Index Catalog // ScholarsArchive@OSU

Planning in multi-agent partially observable domains using sparse sampling

Creator:: Donneau-Golencer, Thierry D.
Abstract:: A large number of sequential decision-making problems in uncertain environments can be modeled as Markov Decision Processes (MDPs). In such settings, an agent can observe at each time step the state of the environment and then executes an action, causing a stochastic transition to a new state of the environment...
Resource Type:: Masters Thesis

Protein secondary structure prediction using conditional random fields and profiles

Creator:: Shen, Rongkun
Abstract:: Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
Resource Type:: Masters Thesis

Mixing time for a 3-cycle interacting particle system : a coupling approach

Creator:: Eves, Matthew Jasper
Abstract:: This thesis examines the mixing times for one-dimensional interacting particle systems. We use the coupling method to study the mixing rates for particle systems on the circle which move according to specific permutations e.g., transpositions and 3-cycles.
Resource Type:: Masters Thesis

Applying hierarchical and adaptive control to coordinating simple robots

Creator:: Knudson, Matthew D.
Abstract:: Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...
Resource Type:: Masters Thesis

Learning MDP action models via discrete mixture trees

Creator:: Wynkoop, Michael S.
Abstract:: This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
Resource Type:: Masters Thesis

Rare category detection using hierarchical mean shift

Creator:: Vatturi, Pavan Kumar
Abstract:: Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
Resource Type:: Masters Thesis

Learning multiple non-redundant codebooks with word clustering for document classification

Creator:: Surve, Akshat Sudhakar
Abstract:: The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
Resource Type:: Masters Thesis

Recognizing human group activities in video through mining optimal features

Creator:: Amer, Mohamed R.
Abstract:: Given a video, we would like to recognize group activities, localize video parts where these activities occur, and detect actors involved in them. To this and, we propose a novel, mid-level feature, called control point, for representing group activities. The control points are aimed at summarizing visual cues, lifting from...
Resource Type:: Masters Thesis

Enhancing macrocell downlink performance through femtocell user cooperation

Creator:: Zaid, Adem Mabruk
Abstract:: This thesis studies cooperative techniques that rely on femtocell user diversity to improve the downlink communication quality of macrocell users. We analytically analyze and evaluate the achievable performance of these techniques in the downlink of Rayleigh fading channels. We provide an approximation of both the bit-error rate (BER) and the...
Resource Type:: Masters Thesis

Analysis of bio-based composites for image segmentation with the aid of games

Creator:: Inouye, Jennifer A.
Abstract:: A fundamental problem in computer vision is to partition an image into meaningful segments. While image segmentation is required by many applications, the thesis focuses on segmentation of computed tomography (CT) images for analysis and quality control of composite materials. The key research contribution of this thesis is a novel...
Resource Type:: Masters Thesis

Predicting activity type from accelerometer data

Creator:: Zheng, Yonglei
Abstract:: The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has...
Resource Type:: Masters Thesis

Fast queuing policies via convex relaxation

Creator:: Nguyen-Huu, Duong
Abstract:: Traditionally, networking protocol designs have placed much emphasis on point-to-point reliability and efficiency. With the recent rise of mobile and multimedia applications, other considerations such as power consumption and/or Quality of Service (QoS) are becoming increasingly important factors in designing network protocols. As such, we present a new flexible framework...
Resource Type:: Masters Thesis

Incorporating labeled features into image classification using generalized expectation

Creator:: Moore, Travis
Abstract:: Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well, and incorporating user feedback into these learning algorithms could help reduce the dependency on large amounts of labeled training data. This process has already been...
Resource Type:: Masters Thesis

Revenue-based spectrum management via Markov decision process

Creator:: Zhu, Pingan
Abstract:: We consider the problem of wireless spectrum management in cognitive wireless networks that maximizes the revenue for a spectrum operator. Specifically, we study the problem on how a wireless spectrum operator can optimally allocate its limited spectrum to various classes users/devices who pay differently for their spectrum per unit time....
Resource Type:: Masters Thesis

Interactive player tracking for videos in American football

Creator:: Bawaskar, Amit
Abstract:: This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...
Resource Type:: Masters Thesis

Object detection in biological images using a search-based framework

Creator:: Lam, Michael
Abstract:: This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
Resource Type:: Masters Thesis

Probabilities of voting paradoxes with three candidates

Creator:: McDonald, Leslie M.
Abstract:: Pardoxes in voting has been an interest of voting theorists since the 1800's when Condorcet demonstrated the key example of a voting paradox: voters with individually transitive rankings produce an election outcome which is not transitive. With Arrow's Impossibility Theorem, the hope of finding a fair voting method which accurately...
Resource Type:: Masters Thesis

Efficient Incremental Panorama Reconstruction from Multiple Videos

Creator:: Feng, Zhongyuan
Abstract:: Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama represents an enhanced still-image representation of an entire scene captured in a set of videos, where each video shows only a part of the scene. Importantly, a panorama shows only the scene background,...
Resource Type:: Capstone Project

Toward Automated Decision-Making in Power Systems Wide-Area Protection

Creator:: Meier, Richard John
Abstract:: In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements and despite targeted efforts to improve the operation and control of the electric grid, the power systems industry has seen relatively small advances in this regard. For instance, today's power system is...
Resource Type:: Masters Thesis

Listing as a Potential Connection between Sets of Outcomes and Counting Processes

Creator:: Erickson, Sarah A.
Abstract:: Counting problems are rich in opportunities for students to make meaningful mathematical connections and develop non-algorithmic thinking; their accessible nature and applications to computer science make counting problems a valuable part of mathematics curricula. However, students struggle in various ways with counting, and while previous studies have indicated that listing...
Resource Type:: Masters Thesis

Open Set Learning with Counterfactual Images

Creator:: Neal, Lawrence W.
Abstract:: In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
Resource Type:: Masters Thesis

Methods for Detection and Recovery of Out-of-Distribution Examples

Creator:: Ratzlaff, Neale J.
Abstract:: Deep neural networks currently comprise the backbone of many applications where safety is a critical concern, for example: autonomous driving and medical diagnostics. Unfortunately these systems currently fail to detect out-of-distribution (OOD) inputs and can be prone to making dangerous errors when exposed to them. In addition, these same systems...
Resource Type:: Masters Thesis

Counterfactual States for Atari Agents via Generative Deep Learning

Creator:: Olson, Matthew L.
Abstract:: Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
Resource Type:: Masters Thesis

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

Action Selection for Heterologous Control of a Continuous-Output Myoelectric Prosthesis

Creator:: Sylwester, Sean T.
Abstract:: Signiﬁcance: Movement intent decoding algorithms can interpret human bioelectrical signals to control prosthetic limbs with many degrees of freedom (DOFs). This work involves decoding volitional movement intent from surface electromyogram (sEMG) signals to control prosthetic arms. To train these algorithms, patients ﬂex their muscles to “follow” a movement prompt, and...
Resource Type:: Masters Thesis

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

Creator:: George, Damon A.
Abstract:: Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
Resource Type:: Masters Thesis

Customizable Visual Traces for Program Explanations

Creator:: Bajaj, Divya
Abstract:: Correctness and efficiency are important properties of programs. However, to support maintenance and debugging, the programs should also be understandable. Program explanations also play a vital role in educational settings, enhancing the understanding of programs among students. Proof trees provide a sound basis for generating dynamic explanations of programs. But...
Resource Type:: Masters Thesis

Learning to Estimate Multi-Relation Aggregation Functions

Creator:: Jadhav, Rashmi Suresh
Abstract:: Multi-relation aggregation queries process the join operator before computing the aggregation function. This join is arguably the most costly operation since traditional join algorithms spend majority of their time trying to join the parts of the relations that do not generate any output tuples. This causes slow response times with...
Resource Type:: Masters Thesis

Collaborative Online Learning For Join Processing

Creator:: Xie, Mian
Abstract:: Relational binary operators, such as join, are arguably the most costly and frequently used operations in relational data systems. In many join algorithms, the majority of the process time is spent on scanning and attempting to join the parts of the relations that do not satisfy the join condition and...
Resource Type:: Capstone Project

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Creator:: Batke, Ryan
Abstract:: As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
Resource Type:: Masters Thesis

Detecting Anomalies in Object Appearance and Motion Dynamics

Creator:: Alotaibi, Mazen
Abstract:: Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...
Resource Type:: Capstone Project

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Creator:: Senewiratne, Yomal K.
Abstract:: We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Resource Type:: Masters Thesis

A tool for visualizing sequence alignments and associating annotations

Creator:: Zhang, Wenpeng
Abstract:: Alignment of genomic sequences from different species is becoming an increasingly powerful method in biology, and is being used for many purposes. The result of sequence alignments is a list of pairs of matched locations between the pattern string and the text string. However, without any proper visualization tools to...
Resource Type:: Capstone Project

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