1081081
Modelling the learning transaction
A model of learning transactions is offered for use in the development of e-learning materials and toolkits. The model identifies the key components required for effective learning, and in doing so, helps the development team ensure the provision of these components to the teacher or learner.
14588645
Community Tools for Repurposing Learning Objects
A critical success factor for the reuse of learning objects is the ease by which they may be repurposed in order to enable reusability in a different teaching context from which they were originally designed. The current generation of tools for creating, storing, describing and locating learning objects are best suited for users with technical expertise. Such tools are an obstacle to teachers who might wish to perform alterations to learning objects in order to make them suitable for their context. In this paper we describe a simple set of tools to enable practitioners to adapt the content of existing learning objects and to store and modify metadata describing the intended teaching context of these learning objects. We are deploying and evaluating these tools within the UK language teaching community.
46393116
Learning Approximate MRFs from Large Transaction Data
In this paper we consider the problem of learning approximate Markov Random Fields (MRFs) from large transaction data. We rely on frequent itemsets to learn MRFs on the data. Since learning exact large MRFs is generally intractable, we resort to learning approximate MRFs. Our proposed modeling approach first employs graph partitioning to cluster variables into balanced disjoint partitions, and then augments important interactions across partitions to capture interdependencies across them. A novel treewidth based augmentation scheme is proposed to boost performance. We learn an exact local MRF for each partition and then combine all the local MRFs together to derive a global model of the data. A greedy approximate inference scheme is developed on this global model. We demonstrate the use of the learned MRFs on the selectivity estimation problem. Empirical evaluation on real datasets demonstrates the advantage of our approach over extant solutions.
33976422
Concurrent product/process design with multiple representations of parts
The need for concurrency in design and manufacturing is supported in a sheet metal design system by doing preliminary process design at the detail product design stage. This design with features approach represents the evolving part form to the designer in multiple process domains and at multiple stages of a sequential process. Each part of part form representations defines the conceptualized process which transforms it from one to the other. At this modeling level, processes are reversible so that design activity can take place in any of the domains and be transferred to the others. This concept is fully implemented in a sheet metal design/manufacturing system in which preliminary process design occurs concurrently with product design and the normal representation ambiguities of wireframe and flat panel models are eliminated.<<ETX>>
7101460
On the Difference Method for Drawing Conic Arcs
Abstract The difference method is an efficient way to generate an inscribed polygon of a conic section. We address the implementation issues of the difference method, that is, how to choose the difference parameter and initial points so that the inscribed polygon has a minimal number of line segments and is within a given tolerance of the conic arc. We also discuss how the difference method can be used to draw conic arcs represented by rational quadratic Bezier curves.
600527
Learning from BDDs in SAT-based bounded model checking
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity as an alternative to BDD-based model checking techniques for finding bugs in large designs. In this paper, we explore the use of learning from BDDs, where learned clauses generated by BDD-based analysis are added to the SAT solver, to supplement its other learning mechanisms. We propose several heuristics for guiding this process, aimed at increasing the usefulness of the learned clauses, while reducing the overheads. We demonstrate the effectiveness of our approach on several industrial designs, where BMC performance is improved and the design can be searched up to a greater depth by use of BDD-based learning.
13998980
Image annotation refinement using random walk with restarts
Image annotation plays an important role in image retrieval and management. However, the results of the state-of-the-art image annotation methods are often unsatisfactory. Therefore, it is necessary to refine the imprecise annotations obtained by existing annotation methods. In this paper, a novel approach to automatically refine the original annotations of images is proposed. On the one hand, for Web images, textual information, e.g. file name and surrounding text, is used to retrieve a set of candidate annotations. On the other hand, for non-Web images that are lack of textual information, a relevance model-based algorithm using visual information is used to decide the candidate annotations. Then, candidate annotations are re-ranked and only the top ones are reserved as the final annotations. To re-rank the annotations, an algorithm using Random Walk with Restarts (RWR) is proposed to leverage both the corpus information and the original confidence information of the annotations. Experimental results on both non-Web images of Corel dataset and Web images of photo forum sites demonstrate the effectiveness of the proposed method.