Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.

Abstract
IdSay is an open domain Question Answering system for Portuguese that was developed from scratch. Its current version can be considered a baseline version, using mainly techniques from the area of Information Retrieval. The only external information that it uses besides the text collections is lexical information for Portuguese. It was submitted to the monolingual Portuguese task of the Question Answering track of the Cross-Language Evaluation Forum 2008 (QA@CLEF) for the first time, in which it answered correctly to 65 of the 200 questions in the first answer, and to 85 answers considering the three answers that could be returned per question. Generally, the types of questions that are answered better by IdSay system are measure factoids, count factoids and definitions, but there is still work to be done in these areas, as well as in the treatment of time. List questions, location and people/organization factoids are the types of question with more room for evolution.

Abstract
Language resources that include semantic equivalences at word level are common, and its usefulness is well established in text processing applications, as in the case of search. Named entities also play an important role for text based applications, but are not usually covered by the previously mentioned resources. The present work describes the WES base, Wikipedia Entity Synonym base, a freely available resource based on the Wikipedia. The WES base was built for the Portuguese Language, with the same format of another freely available thesaurus for the same language, the TeP base, which allows integration of equivalences both at word level and entity level. The resource has been built in a language independent way, so that it can be extended to different languages. The WES base was used in a Question Answering system, enhancing significantly its performance.

Abstract
IdSay is an open domain Question Answering (QA) system for Portuguese. Its current version can be considered a baseline version, using mainly techniques from the area of Information Retrieval (IR). The only external information it uses besides the text. collections is lexical information for Portuguese. It was submitted to the monolingual Portuguese task of the QA track of the Cross-Language Evaluation Forum 2008 (QA@CLEF) for the first time, and it answered correctly to 65 of the 200 questions in the first answer, and to 85 answers considering the three answers that could be returned per question. Generally, the types of questions that are answered better by IdSay system are measure factoids, Count factoids and definitions, but there is still work to be done in these areas, as well as in the treatment of time. List questions, location and people/organization factoids are the types of question with more room for improvement.

Abstract
A mixed corpus of Portuguese is one in which texts of different origins produce different spelling variants for the same word. A new norm, which will bring together the written texts produced both in Portugal and Brazil, giving then a more uniform orthography, has been effective since 2009, but what happens in the perspective of search, to corpora created before the norm came into practice, or within the transition period? Is the information they contain outdated and worthless? Do they need to be converted to the new norm? In the present work we analyse these questions. © 2012 Springer-Verlag.