Asee peer logo
Well-matched quotation marks can be used to demarcate phrases, and the + and - operators can be used to require or exclude words respectively
Displaying all 2 results

Assessment of Large Language Models for Wildcard Match Identification

Conference Session
Computing and Information Technology Division (CIT) Technical Session 4
Collection
2025 ASEE Annual Conference & Exposition
Authors
Claire Han; Abel Andres Reyes-Angulo, Michigan Technological University; Sidike Paheding, Fairfield University
Tagged Topics
Diversity
Tagged Divisions
Computing and Information Technology Division (CIT)
answer about whether the cookie is a wildcard match.The algorithm was trained on a diverse dataset from the Open Cookie Dataset, showing a potential breakthrough forcybersecurity and AI to secure online safety. Preliminary results provide a promising performance of above 95% ac-curacy and 0.77 MCC score in cookie wildcard match identification.Keywords— Cybersecurity, cookies wildcards match, Large Language Models1 IntroductionIn today’s society, everyday life is heavily dependent on technology. The world has become more interwoven, and communication,entertainment, and knowledge have become convenient, leading to the widespread use of electronics and the Internet. Now, fromwatching television shows to checking bank finances, the Internet has
Relevance Score: 0.013028511

Leading in the AI Era: An Interactive Experiential Hands-On Learning Approach for Professionals and Leaders

Conference Session
Computing and Information Technology Division (CIT) Technical Session 3
Collection
2024 ASEE Annual Conference & Exposition
Authors
Sharifa Alghowinem, Massachusetts Institute of Technology; Aikaterini Bagiati, Massachusetts Institute of Technology; Andrés F. Salazar-Gómez, Massachusetts Institute of Technology; Cynthia Breazeal, Massachusetts Institute of Technology
Tagged Divisions
Computing and Information Technology Division (CIT)
Paper ID #42555Leading in the AI Era: An Interactive Experiential Hands-On Learning Approachfor Professionals and LeadersDr. Sharifa Alghowinem, Massachusetts Institute of Technology Dr. Alghowinem earned her PhD in multimodal AI from the Australian National University in 2015, following an MSc in Software Engineering at the University of Canberra in 2010 and a BSc in Computer Applications at King Saud University in 2004. Stationed at MIT’s Personal Robots Group as a research scientist, she develops AL models that provide insights for enhanced human-robot interaction. With an expertise in multimodal AI, Dr. Sharifa
Relevance Score: 0.012816065