Abstract
This paper reports the design and development process of an artistic robot by a team of five engineering and design students from Belgian, Finland, Poland, Romania and Scotland. To contribute to this goal, the team designed and assembled GraphBot, a voice commanded drawing robot prototype, following the EPS@ISEP process. In addition, the team specified their target as young children and, in particular girls, and stated that their motivation was to introduce young generations to the world of science, technology, engineering and mathematics (STEM). In terms of outcomes, this project is expected to go beyond the boundaries of the traditional development of scientific and technical competences, by providing the students with a holistic learning experience, fostering also the development of personal and inter-personal skills within a multidisciplinary and multicultural teamwork set-up.

Abstract

Abstract
Coverage path planning (CPP) is a fundamental agricultural field task required for autonomous navigation systems. It is also important for resource management, increasingly demanding in terms of reducing costs and environmental polluting agents as well as increasing productivity. Additional problems arise when this task involves irregular agricultural terrains where the crop follows non-uniform configurations and extends over steep rocky slopes. For mountain vineyards, finding the optimal path to cover a restricted set of terraces, some of them with dead ends and with other constraints due to terrain morphology, is a great challenge. The problem involves other variables to be taken into account such as speed, direction and orientation of the vehicle, fuel consumption and tank capacities for chemical products. This article presents a decision graph-based approach, to solve a Rural Postman Coverage like problem using A* and Dijkstra algorithms simultaneously to find the optimal sequence of terraces that defines a selected partial coverage area of the vineyard. The decision structure is supported by a graph that contains all the information of the Digital Terrain Model (DTM) of the vineyard. In this first approach, optimality considers distance, cost and time requirements. The optimal solution was represented in a graphical user OpenGL application developed to support the path planning yprocess. Based on the results, it was possible to prove the applicability of this approach for any vineyards which extend like routes. Near optimal solutions based on other specific criteria could also be considered for future work.

Abstract
The CloudMdsQL polystore provides integrated access to multiple heterogeneous data stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such as MapReduce or Spark. The CloudMdsQL language is a functional SQL-like query language with a flexible nested data model. A major capability is to exploit the full power of each of the underlying data stores by allowing native queries to be expressed as functions and involved in SQL statements. The CloudMdsQL polystore has been validated with a good number of different data stores: HDFS, key-value, document, graph, RDBMS and OLAP engine. In this paper, we introduce the benchmarking of the CloudMdsQL polystore and evaluate the performance benefits of important features enabled by the query language and engine.

Abstract
Data stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear or evolve over time. Most of the works found in the ND literature presents it as a binary classification task. In several data stream real life problems, ND must be treated as a multiclass task, in which, the known concept is composed by one or more classes and different new classes may appear. This work proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a multiclass task. In the initial training phase, MINAS builds a decision model based on a labeled data set. In the online phase, new examples are classified using this model, or marked as unknown. Groups of unknown examples can be used later to create valid novelty patterns (NP), which are added to the current model. The decision model is updated as new data come over the stream in order to reflect changes in the known classes and allow the addition of NP. This work also presents a set of experiments carried out comparing MINAS and the main novelty detection algorithms found in the literature, using artificial and real data sets. The experimental results show the potential of the proposed algorithm.

Abstract
Learning from data streams is a challenge faced by data science professionals from multiple industries. Most of them struggle hardly on applying traditional Machine Learning algorithms to solve these problems. It happens so due to their high availability on ready-to-use software libraries on big data technologies (e.g. SparkML). Nevertheless, most of them cannot cope with the key characteristics of this type of data such as high arrival rate and/or non-stationary distributions. In this paper, we introduce a generic and yet simplistic framework to fill this gap denominated Concept Neurons. It leverages on a combination of continuous inspection schemas and residual-based updates over the model parameters and/or the model output. Such framework can empower the resistance of most of induction learning algorithms to concept drifts. Two distinct and hence closely related flavors are introduced to handle different drift types. Experimental results on successful distinct applications on different domains along transportation industry are presented to uncover the hidden potential of this methodology.