Abstract
Nowadays there are specific video equipments which allow acquisition at very high frame rates, e.g., up to 5000 fps. When observing a high frame rate sequence, it becomes noticeable that a great percentage of the frames' pixels values do not suffer great changes along the sequence. Hence, if a pixel's value remains the same, then a unique sample is enough to represent that particular pixel over several frames. Only pixels that suffer great variations require a higher number of samples to be correctly represented. This work reports a methodology that combines compression and reconstruction techniques to evaluate an alternative to the traditional compression methods 25/30 fps. In the proposed method, sets of pixels are extracted along the temporal direction and by exploiting the high temporal correlation of the signals, a proper decimation process is applied in order to create a sequence with a lower number of frames and consequently smaller rile size. The main objective is to propose a new compression methodology that will take advantage of the signal characteristics using both compression and reconstruction techniques.

Abstract
Companies use multiple solutions of information technologies and information systems to support their activities. Among the systems adopted by companies we can find Enterprise Resource Planning systems, Customer Relationship Management systems, Business Intelligence systems and many others. Such systems assure a wide spectrum of activities, ranging from operational support to the strategic support of the company. With the purpose of identifying and characterizing which systems the large Portuguese companies have been implementing, in which systems they intend to invest in short-term and which is the current role of information technology in the organization, we developed a study with the participation of several chief information officers. The findings of this study reveal that in Portuguese large companies the adoption of Enterprise Resource Planning systems are consolidated, while the adoption of systems like Business Intelligence, Collaboration, Workflow and Groupware will increase in the near future.

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Abstract
Question Answering (QA) has been an area of interest for researchers, in part motivated by the international QA evaluation forums, namely the Text REtrieval Conference (TREC), and more recently, the Cross Language Evaluation Forum (CLEF) through QA@CLEF, that since 2004 includes the Portuguese language. In these forums, a collection of written documents is provided, as well as a set of questions, which are to be answered by the participating systems. Each system is evaluated by its capacity to answer the questions, as a whole, and there are relatively few results published that focus on the performance of its different components and their influence on the overall system performance. That is the case of the Information Retrieval (IR) component, which is broadly used in QA systems. Our work concentrates on the different options of preprocessing Portuguese text before feeding it to the IR component, evaluating their impact on the IR performance in the specific context of QA, so that we can make a sustained choice of which options to choose. From this work we conclude the clear advantage of the basic preprocessing techniques: case folding and removal of punctuation marks. For the other techniques considered, stop word removal enhanced the performance of the IR system but that was not the case as far as Stemming and Lemmatization are concerned. © 2007 ACM.

Abstract
Automatic morphosyntactic tagging of corpora is usually imperfect. Wrong or strange tagging may be automatically repeated following some patterns. It is usually hard to manually detect all these errors, as corpora may contain millions of tags. This paper presents an approach to detect sequences of part-of-speech tags that have an internal cohesiveness in corpora. Some sequences match to syntactic chunks or correct sequences, but some are strange or incorrect, usually due to systematically wrong tagging. The amount of time spent in separating incorrect bigrams and trigrams from correct ones is very small, but it allows us to detect 70% of all tagging errors in the corpus.