Abstract
In this paper we describe an algorithm that produces a post mission estimate of the spatial evolution of the Isurus AUV. To make this post processing possible, the navigation system records on the vehicle logging system all the navigation data received during the mission execution. The data comprise the depth of the vehicle, the outputs of the tilt sensors and digital compass, the angular velocity of the propeller, as well as acoustic range measurements to a set of transponders. After mission completion, the logged data is then processed to produce the estimate of the evolution of the vehicle. The algorithm used to process this data is based on a fixed interval nonlinear stochastic smoothing scheme and produces an estimate that evolves continuously in time. For each instant of time, the post mission position estimate is based on all the information collected during the mission, as opposed to real time estimates that can only take into account past data.

Abstract
SINUP consists of a geographical information system whose purpose is to store, in a coherent manner, data resulting from key activities of Oporto local authority, allowing to better structure the knowledge about the urban reality. In the possession of such knowledge, and with the revision of Oporto's Municipal Master Plan taking place soon, the municipality is making an effort to develop an electronic citizen service that will allow a large number of citizens to consult it, and more important, participate in its public discussion prior to approval thus creating a major instrument of e-democracy in Oporto's municipality.

Abstract
Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability class distribution associated with each training example. In this paper we argue that Iterative Bayes minimizes a quadratic loss function instead of the 0-1 loss function that usually applies, to classification problems. Experimental evaluation of Iterative Bayes on 27 benchmark data sets shows consistent gains in accuracy. An interesting side effect of our algorithm is that it shows to be robust to attribute dependencies.

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Abstract
The holonic manufacturing paradigm allows a new approach to the emergent requirements faced by the manufacturing world, through the concepts of modularity, decentralisation, autonomy, re-use of control software components. The formal modelling and validation of the structural and behavioural specifications of holonic control systems assumes a critical role. This paper discusses the formal validation of the Petri Net models designed to represent the behaviour and specifications of the holon classes defined at ADACOR architecture.