An architecture for a collaborative authoring system

Collaborative working with the aid of computers is increasing rapidly due to the widespread use of computer networks, geographic mobility of people, and small powerful personal computers. For the past ten years research has been conducted into this use of computing technology from a wide variety of perspectives and for a wide range of uses. This thesis adds to that previous work by examining the area of collaborative writing amongst groups of people. The research brings together a number of disciplines, namely sociology for examining group dynamics, psychology for understanding individual writing and learning processes, and computer science for database, networking, and programming theory. The project initially looks at groups and how they form, communicate, and work together, progressing on to look at writing and the cognitive processes it entails for both composition and retrieval. The thesis then details a set of issues which need to be addressed in a collaborative writing system. These issues are then followed by developing a model for collaborative writing, detailing an iterative process of co-ordination, writing and annotation, consolidation, and negotiation, based on a structured but extensible document model. Implementation issues for a collaborative application are then described, along with various methods of overcoming them. Finally the design and implementation of a collaborative writing system, named Collaborwriter, is described in detail, which concludes with some preliminary results from initial user trials and testing.

A systems approach to public sector budgeting: the case of the planning, programming, budgeting system for the health and personal social services

The aim of this thesis is to examine the specific contextual factors affecting the applicability and development of the planning, programming, budgeting system (P.P.B.S.) as a systems approach to public sector budgeting. The concept of P.P.B.S. as a systems approach to public sector budgeting will first be developed and the preliminary hypothesis that general contextual factors may be classified under political, structural and cognitive headings will be put forward. This preliminary hypothesis will be developed and refined using American and early British experience. The refined hypothesis will then be tested in detail in the case of the English health and personal social services (H.P.S.S.), The reasons for this focus are that it is the most recent, the sole remaining, and the most significant example in British central government outside of defence, and is fairly representative of non-defence government programme areas. The method of data collection relies on the examination of unpublished and difficult to obtain central government, health and local authority documents, and interviews with senior civil servants and public officials. The conclusion will be that the political constraints on, or factors affecting P.P.B.S., vary with product characteristics and cultural imperatives on pluralistic decision-making; that structural constraints vary with the degree of coincidence of programme and organisation structure and with the degree of controllability of the organisation; and finally, that cognitive constraints vary according to product characteristics, organisational responsibilities, and analytical effort.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.

From phenomenography study to planning teaching

A phenomenographic study uncovers variations in the way that the subjects are aware of a phenomenon. In the categories of description that represent the variations in awareness there are features that, through their variation, define the characteristics of the categories. Teaching seeks to foster a change in the way that the learner is aware of a phenomenon through opening up a space of learning. This paper outlines the way that the outcome spaces from a phenomenographic study can be used to plan a teaching programme that utilises variations in the features. It discusses a strategy for teaching programming based on a phenomenographic study of practitioner conceptions of an object-oriented program. The strategy covers features related to the nature of an object-oriented program. Copyright 2010 ACM.