BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260629T193552Z UID:45DA727D-02FE-4A9F-AFA9-ACFC34DB3F90 DTSTART;TZID=America/New_York:20260629T140000 DTEND;TZID=America/New_York:20260629T150000 DESCRIPTION:[]\n\nJoin the IEEE Toronto Instrumentation & Measurement – R obotics & Automation Joint Chapter for a technical talk presented by Dr. A lvine B. Belle from York University.\n\nMonday\, June 29\, 2026 @ 2:00 – 3:00 PM (EST)\n\nAbstract:\n\nJustifying the correct implementation of th e non-functional requirements (e.g.\, safety\, security\, reliability) of mission-critical systems is crucial to prevent system failure. The latter could have severe consequences such as the death of people\, financial los ses\, and environmental damage. Assurance cases (e.g.\, safety cases\, sec urity cases) can be used to prevent system failure. They therefore support system assurance. Assurance cases are structured sets of arguments suppor ted by evidence and aiming at demonstrating that a system's non-functional requirements have been correctly implemented. However\, although the avai lability of complete assurance cases is crucial to allow the research comm unity to contribute to the system assurance field\, it remains very challe nging to access complete assurance cases due to several concerns such as c onfidentiality issues. Furthermore\, assurance cases are usually very larg e documents. Still\, their creation remains a manual\, labor-intensive\, a nd error-prone process that heavily relies on domain expertise. Therefore\ , relying on (semi-)automated techniques such as those supported by genera tive AI through LLMs (Large Language Models) could alleviate the task of a ssurance case developers by facilitating the execution of all activities r elated to the assurance case lifecycle.\n\nIn this talk\, Dr. Belle will p resent the current solutions on LLM-based system assurance to inform futur e research on this topic. She will illustrate these solutions with various case studies spanning several application domains (e.g.\, healthcare\, au tomotive\, and nuclear).\n\nSpeaker(s): Alvine B. Belle\, PhD\, \n\nVirtua l: https://events.vtools.ieee.org/m/560709 LOCATION:Virtual: https://events.vtools.ieee.org/m/560709 ORGANIZER:s.sedghizadeh.ca@ieee.org SEQUENCE:48 SUMMARY:System Assurance in The Era of Large Language Models (LLMs) URL;VALUE=URI:https://events.vtools.ieee.org/m/560709 X-ALT-DESC:Description: <br /><p><img style="display: block\; margin-left: auto\; margin-right: auto\;" src="https://events.vtools.ieee.org/vtools_ui /media/display/fa73fbfd-105a-4458-80ed-096d3a30af08" alt="" width="940" he ight="529"></p>\n<p>Join the <strong>IEEE Toronto Instrumentation &amp\; M easurement &ndash\; Robotics &amp\; Automation Joint Chapter</strong>&nbsp \;for a technical talk presented by&nbsp\;<strong>Dr. Alvine B. Belle </st rong>from<strong> York University.</strong></p>\n<p><span style="backgroun d-color: rgb(255\, 255\, 255)\; font-size: 14pt\; color: rgb(186\, 55\, 42 )\;"><strong>Monday\, June 29\, 2026 @ 2:00 &ndash\; 3:00 PM (EST)</strong ></span></p>\n<p class="MsoNormal"><strong><span style="font-size: 12.0pt\ ;">Abstract</span></strong><span style="font-size: 12.0pt\;">:</span></p>\ n<p class="MsoNormal" style="text-align: justify\;"><span style="mso-ansi- language: EN-US\;">Justifying the correct implementation of the non-functi onal requirements (e.g.\, safety\, security\, reliability) of mission-crit ical systems is crucial to prevent system failure. The latter could have s evere consequences such as the death of people\, financial losses\, and en vironmental damage. Assurance cases (e.g.\, safety cases\, security cases) can be used to prevent system failure. They therefore support system assu rance. Assurance cases are structured sets of arguments supported by evide nce and aiming at demonstrating that a system's non-functional requirement s have been correctly implemented. However\, although the availability of complete assurance cases is crucial to allow the research community to con tribute to the system assurance field\, it remains very challenging to acc ess complete assurance cases due to several concerns such as confidentiali ty issues. Furthermore\, assurance cases are usually very large documents. Still\, their creation remains a manual\, labor-intensive\, and error-pro ne process that heavily relies on domain expertise. Therefore\, relying on (semi-)automated techniques such as those supported by generative AI thro ugh <strong>LLM</strong>s (<strong>Large Language Models</strong>) could a lleviate the task of assurance case developers by facilitating the executi on of all activities related to the assurance case lifecycle. </span></p>\ n<p class="MsoNormal" style="text-align: justify\;"><span style="mso-ansi- language: EN-US\;">In this talk\, <strong>Dr. Belle</strong> will present the current solutions on <strong>LLM-based system assurance</strong> to in form future research on this topic. She will illustrate these solutions wi th various case studies spanning several application domains (e.g.\, healt hcare\, automotive\, and nuclear).&nbsp\;</span></p> END:VEVENT END:VCALENDAR