The purpose of this project was to research the virtual camera systems used in current generation third person action games and to see what could be improved upon. To do this, different camera shots were categorized into camera archetypes, which also include post process and lens effects used. Information about the games was acquired by either looking through the game’s settings or by observing gameplay. Finally the results were compared to each other as well as the film industry and several improvements regarding usage of different features and camera shots were suggested.

This study on engagement in environmental activism can offer valuable insights into how Ontario’s young people come to be responsible stewards of the earth. This research seeks to understand the narrative complexities put forth by teachers and students (Gr. 11-12) about the influence school plays for environmental activists. The teachers’ involvement with activism is mediated by students and the social networks that support their actions. The students’ involvement in action is influenced by teacher mentors, learning about/in the environment, and having a venue for activism. These findings suggest that in order to live up to Acting Today, Shaping Tomorrow schools should be seeking to have at least one environmentally literate teacher who wants to provide students with a venue for action. To assist the teachers and students with activism, there needs to be support for environmental action initiatives from the school administration and the community.

Over the past several years, both government and the profession of pharmacy have acknowledged that pharmacists are not being used to their full potential in our health care system. In order to advance the profession of pharmacy in this area, guidance on how pharmacists can be integrated need to be investigated.<p>The purpose of this study was to identify how to integrate a pharmacist into an already established primary health care team, at the Student Health Centre at the University of Saskatchewan. The project was divided into three phases: defining the role of the pharmacist, implementing the proposed role and then evaluating and prioritizing the role. Using action research, an expert panel consisting of established primary health/ambulatory care pharmacists from across Canada helped to identify possible clinical activities for a Student Health Centre pharmacist. The results were presented to the primary health care team, who then collaborated with the pharmacist and researchers to define the role of the pharmacist. Once an agreement was reached, a pharmacist provided eight weeks of full-time clinical services. Upon completion, focus groups with the primary health care team members were used to evaluate the pharmacists clinical services.<p>The role of the pharmacist was tailored specifically for the student health care centre selected for the study. However, the process of integrating and evaluating the role of the pharmacist, will serve as a template for other pharmacists desiring to be involved in any primary health care team interested in expanding their multidisciplinary service.

After formulating the problem and reviewing relevant research literature, a study was performed that compared reverse engineering with traditional scenario-based techniques in usability evaluation. In this case study, an online tool for creating asthma action plans was created and evaluated through questionnaires and focus groups. The tool was then tested in a controlled study using both a traditional scenario-based approach and the reverse engineering method. A group of twelve users built asthma action plans using each method in a randomized order. Results concerning usability, efficiency and the types of usability problems found were reported, along with recommendations for further research in the use of reverse engineering as a method of usability evaluation.

After formulating the problem and reviewing relevant research literature, a study was performed that compared reverse engineering with traditional scenario-based techniques in usability evaluation. In this case study, an online tool for creating asthma action plans was created and evaluated through questionnaires and focus groups. The tool was then tested in a controlled study using both a traditional scenario-based approach and the reverse engineering method. A group of twelve users built asthma action plans using each method in a randomized order. Results concerning usability, efficiency and the types of usability problems found were reported, along with recommendations for further research in the use of reverse engineering as a method of usability evaluation.

In this thesis, a review of Kenneth Burke's use of the term attitude in his published works as well as in some unpublished notes, drafts and letters, is performed. Three periods of different usage are found. Early works feature a pervasive attitude with elements of both body and mind. This attitude is then subsumed into the pentad and the physiological connection is diminished, but attitude is given an important function as a connective between action and motion. The later Burke reinstates attitude as central to his theory of symbolic action, reconnects it to the physiological and includes it in the Pentad with parsimony-inducing effect. The attitude is then found to aid rhetorical analysis and show promise in being able to help analyse expressions not wholly in the realm of the conscious, be they in the form of a Bourdieu social practice or barely conscious rhetorical markers in conversation.

The recognition of time-varying geometry (TVG) objects (in particular, humans) and their actions is a complex task due to common real-world sensing challenges, such as obstacles and environmental variations, as well as due to issues specific to TVG objects, such as self-occlusion. Herein, it is proposed that a multi-camera active-vision system, which dynamically selects camera poses in real-time, be used to improve TVG action sensing performance by selecting camera views on-line for near-optimal sensing-task performance. Active vision for TVG objects requires an on-line sensor-planning strategy that incorporates information about the object itself, including its current action, and information about the state of the environment, including obstacles, into the pose-selection process. Thus, the focus of this research is the development of a novel methodology for real-time sensing-system reconfiguration (active vision), designed specifically for the recognition of a single TVG object and its actions in a cluttered, dynamic environment, which may contain multiple other dynamic (maneuvering) obstacles. The proposed methodology was developed as a complete, customizable sensing-system framework which can be readily modified to suit a variety of specific TVG action-sensing tasks – a 10-stage pipeline real-time architecture. Sensor Agents capture and correct camera images, removing noise and lens distortion, and segment the images into regions of interest. A Synchronization Agent aligns multiple images from different cameras to a single ‘world-time.’ Point Tracking and De-Projection Agents detect, identify, and track points of interest in the resultant 2-D images, and form constraints in normalized camera coordinates using the tracked pixel coordinates. A 3-D Solver Agent combines all constraints to estimate world-coordinate positions for all visible features of the object-of-interest (OoI) 3-D articulated model. A Form-Recovery Agent uses an iterative process to combine model constraints, detected feature points, and other contextual information to produce an estimate of the OoI’s current form. This estimate is used by an Action-Recognition Agent to determine which action the OoI is performing, if any, from a library of known actions, using a feature-vector descriptor for identification. A Prediction Agent provides estimates of future OoI and obstacle poses, given past detected locations, and estimates of future OoI forms given the current action and past forms. Using all of the data accumulated in the pipeline, a Central Planning Agent implements a formal, mathematical optimization developed from the general sensing problem. The agent seeks to optimize a visibility metric, which is positively related to sensing-task performance, to select desirable, feasible, and achievable camera poses for the next sensing instant. Finally, a Referee Agent examines the complete set of chosen poses for consistency, enforces global rules not captured through the optimization, and maintains system functionality if a suitable solution cannot be determined. In order to validate the proposed methodology, rigorous experiments are also presented herein. They confirm the basic assumptions of active vision for TVG objects, and characterize the gains in sensing-task performance. Simulated experiments provide a method for rapid evaluation of new sensing tasks. These experiments demonstrate a tangible increase in single-action recognition performance over the use of a static-camera sensing system. Furthermore, they illustrate the need for feedback in the pose-selection process, allowing the system to incorporate knowledge of the OoI’s form and action. Later real-world, multi-action and multi-level action experiments demonstrate the same tangible increase when sensing real-world objects that perform multiple actions which may occur simultaneously, or at differing levels of detail. A final set of real-world experiments characterizes the real-time performance of the proposed methodology in relation to several important system design parameters, such as the number of obstacles in the environment, and the size of the action library. Overall, it is concluded that the proposed system tangibly increases TVG action-sensing performance, and can be generalized to a wide range of applications, including human-action sensing. Future research is proposed to develop similar methods to address deformable objects and multiple objects of interest.

The recognition of time-varying geometry (TVG) objects (in particular, humans) and their actions is a complex task due to common real-world sensing challenges, such as obstacles and environmental variations, as well as due to issues specific to TVG objects, such as self-occlusion. Herein, it is proposed that a multi-camera active-vision system, which dynamically selects camera poses in real-time, be used to improve TVG action sensing performance by selecting camera views on-line for near-optimal sensing-task performance. Active vision for TVG objects requires an on-line sensor-planning strategy that incorporates information about the object itself, including its current action, and information about the state of the environment, including obstacles, into the pose-selection process. Thus, the focus of this research is the development of a novel methodology for real-time sensing-system reconfiguration (active vision), designed specifically for the recognition of a single TVG object and its actions in a cluttered, dynamic environment, which may contain multiple other dynamic (maneuvering) obstacles. The proposed methodology was developed as a complete, customizable sensing-system framework which can be readily modified to suit a variety of specific TVG action-sensing tasks – a 10-stage pipeline real-time architecture. Sensor Agents capture and correct camera images, removing noise and lens distortion, and segment the images into regions of interest. A Synchronization Agent aligns multiple images from different cameras to a single ‘world-time.’ Point Tracking and De-Projection Agents detect, identify, and track points of interest in the resultant 2-D images, and form constraints in normalized camera coordinates using the tracked pixel coordinates. A 3-D Solver Agent combines all constraints to estimate world-coordinate positions for all visible features of the object-of-interest (OoI) 3-D articulated model. A Form-Recovery Agent uses an iterative process to combine model constraints, detected feature points, and other contextual information to produce an estimate of the OoI’s current form. This estimate is used by an Action-Recognition Agent to determine which action the OoI is performing, if any, from a library of known actions, using a feature-vector descriptor for identification. A Prediction Agent provides estimates of future OoI and obstacle poses, given past detected locations, and estimates of future OoI forms given the current action and past forms. Using all of the data accumulated in the pipeline, a Central Planning Agent implements a formal, mathematical optimization developed from the general sensing problem. The agent seeks to optimize a visibility metric, which is positively related to sensing-task performance, to select desirable, feasible, and achievable camera poses for the next sensing instant. Finally, a Referee Agent examines the complete set of chosen poses for consistency, enforces global rules not captured through the optimization, and maintains system functionality if a suitable solution cannot be determined. In order to validate the proposed methodology, rigorous experiments are also presented herein. They confirm the basic assumptions of active vision for TVG objects, and characterize the gains in sensing-task performance. Simulated experiments provide a method for rapid evaluation of new sensing tasks. These experiments demonstrate a tangible increase in single-action recognition performance over the use of a static-camera sensing system. Furthermore, they illustrate the need for feedback in the pose-selection process, allowing the system to incorporate knowledge of the OoI’s form and action. Later real-world, multi-action and multi-level action experiments demonstrate the same tangible increase when sensing real-world objects that perform multiple actions which may occur simultaneously, or at differing levels of detail. A final set of real-world experiments characterizes the real-time performance of the proposed methodology in relation to several important system design parameters, such as the number of obstacles in the environment, and the size of the action library. Overall, it is concluded that the proposed system tangibly increases TVG action-sensing performance, and can be generalized to a wide range of applications, including human-action sensing. Future research is proposed to develop similar methods to address deformable objects and multiple objects of interest.

This dissertation describes a research study that investigated cooperative inquiry as a strategy for professional learning of inner-city school principals in a large urban centre in Western Canada. The study attempted to identify the central issues of concern and means of redress for school leaders in high-poverty communities, many of which focused on educational leadership, school management, the context of their schools within impoverished communities, and the challenges of personal well-being. The findings suggest that cooperative inquiry was an effective strategy in that the approach was participatory, democratic, empowering, life-enhancing, and fostered community-building among participants. The findings also suggest that the approach was effective in that it was grounded in the action research cycle of planning, action, observation, and reflection. The study further examined the use of dialogue as a means of constructing knowledge regarding these issues, and identified the ways in which such knowledge impacts upon the professional practice of these principals. Findings suggest that participants gained knowledge from each other, offered knowledge from others, constructed knowledge together as a group, and developed deeper understandings of their own perspectives. Findings also suggest that meaning is lost when dialogic interactions are transcribed into print. Thus, dialogue is a form of communication in and of itself, one that cannot simply be transformed into the written word without losing part of that dialogic essence. Further, this study posits that dialogue has unique power to be both a process for meaning making, as well as an ontological means of clarifying one’s own sense of reality. / October 2008

This study on engagement in environmental activism can offer valuable insights into how Ontario’s young people come to be responsible stewards of the earth. This research seeks to understand the narrative complexities put forth by teachers and students (Gr. 11-12) about the influence school plays for environmental activists. The teachers’ involvement with activism is mediated by students and the social networks that support their actions. The students’ involvement in action is influenced by teacher mentors, learning about/in the environment, and having a venue for activism. These findings suggest that in order to live up to Acting Today, Shaping Tomorrow schools should be seeking to have at least one environmentally literate teacher who wants to provide students with a venue for action. To assist the teachers and students with activism, there needs to be support for environmental action initiatives from the school administration and the community.