Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

We need to have a conversation about OpenURL, a close look at a corpus of error reports from the 2013 academic year. Or, How I learned to stop worrying and give up on clean metadata.

Presented at Access 2014, winner of poster contest.

Perceptions of Adolescent Males and Their Parents as to Factors That Influence the Young Men's Academic Performance

Postsecondary enrolments of young males has been declining since the mid-1980s. The decline can be attributed, at least in part, to boys and young men being unable to compete for a fixed number of available places in institutions of higher learning, whether in community college or university. This inability to compete stems from their academic performance in secondary school. This study interviewed adolescent males and their parents as to their perceptions of a number of factors that may contribute to their academic performance. Those factors included noncognitive skills, dimensions of character, perceptions of teachers, general attitudes towards school, and likes and dislikes on a range of course subjects. One of the most important findings was that only one of the seven adolescent male participants was considering a future career that would require a university degree. Other findings showed the young men's noncognitive skills were weak, particularly in relation to time management skills and their unwillingness to ask for help with schoolwork and homework. Most of the young men expressed a dislike for mathematics beyond high school, a subject key to the study, of the natural sciences, engineering, technology, and business. Recommendations include school reforms both inside the classroom and beyond. Additionally, a framework using project management theory and practice has been proposed to improve noncognitive skills, dimensions of character, and executive function.

Copyright in the Stacks: The Chilling Effects of Unclear Copyright Interpretations in the Canadian Academic Library

Presented at the Annual Conference of the Canadian Political Science Association, Brock University, St. Catharines, Ontario, May 27, 2014

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

The Mediating Role of Perceived Scholastic Competence in the Relationship Between Motor Coordination and Academic Performance

Children with developmental coordination disorder (DCD) are often referred to as clumsy because of their compromised motor coordination. Clumsiness and slow movement performances while scripting in children with DCD often result in poor academic performance and a diminished sense of scholastic competence. This study purported to examine the mediating role of perceived scholastic competence in the relationship between motor coordination and academic performance in children in grade six. Children receive a great deal of comparative information on their academic performances, which influence a student's sense of scholastic competence and self-efficacy. The amount of perceived academic self-efficacy has significant impact on academic performance, their willingness to complete academic tasks, and their self-motivation to improve where necessary. Independent t-tests reveal a significant difference (p < .001) between DCD and non-DCD groups when compared against their overall grade six average with the DCD group performing significantly lower. Independent t-tests found no significant difference between DCD and non-DCD groups for perceived scholastic competence. However, multiple linear regression analysis revealed a significant mediating role of 15% by perceived scholastic competence when examining the relationship between motor coordination and academic performance. While children with probable DCD may not rate their perceived scholastic competence as less than their healthy peers, there is a significant mediating effect on their academic performance.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Internationalization in German Academic Libraries: Moving beyond North American Perspectives

This paper explores how internationalization is understood and experienced in German academic libraries. Its main purpose is to move the discussion of internationalization in academic libraries beyond the boundaries of English-speaking North America by investigating a European perspective. Its secondary purpose is to investigate the role of English in German academic libraries. An online survey and a series of in-person interviews conducted in Germany in April 2015 provided the data for this study. What emerged are a series of stated differences and similarities between North America and Germany informed by the two overarching themes of implicit internationalization and plurilingualism, the ability to switch from one language to another as required.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Stories of Informal Mentorship: Recognizing the Voices of Mentees in Academic Libraries

Based on the 2014 OLA Super Conference session “Mentorship in Academic Libraries: A Universe of Possibilities,” this article explores the benefits of informal mentorship in its various forms and how librarians are embracing a new way of thinking about mentorship both individually and organizationally. The lived experiences of two professional academic librarians are shared as they argue that informal mentorship offers the opportunity to co-create a meaningful mentorship experience by recognizing the importance of the mentee’s voice. This paper will discuss the value of informal mentorship and how, when certain elements are present within it, this model can allow us to reimagine mentorship in academic libraries. Concepts such as “accidental” mentorship, “purposeful” mentorship, mentorship “network,” and “peer” mentorship are discussed.

Investigation of Theories Supporting Engagement of Resistant Learners in Formal Academic Settings and Curriculum

This study sought to explore ways to work with a group of young people through an arts-based approach to the teaching of literacy. Through the research, the author integrated her own reflexivity applying arts methods over the past decade. The author’s past experiences were strongly informed by theories such as caring theory and maternal pedagogy, which also informed the research design. The study incorporated qualitative data collection instruments comprising interviews, journals, sketches, artifacts, and teacher field notes. Data were collected by 3 student participants for the duration of the research. Study results provide educators with data on the impact of creating informal and alternative ways to teach literacy and maintain student engagement with resistant learners.