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Filtering by: Creator Oakley, Brian B. Remove constraint Creator: Oakley, Brian B. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Image thinning by hexagonal grid

    Creator:
    Carstens, Brian
    Abstract:
    One of the tasks of image processing systems is to characterize images by thinning, or skeletonizing techniques. The standard method, based on a disk growing technique, often results in disconnected skeletons. Since the standard technique is based on a square grid system, the disk shape is also square. The. purpose...
    Resource Type:
    Capstone Project
    Full Text:
    I u u IMAGE THINNING BY HEXAGONAL GRID by Brian

  • A tutorial interface for a problem-solving production system

    Creator:
    Lantz, Brian Scott
    Abstract:
    The system described is an interface between a student and a problem-solving production system that solves some class of problems. Its purpose is to help a student learn some part of the realm of problem solving. As a student attempts to solve a problem the system controls the firing of...
    Resource Type:
    Masters Thesis
    Full Text:
    system AN ABSTRACT OF THE THESIS OF Brian Lantz for the degree of Master of Science in Computer

  • The empire infrastructure for testing experimentation

    Creator:
    Davia, Brian
    Abstract:
    It is widely believed that there is not enough experimentation in the field of computer science. One area in particular in which additional experimentation is needed involves regression testing of large, real software. Regression testing is the expensive process of validating changes made to previously tested software. One reason why...
    Resource Type:
    Capstone Project
    Full Text:
    Brian Davia a research paper submitted to Oregon State University in partial fulfillment of the

  • Machine Learning and Optimization Applications in Agriculture

    Creator:
    Molotkov, Alexander
    Abstract:
    The ever-increasing global population presents looming problems for the field of agriculture. Global food demand will, at some point, increase to the point where there is not enough crop-ready land to keep up. This creates an additional incentive, other than economics, for growers to increase their yield-per-acre and make sure...
    Resource Type:
    Honors College Thesis
    Full Text:
    Manufacturing Engineering Stefan Lee, Committee Member, representing Electrical and Computer Engineering Brian

  • 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
    Full Text:
    CNNs Abstract approved: Rakesh B. Bobba Emergence of highly accurate Convolutional Neural Networks

  • 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
    Full Text:
    approved: Anita Sarma Rakesh B. Bobba Many home users nowadays use various smart devices to improve the

  • 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

  • Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks

    Creator:
    Deng, Dezhong
    Abstract:
    Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks...
    Resource Type:
    Dissertation
    Full Text:
    the Transformer decoder layer. . . . . . . . . . . . 12 3.1 RNA structure types. B shows a hairpin

  • Contributions to Collaborative Filtering Research

    Creator:
    Nong, Shuzhen
    Abstract:
    In this report, I first introduce three areas of interest to collaborative filtering researchers, namely: (a) how to solve the sparsity and scalability problems in recommendation systems; (b) how to rapidly develop and test collaborative filtering algorithms; and (c) how to apply collaborative filtering to unbounded numeric preference data. In...
    Resource Type:
    Capstone Project
    Full Text:
    challenged by, namely: (a) how to solve the sparsity and scalability problems in recommendation systems; (b

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
    Full Text:
    bag-level labels; b) clustering with constraints, where the partial supervision is expressed as