The Montreal Chapters of the IEEE Control Systems (CS), Systems, Man & Cybernetics (SMC), and Circuits & Systems (CAS) cordially invite you to attend the following in-person talk, to be given by Dr. Marzieh Amini from Carleton University. Co-sponsored by: Concordia University Speaker(s): Dr. Marzieh Amini Room: EV002.309, Bldg: EV Building, Concordia University , Montreal, Quebec, Canada, H3G 1M8

In her talk Prof. Hatice KOSE will be presenting some of her recent robotic and AI based projects for children with disabilities and the emotion recognition models her team developed on different data modalities such as vision, audio, and physiological data and multimodal fusion of these. She will also share her experience on designing robotic experiments for children, and ethical use of AI and data in studies involving vulnerable groups. BIO: Hatice Kose holds a PhD in computer engineering and she is a full-time professor in AI and Robotics at the Faculty of Computer and Informatics Engineering, Istanbul Technical University (ITU), Turkey. She is the founder and coordinator of the Cognitive Social Robotics Lab, and Game and Interaction Technologies Lab. She is involved with the administration of AI and Data Engineering Department, Computer Engineering, and Game and Interaction Technologies Graduate programs. She is an IEEE Senior Member and has been working in Robotics and Artificial Intelligence for more than 20 years. Her research focuses on developing affective and social robotic systems for children with disabilities over a decade. Her expertise includes the Ethical use of AI in human centric applications, especially applications involving vulnerable groups and AI for Good. She pioneered in robotics for sign language tutoring, robotics and AI based research for children with hearing impairments. Her research team is developing robotic applications for humanoid robots and affective models including ML/DL models for emotion/stress/interaction recognition especially for children, in both application and theoretical levels. Their main motivation is to develop affective social robots for education, therapy and health applications using these models. She is part of several national and international research projects funded by European Union in collaboration with the leading European research facilities and universities, involving robotic and AI assisted health applications for children with disabilities such as hearing impairment, autism and cerebral palsy. Recently, her work is recognized by the EELISA European consortium, and she received 1st EELISA Diversity Award in October, 2023 with her research activities. Virtual: https://events.vtools.ieee.org/m/451115

Discuss recent advancements; Meet new member; Discuss future plans Room: M-6002, Polytechnique Montréal, Montréal, Quebec, Canada

Concrete encased electrodes for grounding electrical power systems, also called Ufer grounds, are highly effective grounding systems when installed correctly. James will explain what constitutes a Ufer ground and how one can be easily constructed in accordance with the IEEE Standard 142-2007 (the Green Book) and the National Electrical Code (NEC). He will also explain what common errors and myths surround Ufer grounds as well as an error in the IEEE Green Book, and errors in interpreting the NEC. James will also explain how Ufer grounds are essential to effective lightning protection systems. James’ background as an experienced construction electrician and a B.S. Civil Engineer makes him uniquely qualified to explain civil/structural concepts and practices, and how these are relevant to Electrical Engineers. Co-sponsored by: IEEE Hamilton PES Chapter, and other PES Chapters in R7 Speaker(s): James J. Mercier, Virtual: https://events.vtools.ieee.org/m/450987

[] Free Registration (with a Zoom account; you can get one for free if you don't already have it): https://sjsu.zoom.us/meeting/register/tZcsc-CoqjwpG9aPDHfg6Axqvn90i4uQRmqr Synopsis: For a long time, the AI/ML community relied on traditional evaluation metrics such as the confusion matrix, accuracy, precision, and recall for assessing the performance of machine learning models. However, the rapidly evolving field has been raising several ethical concerns, which calls for a more comprehensive evaluation scheme. In easy-to-understand language, this talk will delve into the quantitative analysis of model performance, emphasizing the critical importance of explainability. As ML models become increasingly complex and pervasive, understanding their decision-making processes is paramount. We'll explore various performance metrics, their limitations, and the growing need for transparency. Topics covered include Cohen’s Kappa Statistic, Matthew's correlation coefficient (MCC), Confusion Matrix, Precision, Recall, G-measure, ROC Curve, Youden's J statistic, Type II Adversarial attack, R-squared, LIME, SHAP, and more. Speaker(s): Dr. Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/442073