Integration of Behavioral Economic Models to Optimize ML performance and interpretability: a sandbox example

This paper presents a sandbox example of how the integration of models borrowed from Behavioral Economic (specifically Protection-Motivation Theory) into ML algorithms (specifically Bayesian Networks) can improve the performance and interpretability of ML algorithms when applied to Behavioral Data. The integration of Behavioral Economics knowledge to define the architecture of the Bayesian Network increases the accuracy of the predictions in 11 percentage points. Moreover, it simplifies the training process, making unnecessary training computational efforts to identify the optimal structure of the Bayesian Network. Finally, it improves the explicability of the algorithm, avoiding illogical relations among variables that are not supported by previous behavioral cybersecurity literature. Although preliminary and limited to 0ne simple model trained with a small dataset, our results suggest that the integration of behavioral economics and complex ML models may open a promising strategy to improve the predictive power, training costs and explicability of complex ML models. This integration will contribute to solve the scientific issue of ML exhaustion problem and to create a new ML technology with relevant scientific, technological and market implications

Deep Learning Architectures for Diagnosis of Diabetic Retinopathy

For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of self-attention-based networks to the field of computer vision through ViTs seems to have changed the trend of using standard convolutions. Throughout this work, we apply different architectures such as U-Net, ViTs and ConvMixer, to compare their performance on a medical semantic segmentation problem. All the models have been trained from scratch on the DRIVE dataset and evaluated on their private counterparts to assess which of the models performed better in the segmentation problem. Our major contribution is showing that the best-performing model (ConvMixer) is the one that shares the approach from the ViT (processing images as patches) while maintaining the foundational blocks (convolutions) from the U-Net. This mixture does not only produce better results (DICE=0.83) than both ViTs (0.80/0.077 for UNETR/SWIN-Unet) and the U-Net (0.82) on their own but reduces considerably the number of parameters (2.97M against 104M/27M and 31M, respectively), showing that there is no need to systematically use large models for solving image problems where smaller architectures with the optimal pieces can get better results

Tiny ML: La nueva revolución en la IoT

A diferencia de la inteligencia artificial o la computación cuántica, hay otra revolución tecnológica que ha aparecido de una forma muy sutil y por la puerta de atrás. Hoy a nadie se le pasa que todo está conectado mediante una serie de sensores que pueden recoger cualquier tipo de actividad sea humana, industrial económica o social. Todo este mundo interconectado se conoce como Internet de las Cosas. En paralelo, crece el auge de los microcontroladores, los pequeños dispositivos electrónicos con precios muy bajos, diseñados para realizar una determinada tarea, o programa, dentro de un dispositivo, como sistemas embebidos o empotrados, ya que están integrados en los dispositivos que controlan. Si el lector mira a su alrededor estos sistemas están por todas partes. Se prevé que para el año 2023 se vendan 33.000 millones de microcontroladores, con un gran impacto económico. TinyML une el mundo de los dispositivos de bajo coste y bajo consumo energético con el campo de las tecnologías de aprendizaje máquina y aprendizaje profundo, lo que permite realizar análisis de datos de sensores en el dispositivo con un consumo de energía extremadamente bajo. Las ventajas son innumerables, desde la baja latencia y el bajo consumo de energía a un reducido ancho de banda y mayor privacidad, unas características que permiten aplicar esta tecnología en diversos ámbitos, como el mantenimiento predictivo de la maquinaria; la monitorización sanitaria de las personas vulnerables o el control de enfermedades transmitidas por mosquitos; la optimización de la agricultura y la ganadería, como el mejor aprovechamiento del agua o la reducción de la huella de carbono en todos los procesos; la detección automática de especies para hacer un seguimiento de las pérdidas en el planeta; o los avances que harán posible la pendiente implementación smart de las ciudades inteligentes. Asumiendo la importancia de todas estas dimensiones, la Fundació Parc Científic Universitat de València (FPCUV) pone a tu disposición esta monografía, coordinada por Emilio Soria Olivas, catedrático y fundador del grupo de investigación Intelligent Data Analysis Laborator (IDAL) en la Escuela Técnica Superior de Ingeniería (ETSE) de la Universitat de València, y Mariano Serra Bondia, responsable de los Sistemas TIC de la FPCUV, que pretende ser la primera piedra de lo que seguro será una gran catedral en la bibliografía sobre esta temática. La obra se enmarca en Transforma Difusión, proyecto que cuenta con el apoyo de la Agència Valenciana de la Innovació (AVI).S0648000/2021Unlike artificial intelligence or quantum computing, there is another technological revolution that has appeared in a very subtle way and through the back door. Today no one forgets that everything is connected through a series of sensors that can pick up any type of activity, be it human, industrial, economic or social. This entire interconnected world is known as the Internet of Things. In parallel, the rise of microcontrollers is growing, small electronic devices with very low prices, designed to perform a certain task, or program, within a device, such as embedded or embedded systems, since they are integrated into the devices they control. If the reader looks around, these systems are everywhere. It is expected that by the year 2023, 33,000 million microcontrollers will be sold, with a great economic impact. TinyML bridges the world of low-cost, low-power devices with the field of machine learning and deep learning technologies, enabling on-device sensor data analysis with extremely low power consumption. The advantages are innumerable, from low latency and low energy consumption to reduced bandwidth and greater privacy, characteristics that allow this technology to be applied in various fields, such as predictive maintenance of machinery; health monitoring of vulnerable people or control of diseases transmitted by mosquitoes; the optimization of agriculture and livestock, such as the best use of water or the reduction of the carbon footprint in all processes; automatic species detection to track losses on the planet; or the advances that will make possible the pending smart implementation of smart cities. Assuming the importance of all these dimensions, the Fundació Parc Científic Universitat de València (FPCUV) puts at your disposal this monograph, coordinated by Emilio Soria Olivas, professor and founder of the Intelligent Data Analysis Laboratory (IDAL) research group at the Higher Technical School of Engineering (ETSE) of the Universitat de València, and Mariano Serra Bondia, responsible for the ICT Systems of the FPCUV, which aims to be the first stone of what will surely be a great cathedral in the bibliography on this subject. The work is part of Transforma Difusión, a project that has the support of the Valencian Innovation Agency (AVI)