Computer Sciences Commons^™

Quantum Pseudorandom Primitives Beyond Pseudorandom States, Chuhan Lu

Dissertations and Theses

Quantum pseudorandomness is an emerging research area. Ji, Liu, and Song defined pseudorandom states (PRSs) and pseudorandom unitaries (PRUs) as quantum analogs of pseudorandom generators and pseudorandom functions. A unitary oracle separation result between one-way functions and PRSs/PRUs, established by Kretschmer, suggests that certain quantum primitives may remain secure even if classical cryptography is compromised. This insight has spurred extensive work on quantum pseudorandomness and its applications in quantum cryptography.

Many constructions of PRSs have been established under standard assumptions, yet building a secure PRU was a long-standing open problem. This dissertation aims to narrow the gap between PRSs and …

Towards Scalable Schema Mapping Using Large Language Models, Christopher Buss, Mahdis Safari, Arash Termehchy, David Maier, Stefan Lee

Computer Science Faculty Publications and Presentations

The growing need to integrate information from many diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex and costly to maintain. While recent advances suggest that large language models (LLMs) can assist in automating schema mapping, key challenges remain. We motivate future research in schema mapping generation by highlighting key challenges, presenting a competitive bidirectional schema matching pipeline, and exploring the limitations of current methods for generating more complex mappings.

Imputation Via Domain Adaptation: Rethinking Variable Subset Forecasting From Knowledge Transfer, Runchang Liang, Qi Hao, Yue Gao, Kunpeng Liu, Lu Jiang, Pengyang Wang, Minghao Yin

Computer Science Faculty Publications and Presentations

Multivariate time series forecasting in practical deployment faces a critical challenge termed Variable Subset Forecasting (VSF), where certain variables accessible during training are entirely missing during inference. This creates a stark discrepancy between the training (source domain with full variables) and inference (target domain with partial variables) environments, disrupting cross-variable dependencies and fragmenting global temporal patterns. Existing imputation methods, limited to transferring local knowledge (e.g., temporal neighbors or pairwise correlations), fail to capture essential global dynamics, leading to severe performance degradation under distribution shifts. To address these challenges, we redefine VSF as a cross-domain knowledge transfer problem and propose VIDA, …

Coli@Fire2024: Findings Of Word-Level Code-Mixed Language Identification In Dravidian Languages, Asha Hegde, Fazlourrahman Balouchzahi, Sabur Butt, Sharal Coelho, Kavya G, Harshitha S. Kumar, Sonith D, Shashirekha H. L., Ameeta Agrawal

Computer Science Faculty Publications and Presentations

Code-mixing, a linguistic phenomenon where multiple languages are blended within a single text, has become increasingly prevalent in multilingual societies, particularly in digital communication. The CoLI-Dravidian shared task, organized as part of Forum for Information Retrieval and Evaluation (FIRE) 2024, aimed to address these challenges by inviting researchers to develop models capable of classifying words in code-mixed texts involving Dravidian languages — Tamil, Kannada, Malayalam, and Tulu - interwoven with English. The task presents significant challenges due to the complexity of linguistic structures, mixed-language tokens, and dialectal variations, especially in low-resource languages like those in the Dravidian family. The participating …

Alignment Of Perceptual Similarity Metrics With Human Perception, Abhijay Ghildyal

Dissertations and Theses

Perceptual similarity metrics are used for quantitatively evaluating the similarity between two images as it would appear to human perception. These metrics aim to mimic the human visual system, providing a more accurate assessment of visual similarity. Such visual assessments are considered to be more advanced than simple pixel-wise comparisons such as ℓ_p norm distances. Thus, a human-like assessment of visual similarity, makes the metrics valuable for applications in image compression, restoration, and enhancement, where evaluating perceptual quality is crucial. Perceptual similarity metrics have progressively become more correlated with human judgments on perceptual similarity; however, despite recent advances, the …

Robust Contraction Decomposition For Minor-Free Graphs And Its Applications, Bandyapadhyay Sayan, William Lochet, Daniel Lokshtanov, Dániel Marx, Pranabendu Misra, Multiple Additional Authors

Computer Science Faculty Publications and Presentations

We prove a robust contraction decomposition theorem for H-minor-free graphs, which states that given an H-minor-free graph G and an integer p, one can partition in polynomial time the vertices of G into p sets Z₁,… ,Z_p such that tw(G/(Z_i ⧵ Z')) = O(p + |Z'|) for all i ∈ [p] and Z' ⊆ Z_i. Here, tw(⋅) denotes the treewidth of a graph and G/(Z_i ⧵ Z') denotes the graph obtained from G by contracting all edges with both endpoints in Z_i ⧵ Z'. Our result generalizes earlier results by Klein [SICOMP 2008] and Demaine et al. [STOC 2011] based …

Forecasting Influenza Rates Using Machine Learning: A Study Of Chatgpt's Predictive Accuracy, Sara Saleh

University Honors Theses

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall …

An Exposition Of "Probabilistic Polynomials And Hamming Nearest Neighbors", Vivek Srirama

University Honors Theses

This paper is an exposition of the paper Probabilistic Polynomials and Hamming Nearest Neighbors by Josh Alman and Ryan Williams. It presents the findings of this paper in a more accessible format for Computer Science students earlier in their career who may not be as familiar with Computational Theory and its concepts as their PhD counterparts are. The paper assumes that the reader has a basic understanding of Algorithms and Complexity, typically obtained in an introductory level Algorithms course.

The paper by Alman and Williams analyzes a specific problem known as the Hamming Nearest Neighbor problem. All known solutions for …

Advanced Machine Learning Techniques For Social Support Detection On Social Media, Olga Kolesnikova, Moein Shahiki Tash, Zahra Ahani, Ameeta Agrawal, Raúl Monroy, Grigori Sidorov

Computer Science Faculty Publications and Presentations

The widespread use of social media highlights the need to understand its impact, particularly the role of online social support. In this study, we present a dataset of YouTube comments, initially comprising 66,272 entries, which was refined to 42,695, with a subset of 10,000 comments selected for detailed analysis without additional filtering. The dataset is annotated for three classification tasks: (1) distinguishing supportive from non-supportive comments, (2) determining whether the support is directed at an individual or a group, and (3) further categorizing group support into six subtypes (Nation, LGBTQ, Black Community, Women, Religion, and Other). To address data imbalances …

Helmholtz Cage: Software Development And Implementation For Cubesat Testing, Gustavo A. Cotom Lopez

University Honors Theses

This paper details the successful development and deployment of a Helmholtz cage system, designed to produce precisely controlled magnetic fields for testing and calibration purposes. The core focus was on creating a robust, modular software architecture enabling independent current modulation on each axis, comprehensive serial communication between multiple microcontrollers, and real-time data acquisition from the MR3 magnetometer. All software components, including the serial communication drivers, control algorithms, command line interface, and calibration routines, were developed from the ground up. The system was fully operational upon completion: all hardware components functioned as intended, serial communication with each subsystem was reliable, and …

Book Review Of, Participatory Engineering Of Algorithmic Socialtechnologies: An Extended Book Review Of David G. Robinson's Voices Inthe Code, Rajesh Venkatachalapathy

Complex Systems Faculty Publications and Presentations

David G. Robinson’s Voices in the Code, reviewed here, approaches the issue of governance and regulation of algorithmic social technologies with a case study orientation that offers a grounding in the details, illuminating the fundamentally sociological and processual nature of modern governance issues that are mostly missed or left implicit in academic, regulatory, and legislative discussions. This important book urges us to design adaptive, malleable, and evolvable participatory organizational structures instead. Accepting the author’s invitation to carry the torch forward, this review builds on and extends the book’s arguments by presenting broad and transferable insights on regulation and governance …

Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived …

Weak Formulation For Solving Inverse Problems In Reproducing Kernel Hilbert Spaces (With Applications To Learning Dynamical Systems), Victor William Rielly

Dissertations and Theses

We combine numerical and machine learning techniques to present a general framework for solving inverse problems using vector valued reproducing kernel Hilbert spaces in a variational formulation. We present this framework in two papers. In the first paper, we present an original state-of-the-art method derived in the context of our general framework for learning dynamical systems. In the second paper, we generalize the method from our first paper to arrive at the framework for solving inverse problems. Then we apply our general framework to the task of learning dynamical systems. In both papers we consider numerous applications of our methods …

Unremarkable To Remarkable Ai Agent: Exploring Boundaries Of Agent Intervention For Adults With And Without Cognitive Impairment, Mai Lee Chang, Samantha Reig, Alicia (Hyun Jin) Lee, Anna Huang, Hugo Simão, Nara Han, Neeta M. Khanuja, Abdullah Ubed Mohammad Ali, Rebekah Martinez, John Zimmerman, Jodi Forlizzi, Aaron Steinfeld

Computer Science Faculty Publications and Presentations

As the population of older adults increases, there is a growing need for support for them to age in place. This is exacerbated by the growing number of individuals struggling with cognitive decline and shrinking number of youth who provide care for them. Artificially intelligent agents could provide cognitive support to older adults experiencing memory problems, and they could help informal caregivers with coordination tasks. To better understand this possible future, we conducted a speed dating with storyboards study to reveal invisible social boundaries that might keep older adults and their caregivers from accepting and using agents. We found that …

Investigating Key Structures In Protective Scenes For Llms, Eben M. Weisman

University Honors Theses

This research delves into the realm of "protective scenes" within Large Language Models (LLMs), exploring their impact on bias mitigation, deception, and context preservation. The study investigates the use of roleplay prompting human-like behavior and reasoning in LLMs, focusing on the Character-LLM framework's concept of protective scenes with graduated levels of protection. By combining insights from psychology, cognitive science, and computational analysis, this research aims to develop a framework for understanding how protective scenes influence roleplay performance in LLMs, ultimately contributing to the development of more reliable and ethical AI systems.

Next Arrival And Destination Prediction Via Spatiotemporal Embedding With Urban Geography And Human Mobility Data, Pengjiang Li, Zaitian Wang, Xinhao Zhang, Pengfei Wang, Kunpeng Liu

Computer Science Faculty Publications and Presentations

With the development of transportation networks, countless trajectory data are accumulated, and understanding human mobility from traffic data could be helpful for smart cities, urban computing, and urban planning. Extracting valuable insights from traffic data, such as taxi trajectories, can significantly improve residents’ daily lives. There are many studies on spatiotemporal data mining. As we know, arrival prediction or regional function detection encompasses important tasks for traffic management and urban planning. However, trajectory data are often mutilated because of personal privacy and hardware limitations, i.e., we usually can only obtain partial trajectory information. In this paper, we develop an embedding …

Model Explanations For Gender And Ethnicity Bias Mitigation In Ai-Generated Narratives, Martha Otisi Dimgba

Dissertations and Theses

Large Language Models (LLMs) are increasingly utilized in diverse applications, ranging from professional content creation to decision-making systems. However, their outputs often amplify the biases present in their training data, perpetuating stereotypes and reinforcing societal inequities, particularly regarding gender and ethnicity. Such biases can cause tangible harm, especially for underrepresented groups, and require awareness and effective mitigation strategies.

This work explores gender and ethnicity representation in narratives created by generative AI describing 25 occupational fields defined by the U.S. Bureau of Labor Statistics. We examine three large language models (LLMs)--Llama 3.1 70B Instruct, Claude 3.5 Sonnet, and GPT 4.0 Turbo. …

Foundation Models Boost Low-Level Perceptual Similarity Metrics, Abhijay Ghildyal, Nabajeet Barman, Saman Zadtootaghaj

Computer Science Faculty Publications and Presentations

For full-reference image quality assessment (FR-IQA) using deep-learning approaches, the perceptual similarity score between a distorted image and a reference image is typically computed as a distance measure between features extracted from a pretrained CNN or more recently, a Transformer network. Often, these intermediate features require further fine-tuning or processing with additional neural network layers to align the final similarity scores with human judgments. So far, most IQA models based on foundation models have primarily relied on the final layer or the embedding for the quality score estimation. In contrast, this work explores the potential of utilizing the intermediate features …

Drta: Dynamic Reward Scaling For Reinforcement Learning In Time Series Anomaly Detection, Bahareh Golchin, Banafsheh Rekabdar, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with limited labeled data, high false-positive rates, and difficulty generalizing to novel anomaly types. To overcome these challenges, we propose a reinforcement learning-based framework that integrates dynamic reward shaping, Variational Autoencoder (VAE), and active learning, called DRTA. Our method uses an adaptive reward mechanism that balances exploration and exploitation by dynamically scaling the effect of VAE-based reconstruction error and classification rewards. This approach enables the agent to detect anomalies effectively in low-label systems while maintaining high precision and …

Scitopic: Enhancing Topic Discovery In Scientific Literature Through Advanced Llm, Pengjiang Li, Zaitian Wang, Xinhao Zhang, Ran Zhang, Lu Jiang, Pengfei Wang, Yuanchun Zhou

Computer Science Faculty Publications and Presentations

Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scientific information retrieval. Many machine learning methods, particularly deep embedding techniques, have been applied to discover research topics. However, most existing topic discovery methods rely on word embedding to capture the semantics and lack a comprehensive understanding of scientific publications, struggling with complex, high-dimensional text relationships. Inspired by the exceptional comprehension of textual information by large language models (LLMs), we propose an advanced topic discovery method enhanced by LLMs to improve scientific topic identification, namely SciTopic. Specifically, we …

Polynomial-Time Constant-Approximation For Fair Sum-Of-Radii Clustering, Sina Nezhad, Sayan Bandyapadhyay, Tianzhi Chen

Computer Science Faculty Publications and Presentations

In a seminal work, Chierichetti et al. [20] introduced the (t,k)-fair clustering problem: Given a set of red points and a set of blue points in a metric space, a clustering is called fair if the number of red points in each cluster is at most t times and at least 1/t times the number of blue points in that cluster. The goal is to compute a fair clustering with at most k clusters that optimizes certain objective function. Considering this problem, they designed a polynomial-time O(1)- and O(t)-approximation for the k-center and the k-median objective, respectively. Recently, Carta et …

“Lost-In-The-Later”: Framework For Quantifying Contextual Grounding In Large Language Models, Yufei Tao, Adam Hiatt, Rahul Seetharaman, Ameeta Agrawal

Computer Science Faculty Publications and Presentations

Large language models (LLMs) are capable of leveraging both contextual and parametric knowledge but how they prioritize and integrate these sources remains underexplored. We introduce CoPE, a novel framework for systematically quantifying contextual grounding in LLMs. CoPE distinguishes between contextual knowledge (CK) and parametric knowledge (PK), enabling fine-grained attribution across languages and tasks. Using our newly created MultiWikiAtomic dataset in English, Spanish, and Danish, we analyze how LLMs integrate context, prioritize information, and incorporate PK in open-ended question answering. We find that across models and languages, only around 50 to 76 percent of outputs are grounded in the given context, …

Retrieval-Augmented Feature Generation For Domain-Specific Classification, Xinhao Zhang, Jinghan Zhang, Fengran Mo, Dakshak Keerthi Chandra, Yu-Zhong Chen, Fei Xie, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current feature space using existing features and enriching the informational content. However, generating new, interpretable features usually requires domain-specific knowledge on top of the existing features. In this paper, we introduce a Retrieval-Augmented Feature Generation method, RAFG, to generate useful and explainable features specific to domain classification tasks. To increase the interpretability of the generated features, we conduct knowledge retrieval among the existing features in the domain to identify potential feature associations. These associations are expected …

Osa-Diff: An Origin Sampling Based Adversarial Attack Using Diffusion Models, Shayan Jalalipour, Banafsheh Rekabdar

Computer Science Faculty Publications and Presentations

Diffusion models are becoming an increasingly popular emerging technology, however their use in adversarial attacks remains a scarcely explored topic. We show that diffusion models can be used to create end-to-end hidden adversarial perturbations with high rate of success, and propose a novel diffusion based adversarial attack that allows for substantially faster training time (through improved convergence on high quality images) and with substantially less computational overhead than typical diffusion model training

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

Efficient Visual Data Processing Approaches To Improve Resource Utilization, Yeganeh Jalalpour

Dissertations and Theses

With continuous advancements in technology, the volume of visual data being captured, distributed, and consumed across various applications is rapidly increasing. Enhancing the efficiency of visual data processing can improve resource consumption, making the storage, transmission, and use of this growing visual content more effective.

Image and video data constitute a significant share of internet traffic, making video compression a critical factor in enhancing overall data throughput by improving the video compression ratio. Capturing, transferring, and storing raw video data is challenging due to the substantial resources required for both storage and computation. Video compression, however, can significantly mitigate these …

Tag-Based Security In C: Writing And Specifying Flexible Protection, Sean Noble Anderson

Dissertations and Theses

The C language is ubiquitous and insecure. Tag-based security policies offer a flexible toolkit for runtime protection, including policies that mitigate the effects of undetected programming bugs and those that enforce security properties of the program logic. But tag policies can be difficult to define, and the protection that they offer can be difficult to specify and to prove.

This dissertation builds from an assembly-level specification of stack safety, encompassing the essential control-flow structure of C and other high level languages, to a C source-level policy definition framework called Tagged C. Tagged C includes a variant C semantics parameterized by …

Applying Positive Unlabeled Learning Techniques And Using The Kullback-Leibler Divergence To Improve Geothermal Surveying Assessments, Martín Thomas Rodriguez

Dissertations and Theses

As we face the current climate crisis, the discovery of geothermal energy resources has the potential to greatly reduce our dependence on fossil fuels worldwide. However, the development of any new energy infrastructure is expensive and depends on the willingness of energy agencies and developers to make initial investments based on calculated risk measures. One such measure, called geothermal favorability, is the likelihood that a site has conditions favorable for geothermal systems containing recoverable energy potential. Its prediction from existing geophysical datasets proves to be a nontrivial task. The prediction of geothermal favorability can be framed as a binary classification …

Automated Verification Of Compiler Transformations, Yanzhao Wang

Dissertations and Theses

The ever-growing complexity of software and its target hardware makes it increasingly challenging to develop reliable compilers that preserve the semantics of source code during compilation. Traditional testing methods often lack sufficient test coverage and fail to identify subtle errors and undefined behaviors that can lead to compiler malfunctions. Furthermore, while formal compiler certification ensures semantic preservation through theorem proving, the inherent complexity of this process makes re-certification after each compiler revision substantially labor-intensive. This often significantly hinders the improvement of compilers.

This research aims to bridge the gap between increasing compiler complexity and the limited scalability of formal verification …

Tabular Data-Centric Ai: Challenges, Techniques And Future Perspectives, Yanjie Fu, Dongjie Wang, Hui Xiong, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as …