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In this document we outline a Ph. D. research plan and a summary of preliminary results on test automation for distributed and heterogeneous systems.

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This paper presents MicroFactory - a simplified version of the Robot@Factory competition. This version of the competition was conceived to be low-cost and easily implementable in a small space, be it a classroom or the school robotics club. The factory scenario size was originally 3.5m by 2.5m. The floor is now an A0 printed sheet and the warehouses and machines dimensions are so that they can be 3D printed or made out of LEGO (TM) bricks. Both machines and parts had active elements with leds; now they are passive. Robot@Factory is a Portuguese robotic competition whose first edition was held in 2011 in Lisbon. The scenario of the competition simulates a factory which has an Incoming Warehouse, an Outgoing Warehouse, and 8 processing machines. The robots must collect, transport and position the materials, self-localize and navigate while avoiding collisions with walls, obstacles and other robots. Participants' research contributes to improve AGVs (Automated Guided Vehicle systems) technology. Robot@Factory is now integrated in Festival Nacional de Robotica, a yearly event which attracts lots of public, contributing also to STEM (Science, Technology, Engineering and Mathematics) popularization. MicroFactory's main contribution is different - enhancing learning and the undergraduate experience in robotics. While Robot@Factory is intended for groups with high skills, MicroFactory is supposed to attract younger and less skilled people. So, the proposed challenges were simplified. It was also designed an official robot for the MicroFactory competition. It's a 3D printed robot, based on Arduino and low cost common electronic parts. CAD files for the mechanics (and every bit of the factory scenario), the hardware schematics and most of the software can be made available to the organizers or teachers trying to implement didactic experiences involving robotics. The challenge may then be reduced from developing a robot from scratch to implementing just a small part like programming the navigation algorithm. The presented work is part of a wider Open Source project, aiming to develop project-based collaborative didactic experiences involving robotics to foster STEM education, and low-cost 3D printed educational robots based on generic electronics to support those experiences.

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Machine Learning and Data Mining research strongly depend on the quality and quantity of the real world datasets for the evaluation stages of the developing methods. In the context of the emerging Online Multi-Target Regression and Multi-Label Classification methodologies, datasets present new characteristics that require specific testing and represent new challenges. The first difficulty found in evaluation is the reduced amount of examples caused by data damage, privacy preservation or high cost of acquirement. Secondly, few data events of interest such as data changes are difficult to find in the datasets of specific domains, since these events naturally scarce. For those reasons, this work suggests a method of producing synthetic datasets with desired properties(number of examples, data changes events, ... ) for the evaluation of Multi-Target Regression and Multi-Label Classification methods. These datasets are produced using First Principle Models which give more realistic and representative properties such as real world meaning ( physical, financial, ... ) for the outputs and inputs variables. This type of dataset generation can be used to produce infinite streams and to evaluate incremental methods such as online anomaly and change detection. This paper illustrates the use of synthetic data generation through two showcases of data changes evaluation.

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This paper describes the application of the Opt-aiNet algorithm to the reconfiguration problem of distribution systems considering variable demand levels. The Opt-aiNet algorithm is an optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with variable demands is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of power losses along an extended operation period. This work includes results of the application of the Opt-aiNet algorithm to distribution systems with 33, 84, 136 and 417 buses. These results demonstrate the robustness and efficiency of the proposed approach.

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This paper presents Enki, a web-based IDE that integrates several pedagogical tools designed to engage students in learning programming languages. Enki achieves this goal (1) by sequencing educational resources, either expository or evaluative, (2) by using gamification services to entice students to solve activities, (3) by promoting social interaction and (4) by helping students with activities, providing feedback on submitted solutions. The paper describes Enki, its concept and architecture, details its design and implementation, and covers also its validation.