Abstract
Considering the characteristics of biological sequence databases, which typically have a small alphabet, a very long length and a relative small size (several hundreds of sequences), we propose a new sequence mining algorithm (gIL). gIL was developed for linear sequence pattern mining and results from the combination of some of the most efficient techniques used in sequence and itemset mining. The algorithm exhibits a high adaptability, yielding a smooth and direct introduction of various types of features into the mining process, namely the extraction of rigid and arbitrary gap patterns. Both breadth or a depth first traversal are possible. The experimental evaluation, in synthetic and real life protein databases, has shown that our algorithm has superior performance to state-of-the art algorithms. The use of constraints has also proved to be a very useful tool to specify user interesting patterns.

Abstract
This paper presents extensions to Spec Explorer to automate the testing of software applications through their GUIs based on a formal specification in Spec. Spec Explorer, a tool developed at Microsoft Research, already supports automatic generation and execution of test cases for API testing, but requires that the actions described in the model are bound to methods in a Net assembly. The tool described in this paper extends Spec Explorer to automate GUI testing: it adds the capability to gather information about the physical CUI objects that are the target of the user actions described in the model; and it automatically generates a Net assembly with methods that simulate those actions upon the GUI application under test. The GUI modelling and the overall test process supported by these tools are described. The approach is illustrated with the Notepad application.

Abstract
Recent work points towards advantages in decomposing multi-class decision problems into multiple binary problems. There are several strategies for this decomposition. The most used and studied are All-vs-All, One-vs-All and the Error correction output codes (Ecocs). Ecocs appeared in the scope of telecommunications thanks to the capacity to correct transmission errors. This capacity is due to introducing redundancy when codifying messages. Ecocs are binary words and can be adapted to be used in classifications problems. They must, however, respect some specific constraints. The binary words must be further apart as much as possible. Equal or complementary columns cannot exist and no column can be constant (either 1 or 0). Given two ecocs satisfying these constrains, which one is more appropriate for classification purposes? In this work we suggest a function for evaluating the quality of Ecocs. This function is used to guide the search in the persecution algorithm, a new method to generate Ecocs for classifications purposes. The binary words that form the Ecocs can have several dimensions for the same number of classes that it intends to represent. The growth of these possible dimensions is exponential with the number of classes of the multi-class problem. In this paper we present a method to choose the dimension of the Ecoc that assure a good tradeoff between redundancy and error correction capacity. The method is evaluated in a set of benchmark classification problems. Experimental results are competitive against standard decomposition methods.

Abstract
In this paper, we present a novel smart composite based on single mode optical fibres embedded in a hybrid composite laminated. This smart composite comprehended three optical fibres: an optical fibre positioned between two layers of carbon fibres; other optical fibre embedded in two layers of glass fibres; and another optical fibre inserted between the two different composite laminates. Due to cure process using hot plate press, different optical attenuations were obtained for the three optical fibres. The optical fibre positioned between the two different layers (carbon/glass) presented higher losses when compared with the two other optical fibres embedded between equal types of layers. The losses result from the different diameter of carbon/glass and the different coefficient of thermal expansion of the composite material. The smart composite was characterised in terms of its sensitivity to temperature and pressure, independently. Using a matrix method, it was possible to discriminate the pressure and the temperature with only one measurement. Maximum errors of 2.45 degrees C and 0.6 kN/m(2) were found to 60 degrees C and 2500 kN/m(2) measurement ranges.

Abstract
In this paper we propose a new method to perform incremental discretization. This approach consists in splitting the task in two layers. The first layer receives the sequence of input data and stores statistics of this data, using a higher number of intervals than what is usually required. The final discretization is generated by the second layer, based on the statistics stored by the previous layer. The proposed architecture processes streaming examples in a single scan, in constant time and space even for infinite sequences of examples. We demonstrate with examples that incremental discretization achieves better results than batch discretization, maintaining the performance of learning algorithms. The proposed method is much more appropriate to evaluate incremental algorithms, and in problems where data flows continuously as most of recent data mining applications.

Abstract
The hierarchical structuring of a classified image with N classes provides a set of solutions for a classification problem, with N, N-1,..., 2 classes. The visual analysis of this set of images requires that a consistent color indexing is available for the whole structure. This paper addresses this issue, proposing a number of methods for the automatic assignment of lookup tables for the classified images at the various levels of a hierarchical structure. The various strategies are compared and some methods illustrated with a section of a Landsat TM image classified in 15 classes.