Abstract
In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and chi(2). We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.

Abstract

Abstract
The computational requirements of full global illumination rendering are such that it is still not possible to achieve high-fidelity graphics of very complex scenes in a reasonable time on a single computer. By identifying which computations are more relevant to the desired quality of the solution, selective rendering can significantly reduce rendering times. In this paper we present a novel component-based selective rendering system in which the quality of every image, and indeed every pixel, can be controlled by means of a component regular expression (crex). The crex provides a flexible mechanism for controlling which components are rendered and in which order. It can be used as a strategy for directing the light transport within a scene and also in a progressive rendering framework. Furthermore, the crex can be combined with visual perception techniques to reduce rendering computation times without compromising the perceived visual quality. By means of a psychophysical experiment we demonstrate how the crex can be successfully used in such a perceptual rendering framework. In addition, we show how the crex's flexibility enables it to be incorporated in a predictive framework for time-constrained rendering. Copyright © 2005 by the Association for Computing Machinery, Inc.

Abstract
In this paper we propose a novel probabilistic broadcast protocol that reduces the average end-to-end latency by dynamically adapting to network topology and traffic conditions. It does so by using an unique strategy that consists in adjusting the fanout and preferred targets for different gossip rounds as a function of the properties of each node. Node classification is light-weight and integrated in the protocol membership management. Furthermore, each node is not required to have full knowledge of the group membership or of the network topology. The paper shows how the protocol can be configured and evaluates its performance with a detailed simulation model.

Abstract
In this paper we propose the mutable consensus protocol, a pragmatic and theoretically appealing approach to enhance the performance of distributed consensus. First, an apparently inefficient protocol is developed using the simple stubborn channel abstraction for unreliable message passing. Then, performance is improved by introducing judiciously chosen finite delays in the implementation of channels. Although this does not compromise correctness, which rests on an asynchronous system model, it makes it likely that the transmission of some messages is avoided and thus the message exchange pattern at the network level changes noticeably. By choosing different delays in the underlying stubborn channels, the mutable consensus protocol can actually be made to resemble several different protocols. Besides presenting the mutable consensus protocol and four different mutations, we evaluate in detail the particularly interesting permutation gossip mutation, which allows the protocol to scale gracefully to a large number of processes by balancing the number of messages to be handled by each process with the number of communication steps required to decide. The evaluation is performed using a realistic simulation model which accurately reproduces resource consumption in real systems.

Abstract
Version vectors play a central role in update tracking under optimistic distributed systems, allowing the detection of obsolete or inconsistent versions of replicated data. Version vectors do not have a bounded representation; they are based on integer counters that grow indefinitely as updates occur. Existing approaches to this problem are scarce; the mechanisms proposed are either unbounded or operate only under specific settings. This paper examines version vectors as a mechanism for data causality tracking and clarifies their role with respect to vector clocks. Then, it introduces bounded stamps and proves them to be a correct alternative to integer counters in version vectors. The resulting mechanism, bounded version vectors, represents the first bounded solution to data causality tracking between replicas subject to local updates and pairwise symmetrical synchronization.