BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH X-WR-TIMEZONE:America/New_York PRODID:-//Apple Inc.//iCal 3.0//EN CALSCALE:GREGORIAN X-WR-CALNAME:Park School X-APPLE-CALENDAR-COLOR:#222222 BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT SEQUENCE:711 DTSTART;TZID=America/New_York:20200205T140000 SUMMARY:Air Force Research Laboratory Presentation DESCRIPTION:by Dr. Alex J. Aved Technical Advisor of Air Force Research Laboratory Abstract With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety are paramount. Recent sensor technologies from IoT to video are typically leveraged, resulting in an explosion of real-time content. Accordingly, there is a need the data to be monitored effectively and efficiently. However, leveraging human operators to constantly monitor all the data and video streams is not scalable or cost effective. Humans can become fatigued, subjective, and even disinterested and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation. Overcoming these limitations requires a Live Video Computing (LVC) framework for managing and fusing live motion imagery and data via user-defined queries. LVC enables rapid development of video surveillance software much like traditional database applications of today. Such developed video stream processing applications and user-defined queries are able to "reuse" advanced image processing techniques and templates that have been developed. This results in lower software development and maintenance costs. A user requesting information provides refinement of the analysis via queries which are typically semantic in nature the enables multimedia information fusion. A query that is continuously refined reduces uncertainty and can update ontologies as part of the source, evaluation, and information quality. For example, a user receiving a response to a query can determine if the response meets the quality, evaluation, and source criteria. Demonstrated examples are presented for surveillance, networks, and emergency preparation. Biography Dr. Alex J. Aved received the BA degree in Computer Science and Mathematics in 1999 from Anderson University in Anderson, Indiana, an MS in Computer Science from Ball State University and PhD in Computer Science in 2013 (focus area: real-time multimedia databases) from the University of Central Florida. He is currently a technical advisor at the Air Force Research Laboratory Information Directorate in Rome, NY. Alex’s research interests include multimedia databases, stream processing (via CPU, GPU or coprocessor) and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. For questions contact Min Song at msong6@stevens.edu. DTEND;TZID=America/New_York:20200205T150000 END:VEVENT END:VCALENDAR