In the last few decades, there has been growing attention to situated or distributed perspectives on learning and cognition. The purpose of this study was to examine science learning in classroom settings through the lens of distributed cognition. A particular focus of this study was on the public space of interaction that includes participants' interactions with each other and with artefacts in the environment. / Focusing on the event of student experiment design, two science lessons were videotaped in this study, in which a class of Grade-seven participants was asked to investigate the scientific theme of gravity by designing parachutes and pendulums. The video-stimulated post-lesson interviews with both teacher and student provided complementary data in order to understand their practice in these lessons. / The analysis of two science lessons reveals the different functions of language, gestures, and material objects and their relative significance in the process of student meaning making and knowledge construction. It shows that (1) the language of science is best understood as an artefact employed by the participants to achieve mutual understanding; (2) gestures and other forms of non-verbal acts build the connections between the conceptual and the physical worlds, and provided perceptual resources that foregrounded the salient aspects of their environment; and (3) material objects helped the students to understand each other by disambiguating references to objects, but (4) material objects constrained student sense-making. The analysis also demonstrated that (5) the learning activity was enacted through the participants' deployment of a range of artefacts, and (6) the manipulation of conceptual artefacts was interdependent of the manipulation of material objects. / Building on the theoretical framework of distributed cognition, this study was able to document the students' learning processes by investigating classroom interactions in great detail. The findings and techniques resulting from this study will help teachers and researchers to achieve a better understanding of science learning in classrooms and the role of artefacts in this learning and assist them to improve the learning environments.

Distributed denial of service attacks (or DDoS) are a common occurrence on the internet and are becoming more intense as the bot-nets, used to launch them, grow bigger. Preventing or stopping DDoS is not possible without radically changing the internet infrastructure; various DDoS mitigation techniques have been devised with different degrees of success. All mitigation techniques share the need for a DDoS detection mechanism. DDoS detection based on traffic self-similarity estimation is a relatively new approach which is built on the notion that undis- turbed network traffic displays fractal like properties. These fractal like properties are known to degrade in presence of abnormal traffic conditions like DDoS. Detection is possible by observing the changes in the level of self-similarity in the traffic flow at the target of the attack. Existing literature assumes that DDoS traffic lacks the self-similar properties of undisturbed traffic. We show how existing bot- nets could be used to generate a self-similar traffic flow and thus break such assumptions. We then study the implications of self-similar attack traffic on DDoS detection. We find that, even when DDoS traffic is self-similar, detection is still possible. We also find that the traffic flow resulting from the superimposition of DDoS flow and legitimate traffic flow possesses a level of self-similarity that depends non-linearly on both relative traffic intensity and on the difference in self-similarity between the two incoming flows.

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed.

Thesis (M.S.)--Ohio University, June, 2000. / Title from PDF t.p.

Thesis (Ph.D.) - University of Glasgow, 2008. / Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

Thesis (M. App. Sc.)--Carleton University, 2003. / Includes bibliographical references (p. 99-102). Also available in electronic format on the Internet.

Thesis (Ph.D.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 117-126)

Project (M.Eng.) - Simon Fraser University, 2004. / Theses (School of Engineering Science) / Simon Fraser University.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 49-58). Also available on the World Wide Web.

Thesis (Ph. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains ix, 141 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 136-141).