IEEE URUCON 2024 Conference sponsored by IEEE Cono Sur Council and IEEE Uruguay Section, invites you to participate in the third URUCON IEEE Hybrid Conference, that will be held in Montevideo, from the 18th to the 20th of November 2024. Chair: Ing. Pablo Thomasset, Vice Chair: Dr. Ing. Gonzalo Casaravilla Technical Co-Chairs: Dr. Ing. Rodrigo Alonso, Dr. Ing. Alfredo Arnaud, Dr. Ing. Daniel Slomovitz Conference Topics: Communications, Power & Energy, Instrumentation & Measurements, Biomedical, Computing, Circuits & Systems, Control Broadcast, Signal Processing, Education and Technology Management. [] Agenda: Important Dates: Initial paper submission (up to 4 pages): June 30, 2024. Notification of acceptance: August 31, 2024. Final paper submission (up to 5 pages): September 15, 2024. Conference: 18th to the 20th of November 2024 at Montevideo, Uruguay Montevideo, Montevideo, Uruguay, Virtual: https://events.vtools.ieee.org/m/415738

[]💡 ¿Listo para sumergirte en el fascinante mundo del modelado 3D? 🎨 Únete a nuestro apasionante curso y descubre cómo transformar tus ideas en modelos tridimensionales asombrosos. Desde principiantes hasta expertos, todos son bienvenidos🌟 📆 Fechas: - Sábado 01/06 - Sábado 08/06 - Sábado 15/06 - Sábado 22/06 ⏰ Horario: De 08:00 a 12:00 hs 📍 Lugar: UCSA Campus - Laboratorio de Simulación 📲 Link de Inscripción: https://forms.gle/K6jWEcG6t6AcvvY5A 🚀 ¡No esperes más para comenzar tu viaje creativo! Asunción, Central, Paraguay

Rickard Stureborg Abstract: Considerable research effort has been put into improving Large Language Models (LLMs) to evaluate NLP tasks automatically. This work generally tries to achieve high correlations with human judgements on the same task. However, it is still unclear what level of correlation is good enough for practical applications of LLM-based automatic evaluation systems. This paper characterizes these LLM evaluators’ confidence in ranking candidate NLP models and develops a configurable Monte Carlo simulation method. We show that even automatic metrics with low correlation with human judgement can reach high-confidence rankings of candidate models with reasonable evaluation set sizes (100s of examples). Further, we describe tradeoff curves between the LLM evaluator performance (i.e., correlation with humans) and evaluation set size; loss in correlation can be compensated with modest increases in the evaluation set size. We validate our results on RoSE, a text summarization dataset, and find our estimates of confidence align with empirical observations. Bio: Rickard is a PhD candidate in Computer Science, where his research focuses on high-subjectivity tasks in natural language processing, including applications for misinformation and automatic evaluation of machine-generated text. He works as a researcher at Grammarly, where he helps build the next generation of tools to integrate AI into people’s writing workflow. Rich’s interdisciplinary research has been featured in the most prestigious peer-reviewed research venues across several fields, including natural language processing (*ACL conferences), artificial intelligence (NeurIPS and AAAI workshops), human-computer interaction (CHI), optics (SPIE, Journal of Biomedical Optics), and public health (Vaccine). Rich is serving a three-year term on Duke’s Board of Trustees, where he is a member of the Graduate Education and Research Committee. Co-sponsored by: Jose Delpiano Speaker(s): Rich Stureborg Room: Sala I-103, Bldg: Edificio de Ingeniería, San Carlos de Apoquindo 2500, Las Condes, Universidad de los Andes, Santiago, Region Metropolitana, Chile

Reunión Responsables de Grupos de Afinidad de Miembros Vitalicios de Región 9 Virtual: https://events.vtools.ieee.org/m/422684

La CD de la rama en conjunto con el Capítulo EMBS de la Sección Argentina y la Universidad Nacional de Córdoba organizan una serie de seminarios sobre herramientas de IA aplicadas a imágenes. Docentes: Dr. Ing. Diego Sebastián Comas; Dr. Ing. Gustavo Javier Meschino; Lic. Agustín Amalfitano del Instituto de Investigaciones Científicas y Técnicas en Electrónica (ICYTE, CONICET-UNMDP). **Fechas: *Seminario #1 (05/06/2024 – 18.30 – 20.30hs): Introducción a la Inteligencia Artificial. Aprendizaje supervisado y no supervisado. Lógica Difusa en Imágenes Médicas. Redes neuronales artificiales. Deep-Learning. Cálculo de error. -Link de inscripción: https://forms.gle/gxMJj9FBatJxMsVx9 Virtual: https://events.vtools.ieee.org/m/417041