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Articles 271 - 300 of 18466
Full-Text Articles in Entire DC Network
Exploring Math Word Problem Generation With Llms, Trung Hieu Vuong
Exploring Math Word Problem Generation With Llms, Trung Hieu Vuong
Master's Theses
Math Word Problem (MWP) is an important building block for learning math. This type of problem is particularly useful for younger audiences because solving it involves two simultaneous skill sets: reading comprehension and mathematical reasoning. With publicly available large language models (LLMs), generating additional MWPs is readily achievable. While researchers have started using LLMs as MWP facilitators, there still exists a gap in studies about the diversity of MWPs generated by unmodified, publicly accessible LLMs. For that reason, our study focused on two goals: (1) to evaluate the diversity of MWPs generated by publicly available LLMs when provided with examples …
Developing A Vietnamese Text Summarization Large Language Model On Limited Hardware, Tin Pho
Developing A Vietnamese Text Summarization Large Language Model On Limited Hardware, Tin Pho
Master's Theses
Text summarization models have achieved significant growth during the last few years because of major Large Language Model (LLM) technological advancements. The application of LLMs are widely used in news distribution (TL;DR news), translation tools (DeepL Translate), or virtual assistants (ChatGPT, DeepSeek, Claude, etc.). However, the progress has not yet reached all languages equally. The Vietnamese language is used by more than 90 million people, but the language is not as highly developed for LLM as it has many homophones, five different tones that affect meaning of words, and irregular grammar compared to other languages (e.g. English, Spanish, etc.). Also, …
Automatic Guitar Transcription Of Polyphonic Music, Ritwik Patil
Automatic Guitar Transcription Of Polyphonic Music, Ritwik Patil
Master's Theses
Transcribing guitar music automatically is a complex task due to polyphonic overlap, tuning variations, and diverse playing techniques. Current transcription systems focus on identifying note pitches and timing while ignoring performance techniques that describe how the notes are played, treating guitar recordings as generic polyphonic audio and producing MIDI-like outputs that lose important information about articulation and style. To address these challenges, we propose an end-to-end transformer model for automatic guitar transcription. The system uses a T5-based encoder-decoder architecture that processes the Constant-Q Transform (CQT) of stereo audio input. The stereo representation helps separate individual guitar parts within a mix …
Latent Action Trajectory Optimization, Rahul Milind Kandekar
Latent Action Trajectory Optimization, Rahul Milind Kandekar
Master's Theses
Learning from demonstrations offers a path to bypass the sample inefficiency of reinforcement learning, but obtaining action-labeled expert demonstrations remains expensive and often impractical. Learning from Observations (LFO) addresses this by learning policies from observation-only demonstrations. Recent LFO work relies heavily on behavior cloning: VPT and LAPO use observation-only data combined with limited action labels to train BC policies, while AIME offers an alternative policy inference approach but requires the majority of its training data to have action labels. Through systematic experiments in the Lunar Lander environment, we investigate whether latent action methods can function when state and action dimensionalities …
Lithium–Sulfur Batteries: 3d Printed Tools And Assembly Techniques For Repeatable Lab-Scale Coin Cell Manufacturing, Daryl Miranda, Tosif Aliyev, Lamija Kovacevic, Sivapriya Voleti, Jaydie Lee, Kathleen Meehan, Monica C. So, Philip T. Dirlam
Lithium–Sulfur Batteries: 3d Printed Tools And Assembly Techniques For Repeatable Lab-Scale Coin Cell Manufacturing, Daryl Miranda, Tosif Aliyev, Lamija Kovacevic, Sivapriya Voleti, Jaydie Lee, Kathleen Meehan, Monica C. So, Philip T. Dirlam
Faculty Research, Scholarly, and Creative Activity
The coin cell form factor has become a standard in energy storage research owing to its relatively low cost, low quantity of requisite electroactive material, fast experimental turnover, and capability to generate publishable quality data. Coin cell assembly at the lab-scale requires precise manual alignment of the many internal components of a cell to yield a properly functioning device. Truly repeatable device assembly presents a significant challenge to the acquisition of reliable data. In this report, we present a three-dimensional (3D) printable design for a coin cell alignment device and describe techniques for a generalized workflow to achieve reproducible coin …
Attitudes And Perception Of Lis Students Towards Artificial Intelligence(Ai), Shahzadi Humbhi, Shabbir Tareen, Alia Humbhi
Attitudes And Perception Of Lis Students Towards Artificial Intelligence(Ai), Shahzadi Humbhi, Shabbir Tareen, Alia Humbhi
Library Philosophy and Practice (e-journal)
Artificial Intelligence refers to computer systems that can think and behave like humans. It is becoming essential in the education system as it has the potential to transform the way of teaching and learning in an academic environment. Therefore, the current study aims to investigate LIS students' attitudes and perceptions of AI, as well as how these technologies benefit their academic performance. The study also identified barriers that LIS students confront when adopting AI tools. For this purpose, a survey questionnaire was designed to assess students' knowledge and attitudes concerning AI, as well as its applications to improve academic performance. …
Parameter-Efficient Multimodal Adaptation: Ocr-Integrated Lora For Textvqa And Captioning, Karthik Ganesh Malini
Parameter-Efficient Multimodal Adaptation: Ocr-Integrated Lora For Textvqa And Captioning, Karthik Ganesh Malini
Master's Theses
Vision-Language Models (VLMs) have emerged as transformative technologies for multimodal AI, yet they face significant hurdles in processing text-rich images required for enterprise applications like document understanding, medical imaging, and industrial inspection. Current VLMs struggle with accurate text extraction and reasoning, often exhibiting high hallucination rates and poor Optical Character Recognition (OCR) token utilization. To address these limitations, this research presents a comprehensive framework for optimizing parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning strategies on state-of-the-art architectures, including LLaVA-1.5 and BLIVA-FlanT5. Our methodology integrates enhanced OCR token utilization, faithful caption generation, and specific hallucination mitigation techniques. We employ a multi-dimensional evaluation protocol …
Bicultural Identity, Work-Life Balance And Mental Health Outcomes In Swana Students, Julie M. Salama
Bicultural Identity, Work-Life Balance And Mental Health Outcomes In Swana Students, Julie M. Salama
Master's Theses
Southwest Asian and North African (SWANA) college students navigate academic demands alongside immigration-salient stressors that may shape their mental health and academic performance. This study examined generational status (Gen 1 vs. Gen 2+), identity conflict, ethnic identity, and work–life balance (WLB) in overall GPA and major GPA, and mental health outcomes of depression (PHQ-9), and anxiety (GAD-7) among SWANA students (N = 35). Two one-way MANOVAs found no generational differences for academics (overall/major GPA) or mental health (PHQ-9/GAD-7). Identity conflict was unrelated to GPA or symptoms but was positively associated with ethnic identity, while WLB was negatively correlated with depression …
Stream Behavior Characterization Of Marin County Coastal Watersheds And Their Influence On Salmonid Habitat, Ellie L. Murdoch
Stream Behavior Characterization Of Marin County Coastal Watersheds And Their Influence On Salmonid Habitat, Ellie L. Murdoch
Master's Theses
Coastal watersheds in California are increasingly affected by climatic variability, threatening stream behavior, ecological function, and salmonid habitat suitability. This study evaluated streamflow behavior of seven watersheds in Marin County, California, with detailed analysis of Corte Madera, Olema, and Redwood watersheds, which are not influenced by reservoir releases. Using USGS stream gauge records, PRISM climate data, SSURGO soils, and geologic and vegetation information, streamflow metrics critical to salmonid life cycles were quantified for water years 2014 to 2023. Watersheds with greater storage capacity, such as Redwood, exhibited muted hydrologic responses, including delayed flow activation, lower peak flows, slower recession rates, …
A Methodology To Reconstruct The Extent And Dynamics Of Historic Wildfires (1850-1950) In The Santa Cruz Mountains Of California, David Vásquez Ospina
A Methodology To Reconstruct The Extent And Dynamics Of Historic Wildfires (1850-1950) In The Santa Cruz Mountains Of California, David Vásquez Ospina
Master's Theses
This thesis reconstructs the extent and dynamics of historic wildfires in the Santa Cruz Mountains of California between 1850 and 1950 using qualitative archival evidence from historical newspapers, maps, and related documents. A systematically coded database of 225 reports, consolidated into 155 unique wildfire events, was developed to analyze temporal and spatial patterns, ignition sources, vegetation types, suppression strategies, and impacts on communities and infrastructure. Historical rainfall records from the Boulder Creek station were integrated using water-year totals, three-year rolling averages, and standardized precipitation indices to examine how multi-year dryness relates to fire occurrence and size. Linear regressions show only …
Spartan Daily, December 4, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, December 4, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 42
Building A Data-Driven Security Ai Framework Using Machine Learning Models, Christopher Chan Vi
Building A Data-Driven Security Ai Framework Using Machine Learning Models, Christopher Chan Vi
Master's Theses
This research project explores a modern approach to Intrusion Detection System (IDS) anomaly detection by leveraging Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLM), and Explainable AI (XAI). The purpose is to assess the effectiveness of these technologies in enhancing the understanding of intrusion events. This research study addresses the challenges of threshold determination and the interpretability of anomaly detection results. The proposed solution involves an LLM-based framework with XAI capabilities, integrated with a Retrieval Augmented Generation (RAG) architecture, to provide clear explanations for detected anomalies, utilizing both custom and pre-trained datasets. The study navigated inherent challenges, including …
Spartan Daily, December 2, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, December 2, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 40
An Assessment Of The Viva Callesj September 2025 Event In San José, Richard Kos, Adam Cohen
An Assessment Of The Viva Callesj September 2025 Event In San José, Richard Kos, Adam Cohen
Mineta Transportation Institute
Viva CalleSJ is an open-streets initiative where several miles of the City of San José’s streets are closed to vehicular traffic, allowing residents to walk, bike, scooter, and skate freely. This report evaluates the Viva CalleSJ event held in San José on September 7, 2025, which attracted more than 150,000 participants. Using a mixed-methods approach, this evaluation examines aspects such as attendance, activities, modal access, and economic impacts, utilizing data from participant surveys (in English, Spanish, and Vietnamese), interviews, and observational analysis. The findings indicate that the Viva CalleSJ September 2025 event was largely successful in achieving its objectives, including …
What Do Americans Think About Federal Tax Options To Support Transportation? Results From Year Sixteen Of A National Survey, Asha Weinstein Agrawal, Hilary Nixon
What Do Americans Think About Federal Tax Options To Support Transportation? Results From Year Sixteen Of A National Survey, Asha Weinstein Agrawal, Hilary Nixon
Mineta Transportation Institute
This report summarizes the results from the sixteenth year of a national public opinion survey asking U.S. adults questions related to their views on federal transportation taxes. A nationally representative sample of 2,539 respondents completed the online survey from February 3 to February 27, 2025. The questions test public opinions about raising the federal gas tax rate, replacing the federal gas tax with a new mileage fee, and imposing a mileage fee just on commercial travel. In addition to asking directly about support for these tax options, the survey collected data on respondents’ views on the quality of their local …
Automated Design Of Scaffold-Free Dna Wireframe Nanostructures, Wen Wang, Abdulmelik Mohammed, Rong Chen, Antti Elonen, Silian Chen, Mengfan Tian, Junhao Yang, Ye Xiang, Pekka Orponen, Bryan Wei
Automated Design Of Scaffold-Free Dna Wireframe Nanostructures, Wen Wang, Abdulmelik Mohammed, Rong Chen, Antti Elonen, Silian Chen, Mengfan Tian, Junhao Yang, Ye Xiang, Pekka Orponen, Bryan Wei
Faculty Research, Scholarly, and Creative Activity
Computer-aided design has become common practice in DNA nanotechnology, and many programs are available that make the sophisticated design processes accessible to both the core research community and curious scientists in other fields. However, most of the design tools are committed to the scaffolded DNA origami method. Here we present an automated design pipeline for creating DNA wireframe nanostructures based on a scaffold-free molecular self-assembly approach. Unlike in the DNA origami method, scaffold-free designs are not built around a global backbone strand but are constituted entirely of short, locally intertwined oligonucleotides. This overcomes many limitations inherent in scaffolded nanostructure designs, …
A Novel Microfluidic Approach To Quantify Pore-Scale Mineral Dissolution In Porous Media, Rafid Musabbir Rahman, Elliott Niemur, Gianluca Blois, Farzan Kazemifar, Myeongsub Kim, Yaofa Li
A Novel Microfluidic Approach To Quantify Pore-Scale Mineral Dissolution In Porous Media, Rafid Musabbir Rahman, Elliott Niemur, Gianluca Blois, Farzan Kazemifar, Myeongsub Kim, Yaofa Li
Faculty Research, Scholarly, and Creative Activity
Mineral dissolution in porous media coupled with single- and/or multi-phase flows is pervasive in natural and engineering systems. Dissolution modifies the physical, hydrological, and geochemical properties of the solid matrix, resulting in a complex coupling between local dissolution rate and pore-scale flow. The work reports a microfluidic approach that includes 2D reactive porous media and advanced pore flow diagnostics for the study of pore-scale dissolution in porous media with unprecedented details. The 2D microfluidic porous media, called micromodels, were fabricated in calcite by combining photolithography and wet etching, which not only offers precise control over the structural and chemical properties, …
Genetic And Environmental Determinants Of Streaming And Aggregation In Myxococcus Xanthus, Trosporsha T. Khan, Patrick Murphy, Jiangguo Zhang, Oleg A. Igoshin, Roy D. Welch
Genetic And Environmental Determinants Of Streaming And Aggregation In Myxococcus Xanthus, Trosporsha T. Khan, Patrick Murphy, Jiangguo Zhang, Oleg A. Igoshin, Roy D. Welch
Faculty Research, Scholarly, and Creative Activity
Under starvation conditions, a spot of a few million Myxococcus xanthus cells on agar will migrate inward to form aggregates that mature into dome-shaped fruiting bodies. This migration is thought to occur within structures called ‘streams,’ which are considered crucial for initiating aggregation. The prevailing traffic jam model hypothesizes that intersections of streams cause cell crowding and ‘jamming,’ thereby initiating the process of aggregate formation. However, this hypothesis has not been rigorously tested, in part due to the lack of a standardized, quantifiable definition of streams. To address this gap, we captured time-lapse movies and conducted fluorescent cell tracking experiments …
Secondhand Social Capital And Idea Quality In Open Innovation Communities, Elisa Mattarelli, Aaron Schecter, Pamela Hinds, Noshir Contractor
Secondhand Social Capital And Idea Quality In Open Innovation Communities, Elisa Mattarelli, Aaron Schecter, Pamela Hinds, Noshir Contractor
Faculty Research, Scholarly, and Creative Activity
Open innovation communities provide valuable opportunities for creators from diverse backgrounds to collaboratively generate and refine new ideas that companies can implement in new products or services. However, many submitted ideas in these communities are underdeveloped or misaligned with companies' expectations, raising questions about what drives high-quality ideas and, more specifically, innovation potential. We focus on the role of secondhand social capital, i.e., the indirect network benefits an idea accrues through feedback from providers who are themselves actively engaged with other influential ideas in the community. While prior research has explored ego-centric or first-hand networks, we extend this work by …
Retrieval Augmented Framework For Deepfake Audio Detection, Avinash Saxena
Retrieval Augmented Framework For Deepfake Audio Detection, Avinash Saxena
Master's Theses
The widespread use of AI-based audio deepfakes threatens severely to undermine media integrity and public trust. Speech synthesis techniques have improved dramatically in voice conversion (VC) and text-to-speech (TTS) in recent years, making forgeries sound highly realistic, and concerns are raised about possible malevolent uses. Existing state-of-the-art techniques for identifying fake speech have proven to be effective in some cases but are still limited in application and robustness when faced with novel attacking strategies, different acoustic conditions, or alternative linguistic domains. To address some of these limitations, the current research presents a novel deepfake audio detection system based on personalized …
Towards A Generalized And Optimized Apriori Approach, Artem Abdikov
Towards A Generalized And Optimized Apriori Approach, Artem Abdikov
Master's Theses
Apriori is a machine learning algorithm developed in 1994 by R. Agrawal and R. Srikant for association rule mining purposes. This family of algorithms takes transactional data and analyzes relationships between variables in large datasets. The typical output of such algorithms is a prediction that if users choose item X, it is highly likely that they will also choose item Y. Apriori is known to be a robust algorithm and is used by many large companies in order to analyze user tendencies and even make recommendations. Although Apriori is a powerful algorithm, its original implementation is known to have limitations, …
Spartan Daily, November 25, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, November 25, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 39
A Triple Bottom Line Analysis Of Food Waste-To-Bioenergy At California University Campuses: Social Enablers Break Down Economic Barriers, Anjali Sharma
Master's Theses
Greenhouse gas emissions from food waste (FW) and energy generation are two major hurdles to the climate crisis. Previous research shows that waste-to-energy systems may provide a solution to both these challenges, particularly at university campuses, due to their high FW generation and high energy needs. Despite this, few academic institutions in California, US, have adopted waste-to-energy systems, and campus-specific challenges and opportunities associated with these systems remain underexplored. For this study, I use an Environmental, Economic, and Social (EES) lens to document the current landscape of FW management and implementation of food waste-to-bioenergy at campuses in each of the …
An Exploration Of Image Segmentation Techniques For Real-Time Product Detection, Andrew C. Dunton
An Exploration Of Image Segmentation Techniques For Real-Time Product Detection, Andrew C. Dunton
Master's Theses
Recent progress in LLMs enables advanced multimodal understanding, but their high computational cost necessitates monetization strategies like interactive advertising. While bounding boxes show promise for this concept, they can lack precision and visual appeal. Image segmentation offers a superior solution but faces a dual problem: traditional models demand scarce, costly training data, and open-vocabulary segmentation models like SAM are class-agnostic, unable to semantically identify a "consumer product" object class. In this research, we address these limitations by: 1) developing the Prompt-Guided Inpainting Framework (PGIF), which injects negative prompts to generate robustly annotated synthetic segmented product images; 2) investigating the Class-Agnostic …
Spartan Daily, November 18, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, November 18, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 36
Radiation Hardness Study Of Finfet And Gate-All-Around Fet Sram, Albert Lu
Radiation Hardness Study Of Finfet And Gate-All-Around Fet Sram, Albert Lu
Master's Theses
Radiation, such as alpha particles or neutrons, is hard to control and can come from sources such as trace impurities or cosmic rays from space. These can then strike a semiconductor device, such as a Static Random Access Memory (SRAM) cell, and cause the SRAM to have its bit flipped. This bit flip is often just a temporary effect and is thus called a single event upset (SEU). Although it is temporary, a bit flip can cause severe consequences, such as affecting program functionality by causing incorrect data to be used. This is made worse in critical environments that need …
Forecasting Pediatric Emergency Department Arrivals: Evaluating The Role Of Exogenous Variables Using Deep Learning Models, Egbe Etu Etu, Jordan Larot, Kindness Etu, Joshua Emakhu, Sara Masoud, Imokhai Tenebe, Gaojian Huang, Satheesh Gunaga, Joseph Miller
Forecasting Pediatric Emergency Department Arrivals: Evaluating The Role Of Exogenous Variables Using Deep Learning Models, Egbe Etu Etu, Jordan Larot, Kindness Etu, Joshua Emakhu, Sara Masoud, Imokhai Tenebe, Gaojian Huang, Satheesh Gunaga, Joseph Miller
Faculty Research, Scholarly, and Creative Activity
Background Forecasting pediatric emergency department (ED) demand remains a critical challenge in healthcare operations. This study aimed to identify exogenous variables influencing pediatric ED visits and evaluate the performance of different forecasting models. Method Using a retrospective observational design, we analyzed 192,347 pediatric ED visits across nine hospitals in Southeast Michigan between 2017 and 2019. Patient data were aggregated into daily arrival counts and enriched with exogenous variables such as weather, air quality, pollen, calendar, Google search trends, and chief complaints. Feature selection was performed using XGBoost and SHapley Additive exPlanations to identify the most influential predictors. Three forecasting models …
Spartan Daily, November 11, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, November 11, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 34
Embedding-Driven Synthetic Malware Generation With Autoencoders And Cluster-Tangent Diffusion, Gunnika Kapoor, Sathvika Nadipalli, Fabio Di Troia
Embedding-Driven Synthetic Malware Generation With Autoencoders And Cluster-Tangent Diffusion, Gunnika Kapoor, Sathvika Nadipalli, Fabio Di Troia
Faculty Research, Scholarly, and Creative Activity
Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract. As a result, generating high quality synthetic samples from scarce data sets becomes a crucial method for strengthening detection software. This paper focuses on presenting generation techniques that optimize the embedding space to produce high-quality synthetic samples, even under constrained datasets. The dataset used in this paper consists of 500 Windows malware API call samples that were processed using embedding and Generative AI (Gen AI) techniques to generate synthetic …
Spartan Daily, November 4, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, November 4, 2025, San Jose State University, School Of Journalism And Mass Communications
Spartan Daily, 2025
Volume 165, Issue 31