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Articles 1 - 30 of 438
Full-Text Articles in Computer Engineering
Rthermal: Gate Level Power And Thermal Simulation For 3d-Stacked Chips, Peter Xiong
Rthermal: Gate Level Power And Thermal Simulation For 3d-Stacked Chips, Peter Xiong
Master's Theses
As the number of transistors in modern processors increases, heat dissipation has become a major bottleneck to scalability. The use of 3D stacking further intensifies this problem, as heat from multiple layers can accumulate vertically. These challenges create a growing need for tools that can accurately and efficiently simulate the thermal behavior of 3D chips during design and validation. Several existing tools model thermal behavior for 3D-stacked chips and can simulate average heat over large spatial regions or long time intervals. However, when heat is concentrated in a small area or over a short time window, such models can miss …
Adaptive Task-Driven Lidar Point Cloud Compression For Autonomous Driving, Su Hyun Kim
Adaptive Task-Driven Lidar Point Cloud Compression For Autonomous Driving, Su Hyun Kim
Master's Theses
Autonomous-driving systems generate large LiDAR point clouds, but compression can damage the sparse object-support structure needed by 3D detectors even when reconstructions appear visually plausible. This thesis asks whether adaptive LiDAR compression can preserve downstream detection better than uniform compression by allocating more fidelity to detector-relevant regions. The main study builds a mask-aware range-image codec with an encoder-decoder bottleneck, an importance head, and an adaptive quantization variant. It compares this adaptive variant package with a confirmed masked uniform baseline under one fixed RangeDet evaluation surface and one fixed KITTI validation subset. Two supporting studies bound the result: a projection-reconstruction PointPillars …
Early Failure Detection In Web Navigation Agents Via Closed Sequential Pattern Mining, Sergio Talavera
Early Failure Detection In Web Navigation Agents Via Closed Sequential Pattern Mining, Sergio Talavera
Master's Theses
LLM-based web navigation agents fail on the majority of tasks while consuming substantial computational resources before failure becomes apparent. This thesis investigates whether closed sequential pattern mining on the first K steps of agent execution traces can predict task failure early enough to enable meaningful computational savings with interpretable justification. We develop a two-phase system: an offline pipeline that symbolizes agent traces, extracts K-step prefixes, mines closed patterns via BIDE+, and ranks them by failure precision; and an online detector that matches live executions against the resulting pattern library. We evaluate on 1,544 MiniWoB++ traces across three open-weight language models …
Data Augmentation For Vision-Language-Action Models: Bridging Vision And Language, Miaosen Zhou
Data Augmentation For Vision-Language-Action Models: Bridging Vision And Language, Miaosen Zhou
Master's Theses
This thesis focuses on real-time task execution and object detection for autonomous robots through dataset augmentation. We propose a data augmentation approach to address dataset imbalance in Vision-Language-Action (VLA) models across both image and text modalities during the fine-tuning process. The proposed method takes an image as input and generates a structured textual description using a prompt engineering strategy to augment the textual input. The generated augmented text includes key elements such as the task goal, scene description, reasoning, and execution plan, along with other relevant contextual information. This enriched representation improves the quality of the training data and supports …
Moral: Multimodal Reasoning For Autonomous Language Models With Sensor-Grounded Spatial Bev Rendering, Ambarish Govindarajulu Kaliamurthi
Moral: Multimodal Reasoning For Autonomous Language Models With Sensor-Grounded Spatial Bev Rendering, Ambarish Govindarajulu Kaliamurthi
Master's Theses
Autonomous-driving vision-language models describe scenes fluently but reason poorly about metric, safety-critical spatial relationships because they do not read sensor geometry in a grounded way. This thesis presents MoRAL (Multimodal Reasoning for Autonomous Language Models), a two-stage fine-tuning pipeline that teaches a compact 2-billion-parameter VLM to decode a physics-encoded Bird’s Eye View (BEV) representation – LiDAR distance as color, object class as cluster shape, radar Doppler velocity as directional wedges – and then trains it to reason over that representation for driving decisions. Stage 2 then fine-tunes on 57,696 teacher-generated chain-of-thought examples across eight question types, using Cosmos-Reason2-8B as teacher …
The Zeal Instruction Set Architecture, Joseph A. Gerani
The Zeal Instruction Set Architecture, Joseph A. Gerani
Master's Theses
The Instruction Set Architecture of a CPU (Central Processing Unit) determines what type of instructions the CPU is able to understand, how those instructions are encoded, and what it should output upon receiving those instructions as input. There are currently three popular ISAs meant for the consumer market: x86, RISC-V, and ARM, as well as a fourth that mostly now exists in the server market by the name of Power. One of the most important parts of an ISA is for engineers to be able to understand it and make use of it. If an ISA is too complicated, nobody …
Towards Neural Network Optimization: Addressing Issues With Corrupted Weights Within Models, Nick Najafizadeh
Towards Neural Network Optimization: Addressing Issues With Corrupted Weights Within Models, Nick Najafizadeh
Master's Theses
Neural networks are a recent popular technology inspired from human brains. Much of their popularity arises from how they excel in reasoning and logic, and are generally rather efficient in their tasks. With those strengths, they are frequently used in transportation and business among many other fields. However, neural networks have many factors that can deteriorate their performance, one of the most critical being weight corruption. Therefore, it is of utmost importance to detect and handle them as soon as possible so as to minimize the negative impact on a network’s performance. The optimization of neural networks would be especially …
Pipeline Optimization Of Agentic Llms For Text-To-Sql, Peter Conant
Pipeline Optimization Of Agentic Llms For Text-To-Sql, Peter Conant
Master's Theses
Current database interfaces limit users’ interaction to those with technical skills, creating timely roadblocks for non-technical professionals. Text-to-SQL aims to simplify database interactions by translating natural language questions into database queries, but long-standing challenges like question understanding, question-schema linking, and SQL generation have held the field back. In the AI era, foundational LLMs prove to be very capable of question understanding SQL, perform well in Schema Linking, Generation, and Evaluation tasks. However the cost to run these model is a hurdle for many organization with low funds and resources. Text-to-SQL solutions often operate across large enterprise size databases with, and …
Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti
Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti
Library Philosophy and Practice (e-journal)
This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …
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 …
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 …
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 …
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, …
Sensing The Pulse Of A Data Stream In Real Time, Vishnu S. Pendyala
Sensing The Pulse Of A Data Stream In Real Time, Vishnu S. Pendyala
Open Educational Resources
In an era where data never sleeps, streaming algorithms offer a powerful toolkit for extracting meaningful insights from high-velocity data flows. This talk explores some foundational techniques that enable efficient, real-time analytics with minimal memory requirements. The algorithms covered include a clever bit-based strategy for approximating the count of 1s in a sliding window, ideal for binary streams where space efficiency is paramount. Another algorithm helps estimate statistical moments (mean, variance, skewness) using compact sketches, enabling a deeper understanding of stream distributions without storing the entire dataset. One other algorithm identifies trending items with exponential decay, giving more weight to …
Understanding And Evaluating Genomic Language Models, Aadit Kapoor
Understanding And Evaluating Genomic Language Models, Aadit Kapoor
Master's Theses
Large Language Models (LLMs) have shown remarkable capabilities in interpreting complex patterns across various domains, yet their application to genomic data remains limited. We see great potential in leveraging LLMs for vital biological tasks, such as predicting transcription factor binding sites and identifying antibiotic-resistant genes. This emergent behavior positions LLMs as powerful tools for enhancing our understanding of intricate biological language. LLMs trained specifically on genomic data, such as DNA sequences, operate distinctly compared to those trained on natural language. This difference is evident not only in the architectural landscape of the models but also in the methodologies employed by …
Cost-Effective Automated Uhi Mapping With Ai: A Case Study Of A Scalable Framework For Climate Equity In San José, California, Martin Alvarez Lopez
Cost-Effective Automated Uhi Mapping With Ai: A Case Study Of A Scalable Framework For Climate Equity In San José, California, Martin Alvarez Lopez
Master's Theses
Urban areas experience the Urban Heat Island (UHI) effect, with higher temperatures than rural areas, disproportionately impacting low-income communities. Mapping UHIs is a process that usually requires significant amount of human resources, and is not scalable. The lack of accurate and detailed UHI maps makes it difficult for decision makers to design effective mitigation strategies. In this work we introduce a cost-effective, scalable, and universally applicable UHI mapping framework that leverages open-source data and AI-driven feature extraction from remote sensing imagery. Using various causative factors such as city characteristics, anthropogenic heat, city canyons, and meteorological variables, we create UHI maps …
Quantization On Graph Neural Networks For Image Classification, Rithik Reddy Katpally
Quantization On Graph Neural Networks For Image Classification, Rithik Reddy Katpally
Master's Theses
Quantization has become a key approach for reducing storage and computational demands of deep neural networks while maintaining high accuracy. Although 8-bit quantization is well-established for convolutional architectures such as ResNet50 and MobileNetV2, its application to graph-based vision models remains underexplored. In this work, we extend quantization-aware training to Vision Graph Neural Networks (ViGs) and conduct comparisons with quantized CNNs on the CIFAR-100 dataset. To ensure parity, all models have same training hyperparameters such as learning rate, batch size, optimizer, number of epochs. We used numerous techniques to preserve performance for low-bit precision. First, Pauta Quantization clips activation outliers based …
Stead: Spatio-Temporal Efficient Anomaly Detection For Time And Compute Sensitive Applications, Andrew Gao
Stead: Spatio-Temporal Efficient Anomaly Detection For Time And Compute Sensitive Applications, Andrew Gao
Master's Theses
This paper presents a new method for anomaly detection in automated systems with time and compute sensitive requirements, with unparalleled efficiency. As these systems become increasingly popular, ensuring their safety has become more important than ever. Therefore, this paper focuses on how to quickly and effectively detect various anomalies in the aforementioned systems, with the goal of making them safer and more effective. Many detection systems have been developed with great success under spatial contexts; however, there is still significant room for improvement when it comes to temporal context. While there is substantial work regarding this task, there is minimal …
Using Facial Recognition For Selective Pose Detection, William J. Parker
Using Facial Recognition For Selective Pose Detection, William J. Parker
Master's Theses
Pose detection involves locating and identifying key body points for all individuals within a frame. This enables the ability to convert the pose into a digital format, which can then be recorded and analyzed for a variety of purposes. Advancements in the field have already opened applications in areas such as digital fitness coaches, fall detection, and virtual reality. Existing approaches primarily focus on tracking all detected individuals, which limits the practical applications when attempting to analyze a single or specific subject when there are other people in frame. Previous work has discussed integrating identification, but these approaches use identification …
Quantum Algorithm Emulation Using Fpgas, Samuel Petruescu
Quantum Algorithm Emulation Using Fpgas, Samuel Petruescu
Master's Theses
Field Programmable Gate Arrays (FPGAs) have been used in most of the physics sub-fields for various unique purposes. This includes particle physics, quantum optics, and, more recently, quantum computing. FPGAs boast many benefits over previous experimental and computational setups. They are versatile, easy to program, and cost-effective, leading to an understandable desire to incorporate them into the new field of quantum computing. While FPGAs have been used to help control the readout and control of physical qubits, they can also be a good tool for improving algorithm simulations, which is the focus of this paper. Different algorithms have different computational …
Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia
Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia
Master's Theses
Billions of people today rely on traffic predictions to optimize their travels. Digital mapping services deliver accurate predictions by learning from vast troves of historical data. Impressive as these systems are, their assumptions do not always apply. They depend on an endless flow of sensitive user data to a central authority, a stable Internet connection, and trustworthiness on both sides of the traditional client-server model. This thesis explores a novel architecture which bucks those assumptions. In the proposed model, traffic data remains on edge devices which individually train models via federated learning. Beyond the obvious privacy benefits, this architecture enables …
Unveiling The Transformative Power Of Unsupervised Machine Learning Through Clustering, Vishnu S. Pendyala
Unveiling The Transformative Power Of Unsupervised Machine Learning Through Clustering, Vishnu S. Pendyala
Open Educational Resources
Clustering methods demonstrated their transformative potential across various industries through image segmentation, anomaly detection, bioinformatics, and customer segmentation. The presentation explores these techniques in unsupervised machine learning, focusing on foundational clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN. Through an in-depth analysis of their underlying principles and computational intricacies, the presentation highlights how these methods have evolved to address complex, high-dimensional data problems. The presentation provides insights into how K-means remains a versatile tool for partitioning data in linear spaces. It delves into Hierarchical Clustering's unique approach to building dendrograms and capturing multi-scale data relationships, and how DBSCAN's density-based …
Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng
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. …
Homomorphically Encrypted Faceted Values, Tanmay Singal
Homomorphically Encrypted Faceted Values, Tanmay Singal
Master's Projects
Faceted values prevent the implicit flow of sensitive information by controlling the visibility of program data. They achieve this by maintaining two facets for each variable: a public facet, which is observable, and a private facet, which remains hidden. Although this method secures the flow of sensitive data, it can be leaked if the server storing the faceted values is compromised. While faceted values may be encrypted on the server, doing so would necessitate that the private facets be briefly decrypted during execution to allow arithmetic operations to be performed on them, creating an attack vector for information to be …
Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb
Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb
Master's Projects
Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …
Mitigating Cold Start Problem Through Metadata Integration And User Preference Analysis, Prabaljit Walia
Mitigating Cold Start Problem Through Metadata Integration And User Preference Analysis, Prabaljit Walia
Master's Projects
Recommendation systems power the most popular platforms in the world: from content catalogs on Netflix to custom feeds on TikTok – the importance of recommendation systems is significant. Collaborative filtering, the most popular recommendation technique, is essentially based on the idea of leveraging collective user intelligence i.e., creating recommendations by finding similar users. But this technique suffers when there is not enough data in the profiles of users, formally termed as the cold start problem. This research focuses on this problem by introducing an approach that integrates metadata-driven similarity measures with profile expansion techniques. Our approach combines traditional collaborative filtering …