Index Catalog // ScholarsArchive@OSU

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

Secure Data Analytics under Data Integrity Attacks

Creator:: De Silva, Shashini A.
Abstract:: There has been tremendous growth in using data analytic and machine learning algorithms to make critical decisions, such as in the national power grid, healthcare operations, and autonomous vehicles. Employing data analytic for decision-making allows cyber attackers to manipulate the decisions of these algorithms through data falsification. Hence, the trustworthiness...
Resource Type:: Dissertation

Novel Approaches to Porous Material Discovery

Creator:: Sturluson, Arni
Abstract:: Uses for materials with a large surface area and high porosity have grown sig-niﬁcantly in recent years. Porous materials have found usage in applications such as separation, gas storage, sensing, puriﬁcation and more, prompting researchers to ﬁnd and discover numerous new porous materials to suit a speciﬁc purpose. Hundreds of...
Resource Type:: Dissertation
Full Text:: valuable insight into the latent representations in Chapter 4. Grant McConachie and Samuel Hough helped in

Interpretable Machine Learning: Applications in Biology and Genomics

Creator:: Danaee, Padideh
Abstract:: Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
Resource Type:: Dissertation

Information-theoretic Approach to Design and Evaluate Privacy-preserving and Fair Frameworks for Continuous High-dimensional Data

Creator:: Alsulaimawi, Zahir Ahmed Hussein
Abstract:: Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are circumventing them. Further, there have been concerns about the possibility of bias and discrimination in predictive applications. In order to address these issues, we propose an...
Resource Type:: Dissertation

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

Creator:: Raghavan, Aswin
Abstract:: Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
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

Applying Machine Learning Algorithms to Identify Problematic Nuclear Data within Nuclear Transport Simulations

Creator:: Grechanuk, Pavel A.
Abstract:: The primary goal of this dissertation is to improve the quality of nuclear data available to the nuclear science community. We propose to accomplish this by applying machine learning algorithms to the large number of available benchmark experiments and simulations, with the goal of determining which nuclear data have strong...
Resource Type:: Dissertation

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

ScholarsArchive@OSU

Towards Direct Simultaneous Speech Translation

Secure Data Analytics under Data Integrity Attacks

Novel Approaches to Porous Material Discovery

Interpretable Machine Learning: Applications in Biology and Genomics

Information-theoretic Approach to Design and Evaluate Privacy-preserving and Fair Frameworks for Continuous High-dimensional Data

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

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

Applying Machine Learning Algorithms to Identify Problematic Nuclear Data within Nuclear Transport Simulations

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

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