Taller sobre LabVIEW y Processing; sus aplicaciones en la ingeniería Speaker(s): Alberto Marroquín, Bldg: Edificio 3 CITT, Universidad Don Bosco, Campus Soyapango, Soyapango, San Salvador, El Salvador
Reunião para assuntos administrativos do IEEE Conselhos de Sensores e Sistemas – Seção Sul Brasil Virtual: https://events.vtools.ieee.org/m/361499
Estimados miembros de IEEE Chile Centro, Como presidente de la Rama estudiantil IEEE USS junto con los capítulo Chile Centro y Argentina Section de la IEEE Computer Society & Computer Intelligence Society tengo el agrado de invitarlos cordialmente al evento USS: Insider Sight, un ciclo de charlas profesionales donde su primera versión contará con la temática: IA aplicada en Videojuegos. Este emocionante evento se llevará a cabo los días 19 y 20 de junio en la Universidad San Sebastián, tanto de forma presencial como a través de la plataforma Blackboard en formato online. USS: Insider Sight reunirá a destacados expertos de la industria de los videojuegos y la inteligencia artificial para compartir sus conocimientos, experiencias y perspectivas en este campo en constante evolución. Si desean participar en este evento único, les pedimos que completen el formulario de inscripción que proporcionamos a continuación. A través de este formulario, les enviaremos información más detallada sobre el programa de cada uno de los días, así como cualquier actualización importante relacionada con el evento. Formulario: https://forms.office.com/r/Zwp25jrCvn Agradecemos de antemano su interés y participación en el USS: Insider Sight. Esperamos contar con su presencia y contribución en este importante evento para la comunidad de profesionales y entusiastas de la IA y los videojuegos. Si tienen alguna pregunta o consulta, no duden en comunicarse con nosotros al siguiente correo sb-uss@ieee.org. Estaremos encantados de ayudarles en lo que sea necesario. Co-sponsored by: Facultad ingeniería, arquitectura y diseño, Universidad San Sebastian, Chile. Speaker(s): Dr.Daniela Lopez de Louise , Alexander Dockhorn , Mg.Xavier Murillo Sanchez, Dr.Kostas Karpouzis, Dr.Nicolás Barriga Agenda: Día lunes 19/06: Título: IA en Videojuegos. Expone: Daniela Lopez de Louise Directora del CI2S Lab e investigadora en Computational Intelligence & Information Systems Lab. Título: Inside Games Research: More Than Playing Games. Expone: Alexander Dockhorn Juniorprofessor at the Institute for Information Processing of the Gottfried Wilhelm Leibniz University Hannover, Germany. Día martes 20/06 Título: Generación Procedural de Contenidos para Videojuegos. Expone: Nicolás Barriga Profesor del Departamento de Visualización Interactiva y Realidad Virtual de la Universidad de Talca, Chile. Titulo: Técnicas de inteligencia artificial para el desarrollo de Videojuegos. Expone: Xavier Murillo Ingeniero mecatrónico de profesión, maestría en Inteligencia artificial y Big Data, diplomado en IoT y educación superior, Bolivia. Titulo: Ready, Player GPT: How AI interacts and evolves games. Expone: Kostas Karpouzis Assistant Professor at the Panteion University of Social and Political Sciences, Greece. Bellavista 7, Santiago, Region Metropolitana, Chile, 8320000, Virtual: https://events.vtools.ieee.org/m/362423
The concept of risk is a combination of threat probabilities, vulnerabilities and expected consequences. In traditional risk modeling and evaluation approaches, analyses are performed at design time and possibly repeated periodically, or at any relevant system change. With such approaches, there is no possibility to evaluate how the risk evolves over time as a condition of actual system state and detected threats. One challenging objective in the field of connected cyber-physical systems (CPS) and the Internet of Things (IoT) is to improve resilience by providing non-trivial mechanisms for run-time threat detection, risk estimation and system reconfiguration following Self-X principles like self-diagnostics and self-healing. Threats include faults, errors and failures, and can be either intentional (e.g., security attacks) or unintentional (e.g., random faults). A central issue is to develop model-based approaches allowing for run-time risk evaluation accounting for uncertainties in system itself and in the surrounding environment. Those models should be such to account for growing complexity (size, distribution, heterogeneity) and criticality of modern CPS. Multi-paradigm modeling can combine probabilistic modelling languages borrowed from Artificial Intelligence (e.g., Bayesian Networks) with formalisms like high-level Petri Nets, in order to find the optimal balance and trade-off between ease of use, expressive power and solving efficiency. Models used in static risk assessment at design time can be reused and integrated in appropriate frameworks to allow online monitoring of relevant system parameters, threat detection and dynamic adaptation to respond to threats. In critical applications, the reuse of suitable models already employed for system certification together with run-time model-checking supports explainable Artificial Intelligence (XAI) that is requested to build trustworthy autonomous CPS like self-driving vehicles. The next generation of run-time risk models will act as Digital Twins to anticipate threats and enable novel paradigms like proactive dependability and collaborative security as a support to prognostics and preventive maintenance in Industry 4.0 and other smart-X applications (e.g., smart-houses, smart-cities, smart-transportation, etc.). In fact, Digital Twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, in this talk we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that aim, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argue that the convergence between DT and self-adaptation is the key to build smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and – ultimately – self-heal. The conceptual framework eases dependability assessment, which is essential for the certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications. Speaker(s): Francesco Flammini Virtual: https://events.vtools.ieee.org/m/360891
