Flexible self-adjustment of task deployment in dynamic environments

This paper presents an investigation into dynamic self-adjustment of task deployment and other aspects of self-management, through the embedding of multiple policies. Non-dedicated loosely-coupled computing environments, such as clusters and grids are increasingly popular platforms for parallel processing. These abundant systems are highly dynamic environments in which many sources of variability affect the run-time efficiency of tasks. The dynamism is exacerbated by the incorporation of mobile devices and wireless communication. This paper proposes an adaptive strategy for the flexible run-time deployment of tasks; to continuously maintain efficiency despite the environmental variability. The strategy centres on policy-based scheduling which is informed by contextual and environmental inputs such as variance in the round-trip communication time between a client and its workers and the effective processing performance of each worker. A self-management framework has been implemented for evaluation purposes. The framework integrates several policy-controlled, adaptive services with the application code, enabling the run-time behaviour to be adapted to contextual and environmental conditions. Using this framework, an exemplar self-managing parallel application is implemented and used to investigate the extent of the benefits of the strategy

From ALIBI to COLUMBUS. The long march to self-aware, reflective computational models of humor

One thing is (a) to develop a system that handles some task to one's satisfaction, and also has a universally recognized myrthful side to its output. Another thing is (b) to provide an analysis of why you are getting such a byproduct. Yet another thing is (c) to develop a model that incorporates reflection about some phenomenon in humor for its own sake. This paper selects for discussion especially Alibi, going on to describe the preliminaries of Columbus. The former, which fits in (a), is a planner with an explanatory capability. It invents pretexts. It's no legal defense, but it is relevant to evidential thinking in AI & Law. Some of the output pretext are myrthful. Not in the sense they are silly: they are not. A key factor seems to be the very alacrity at explaining out detail after detail of globally damning evidence. I attempt a reanalysis of Alibi in respect of (b). As to Columbus, it fits instead in (c). We introduce here the basics of this (unimplemented) model, developed to account for a sample text in parody.

Reading the landscape: the development of a typology of literary trails to inform experiential design

This article explores the under-researched field of self-guided trails. The focus of the research is on the experiential aspects of self-guided literary trails from the perspective of both the developer and user. An examination of existing literature on self-guided trails and literary tourism was undertaken and supplemented with a review of experiential design principles. Content analysis of a sample of literary heritage trails was then carried out and three distinctive typologies were developed, informed by aspects of experiential design. The research reveals that few literary trails developers utilise these principles and the article concludes with proposals for the design of more effective literary trail experiences.