This report begins with a literature review of computing education in Africa. We found a substantial body of work, scattered over more than 80 venues, which we have brought together here for the first time. Several important themes emerge in this dataset, including the need to contextualise computing education. In the second part of this report we investigate contextualisation further. We present a pilot study, grounded in the literature review, of the development of course materials, sample code, and programming assignments for introductory programming, contextualised for six African countries: Botswana, Egypt, Ghana, Nigeria, South Africa, and Zambia. We include the …

Spartan Daily, January 23, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 164, Issue 1

The Impact Of Artificial Intelligence On Data Privacy: A Risk Management Perspective, Norman Mooradian, Patricia C. Franks, Amitabh Srivastav

Faculty Research, Scholarly, and Creative Activity

Purpose: The purpose of this paper is to increase the artificial intelligence (AI) ethical literacy of information governance professionals by explaining how AI intensifies familiar data privacy issues by virtue of its dependency on data and ability to create new personal information, to explicate emerging privacy enhancing methods and to show their continuity with existing privacy and information governance principles. Design/methodology/approach: The paper uses an interdisciplinary design research methodology that extends current governance frameworks by combining information science, information governance and applied ethics concepts. Three information sources were referenced: 1) academic papers; 2) standards and best practices published by governmental …

Dynamic Imprints Of Colliding-Wind Dust Formation From Wr 140, Emma P. Lieb, Ryan M. Lau, Jennifer L. Hoffman, Michael F. Corcoran, Macarena Garcia Marin, Theodore R. Gull, Kenji Hamaguchi, Yinuo Han, Matthew J. Hankins, Olivia C. Jones, Thomas I. Madura, Sergey V. Marchenko, Hideo Matsuhara, Florentin Millour, Anthony F.J. Moffat, Mark R. Morris, Patrick W. Morris, Takashi Onaka, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

Carbon-rich Wolf-Rayet (WR) binaries are a prominent source of carbonaceous dust that contribute to the dust budget of galaxies. The “textbook” example of an episodic dust-producing WR binary, WR 140 (HD 193793), provides us with an ideal laboratory for investigating the dust physics and kinematics in an extreme environment. This study is among the first to utilize two separate JWST observations, from Cycle 1 ERS (2022 July) and Cycle 2 (2023 September), to measure WR 140’s dust kinematics and confirm its morphology. To measure the proper motions and projected velocities of the dust shells, we performed a novel point-spread function …

To Be Or Not To Be Included In The S&P 500: Cost Of Debt Implications, Matthew Faulkner, Tracie Frost, Stoyu I. Ivanov

Faculty Research, Scholarly, and Creative Activity

Purpose: We examine the changes in a firm’s cost of debt after it is included in or removed from the S&P 500. The extant literature on index composition focuses on the cost of equity and lacks an understanding of the impacts on a firm’s cost of debt capital upon inclusion in or removal from a major stock market index. Therefore, we address the following question: Does a firm’s cost of debt change around its inclusion in or removal from the S&P 500? Design/methodology/approach: We develop two hypotheses based on the research question and use univariate and multivariate fixed-effects analyses to …

In Touch With Those In Need: How Touchscreen Devices Lead Consumers To Support Social Causes, Aekyoung Kim, Hyojin Lee, Saerom Lee

Faculty Research, Scholarly, and Creative Activity

As consumers are increasingly using touchscreen devices (e.g., tablets or smartphones) when exposed to various marketing messages, this research explores how touchscreen devices alter consumer reactions to social cause marketing. This research shows that using touchscreen (vs. nontouchscreen) devices while viewing online charity ads featuring human victims increases consumers’ support for the advertised social causes. Four studies demonstrate this effect by examining consumers’ social media activities, donation intentions, and actual donations in field and lab settings. Mediation analysis reveals that using touchscreen (vs. nontouchscreen) devices enhances consumer support through increased empathy for the victims featured in charity ads. Together, this …

Spherical And Sessile Droplet Dynamics By Fluctuating Hydrodynamics, John B. Bell, Andrew Nonaka, Alejandro L. Garcia

Faculty Research, Scholarly, and Creative Activity

We simulate the mesoscopic dynamics of droplets formed by phase-separated fluids at nanometer scales where thermal fluctuations are significant. Both spherical droplets fully immersed in a second fluid and sessile droplets which are also in contact with a solid surface are studied. Our model combines a Cahn-Hilliard formulation with incompressible fluctuating hydrodynamics; for sessile droplets, the fluid-solid contact angle is specified as a boundary condition. Deterministic simulations with an applied body force are used to measure the droplets' mobility from which a diffusion coefficient is obtained using the Einstein relation. Stochastic simulations are independently used to obtain a diffusion coefficient …

Action Research As A Pathway To Discovery In The School Library, David Vickers Loertscher, Michelle Young

Learning Hub

No abstract provided.

(Missed) Opportunities To Build Solidarity: A Critical Analysis Of Spanish-English Bilingual Children’S Books, Claudia Rodriguez-Mojica, Allison Briceño

Faculty Research, Scholarly, and Creative Activity

Two-way dual language (TWDL) programs have traditionally served white and non-Black Latinx students, with limited attention to other cultures and races. This limitation can result in the erasure of Afro-Latinx culture from bilingual programs. This study explores the ways Black characters are represented in bilingual picturebooks, which can serve to perpetuate or combat anti-blackness within bilingual education. We completed a critical content analysis of Spanish-English bilingual picturebooks that include Black or Afro-Latinx characters, employing LatCrit and BlackCrit theoretical frameworks to explore how Black characters are positioned. Findings show a significant dearth of bilingual books that include Black or Afro-Latinx characters. …

Tsao, H.-S. Jacob, San Jose State University

Emeritus and Retired Faculty Biographies

University of California, Berkeley, Operations Research, Ph.D. 1984

University of Texas at Dallas, Mathematical Statistics, MS, 1979

National Chiao-Tung University, Taiwan, Applied Mathematics, BS, 1976

Characteristics Of World Class School Librarians, David V. Loertscher, Michelle Young, Charlene Peterson

Learning Hub

Over 30 school librarians across the U.S. and Canada and who had been nominated by their peers for their excellence were interviewed as a part of the AliveLibrary Project at San Jose State University. The major question was “What are the major characteristics of world class school librarians.” Each was read and rated by three researchers, and in several rounds resulted in eight major characteristics that seem to undergird high impact on teaching and learning across a wide spectrum of locations, affluence, diversity, and communities.

Science, Physiology, And Nutrition For The Nonscientist, Judi S. Morrill

Open Educational Resources

A wonderful blend of physiology, nutrition, biochemistry, genetics, biology, evolution, chemistry--what we all need to know as informed citizens. A basic knowledge of the life sciences and how our bodies work--to promote our own good health, especially as we're bombarded with misleading advertisements, soundbites, and the like. DNA fingerprinting, calorie requirements, dietary advice, genetic engineering (including gene editing with CRISPR cas9)--all in an easy-to understand book.

Carbohydrate Counting: Traditional Filipino Food Lists For Management And Prevention Of Diabetes Mellitus, Ashwini Wagle

Open Educational Resources

The Carbohydrate Counting tool was created to address the specific dietary needs of the Filipino population. It is an original intellectual work by Ashwini Wagle, Ed.D., MS, RD, Chair of the Department of Nutrition, Food Science, and Packaging at San Jose State University (SJSU).

The development of this tool was made possible through the collaboration and support of several contributors. Serena Starr, MS; Justine Martinez Cuaresma, MS, RD; Nazli Goudarzi, MS; Leah Olaivar; and Joe Johnston—a graduate student in the Department—played integral roles in the project. Their contributions included verifying the authenticity of traditional foods, designing culturally appropriate meal plans, …

Words And Stardom: Modernist Poetic Responses To Asta Nielsen, Karl E. Toepfer

Open Educational Resources

This essay discusses how four modernist poets portrayed the Danish movie star Asta Nilesen (1881-1972). German Hans Schiebelhuth (1895-1944) describes the "demonic" power of Nielsen's image to awaken dark aspects of his adolscent personality. For the Flemish poet Paul van Ostaijen (1896-1928), Nielsen's cinematic image provokes a radical shattering of language into extravagant "word-images." Danish writer Rudolf Broby-Johansen (1900-1987) linked Nielsen's image to a radical (Marxist) critique of oppressive sexual politics within a capitalist society. German Franz Behrens (1895-1977) composed a lengthy, expressionist ode to Nielsen from the perspective of one who actually worked as a screenwriter in the German …

Application Of Root Cause Analysis For Fall Prevention: A Quality Improvement Initiative For Older Adults In A Skilled Nursing And Long-Term Care Facility, Terrence Ranjo

Doctoral Projects

Falls in older adults are common and often have severe outcomes. They are the leading cause of fatal and non-fatal injuries among people aged 65 and older and continue to increase. Half of residents in nursing facilities fall annually, and one in every ten falls will lead to a severe injury. Federal regulations like the Centers for Medicare and Medicaid Services require long-term care (LTC) facilities to incorporate quality assurance performance improvement initiatives to address identified quality concerns in LTC facilities. Root cause analysis (RCA) can help clinicians identify several root causes of falls and guide clinicians in developing personalized …

Advocating For The Field Of Occupational Therapy Among High School Students, Linda Crabtree

Doctoral Projects

The field of occupational therapy (OT) is a healthcare field that is impactful on the lives of many individuals however there is currently a lack of awareness of the field of OT due to a lack of knowledge of and interest in OT among healthcare providers and the public (Richards and Valleé, 2020). The Aim of Research. The purpose of this project is to advocate for the field of OT and assess if an introductory presentation and hands-on lab about OT’s scope can improve the interest in OT among high school students in both rural and urban environments. Methods. The …

Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra

Master's Projects

Distributed systems are difficult to trace using traditional methods due to the scale of data volume and complexity, and they usually require a lot of manual analysis. TraceAI tries to solve these problems by integrating Large Language Models with the tracing tools to automatically enhance the trace data evaluation. The project aims to provide an AI-driven solution for monitoring and understanding the flow of requests across services, anomaly detection, root cause analysis and performance optimization. It can thus automate finding out systems problems using LLMs thereby carrying out large scale trace data analysis. Anticipated results from the effort will be …

Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan, Crystal Kwong

Master's Projects

Generative adversarial networks (GANs) are known for their ability to generate high quality
images mimicking real life or even particular art styles. Yet for all their capability, casually
training a GAN on an average machine can be infeasible as GANs require an enormous amount
of time and data to train. Even with a trained GAN, model inference demands heavy
computations, making GANs difficult to deploy on applications. To address these limitations,
techniques such as transfer learning and quantization have been leveraged to speed up training of
GANs and lighten computational cost of GAN inference. This project aims to use such …

An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect …

Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng

Master's Projects

Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. …

Moving Target Defense With Quantized Morphence: Defense Quantification Against Common Adversaries In Image And Time Series Problems, Rithika Dhamala

Master's Projects

In recent years, the vulnerability of deep learning models to adversarial attacks has emerged as a serious threat, particularly in domains where reliability and robustness are critical. This project builds upon the Morphence framework, a Moving Target Defense (MTD) strategy designed to counter adversarial threats by maintaining a dynamic pool of models and introducing randomness at inference time. While Morphence was originally developed for image classification tasks, this work not only reproduces the original architecture using MNIST and CIFAR-10 datasets but also extends the core principles to an entirely new domain: time series forecasting. The project proposes a unified defense …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla

Master's Projects

The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is …

Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei

Master's Projects

In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …

Photoproof: A Mobile Application For Verifying The Authenticity Of Images, Pruthviraj Urankar

Master's Projects

The easy access to artificial intelligence (AI) technologies, such as deepfakes and generative adversarial networks (GANs), has facilitated the creation of highly realistic artificial images, thereby undermining the authenticity of photos in today’s digital age. Misinformation and manipulation are key dangers to digital content due to this advancement. Therefore, the need for reliable methods of photo verification and authentication has become increasingly important. This report presents a decentralized iOS app that uses blockchain to ensure photo authenticity. The app leverages Ethereum smart contracts and cryptographic hashing to securely log image metadata. When a user takes a photo, the app hashes …

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …