Worldwide, chronic pain has established as one of the foremost medical issues due to its 35% of comorbidity with depression and many other psychological problems. Traditionally, self-report (VAS scale) or physicist inspection (OPI scale) perform the pain assessment; nonetheless, both methods do not usually coincide [14]. Regarding self-assessment, several patients are not able to complete it objectively, like young children or patients with limited expression abilities. The lack of objectivity in the metrics draws the main problem of the clinical analysis of pain. In response, various efforts have tried concerning the inclusion of objective metrics, among which stand out the Prkachin and Solomon Pain Intensity (PSPI) metric defined by face appearance [5]. This work presents an in-depth learning approach to pain recognition considering deep facial representations and sequence analysis. Co...

The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU), the Office of Research of Universidad Nacional de Ingeniería (VRI - UNI) and the research management office (OGI - UNI).

Objective: To build and validate an instrument to measure the level of knowledge about the Law that regulates the work of the dental surgeon in the Peruvian territory (Law No. 27878) of postgraduate students and teachers of the Stomatology Career of the Scientific University of South. Peru. Material and methods: The sample consisted of 38 dental surgeons, participants in the postgraduate program (20) and professors (18) of the Stomatology Career at the Universidad Científica del Sur. A structured questionnaire of 15 questions was developed based on the articles of Law No. 27878. Results: It was approved by Expert Judgment and submitted to Aiken's V with an average result of 0.95. A moderate level of correlation was obtained through the Pearson Correlation Coefficient and a Cronbach's Alpha of 0.8. Through the Exploratory Factor Analysis, 3 dimensions were formed. Conclusions: The instru...

The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Office Research of Universidad Nacional de Ingeniería (VRI - UNI).

The unpaired image-to-image translation consists of transferring a sample a in the domain A to an analog sample b in the domain B without intensive pixel-to-pixel supervision. The current vision focuses on learning a generative function that maps both domains but ignoring the latent information, although its exploration is not explicit supervision. This paper proposes a cross-domain GAN-based model to achieve a bi-directional translation guided by latent space supervision. The proposed architecture provides a double-loop cyclic reconstruction loss in an exchangeable training adopted to reduce mode collapse and enhance local details. Our proposal has outstanding results in visual quality, stability, and pixel-level segmentation metrics over different public datasets.

Forest census allows getting precise data for logging planning and elaboration of the forest management plan. Species identification blunders carry inadequate forest management plans and high risks inside forest concessions. Hence, an identification protocol prevents the exploitation of non-commercial or endangered timber species. The current Peruvian legislation allows the incorporation of non-technical experts, called “materos”, during the identification. Materos use common names given by the folklore and traditions of their communities instead of formal ones, which generally lead to misclassifications. In the real world, logging companies hire materos instead of botanists due to cost/time limitations. Given such a motivation, we explore an end-to-end software solution to automatize the species identification. This paper introduces the Peruvian Amazon Forestry Dataset, which includ...