Nasby's lament : over the New York nominations /

Mode of access: Internet.From the Thomas A. Edison Collection of American Sheet Music

Activity-based scenarios for and approaches to ubiquitous e-Learning

This paper presents scenarios for ubiquitous e-Learning in heterogeneous networks. It concludes by arguing for the development of a learning-focused analogue, activity-based e-Learning extensions (ABLE), of activity-based computing (ABC). The goal would be to offer the learning-support/performance-support equivalent of ABC’s support for human activities in a ubiquitous computing environment, relevant to areas that are hard to model today: informal on-the-job learning; peer-to-peer support and informal sharing of content in ad hoc work groups; formal and informal ways to capture and share knowledge-focused insights and processes; content and systems to aid reflection. Just as ABC supplements traditional computing approaches (in ABC, data- and application-oriented) to suit ‘multiple, parallel and mobile work activities’ (Bardram et al. in Support for ABC in a personal computing operating system. CHI 2006 proceedings. Montréal, Québec, Canada, 22–27 April 2006, pp 211–220), so ABLE could supplement traditional e-learning approaches (often largely content-focused, sometimes little more than page-turning) to suit those same work activities, and make e-Learning potentially more resilient to interruptions, more fun and more memorable

Evaluating the quality of discovered process models

In the domain of process mining the evaluation of models (i.e., \How can we measure the quality of a mined process model?") is still subject to ongoing research. Because the types of models used in process mining are typically on a higher level of abstraction (they, for example, allow to capture concurrency), the problem of model evaluation is challenging. In this paper, we elaborate on the problem of process model evaluation, and we evaluate both new and existing fitness metrics for different levels of noise. The new metrics and the noise generation are based on Hidden Markov Models (HMMs)

Typed meta-interpretive learning of logic programs

Meta-interpretive learning (MIL) is a form of inductive logic programming that learns logic programs from background knowledge and examples. We claim that adding types to MIL can improve learning performance. We show that type checking can reduce the MIL hypothesis space by a cubic factor. We introduce two typed MIL systems: Metagol T and HEXMIL T , implemented in Prolog and Answer Set Programming (ASP), respectively. Both systems support polymorphic types and can infer the types of invented predicates. Our experimental results show that types can substantially reduce learning times