Abstract
Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries "Golden Globe Award", "September 11th" or "Halloween", which refer to seasonal events that occur or have occurred at a specific occasion and for which, people often search in a planned and cyclic manner. Understanding this seasonal behavior, may help search engines to provide better ranking approaches and to respond with temporally relevant results leading into user's satisfaction. Detecting the diverse types of seasonal queries is therefore a key step for any search engine looking to present accurate results. In this paper, we categorize web search queries by their seasonality into 4 different categories: Non-Seasonal (NS, e.g., "Secure passwords"), Seasonal-related to ongoing events (SOE, "Golden Globe Award"), Seasonal-related to historical events (SHE, e.g., "September 11th") and Seasonal-related to special days and traditions (SSD, e.g., "Halloween"). To classify a given query we extract both time series (using the document publish date) and content features from its relevant documents. A Random Forest classifier is then used to classify web queries by their seasonality. Our experimental results show that they can be categorized with high accuracy. © 2017 Copyright held by the owner/author(s).

Abstract
Neste artigo, é apresentada uma pesquisa-ação com o objetivo de motivar os alunos a desenvolverem suas aprendizagens de programação de computadores no ensino superior, particularmente na transição da programação de nível iniciante para a programação avançada. Para alcançar este objetivo, foi desenvolvida uma abordagem motivacional denominada SimProgramming. A partir das reflexões sobre o processo desta pesquisa, conclui-se que SimProgramming em sua aplicação ao ensino de programação de computadores em turmas intermediárias é promissor e ainda apresenta potencial para ser usado em outros contextos educacionais.;In this paper, an action research is presented to motivate students to develop their learning of computer programming in higher education, particularly in the transition from beginner to advanced programming. To achieve this goal, a motivational approach was developed called SimProgramming. From the reflections on the process of this research, it is concluded that SimProgramming in its application to the teaching of computer programming in intermediate classes is promising and still presents potential to be used in other educational contexts.

Abstract

Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and scalable hash trie map design that fully supports the concurrent search, insert and remove operations on hash maps. To the best of our knowledge, our proposal is the first concurrent hash map design that puts together the following characteristics: (i) be lock-free; (ii) use fixed size data structures; and (iii) maintain the access to all internal data structures as persistent memory references. Experimental results show that our proposal is quite competitive when compared against other state-of-the-art proposals implemented in Java. Its design is modular enough to allow different types of configurations aimed for different performances in memory usage and execution time. © 2017 IEEE.

Abstract
We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of difference-of-saturation (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions.

Abstract
The combination of information obtained from data mining technique from physicochemical and organoleptic data analysis allowed similarities between the wines of the nine sub-regions in the Demarcated Region of Vinho Verde. Through clustering techniques, four clusters were identified, each characterized by its centroid. The measure of information gain, together with supervised rule-based learning, was used to find the differentiating characteristics. This study allowed the interconnection of the characteristics of the wines of these sub-regions, which can improve the decision making on the profiles of these same wines.