A computer vision (CV) system is proposed for real-time prediction of strain by monitoring the color-changing feature of mechanochromic sensors. Pictures of the sensors subjected to calibration tensile tests were treated with standard image processing methods and analyzed using supervised machine learning (ML) algorithms. Visual strain sensing was demonstrated by linear regression models capable of learning a relation between the applied strain and the reflected structural color. The ElasticNet regression model provided the highest accuracy in the strain prediction task, with a remarkable performance in monitoring real-time strain variation of sensors during a tensile-relaxion cycle. Using calibration curves, the predicted strain can also be employed for estimating the tensile force applied on the mechanochromic sensors. Taken together these results point to potential intelligent systems for noninvasive in-situ visual monitoring of deformations and tensions.

Leandro Santos, Edilson Anselmo Corrêa Júnior, Osvaldo Oliveira Jr, Diego Amancio, Letícia Mansur, Sandra Aluísio. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

In machine learning accurate predictors may be obtained by combining predictions of an ensemble of accurate and diverse predictors. Ensembles are efficiently constructed with the random subspace method (RSM) performed in the instance or in the principal components (PCs) spaces. In this paper, we extend RSM to explore the synergy in the characteristics of these two spaces, with a method referred to as RSM-IPCS. Using 24 datasets from the UCI machine learning repository, we show an enhanced performance of RSM-IPCS in comparison to the original RSM and RSM in PCs space, in terms of higher accuracy and smaller variances. Since RSM-IPCS exhibited at least a similar performance to the best method in a separate space, it opens the way for optimization of ensembles based on the combination of multiple spaces.

We present the computational and composition theoretical bases for the design of a collaborative writing tool, based on the critiquing approach, to assist non-native novice researchers in understanding and production of the structure of scientific papers. This critiquing tool is embedded in a suite named AMADEUS that caters for various needs of non-native English users to produce a first draft of a paper, relying on the reuse of contextualized linguistic material as input for the user. Our emphasis is on the architecture and methodology to build the linguistic resources for the critiquing tool. Though originally targeted at non-native authors, the critiquing tool may also be useful for novice native English writers and as a teaching resource for English for Academic Purposes practitioners.