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Spartan Daily, September 11, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 9

Spartan Daily, September 10, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 8

Spartan Daily, September 9, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 7

Spartan Daily, September 4, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 6

Climate Change-Related Threats To Railroads: Implications For Threat, Hazard And Risk Assessment, Frances L. Edwards, Daniel C. Goodrich

Faculty Research, Scholarly, and Creative Activity

Climate change has created new challenges for railroads around the world. The rail systems operate in open environments, subjecting them to increased heat, changing locations of heavy rains, developing sea level rise and storm surge, and a longer and stronger wildfire season. This research investigates how an updated threat and hazard identification and risk assessment (THIRA) can lead to the development of climate adaptation strategies that limit damage and disruption to the railroad enterprise. The THIRA analyzes which threats are of most concern in a specific rail segment, and which climate change adaptation strategies give a beneficial return on investment.

Spartan Daily, September 3, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 5

Acoustic Wave Manipulation Through Sparse Robotic Actuation, Tristan Shah, Noam Smilovich, Feruza Amirkulova, Samer Gerges, Stas Tiomkin

Faculty Research, Scholarly, and Creative Activity

Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, the use of deep neural networks to represent dynamics that are partially observed by robot sensors, as well as effective control using sparse control signals. In this work, we explore a more general problem: the manipulation of acoustic waves, which are partially observed by a robot capable of influencing the waves through spatially sparse actuators. This problem holds great potential for the design of new artificial materials, ultrasonic cutting tools, energy harvesting, and other applications. We develop …

Exploring The Use And Misuse Of Large Language Models, Hezekiah Paul D. Valdez, Faranak Abri, Jade Webb, Thomas H. Austin

Faculty Research, Scholarly, and Creative Activity

Language modeling has evolved from simple rule-based systems into complex assistants capable of tackling a multitude of tasks. State-of-the-art large language models (LLMs) are capable of scoring highly on proficiency benchmarks, and as a result have been deployed across industries to increase productivity and convenience. However, the prolific nature of such tools has provided threat actors with the ability to leverage them for attack development. Our paper describes the current state of LLMs, their availability, and their role in benevolent and malicious applications. In addition, we propose how an LLM can be combined with text-to-speech (TTS) voice cloning to create …

Spartan Daily, August 28, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 4

Spartan Daily, August 27, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 3

Accelerating The Fusion Workforce In The Usa, Carlos Paz-Soldan, Eva Belonohy, Troy Carter, Laleh E. Coté, Evdokiya Kostadinova, Calvin Lowe, Subash L. Sharma, Sybil De Clark, Jaydeep Deshpande, Kate Kelly, Bobbi Makani, David A. Schaffner, Kathreen E. Thome

Faculty Research, Scholarly, and Creative Activity

The fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on 29-30 May 2024, which was funded by the National Science Foundation of the USA. A major goal of the conference was to …

Context-Aware Natural Language Processing For Malware Detection, Helen Liu, Summer Mccune, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

As malware continues to evolve and cyber attacks become increasingly prevalent, it is critical to develop effective malware classification techniques for the detection and prevention of such malicious attacks. In our research, we approach malware classification from a natural language processing perspective to explore how various tokenization techniques on malware opcode features can enhance classification accuracy and combat malware obfuscation and evolution techniques with a deep learning approach, previously unaddressed by existing research. We bridge this gap by conducting extensive hyperparameter tuning experiments that examine the effects of five common tokenization methods, that is, White Space Separation, Top Single Words, …

Poster: Development Of Situation Awareness Measurement For Cybersecurity Professionals, David Schuster, Crystal Fausett, Maiyi Huang, Sabina M. Patel, Jenna Korentsides, Joseph R. Keebler, Elizabeth H. Lazzara

Faculty Research, Scholarly, and Creative Activity

Better understanding of the implications of many aspects of human behavior, especially that of cybersecurity professionals, can help develop the cybersecurity workforce. Despite efforts aimed at documenting and understanding cybersecurity professionals' knowledge, understanding of how cognition supports human performance in specific cybersecurity tasks remains limited. Specifically, understanding of the elements of situation awareness (SA), defined as goal-directed knowledge, is necessary to support human-centered evaluation, selection, training, and recruitment strategies. In this poster, we propose a framework for developing measures of situation awareness for cybersecurity professionals and a method of capturing initial effectiveness data that does not rely on access to …

Poster: Cloudsweeper: Leveraging Large Language Models To Personalize Sensitive Archive Search, Victor Escuerdo, Sergio Talavera, Gautam Santhanu Thampy, Ivan Torres, Daniel Vega Lojo, Chris Kanich, Magdalini Eirinaki

Faculty Research, Scholarly, and Creative Activity

As cyber threats continue to evolve, overlooked or neglected files stored in cloud services can pose significant risks to personal privacy and data security. In this paper we present Cloudsweeper, a system that aims to improve cloud storage security by creating tools that help users identify and manage sensitive or unwanted files. Cloudsweeper leverages Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) architecture to develop a personalized and privacy-focused archive search system. Cloudsweeper represents an innovative approach to secure archive management, balancing user control and privacy in cloud storage environments.

Poster: Threat Intelligence & Modeling Practices For Iomt Devices Using Wi-Fi Communications, Eva Wilson, Cody Ourique, Bernardo Flores

Faculty Research, Scholarly, and Creative Activity

The integration of internet connectivity into medical devices, known as the Internet of Medical Things (IoMT), is revolutionizing modern healthcare through remote monitoring and advanced diagnostics. Among these devices, the Continuous Positive Airway Pressure (CPAP) machine plays a critical role in managing patients with Sleep Apnea. However, the security of CPAP devices presents significant concerns due to potential vulnerabilities that could lead to critical failures, such as device shutdowns. An analysis was made of the device's connectivity interface, to identify potential exploit points and vulnerabilities. Additionally, we evaluated whether existing threat modeling frameworks - namely the MITRE Adversarial Tactics, Techniques, …

Synthetic Malware Image Generation Based On Generative Models Against Zero-Day Attacks, Arjun Sudheer, Ayesha Ahmed, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware detection is a critical task for protecting our assets from attacks. However, traditional approaches to malware detection often struggle with limited datasets, which hinder the effectiveness of machine learning models. In this paper, we propose a malware image generation system designed to craft high-quality synthetic malware image samples, addressing the challenges posed by small datasets in malware detection. The proposed system utilizes two popular generative models, WGAN-GP and Diffusion, to generate synthetic malware images. It converts malware binary files into image files using four different color spaces: monochrome, grayscale, RGB, and CMYK. These images are then evaluated based on …

Testimonios: The Experiences Of An Undocumented Student In California Post-Daca Rescindment, Isabel Rangel

McNair Research Journal SJSU

After Deferred Action for Childhood Arrival (DACA, 2012) was rescinded

by the Trump Administration in 2017, many students were left in “limbo

legality” (Gonzalez 2012) This study addresses the question, “What are the

experiences of undocumented students regarding their academic motivation

toward graduation now that DACA has been rescinded at San Jos. State

University (SJSU)?” Grounded in a Chicana Feminist Epistemology

approach, the methodology of testimonio is used to capture the experiences

of the author alongside an undocumented graduate student. Findings

suggest that college campuses should incorporate faculty knowledgeable in

policies that affect undocumented students, Undocumented Ally trainings

in college …

Confronting The Digital Epidemic: Excessive Social Media Use (Smu) In The Wellbeing Of Young Adult Populations, Joanna Abellera

McNair Research Journal SJSU

This study will investigate the relationship between social media as a

mediator and its effects on depressive symptoms, using Social Comparisons

theory (SCT; Festinger, 1954) as a framework. A series of 8-10 articles,

obtained through the San Jose State Library Database and PsycINFO, will

explore the nature of social media addiction (SMA) and problematic social

media use (PSMU) and its relation to self-esteem of young adult

populations by comparing theoretical perspectives (i.e., cross-sectional and

experimental designs) in the literature. Studies have found that [increased

SMU has been correlated with negative outcomes such as depression,

loneliness, and increased envy]. Future studies …

Front Matter, Maria Cruz

McNair Research Journal SJSU

No abstract provided.

Full Issue

McNair Research Journal SJSU

No abstract provided.

Spartan Daily, August 26, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 2

Attention In The Age Of Tiktok: Examining The Cognitive Impact Of Short-Form Video Consumption, Samantha Shannon Sutrisno

Master's Theses

With the rapid growth of popularity among short-form video platforms such as TikTok, media consumption has shifted toward fast-paced and highly engaging formats. This trend raises questions about the potential effects of continuous exposure to brief and repetitive audiovisual stimuli on attention. Research on short-form video’s impact on attention is limited, but prior studies suggest it may influence attention in meaningful ways. This study examined the relationship between short-form video consumption and attention among TikTok users (N = 110) by using the Attention Network Task (ANT), which measures the alerting, orienting, and executive control networks of visual attention. Participants were …

A Closer Look At The Relationship Between Cultural Intelligence And Organizational Citizenship Behaviors, Lesli B. Quiroz

Master's Theses

As organizations—particularly in the United States—become more multicultural due to globalization, they increasingly rely on individuals' ability to succeed in complex, cross?cultural environments. Cultural intelligence (CQ), or the ability to successfully adapt to unfamiliar cultural settings, has been previously related to expatriate cross-cultural adjustment, group effectiveness, and more recently, to organizational citizenship behaviors (OCB), which are voluntary extra-role workplace behaviors that lead to positive employee and organizational outcomes. This study mainly sought to theoretically expand the limited research on the relationship between CQ and OCB with some research questions. In a sample of 93 participants based in California, results found …

Spartan Daily, August 20, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 1

Use Of Cigarettes, Cannabis, And Alcohol Among Asian American, Native Hawaiian, And Pacific Islander Adults: Community-Based National Survey Analysis, Vuong Van Do, Van My Ta Park, Nhung Nguyen, Pamela May Ling, Marian Tzuang, Bora Nam, Marcelle M. Dougan, Oanh L. Meyer, Janice Y. Tsoh

Faculty Research, Scholarly, and Creative Activity

Background: Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations have diverse cultural, immigra-tion, and sociodemographic characteristics. Aggregated data could mask substantial differences in substance use between cultural subgroups in this population. Yet, studies examining substance use among the AANHPI population are limited. Objective: This study aimed to describe cigarette, cannabis, and alcohol use among AANHPI adults by cultural subgroup and sex. Methods: We analyzed data from 3411 AANHPI respondents of a multilingual national survey “COMPASS” during December 2021-May 2022. Primary outcomes were self-report current (every day or some days) use of cigarettes, cannabis, and alcohol. Cultural subgroups included Asian …

The Sketches Of Infinite Data And Algorithms For Real-Time Data Insights, Vishnu S. Pendyala

Open Educational Resources

How are machine learning algorithms able to answer questions from any nook and corner of the World Wide Web? How are trending hashtags from the near infinite microblog posts, unique visitors and other distinct counts in the near infinite website traffic determined? How do blogging websites avoid recommending articles a user has previously read? In general, how can we answer complex queries about enormous data streams without storing them entirely, in real-time? The answer often lies in clever approximation algorithms and data "sketches" that capture essential properties using vastly reduced space. The relentless flow of data in modern systems indeed …

Multimodal Emotion Detection And Analysis From Conversational Data, Abhinay Jatoth, Faranak Abri, Tien Nguyen

Faculty Research, Scholarly, and Creative Activity

—Emotion recognition in conversations has become increasingly relevant due to its potential applications across various fields such as customer service, social media, and mental health. In this work, we explore multimodal emotion detection using both textual and audio data. Our models leverage deep learning architectures, including Transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly optimized BERT approach (RoBERTa), Audio Spectrogram Transformer (AST), Wav2Vec2), Bidirectional Long Short-Term Memory (BiL-STM), and four fusion strategies that combine features from multiple modalities. We evaluate our approaches using two widely used emotion datasets, IEMOCAP and EMOV. Experimental results show that fusion models …

Exploring The Ant: How Well Do Three Attention Networks Predict Road Hazard Perception, Xander Philip Martinez

Master's Theses

Hazard perception is the ability of a driver to anticipate emerging dangers. Driving is a complex activity requiring attention to various stimuli to prevent accidents. Prior studies have shown that older, experienced drivers detect more cues and perceive hazards better than younger, inexperienced drivers. Attention is multifaceted, and three visual attention networks were focused on in the study: alerting (readiness), orienting (spatial focus), and executive control (distraction inhibition). A sample of 95 participants completed two tasks: The Attention Network Test was used to assess these three attention networks, and to measure road hazard awareness, participants viewed short dashcam videos (~233 …

Learning Structure With Multivariate Information Bottleneck And Exploration Of New Methods In Sequential Decision Making, Volodymyr Makarenko

Master's Theses

Research in useful information extraction has been motivated by the increasing demand to extract insights from unstructured data, and by the need to store and transmit great volumes of information, often originating in unstructured data such as videos. Research in rate distortion and information bottleneck paved the path for understanding and guiding the design of lossy encoders, capable of extracting relevant information. Independently, research in deep representation learning has enabled numerous applications for unstructured high-dimensional data such as images. However, the interpretability of the deep learning methods remained limited. Several desired properties of learned representations have been suggested, including disentanglement. …

Book Review On The Worlds Of Classical Chinese Aesthetics (By Paul R. Goldin), Lin Wang

Comparative Philosophy

No abstract provided.