Index Catalog // ScholarsArchive@OSU

Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

Creator:: Aziere, Nicolas
Abstract:: This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
Resource Type:: Dissertation

Supporting merge comprehension when resolving merge conflicts

Creator:: Nelson, Nicholas
Abstract:: Distributed version control allows developers to manage software evolution among distributed development teams. But it does not eliminate all consistency and concurrency issues, and instead introduces additional complexity when merging code. And resolving merge conflicts is nontrivial when automated merging fails. In such cases, developers are forced to inspect the...
Resource Type:: Dissertation

Semantic embodied navigation : developing agents that navigate from language and vision

Creator:: Krantz, Jacob
Abstract:: Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
Resource Type:: Dissertation

Visual object discovery and understanding

Creator:: Yuan, Jialin
Abstract:: Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
Resource Type:: Dissertation

Characterizing Server Compromise: Resilience Models, Instantiations, and Impossibilities

Creator:: McQuoid, Ian M.
Abstract:: One of the pervasive problems arising in our modern, digital world surrounds data breaches where an adversary, through zero-day exploitations, phishing, or old-fashioned social engineering attacks, gains access to a service’s data stores. Our society increasingly relies on these cloud-based services for everything from our taxes to personal communication. As...
Resource Type:: Dissertation

New constructions and applications for homomorphic secret sharing and function secret sharing

Creator:: Singh, Jaspal
Abstract:: A secret sharing scheme allows a dealer to distribute a secret with a set of parties, such that only a certain subset of parties can collaborate and learn the shared secret. Traditional secret sharing schemes have been used as building blocks in various subdomains of cryptography. Recently, two new extensions...
Resource Type:: Dissertation

Designing Diversity and Inclusion Processes and Tools for Sustainable Open Source

Creator:: Guizani, Mariam
Abstract:: Over time, Open Source Software (OSS) has become indispensable in the creation and upkeep of software products, serving as the fundamental building block for widely used solutions in our daily lives, including applications that enable communication, entertainment, and productivity. A sustainable OSS ecosystem is one that attracts and retains a...
Resource Type:: Dissertation

Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

Creator:: Fallatah, Abrar
Abstract:: While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
Resource Type:: Dissertation

Communication efficient private set and database operations

Creator:: Garimella, Gayathri Annapurna
Abstract:: Secure Computation is a powerful tool that enables a set of parties to jointly compute any function over their private inputs, without a trusted third party. Private Set Intersection is a specific case of two-party Secure Computation, where Alice (with private set X) and Bob (with private set Y) specifically...
Resource Type:: Dissertation

Automatic Explanations of Computation Results with Value Decompositions and Dominating Sets

Creator:: Kumar, Prashant
Abstract:: In an increasingly computation-driven world, algorithms and mathematical models significantly impact decision making across various fields. To foster trust and understanding, it is crucial to provide users with clear and concise explanations of the reasoning behind the results produced by computational tools, especially when recommendations appear counterintuitive. Legal frameworks in...
Resource Type:: Dissertation

Characterizing and Addressing Dataset Shift with Deep Learning

Creator:: Olson, Matthew L.
Abstract:: This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
Resource Type:: Dissertation

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:: Khorram, Saeed
Abstract:: The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
Resource Type:: Dissertation

Towards Direct Simultaneous Speech Translation

Creator:: Chen, Junkun
Abstract:: Simultaneous speech translation (SimulST) is widely useful in many cross-lingual communication scenarios, including multinational conferences and international traveling. Since text-based simultaneous machine translation (SimulMT) has achieved great success in recent years. The conventional cascaded approach for SimulST uses a pipeline of streaming ASR followed by simultaneous MT but suffers from...
Resource Type:: Dissertation

Efficient Comparative Sequence Analysis Algorithms for RNA Conserved Structure and Region Detection

Creator:: Li, Sizhen
Abstract:: RNAs play important roles in multiple cellular processes, and many of their functions rely on folding to specific structures. To maintain their functions, secondary structures of RNA homologs are conserved across evolution. These conserved structures provide critical targets for diagnostics and therapeutics. Thus, there is a need for developing fast...
Resource Type:: Dissertation

Communication-Efficient Secure Two-Party Computation From Minicrypt and OT

Creator:: Roy, Lawrence
Abstract:: Secure two-party computation (2PC) is the task of performing arbitrary calculations on secret inputs provided by two parties, while maintaining secrecy if at least one party is honest. 2PC has been applied to privacy-preserving record linkage and machine learning, in areas such as medicine where maintaining privacy is crucial. One...
Resource Type:: Dissertation

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:: Koul, Anurag
Abstract:: Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
Resource Type:: Dissertation

Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing

Creator:: Vu, Trung
Abstract:: Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...
Resource Type:: Dissertation

Empowering End Users to Mitigate Privacy and Security Risks in Smart-Home Trigger-Action Apps

Creator:: Saeidi, Mahsa
Abstract:: Many home users nowadays use various smart devices to improve the efficiency and convenience of their home environments. Trigger-action platforms such as “If-This-Then-That” (IFTTT) enable end users to connect different smart devices and services using simple apps to control these devices and automate the tasks (e.g., if the camera detects...
Resource Type:: Dissertation

Elucidating Computational Narratives to Facilitate Code Comprehension

Creator:: Chattopadhyay, Souti
Abstract:: Programming is integrated across the workflow of multiple domains where end-user programmers, those who need to program as a means to an end, regularly need to code. In the modern setting of collaborative development, end-user programmers have to interpret the intentions behind existing code to contribute and build solutions to...
Resource Type:: Dissertation

Divergence Reduction and Dependency Management in GPU Programs using Asynchronous Work Scheduling

Creator:: Cuneo, Braxton
Abstract:: With continuing improvements in performance and capability, GPU processing has gained significant and growing interest across science and industry. With this interest, research has increasingly focused upon methods of processing algorithms with stochastic, non-uniform branching while maintaining low divergence. Central among these methods is thread-data remapping (TDR), whereby data is...
Resource Type:: Dissertation

Stake-Free Evaluations of Black-Box Optimization and Spatio-Temporal Graph Network Algorithms

Creator:: Merrill, Erich, III
Abstract:: Papers proposing novel machine learning algorithms tend to present the algorithm or technique in question in the best possible light. The standard practice is generally for authors to emphasize their proposed algorithms' performance in the precise setting where it is maximally impressive, often by only fully evaluating their best known...
Resource Type:: Dissertation

Deep Learning for Human and Biological Languages

Creator:: Hong, Junki
Abstract:: We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
Resource Type:: Dissertation

Explaining and Improving Neural Machine Translation

Creator:: Yang, Yilin
Abstract:: Machine Translation, the task of automatically translating between human languages has been studied for decades. This task is used to be solved by count-based statistical models, e.g. Phrase-based Statistical Machine Translation (PBSMT), which solves the translation problem by separately training a statistical language model and a translation model. Recently, Neural...
Resource Type:: Dissertation

On Avoiding Power-Seeking by Artificial Intelligence

Creator:: Turner, Alexander M.
Abstract:: We do not know how to align a very intelligent AI agent's behavior with human interests. I investigate whether—absent a full solution to this AI alignment problem—we can build smart {\ai} agents which have limited impact on the world, and which do not autonomously seek power. In this thesis, I...
Resource Type:: Dissertation

A Framework for Evaluating the Comprehensiveness of Continuous Improvement in Academic Computing Programs. A 360-Degree View of the Continuous Improvement Efforts in a Large School of Electrical Engineering and Computer Science

Creator:: Azzouni, Abdullah
Abstract:: Continuous Improvement (CI) of academic computing programs is a main requirement of accreditation. Academic computing programs must have a well-documented CI plan in order to be granted accreditation. Based on the existing literature, we developed a comprehensive CI (or 360-CI) model consisting of 8 components: course, curriculum, administration, faculty, research,...
Resource Type:: Dissertation

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Creator:: Wu, Wenxuan
Abstract:: As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
Resource Type:: Dissertation

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:: Dodge, Jonathan E.
Abstract:: Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
Resource Type:: Dissertation

Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

Creator:: Almadhoun, Eman
Abstract:: In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
Resource Type:: Dissertation

Source-Routed Multicast Schemes for Large-Scale Cloud Data Center Networks

Creator:: Alqahtani, Jarallah Ali m
Abstract:: Data centers (DCs) have been witnessing unprecedented growth in size, number and complexity in recent years. They consist of tens of thousands of servers interconnected by fast network switches, hosting and enabling numerous applications with various traffic characteristics and requirements. As a result, DC networks have been presented with several...
Resource Type:: Dissertation

Learning and Improving Policies for Probabilistic Planning Problems

Creator:: Issakkimuthu, Murugeswari
Abstract:: In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
Resource Type:: Dissertation

Analytical Methods for Improving Causal Inference

Creator:: Sinha, Meghamala
Abstract:: Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
Resource Type:: Dissertation

Linear-time Algorithms for RNA Folding: Partition Function, Stochastic Sampling and RNA-RNA Interaction

Creator:: Zhang, He
Abstract:: RNAs play important roles in the central dogma of molecular biology, and are involved in multiple biology processes such as chromatin modification, transcriptional interference and translation initiation. The functions of RNAs, especially non-coding RNAs, are highly related to its secondary structures, therefore computational methods for RNA structure prediction are of...
Resource Type:: Dissertation

Machine Learning for Precision Medicine: Application to Cancer Chemotherapy Response Prediction and Wound Healing Status Assessment

Creator:: Wei, Qi
Abstract:: Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
Resource Type:: Dissertation

Algorithmic Problems on Simplicial Complexes

Creator:: Maxwell, William
Resource Type:: Dissertation

Efficient Algorithms for Robust Spatiotemporal Data Analysis

Creator:: Moore, Travis W.
Abstract:: Many large-scale data analysis applications involve data that can vary over both time and space. Often the primary goal of analyzing spatiotemporal data is identifying trends, movements, and sudden changes with respect to time, location, or both. This can include a variety of applications in economics (housing prices, unemployment, job...
Resource Type:: Dissertation

Towards Automating Type Changes

Creator:: Ketkar, Ameya S.
Abstract:: Developers frequently change the type of a program element and update all its references for performance, security, concurrency, library migration, or better maintainability. Despite type changes being a common program transformation, it is the least automated and the least studied. Manually performing type changes is tedious since the programmers have...
Resource Type:: Dissertation

Uncertainty in Deep Learning with Implicit Neural Networks

Creator:: Ratzlaff, Neale
Abstract:: The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
Resource Type:: Dissertation

Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

Creator:: Seo, Eugene
Abstract:: Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
Resource Type:: Dissertation

Variational Satisfiability Solving

Creator:: Young, Jeffrey M.
Abstract:: Over the last two decades, satisfiability and satisfiability-modulo theory (SAT/SMT) solvers have grown powerful enough to be general purpose reasoning engines throughout software engineering and computer science. However, most practical use cases of SAT/SMT solvers require not just solving a single SAT/SMT problem, but solving sets of related SAT/SMT problems....
Resource Type:: Dissertation

Theory and Implementation of a Variational Database Management System

Creator:: Ataei, Parisa S.
Abstract:: In this thesis, I present the variational database management system, a formal framework and its implementation for representing variation in relational databases and managing variational information needs. A variational database is intended to support any kind of variation in a database. Specific kinds of variation in databases have already been...
Resource Type:: Dissertation

Predicting and Improving Throughput, Responsiveness and Battery Life of Computer Systems by Machine Learning

Creator:: Li, Yunfan
Abstract:: The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput...
Resource Type:: Dissertation

Visualization of N-ary Relationships

Creator:: Qu, Botong
Abstract:: N-ary relationships, which relate N entities where N is not necessarily two, are omnipresent in real life. In this thesis, we develop a visualization technique for N-ary relationships. First, we propose a visual metaphor that utilizes vertices and polygons to represent entities and N-ary relationships. Based on this visual metaphor,...
Resource Type:: Dissertation

Multiple Instance Multiple Label Learning with Limited Supervision

Creator:: Nguyen, Tam
Abstract:: In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...
Resource Type:: Dissertation

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Creator:: Nguyen, Khoi D.
Abstract:: This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
Resource Type:: Dissertation

Knowledge Base Completion Leveraging Natural Language Data

Creator:: Shahbazi, Hamed
Abstract:: Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
Resource Type:: Dissertation

Meeting End-to-End Deadlines in Real-Time Networks Using Software Defined Networking (SDN)

Creator:: Almahdi, Rafid M.
Abstract:: Network flows in Real-Time (RT) systems need to meet stringent end-to-end deadlines in order for such systems to operate safely and reliably. Today, such systems use custom or domain specific network system designs to meet end-to-end deadlines and other constraints of real-time flows. In this work we explore the design...
Resource Type:: Dissertation

Ideal Thumbnail-Preserving Encryption for Balancing Image Privacy and Usability

Creator:: Tajik, Kimia
Abstract:: In this dissertation, we propose Ideal Thumbnail-Preserving Encryption (Ideal TPE), as a special case of format-preserving encryption, to balance image privacy and usability concerns in a cloud environment. We first introduce a concrete construction for Ideal TPE, that provably leaks nothing about the plaintext (unencrypted) image beyond its thumbnail. We...
Resource Type:: Dissertation

Two Sides of a Coin: Adversarial-Based Image Privacy and Defending Against Adversarial Perturbations for Robust CNNs

Creator:: Rajabi, Arezoo
Abstract:: Emergence of highly accurate Convolutional Neural Networks (CNNs) with the capability to process large datasets, has led to their popularity in many applications, including safety/security-sensitive (e.g. disease recognition, self-driving cars). Despite the high accuracy of convolutional neural networks, they have been found to be susceptible to adversarial noise added to...
Resource Type:: Dissertation

Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

Creator:: Yao, Yao
Abstract:: We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
Resource Type:: Dissertation

Action Segmentation with Limited Supervision

Creator:: Li, Jun
Abstract:: In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
Resource Type:: Dissertation

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