Academic competitions

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed

TurboGP: A flexible and advanced python based GP library

We introduce TurboGP, a Genetic Programming (GP) library fully written in Python and specifically designed for machine learning tasks. TurboGP implements modern features not available in other GP implementations, such as island and cellular population schemes, different types of genetic operations (migration, protected crossovers), online learning, among other features. TurboGP's most distinctive characteristic is its native support for different types of GP nodes to allow different abstraction levels, this makes TurboGP particularly useful for processing a wide variety of data sources

Gesture and Action Recognition by Evolved Dynamic Subgestures

This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets

Reconocimiento de depresión en redes sociales basado en la detección de síntomas

Depression is a common mental disorder that affects millions of people around the world. Recently, several methods have been proposed that detect people suffering from depression by analyzing their language patterns in social media. These methods show competitive results, but most of them are opaque and lack of explainability. Motivated by these problems, and inspired by the questionnaires used by health professionals for its diagnosis, in this paper we propose an approach for the detection of depression based on the identification and accumulation of evidence of symptoms through the users’ posts. Results in a benchmark collection are encouraging, as they show a competitive performance with respect to state-of-the-art methods. Furthermore, taking advantage of the approach’s properties, we outline what could be a support tool for healthcare professionals for analyzing and monitoring depression behaviors in social networks.La depresión es un trastorno mental que afecta a millones de personas en todo el mundo. Recientemente, se han propuesto varios métodos que detectan personas que sufren depresión analizando sus patrones de lenguaje en las redes sociales. Estos métodos han mostrado resultados competitivos, sin embargo la mayoría son opacos y carecen de explicabilidad. Motivados por estos problemas, e inspirados en los cuestionarios utilizados por los profesionales de la salud para su diagnóstico, en este trabajo proponemos un método para la detección de depresión basado en la identificación y acumulación de evidencia de síntomas a través de las publicaciones de los usuarios. Los resultados obtenidos en una colección de referencia son prometedores, ya que muestran un desempeño competitivo con respecto a los mejores métodos actuales. Además, aprovechando las propiedades del método, describimos lo que podría ser una herramienta de apoyo para que los profesionales de la salud analicen y monitoreen las conductas depresivas en las redes sociales