Explainable Hopfield Neural Networks Using an Automatic Video-Generation System

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).University of the Bio-Bio. Vicerrectoria de Investigacion. Facultad de Ciencias Empresariales. Departamento de Sistemas de Informacion

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others

Experiencia en el proceso de construcción de Tecnologías Sociales en el campo de la Soberanía y Seguridad Alimentaria y Nutricional en América del Sur

Experiencia en el proceso de construcción de tecnologías sociales en el campo de la Soberanía y Seguridad Alimentaria y Nutricional en América del Sur, o processo de construção de diálogos entre pesquisadores é retomado, dessa vez do ponto de vista dos parceiros latino-americanos, contando com colaboração de argentinos, bolivianos, chilenos, colombianos e equatorianos, além de brasileiros. Nesse capítulo, os autores apresentam as atividades e as lições aprendidas, mas reconhecem também as descontinuidades e barreiras enfrentadasFil: González, Ana Lía. Universidad Nacional de Córdoba. Facultad de Medicina. Escuela de Nutrición; ArgentinaFil: Fernandes, Anna Christina Pinheiro. Universidad del Desarrollo; ChileFil: Albrecht, Claudia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; ArgentinaFil: Román, María Dolores. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; ArgentinaFil: Araneda Flores, Jacqueline Alejandra. Universidad del Bio Bio; ChileFil: Delgado Burgoa, José Manuel Freddy. Comunidad Pluricultural Andino Amazónico para la Sustentabilidad ; BoliviaFil: Oliveira, María Rita Marques de. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Castillo Carrión, Maritza Janeth. Universidad Técnica Particular de Loja; PerúFil: Popelka, Regina Maria. Universidad Nacional de Córdoba. Facultad de Medicina. Escuela de Nutrición; ArgentinaFil: Martinez Espinosa, Ruth Irene. Universidad Técnica Particular de Loja; PerúFil: Koester Webe, Thabata. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: García Ramírez, Yudi Paulina. Universidade Estadual Paulista Julio de Mesquita Filho; Brasi

Alirocumab and cardiovascular outcomes after acute coronary syndrome

BACKGROUN

Alirocumab and Cardiovascular Outcomes after Acute Coronary Syndrome

BACKGROUN