Abstract
We propose a method for computing the similarity of symbolically-encoded Portuguese folk melodies. The main novelty of our method is the use of a preprocessing melodic reduction at multiple hierarchies to filter the surface of folk melodies according to 1) pitch stability, 2) interval salience, 3) beat strength, 4) durational accents, and 5) the linear combination of all former criteria. Based on the salience of each note event per criteria, we create three melodic reductions with three different levels of note retention. We assess the degree to which six folk music similarity measures at multiple reduction hierarchies comply with collected ground truth from experts in Portuguese folk music. The results show that SIAM combined with 75th quantile reduction using the combined or durational accents best models the similarity for a corpus of Portuguese folk melodies by capturing approximately 84-90% of the variance observed in ground truth annotations.

Abstract

Abstract
Strategic term re-writing and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies to apply term re-write rules in defining large-scale language transformations, while the latter is suitable to express context-dependent language processing algorithms. These two techniques can be expressed and combined via a powerful navigation abstraction: generic zippers. This results in a concise zipper-based embedding offering the expressiveness of both techniques. Such elegant embedding has a severe limitation since it recomputes attribute values. This paper presents a proper and efficient embedding of both techniques. First, attribute values are memoized in the zipper data structure, thus avoiding their re-computation. Moreover, strategic zipper based functions are adapted to access such memoized values. We have implemented our memoized embedding as the Ztrategic library and we benchmarked it against the state-of-the-art Strafunski and Kiama libraries. Our first results show that we are competitive against those two well established libraries.

Abstract
Digital Marketing sets a sequence of strategies responsible for maximizing the interaction between companies and their target audience. One of them, known as Customer Success, establishes long-term techniques capable of projecting the sustainable value of a given customer to a company, monitoring the indexers that translate its activities. Therefore, this paper intends to address the need to develop an innovative tool that allows the creation of a temporal knowledge base composed of the behavioral evolution of customers. The CRISP-DM model benefits the processing and modeling of data capable of generating knowledge through the application and combination of the results obtained by machine learning algorithms specialized in time series. Time Series K-Means allows the clustering and differentiation of consumers characterized by their similar habits. Through the formulation of profiles, it is possible to apply forecasting methods that predict the following trends. The proposed solution provides the understanding of time series that profile the flow of customer activity and the use of the evidenced dynamics for the future prediction of these behaviors. © MIR Labs, www.mirlabs.net/ijcisim/index.html

Abstract
Water bodies are complex and interconnected systems that play a crucial role in both our environment and our economy. Studying these water bodies is therefore essential but collecting and analyzing water samples can be challenging, particularly when dealing with large volumes of water. This article presents a system capable of autonomously filtering large volumes of water through standard marine biology filters and preserving them to later be analyzed. The system's preliminary results are presented in this paper.

Abstract
As the demand for electric charging accelerates, so does the stress on the relatively insufficient public charging infrastructure. To appropriately manage and scale charging infrastructure, there is a need for support tools capable of predicting the utilization and sales of charging stations, as well as the traffic flow of users from their original location to the charging stations. Therefore, this article proposes a generic methodology for infrastructure placement, namely forecasting demand and predicting its flow to the supply points. The methodology is applied in a case study to the electric charging grid of Portugal with real data, in the context of the needs of a particular charging point operator (CPO). Demand is first forecasted at a high-granularity level with a demand disaggregation model, followed by its capture by the grid of chargers using a parameterized gravity model. Validation is performed by comparing actual with predicted sales per charging station. Adequate visualizations to support decision-making are presented.