This research explores the annotation and note-taking practices of graduate students and reports on the sets of activities, habits, objects, tools and methods that define the practice. In particular, this empirical study focuses on understanding the integration of annotation practices within larger scholarly processes. This study therefore aims to describe and analyze annotation not only as material externalities of the research process, but also as crucial epistemic practices allowing students to progress from one research activity to the other. Interviews are supplemented by document collection and analyzed using a multi-perspectival framework. The findings describe an annotation lifecycle and suggest a new model of the scholarly process using annotation practices as units of analysis. The study further discusses annotation as a primitive epistemic practice and examines the productive tensions fostering the student’s progress towards her goals. This research finally proposes requirements for future tools supporting scholarly practice.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das / Stochastic algorithms are widely used in various modeling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals’ behavior such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address ‘convergence’ and ‘diversity’ issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / There is an increasing amount of user-generated information in online documents, includ- ing user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as either positive, negative or neutral based on the words present in the document. Supervised learning approaches have been successfully used for sentiment classification in domains that are rich in labeled data. Some of these approaches make use of features such as unigrams, bigrams, sentiment words, adjective words, syntax trees (or variations of trees obtained using pruning strategies), etc. However, for some domains the amount of labeled data can be relatively small and we cannot train an accurate classifier using the supervised learning approach. Therefore, it is useful to study domain adaptation techniques that can transfer knowledge from a source domain that has labeled data to a target domain that has little or no labeled data, but a large amount of unlabeled data. We address this problem in the context of product reviews, specifically reviews of movies, DVDs and kitchen appliances. Our approach uses an Adapted Naive Bayes classifier (ANB) on top of the Expectation Maximization (EM) algorithm to predict the sentiment of a sentence. We use grams derived from complete syntax trees or from syntax subtrees as features, when training the ANB classifier. More precisely, we extract grams from syntax trees correspond- ing to sentences in either the source or target domains. To be able to transfer knowledge from source to target, we identify generalized features (grams) using the frequently co-occurring entropy (FCE) method, and represent the source instances using these generalized features. The target instances are represented with all grams occurring in the target, or with a reduced grams set obtained by removing infrequent grams. We experiment with different types of grams in a supervised framework in order to identify the most predictive types of gram, and further use those grams in the domain adaptation framework. Experimental results on several cross-domains task show that domain adaptation approaches that combine source and target data (small amount of labeled and some unlabeled data) can help learn classifiers for the target that are better than those learned from the labeled target data alone.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Pitch detection and instrument identification can be achieved with relatively high accuracy when considering monophonic signals in music; however, accurately classifying polyphonic signals in music remains an unsolved research problem. Pitch and instrument classification is a subset of Music Information Retrieval (MIR) and automatic music transcription, both having numerous research and real-world applications. Several areas of research are covered in this thesis, including the fast Fourier transform, onset detection, convolution, and filtering. Basic music theory and terms are also presented in order to explain the context and structure of data used. The focus of this thesis is on the representation of musical signals in the frequency domain. Polyphonic signals with many different voices and frequencies can be exceptionally complex. This thesis presents a new model for representing the spectral structure of polyphonic signals: Uniform MAx Gaussian Envelope (UMAGE). The new spectral envelope precisely approximates the distribution of frequency parts in the spectrum while still being resilient to oscillating rapidly (noise) and is able to generalize well without losing the representation of the original spectrum. When subjectively compared to other spectral envelope methods, such as the linear predictive coding envelope method and the cepstrum envelope method, UMAGE is able to model high order polyphonic signals without dropping partials (frequencies present in the signal). In other words, UMAGE is able to model a signal independent of the signal’s periodicity. The performance of UMAGE is evaluated both objectively and subjectively. It is shown that UMAGE is robust at modeling the distribution of frequencies in simple and complex polyphonic signals. Combined classification (combiners), a methodology for learning large concepts, is used to simplify the learning process and boost classification results. The output of each learner is then averaged to get the final result. UMAGE is less accurate when identifying pitches; however, it is able to achieve accuracy in identifying instrument groups on order-10 polyphonic signals (ten voices), which is competitive with the current state of the field.

The History of the Book in Canada / Histoire du livre et de l’imprimé au Canada and other national book history projects have been a catalyst for research into the local production of print and have highlighted the need for more study of the print trades in smaller centres. In Ontario during the nineteenth century independent weekly newspapers were printed in most villages, while larger towns boasted more than one print shop and often one or more booksellers and stationers. Bookbinders were active members of the book trades selling books and stationery, ruling paper, binding local pamphlets, periodicals, and books, and manufacturing blankbooks for a variety of purposes. Since much local printing was ephemeral in nature, the only evidence of its existence is found in the record books kept by printers and binders. Partial business records and other surviving artifacts of the Guelph Bookbindery, which operated from 1855 to 1978, are both a rich source of evidence of the day-to-day operations of the bookbindery and a key to the intersection of print trades in Guelph, Ontario, and the surrounding counties. This study uses local imprints, blankbooks, authors’ papers, newspapers, directories, maps, assessment records, photographs, museum artifacts, and oral history accounts to reconstruct a history of the bookbindery and its place in the print culture of nineteenth-century Guelph. It documents the transformation of a business selling books, stationery, and wallpaper into a commercial bindery, which along with local printers produced large numbers of pamphlets, ubiquitous then but increasingly rare today. It is a view into the microcosm of a dynamic community where print was a vital medium for communication reflecting the cultural, commercial, and entrepreneurial discourse in nineteenth-century Canadian society that reached far beyond its borders.

The History of the Book in Canada / Histoire du livre et de l’imprimé au Canada and other national book history projects have been a catalyst for research into the local production of print and have highlighted the need for more study of the print trades in smaller centres. In Ontario during the nineteenth century independent weekly newspapers were printed in most villages, while larger towns boasted more than one print shop and often one or more booksellers and stationers. Bookbinders were active members of the book trades selling books and stationery, ruling paper, binding local pamphlets, periodicals, and books, and manufacturing blankbooks for a variety of purposes. Since much local printing was ephemeral in nature, the only evidence of its existence is found in the record books kept by printers and binders. Partial business records and other surviving artifacts of the Guelph Bookbindery, which operated from 1855 to 1978, are both a rich source of evidence of the day-to-day operations of the bookbindery and a key to the intersection of print trades in Guelph, Ontario, and the surrounding counties. This study uses local imprints, blankbooks, authors’ papers, newspapers, directories, maps, assessment records, photographs, museum artifacts, and oral history accounts to reconstruct a history of the bookbindery and its place in the print culture of nineteenth-century Guelph. It documents the transformation of a business selling books, stationery, and wallpaper into a commercial bindery, which along with local printers produced large numbers of pamphlets, ubiquitous then but increasingly rare today. It is a view into the microcosm of a dynamic community where print was a vital medium for communication reflecting the cultural, commercial, and entrepreneurial discourse in nineteenth-century Canadian society that reached far beyond its borders.

Doctor of Philosophy / Agronomy / Kevin Price / Stephen M. Welch / Plant phenotyping has been studied for decades for understanding the relationship between plant genotype, phenotype, and the surrounding environment. Improved accuracy and efficiency in plant phenotyping is a critical factor in expediting plant breeding and the selection process. In the past, plant phenotypic traits were extracted using invasive and destructive sampling methods and manual measurements, which were time-consuming, labor-intensive, and cost-inefficient. More importantly, the accuracy and consistency of manual methods can be highly variable. In recent years, however, photogrammetry and 3D modeling techniques have been introduced to extract plant phenotypic traits, but no cost-efficient methods using these two techniques have yet been developed for large-scale plant phenotyping studies. High-throughput 3D modeling techniques in plant biology and agriculture are still in the developmental stages, but it is believed that the temporal and spatial resolutions of these systems are well matched to many plant phenotyping needs. Such technology can be used to help rapid phenotypic trait extraction aid crop genotype selection, leading to improvements in crop yield. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems, image processing, and 3D reconstruction algorithms to build 2D mosaicked orthophotos and 3D plant models. Chamber-based and ground-level field implementations can be used to measure phenotypic traits such as leaf length, rosette area in 2D and 3D, plant nastic movement, and diurnal cycles. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.

Doctor of Education / Department of Educational Leadership / Gerald D. Bailey / The purpose of this study was to create a handbook that would support museum professionals through the stages of planning, creating, and evaluating a user-centered eMuseum. Planning, Creating, and Evaluating eMuseums: A Step by Step Handbook for Museum Professionals was developed using the research and development methodology (R&D) developed by Borg and Gall (1989). The seven steps in the R&D cycle used in this study included: 1) research analysis and proof of concept, 2) product planning and design, 3) preliminary product development, 4) preliminary field testing, 5) revision of the prototype, 6) main field testing, and 7) revision of the final product. A prototype of the handbook was developed and then evaluated by experts in digital libraries or museum informatics in the preliminary field test. Revisions were made to the handbook based on their feedback. The handbook was then distributed to museum professionals for the main field test. Feedback from the main field test was used to create the final product. Major conclusions from the study were: 1. There was a need for a handbook to guide museum professionals through the steps of developing an eMuseum. Museum leaders indicated a desire to create a stronger online presence for their museums, but did not know how to begin the process. 2.The handbook was most useful to museum professionals. Originally, the handbook was intended for an audience broadly defined as "information professionals", which included both library and museum professionals. 3.Museum leaders and community stakeholders could partner to create eMuseums. Stakeholders included educators who wanted to use eMuseums to incorporate standards-based curriculum into their classroom or graduate students in education looking for collaborative projects to advance their study. 4.Finding new ways to reach audiences was important to museum leaders. Museum leaders were aware that the majority of their visitors expected to find information about their museum on the Internet and wanted to find ways to reach these audience members. 5.Museum professionals found resources listed in the book to be useful. The handbook worked effectively as a reference guide for creating an eMuseum.

Master of Science / Department of Electrical and Computer Engineering / Sanjoy Das / Stephen M. Welch / Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.