Paradigms and prisons: a narrative of translation and transformation : my hero's journey from "at-risk" youth to teacher/learner in a jail setting /

All life is suffering. Life is the pursuit ofhappiness. These are two foundational Buddhist dictums that, in their simplicity, I have entirely misunderstood regarding their depth, misreading them as contradictory. Indeed, my superficial interpretations led me to Thoreau's life ofquiet desperation and deep depression. We come to know and bring understanding to our lives by storying them. My own Hero's Journey, the path from my egoic selftoward the universal Self, can be understood as the resultant translations and transformations. Inevitably each of us is involved in such a story, though most are unaware of the stages along our own Hero's journey. ' Narrative honours writing as a means of knowing. The contemplative reflection allows insight into our imprisoning paradigms, beliefs, behaviours, and blind spots. My research revisits and explores nodal experiences along my Hero's Journey through 4 categories: self, society, soil, and Self. While the value of this process of narrative inquiry lay in its ability to come to know and understand one's self, perhaps its greater value is of a more universal nature. My inquiry, while adding to the body of academic educational narrative literature, may also illuminate a path to educators, students, and all interested, encouraging a response to the call of their own Hero's journey. I am a teacher/learner in a jail setting, working with youth between the ages of 12 and 18 who have committed crimes such as armed robbery, assault, rape, and murder. As this thesis follows my continual development from egoic self/teacher/learner to universal Self/Teacher/Learner, it also enables me to both consciously and unconsciously open the ways in which I expand my care, compassion, and love to work with at-risk youth.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

A Living Educational Theory of Knowledge Translation: Improving Practice, Influencing Learners, and Contributing to the Professional Knowledge Base

This paper captured our joint journey to create a living educational theory of knowledge translation (KT). The failure to translate research knowledge to practice is identified as a significant issue in the nursing profession. Our research story takes a critical view of KT related to the philosophical inconsistency between what is espoused in the knowledge related to the discipline of nursing and what is done in practice. Our inquiry revealed “us” as “living contradictions” as our practice was not aligned with our values. In this study, we specifically explored our unique personal KT process in order to understand the many challenges and barriers to KT we encountered in our professional practice as nurse educators. Our unique collaborative action research approach involved cycles of action, reflection, and revision which used our values as standards of judgment in an effort to practice authentically. Our data analysis revealed key elements of collaborative reflective dialogue that evoke multiple ways of knowing, inspire authenticity, and improve learning as the basis of improving practice related to KT. We validated our findings through personal and social validation procedures. Our contribution to a culture of inquiry allowed for co-construction of knowledge to reframe our understanding of KT as a holistic, active process which reflects the essence of who we are and what we do.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

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.