874 research outputs found
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A Model of Purpose-driven Analogy and Skill Acquisition In Programming
X is a production system model of the acquisition of programming skill. Skilled programming is modelled by the goal-driven application of production rules (productions). Knowledge compilation mechanisms produce new productions that summarize successful problem solving experiences. Analogical problem solving mechanisms use representations of example solutions to overcome problem solving impasses. The interaction of these two mechanisms yields productions that generalize over example and target problem solutions.Simulations of subjects learning to program recursive functions are presented to illustrate the operation of X
Optimization Techniques for Image Restoration
Many fields of study use images to make discoveries about the past, decisions for the present and predictions for the future. Images often acquire degradations such as a blur due to a patient moving during an x-ray or noise picked up through remote sensing imaging equipment. Images may also lose information through compression or
transmission. In this thesis, diffusion based models were used to solve the image restoration problem as these models can simultaneously remove noise, preserve edges and restore lost information. Specifically, numerical schemes were developed and tested for denoising via nonstandard diffusion that are more computationally efficient than the current method. Furthermore, a new model for digital inpainting is proposed based on the nonstandard diffusion model. Numerical results illustrate the effectiveness of both the denoising and inpainting models in image restoration
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Empirical Analyses of Self-Explanation and Transfer in Learning to Program
Building upon recent work on production system models of transfer and analysis-based generalization techniques, we present analyses of three studies of learning to program recursion. In Experiment 1, a production system model was used to identify problem solving that involved previously acquired skills or required novel solutions. A mathematical model based on this analysis accounts for inter-problem transfer. Programming performance was also affected by particular examples presented in instruction. Experiment 2 examined these example effects in finer detail. Using a production system analysis, examples were found to affect the initial error rates, but not the learning rates on cognitive skills. Experiment 3 examined relations between the ways in which people explain examples to themselves and subsequent learning. Results suggest that good learners engage in more metacognition, generate more domain-specific elaborations of examples, make connections between examples and abstract text, and focus on the semantics of programs rather than syntax
An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval
This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR
Using Markov Chains for link prediction in adaptive web sites
The large number of Web pages on many Web sites has raised
navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past
visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability
matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site
Investigating people: a qualitative analysis of the search behaviours of open-source intelligence analysts
The Internet and the World Wide Web have become integral parts of the lives of many modern individuals, enabling almost instantaneous communication, sharing and broadcasting of thoughts, feelings and opinions. Much of this information is publicly facing, and as such, it can be utilised in a multitude of online investigations, ranging from employee vetting and credit checking to counter-terrorism and fraud prevention/detection. However, the search needs and behaviours of these investigators are not well documented in the literature. In order to address this gap, an in-depth qualitative study was carried out in cooperation with a leading investigation company. The research contribution is an initial identification of Open-Source Intelligence investigator search behaviours, the procedures and practices that they undertake, along with an overview of the difficulties and challenges that they encounter as part of their domain. This lays the foundation for future research in to the varied domain of Open-Source Intelligence gathering
'Datafication': Making sense of (big) data in a complex world
This is a pre-print of an article published in European Journal of Information Systems. The definitive publisher-authenticated version is available at the link below. Copyright @ 2013 Operational Research Society Ltd.No abstract available (Editorial
An Editor for Helping Novices to Learn Standard ML
This paper describes a novel editor intended as an aid in the learning of the functional programming language Standard ML. A common technique used by novices is programming by analogy whereby students refer to similar programs that they have written before or have seen in the course literature and use these programs as a basis to write a new program. We present a novel editor for ML which supports programming by analogy by providing a collection of editing commands that transform old programs into new ones. Each command makes changes to an isolated part of the program. These changes are propagated to the rest of the program using analogical techniques. We observed a group of novice ML students to determine the most common programming errors in learning ML and restrict our editor such that it is impossible to commit these errors. In this way, students encounter fewer bugs and so their rate of learning increases. Our editor, C Y NTHIA, has been implemented and is due to be tested on st..
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