Abstract
We propose an approach to subgroup discovery using distribution rules (a kind of association rules with a probability distribution on the consequent) for numerical properties of interest. The objective interest of the subgroups is measured through statistical goodness of fit tests. Their subjective interest can be assessed by the data analyst through a visual interactive subgroup browsing procedure.

Abstract
In this paper we introduce distribution rules, a kind of association rules with a distribution on the consequent. Distribution rules are related to quantitative association rules but can be seen as a more fundamental concept, useful for learning distributions. We formalize the main concepts and indicate applications to tasks such as frequent pattern discovery, sub group discovery and forecasting. An efficient algorithm for the generation of distribution rules is described. We also provide interest measures, visualization techniques and evaluation.

Abstract
Many software systems can be viewed as organizational Systems, where the different components are seen as autonomous entities, interacting with each other, collaborating toward system's aims. In such systems we may not have full control over the behavior of all its components. Normative specification of an organizational system, provides a way of describing the norms that regulate the behavior of a system and of its components, stating how they are expected to behave, assuming however, that they may deviate from that ideal behavior. In this paper we use an action and deontic modal logic for the normative specification of organizational systems. This logical framework allows us to describe expected behavior of agents, detect non-ideal behavior and identify the agents that, direct or indirectly, are responsible for it. We argue that normative specification can be an useful tool to increase trust and security in complex computational systems and propose a responsibility-based trust concept.

Abstract
The computational requirements of a full physically-based global illumination solution are significant, currently precluding its solution on even a powerful modern PC in reasonable let alone real time. A key factor to consider if we are ever to achieve so-called "Realism in Real-Time", is that we are computing images for humans to look at. Although the human visual system is very good, it is by no means perfect. By understanding what the human does, or perhaps more importantly, does not see, enables us to save significant computation effort without any loss of perceptual quality of the resultant image. This paper describes the novel techniques of selective rendering which allow us to direct computational resources to those areas of high perceptual importance while avoiding computing any detail which will not be perceived by the viewer. Such selective rendering methods offer us the real possibility of achieving high fidelity graphics of complex scenes at interactive rates.

Abstract
Precomputed Radiance Transport (PRT) was introduced as a technique to enable interactive navigation and distant environmental real time relighting of rigid scenes. Evaluating radiance transport is, however, a computationally very demanding task, which precludes PRT's utilization during the model design phase, since the user must wait for long periods of time before being able to light and navigate within the model. This paper proposes and validates an approach to provide visual feedback to the user as soon as possible, within PRT context. By resorting to parallel processing and progressive refinement, the user is quickly presented with a lower lighting resolution of the virtual model. This is then progressively refined by incrementally increasing the number of incident directions taken into account on transport computations. PRT is, however, a complex algorithm that requires frequent collective communications of huge volumes of data, thus constraining the maximum achievable speedup on a parallel system. This issue is analysed and an alternative workload distribution is proposed and evaluated on a 12 node dual processor cluster. The final solution ensures a good resource utilization rate, reducing response times from dozens of seconds to a few hundred milliseconds. Copyright © 2006 by the Association for Computing Machinery, Inc.

Abstract
Physically based global illumination rendering at interactive frame rates would enable users to navigate within complex virtual environments, such as archaeological models. These algorithms, however, are computationally too demanding to allow interactive navigation on current PCs. A technique based on image subsampling and spatiotemporal coherence among successive frames is exploited, while resorting to progressive refinement whenever there is available computing power. A physically based ray tracer (Radiance) is used to compute reflected radiance at the model's triangles vertices. Progressive refinement is achieved increasing the sampling frequency by subdividing certain triangles and requesting shading information for the resulting vertices. This paper proposes and evaluates different criteria for selecting which triangles to subdivide. A random criterium and two criteria based on Normalized Luminance Differences are evaluated: one operating on image space, the other on object space. Results, obtained with a model of an old roman town, show that the object space criterium is able to locate and represent visual discontinuities, such as shadows, and does so requiring less triangle subdivisions than the other two.