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Filtering by: Creator Bailey, Kevin M. Remove constraint Creator: Bailey, Kevin M. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • A Study of Temporal Convolutional Neural Networks for Music Language Modeling as Applied to Automatic Music Transcription

    Creator:
    Meyn, Matthew
    Abstract:
    Automatic music transcription (AMT) is the task, given an acoustic representation of music, to recover a symbolic notation of the written notes expressed by the sound. Transcribing music with multiple notes sounding simultaneously is difficult for both humans and machines. Much existing work on AMT has focused on suitable acoustic...
    Resource Type:
    Capstone Project
    Full Text:
    many thanks to Prof. Mike Bailey and Prof. Prasad Tadepalli, not only for being on my committee, but

  • Feedback-Based Random Test Generator for TSTL

    Creator:
    Kaneoka, Kazuki
    Abstract:
    Software testing is the process of evaluating the accuracy and performance of software, and automated software testing allows programmers to develop software more efficiently by decreasing testing costs. We compared two advanced random test generators, a Feedback-Directed Random Test Generator (FDR) and a Feedback-Controlled Random Test Generator (FCR), for an...
    Resource Type:
    Capstone Project
    Full Text:
    nonErrorSeqs is empty then newSeq := {} else newSeq := selectSeqRandomly(nonErrorSeqs) end if m

  • muupi: An Abstract Syntax Tree based Mutation Testing Tool for Python 2.x Programs

    Creator:
    Liu, Xin
    Abstract:
    Mutation testing is one of the effective approaches measuring test adequacy of test suites. It is widely used in both academia and industry. Unfortunately, the adoption and practical use of mutation testing for Python 2.x programs face three obstacles. First, limited useful mutation operators. Existing mutation testing tools support very...
    Resource Type:
    Capstone Project
    Full Text:
    advisor, I would like to thank the rest of my committee: Dr. Prasad Tadepalli and Dr. Mike Bailey, for

  • Offensive Direction Inference in Real-World Football Video

    Creator:
    Lu, Qingkai
    Abstract:
    Automatic analysis of American football videos can help teams develop strategies and extract patterns with less human effort. In this work, we focus on the problem of automatically determining which team is on offense/defense, which is an important subproblem for higher-level analysis. While seemingly mundane, this problem is quite challenging...
    Resource Type:
    Masters Thesis
    Full Text:
    would like to thank the rest of my thesis committee members: Prof. Borello, Prof. Bailey and Prof

  • Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python

    Creator:
    Jothi, Shankar
    Abstract:
    Monte-Carlo Tree Search (MCTS) is an online-planning algorithm for decision-theoretic planning in domains with stochastic and combinatorial structure. The general applicability of MCTS makes it an ideal first choice to investigate when developing planners for complex applications requiring automated control and planning. The first contribution of this thesis is to...
    Resource Type:
    Masters Thesis
    Full Text:
    goals. My sincere gratitude to Dr. Prasad Tadepalli and Dr. Mike Bailey for accepting the invitation

  • OME : A Framework for Running Spatially Explicit System Dynamics Models

    Creator:
    Wingo, Patrick C.
    Abstract:
    The Open Modeling Environment (OME) is a tool developed to address some known shortcomings in ecological System Dynamics (SD) modeling research. OME provides a common set of methods for interacting directly with spatial information, reducing the need for modelers to create their own methods for doing so. The environment is...
    Resource Type:
    Masters Thesis

  • A collaborative filtering algorithm and evaluation metric that accurately model the user experience

    Creator:
    McLaughlin, Matthew
    Abstract:
    "Collaborative filtering algorithms’ performances have been evaluated using a variety of metrics. These metrics, such as Mean Absolute Error and Precision, have often ignored recommendations for which they do not have data. Ignoring these recommendations has provided numbers which do not accurately represent the user experience. Qualitatively we have seen...
    Resource Type:
    Capstone Project
    Full Text:
    like to thank Suzanne, Joe, and Kevin for making college tolerable at the worst of times and highly

  • 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
    Full Text:
    which we could not have proceeded; Kevin McGrath, for keeping me in the PhD program; Forrest Briggs

  • 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
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Jeffrey M. Young for

  • 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