Eduardo R. B. Marques

Keila Lima, Eduardo R. B. Marques, José Pinto et al.

We present Dolphin, an extensible programming language for autonomous vehicle networks. A Dolphin program expresses an orchestrated execution of tasks defined compositionally for multiple vehicles. Building upon the base case of elementary one-vehicle tasks, the built-in operators include support for composing tasks in several forms, for instance according to concurrent, sequential, or event-based task flow. The language is implemented as a Groovy DSL, facilitating extension and integration with external software packages, in particular robotic toolkits. The paper describes the Dolphin language, its integration with an open-source toolchain for autonomous vehicles, and results from field tests using unmanned underwater vehicles (UUVs) and unmanned aerial vehicles (UAVs).

António Filgueiras, Eduardo R. B. Marques, Luís M. B. Lopes et al.

Machine-learning techniques, especially deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms. In this paper, we describe the construction of a dataset for the Portuguese native flora based on publicly available research-grade datasets, and the derivation of a high-accuracy model from it using off-the-shelf deep convolutional neural networks. We anchored the dataset in high-quality data provided by Sociedade Portuguesa de Botânica and added further sampled data from research-grade datasets available from GBIF. We find that with a careful dataset design, off-the-shelf machine-learning cloud services such as Google's AutoML Vision produce accurate models, with results comparable to those of Pl@ntNet, a state-of-the-art citizen science platform. The best model we derived, dubbed Floralens, has been integrated into the public website of Project Biolens, where we gather models for other taxa as well. The dataset used to train the model is also publicly available on Zenodo.

Eduardo R. B. Marques

We present a novel methodology for deriving fine-grained patches of Java software. We consider an abstract-syntax tree (AST) representation of Java classes compiled to the Java Virtual Machine (JVM) format, and a difference analysis over the AST representation to derive patches. The AST representation defines an appropriate abstraction level for analyzing differences, yielding compact patches that correlate modularly to actual source code changes. The approach contrasts to other common, coarse-grained approaches, like plain binary differences, which may easily lead to disproportionately large patches. We present the main traits of the methodology, a prototype tool called aspa that implements it, and a case-study analysis on the use of aspa to derive patches for the Java 2 SE API. The case-study results illustrate that aspa patches have a significantly smaller size than patches derived by binary differencing tools.