BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Sao_Paulo BEGIN:DAYLIGHT DTSTART:20380119T001407 TZOFFSETFROM:-0300 TZOFFSETTO:-0300 RRULE:FREQ=YEARLY;BYDAY=3TU;BYMONTH=1 TZNAME:-03 END:DAYLIGHT BEGIN:STANDARD DTSTART:20190216T230000 TZOFFSETFROM:-0200 TZOFFSETTO:-0300 RRULE:FREQ=YEARLY;BYDAY=3SA;BYMONTH=2 TZNAME:-03 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230621T213121Z UID:5864DC49-5B92-4422-9CF2-DBDF1E16001C DTSTART;TZID=America/Sao_Paulo:20230621T150000 DTEND;TZID=America/Sao_Paulo:20230621T160000 DESCRIPTION:The concept of risk is a combination of threat probabilities\, vulnerabilities and expected consequences. In traditional risk modeling an d evaluation approaches\, analyses are performed at design time and possib ly repeated periodically\, or at any relevant system change. With such app roaches\, there is no possibility to evaluate how the risk evolves over ti me as a condition of actual system state and detected threats. One challen ging objective in the field of connected cyber-physical systems (CPS) and the Internet of Things (IoT) is to improve resilience by providing non-tri vial mechanisms for run-time threat detection\, risk estimation and system reconfiguration following Self-X principles like self-diagnostics and sel f-healing. Threats include faults\, errors and failures\, and can be eithe r intentional (e.g.\, security attacks) or unintentional (e.g.\, random fa ults). A central issue is to develop model-based approaches allowing for r un-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 optima l balance and trade-off between ease of use\, expressive power and solving efficiency. Models used in static risk assessment at design time can be r eused 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 mode ls already employed for system certification together with run-time model- checking supports explainable Artificial Intelligence (XAI) that is reques ted to build trustworthy autonomous CPS like self-driving vehicles. The ne xt generation of run-time risk models will act as Digital Twins to anticip ate threats and enable novel paradigms like proactive dependability and co llaborative security as a support to prognostics and preventive maintenanc e in Industry 4.0 and other smart-X applications (e.g.\, smart-houses\, sm art-cities\, smart-transportation\, etc.). In fact\, Digital Twins (DT) ar e emerging as an extremely promising paradigm for run-time modelling and p erformability prediction of cyber-physical systems (CPS) in various domain s. Although several different definitions and industrial applications of D T exist\, ranging from purely visual three-dimensional models to predictiv e maintenance tools\, in this talk we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that ai m\, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argu e that the convergence between DT and self-adaptation is the key to build smarter\, resilient and trustworthy CPS that can self-monitor\, self-diagn ose and – ultimately – self-heal. The conceptual framework eases depen dability assessment\, which is essential for the certification of autonomo us CPS operating with artificial intelligence and machine learning in crit ical applications.\n\nSpeaker(s): Francesco Flammini\n\nVirtual: https://e vents.vtools.ieee.org/m/360891 LOCATION:Virtual: https://events.vtools.ieee.org/m/360891 ORGANIZER:danielchaves@ieee.org SEQUENCE:19 SUMMARY:Digital Twins for Trustworthy Autonomy URL;VALUE=URI:https://events.vtools.ieee.org/m/360891 X-ALT-DESC:Description: <br /><p>The concept of risk is a combination of th reat probabilities\, vulnerabilities and expected consequences. In traditi onal risk modeling and evaluation approaches\, analyses are performed at d esign 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-physic al 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 sel f-diagnostics and self-healing. Threats include faults\, errors and failur es\, and can be either intentional (e.g.\, security attacks) or unintentio nal (e.g.\, random faults). A central issue is to develop model-based appr oaches allowing for run-time risk evaluation accounting for uncertainties in system itself and in the surrounding environment. Those models should b e such to account for growing complexity (size\, distribution\, heterogene ity) and criticality of modern CPS. Multi-paradigm modeling can combine pr obabilistic modelling languages borrowed from Artificial Intelligence (e.g .\, Bayesian Networks) with formalisms like high-level Petri Nets\, in ord er to find the optimal balance and trade-off between ease of use\, express ive power and solving efficiency. Models used in static risk assessment at design time can be reused and integrated in appropriate frameworks to all ow online monitoring of relevant system parameters\, threat detection and dynamic adaptation to respond to threats. In critical applications\, the r euse 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-dri ving vehicles. The next generation of run-time risk models will act as Dig ital 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 (C PS) in various domains. Although several different definitions and industr ial applications of DT exist\, ranging from purely visual three-dimensiona l models to predictive maintenance tools\, in this talk we focus on data-d riven evaluation and prediction of critical dependability attributes such as safety. To that aim\, we introduce a conceptual framework based on auto nomic systems to host DT run-time models based on a structured and systema tic 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 &ndash\; ultimately &ndash\; self-heal. The c onceptual framework eases dependability assessment\, which is essential fo r the certification of autonomous CPS operating with artificial intelligen ce and machine learning in critical applications.</p> END:VEVENT END:VCALENDAR