Abstract
We are developing a new method for the identification of rib boundaries in chest x-ray images. The identification of rib boundaries is important for radiologist diagnosis of lung diseases as TB. The radiologists use the ribs as reference for location and can be used to eliminate false positives in the detection of abnormalities. Our method automatically identifies rib boundaries from raw images through a sequence of steps using a combination of image processing techniques. Radiographs are still very relevant in practice because in Portugal and many other countries it is the first step for TB detection. We have access a large database of x-ray images provided by the pneumological screening centre (CDP) of Vila Nova de Gaia, in Portugal.

Abstract
We are developing a new method for the identification of rib boundaries in chest x-ray images. The identification of rib boundaries is important for radiologist diagnosis of lung diseases as TB. The radiologists use the ribs as reference for location and can be used to eliminate false positives in the detection of abnormalities. Our method automatically identifies rib boundaries from raw images through a sequence of steps using a combination of image processing techniques. Radiographs are still very relevant in practice because in Portugal and many other countries it is the first step for TB detection. We have access a large database of x-ray images provided by the pneumological screening centre (CDP) of Vila Nova de Gaia, in Portugal.

Abstract
Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that enables the use of common two-dimensional top-N recommender algorithms for the generation of recommendations using additional dimensions (e.g., contextual or background information). We empirically evaluate our approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, on two real world data sets. The empirical results demonstrate that DaVI enables the application of existing two-dimensional recommendation algorithms to exploit the useful information in multidimensional data. © 2011 IEEE.

Abstract
Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Abstract
In the last few years, a huge amount of temporal written information has become widely available on the Internet with the advent of forums, blogs and social networks. This gave rise to a new challenging problem called future retrieval, which consists of extracting future temporal information, that is known in advance, from web sources in order to answer queries that combine text of a future temporal nature. This paper aims to confirm whether web snippets can be used to form an intelligent web that can detect future expected events when their dates are already known. Moreover, the objective is to identify the nature of future texts and understand how these temporal features affect the classification and clustering of the different types of future-related texts: informative texts, scheduled texts and rumor texts. We have conducted a set of comprehensive experiments and the results show that web documents are a valuable source of future data that can be particularly useful in identifying and understanding the future temporal nature of a given implicit temporal query.

Abstract
The World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all implicit temporal queries, i.e., queries without any date but with an underlying temporal intent. In this research, we aim at studying the temporal nature of any given query by means of web snippets or web query logs. For that purpose, we conducted a set of experiments, which goal is to assess the percentage of web snippets or queries (in query logs) having temporal features, thus checking whether they are a valuable source of data to help on inferring the temporal intent of queries, namely implicit ones. Our results show that web snippets, as opposed to web query logs, are an important source of concentrated information, where time clues often appear. As a consequence, they can be particularly useful to identify and understand "on-the-fly" the implicit temporal nature of queries in the context of ephemeral clustering.