Abstract
Automatic Identification System data has been used in several studies with different directions like traffic forecasting, pollution control or anomalous behavior detection in vessels trajectories. Considering this last subject, the intersection between vessels is often related with abnormal behaviors, but this topic has not been exploited yet. In this paper an approach to assist the domain experts in the task of analyzing these intersections is introduced, based on data processing and visualization. The work was experimented with a proprietary dataset that covers the Portuguese maritime zone, containing an average of 6460 intersections by day. The results show that several intersections were only noticeable with the visualization strategies here proposed. Copyright

Abstract
In this paper, we propose a branch-and-price approach for solving the problem of maximizing the expected number of transplants in Kidney Exchange Programs (KEPs). In these programs, the decision on which transplants will be conducted is usually made with the support of optimization models with the assumption that all operations will take place. However, after a plan of transplants is defined, a pair may leave the KEP or a more accurate compatibility evaluation exam may invalidate a transplant. To model these possible events we consider probabilities of failure of vertices and of arcs and the objective of maximizing the expected number of transplants. The proposed approach is based on the so-called cycle formulation, where decision variables are associated with cycles. Built on the concept of type of cycle a branch-and-price algorithm is conceived. One subproblem is defined for each type of cycle. We present computational results of the proposed branch-and-price algorithm and compare them with solving directly the cycle formulation (with a general purpose mixed integer programming solverCPLEX) showing that the proposed approach is the only one suitable for larger instances.

Abstract
As higher education evolves into a multifaceted and complex activity, the incorporation of education management information systems (EMIS) that allows for the production of relevant, organized and structured information, becomes a necessity for both institutions and students. Despite the recognition of this requirement, existing literature does not focus on how EMIS might trigger students' success. With this in mind, an initial proposal of a multi-perspective EMIS success model is presented and a validation on the possible existence of linear correlations between the model contexts is described. Moderate correlations have been detected between the majority of the model contexts and a very strong correlation has been detected between students' satisfaction and the arise of net benefits associated with the use of EMIS.

Abstract
Vine culture is influenced by many factors, such as the weather, soil or topography, which are triggers to phytosanitary issues. Among them are some diseases, that are responsible for major economic losses that can, however, be managed with timely interventions in the field, viable of leading to effective results by preventing damage propagation. While not all symptoms might present a visible evidence, hyperspectral sensors can tackle this aspect with their ability for measuring hundreds of continuously sparse bands that range beyond the eye-perceptible spectrum. Having such research line in mind in this work, a hyperspectral sensor was applied to analyse the spectral status of vine leaves samples, collected in three chronologically distinct campaigns, while costly and destructive laboratory methods were used to track Flavescence Dorée (FD) in the same samples, for a ground truth information. Regarding data processing, machine learning approaches were used, in which several classifiers were selected to detect FD in vine leaves hyperspectral images. The goal was to evaluate and find most suitable classifier for this task. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
Context-free languages are highly important in computer language processing technology as well as in formal language theory. The Pumping Lemma for Context-Free Languages states a property that is valid for all context-free languages, which makes it a tool for showing the existence of non-context-free languages. This paper presents a formalization, extending the previously formalized Lemma, of the fact that several well-known languages are not context-free. Moreover, we build on those results to construct a formal proof of the well-known property that context-free languages are not closed under intersection. All the formalization has been mechanized in the Coq proof assistant.

Abstract
Emerging markets contain the vast majority of the world's population. Despite the enormous number of inhabitants, these markets still lack a proper finance infrastructure. One of the main difficulties felt by customers is the access to loans. This limitation arises from the fact that most customers usually lack a verifiable credit history. As such, traditional banks are unable to provide loans. This paper proposes credit scoring modeling based on non-traditional-data, acquired from smartphones, for loan classification processes. We use Logistic Regression (LR) and Support Vector Machine (SVM) models which are the top linear models in traditional banking. Then we compared the transformation of the training datasets creating boolean indicators against the categorization using Weight of Evidence (WoE). Our models surpassed the performance of the manual loan application selection process, improving the approval rate and decreasing the overdue rate. Compared to the baseline, the loans approved by meeting the criteria of the SVM model presented a decreased overdue rate. At the same time, using the score generated by a SVM model we were able to grant more loans. This paper shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build robust algorithms that can identify good borrowers as in traditional banking.