Markus Endler

Rodrigo Veiga, Markus Endler, Valeria de Paiva

Blockchains provide a mechanism through which mutually distrustful remote parties can reach consensus on the state of a ledger of information. With the great acceleration with which this space is developed, the demand for those seeking to learn about blockchain also grows. Being a technical subject, it can be quite intimidating to start learning. For this reason, the main objective of this project was to apply machine learning models to extract information from whitepapers and academic articles focused on the blockchain area to organize this information and aid users to navigate the space.

Thiago Lamenza, Marcelo Paulon, Breno Perricone et al.

This technical report describes GrADyS-SIM, a framework for simulating cooperating swarms of UAVs in joint mission in hypothetical landscape and communicating through RF radios. The framework was created to aid and verify the communication, coordination and context-awareness protocols being developed in the GrADyS project. GrADyS-SIM uses the OMNeT++ simulation library and its INET model suite and and allows for addition of modified or customized versions of some simulated components, network configurations and vehicle coordination, so that new coordination protocols can be developed and tested through the framework. The framework simulates UAV movement dictated by file containing some MAVLink instructions and affected on the fly by different network situations. The UAV swarm coordination protocol emerges from individual interactions between UAVs and has the objective of optimizing the collection of sensor data over an area. It also allows for the simulation of some types of failures to test the protocol adaptability. Every node in the simulation is highly configurable making testing different network opographies, coordination protocols, node hardware configurations and more a quick task.

Marcio Ferreira Moreno, Guilherme Lima, Rodrigo Costa Mesquita Santos et al.

In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI.