Biography:

Soo-Young Lee has worked on the artificial cognitive systems with human-like intelligent behavior based on the biological brain information processing. His research includes speech and image recognition, natural language processing, situation awareness, top-down attention, internal-state recognition and human-like dialog systems. Especially, among many internal states, he is interested in emotion, sympathy, trust and personality. He led Korean Brain Neuroinformatics Research Program from 1998 to 2008 with dual goals, i.e., understanding brain information processing mechanism and developing intelligent machine based on the algorithm. He is currently the Director of KAIST Institute for Artificial Intelligence and leading Emotional Conversational Agent Project and a Korean National Flagship AI Project. He was the President of Asia-Pacific Neural Network Society in 2017 and had received Presidential Award from INNS and Outstanding Achievement Award from APNNA.

Abstract:

For the successful interaction between human and digital companion, i.e., machine agents, the digital companions need be able to bind with human-like ethics as well as to make emotional dialogue, understand human emotion and express its own emotion. In this talk we present our approaches to develop human-like ethical and emotional conversational agents as a part of the Korean Flagship AI Program. The emotion of human users is estimated from text, audio and visual face expression during verbal conversation and the emotion of intelligent agents is expressed by speech and facial expression. Specifically, we will show how our ensemble networks won the Emotion Recognition in the Wild (EmotiW2015) challenge with 61.6% accuracy to recognize seven emotions from facial expression. Then, a multimodal classifier combines text, voice and facial video for better accuracy. Also, a deep learning based Text-to-Speech (TTS) system will be introduced to express emotions. These emotions of human users and agents interacts each other during the dialogue. Our conversational agents have chitchat and Question-and-Answer (Q&A) modes and the agents respond differently for different emotional states in chitchat mode. Then, the internal states will be further extended into trustworthiness, implicit intention and personality. Also, we will discuss how the agents may learn human-like ethics during the human-machine interactions.

Biography:

Abstract:

Biography:

Qingsong Xu is the Director of Smart and Micro/Nano Systems Laboratory and Associate Professor of Electromechanical Engineering at the University of Macau. He was a Visiting Scholar at the University of California, Los Angeles (UCLA), USA in 2016, the RMIT University, Melbourne, Australia in 2016, the National University of Singapore, Singapore in 2012 and the Swiss Federal Institute of Technology (ETH Zurich), Switzerland in 2011. His current research area involves micro/nano-mechatronics and systems, control and automation and applications of computational intelligence. He is a Senior Member of IEEE and a Technical Editor of IEEE/ASME Transactions on Mechatronics. He has published three monographs and over 240 technical papers in international journals and conferences.

Abstract:

Robotic micromanipulation systems are demanding devices to realize automated manipulation of biological samples. Majority of existing robotic bio-micromanipulation systems work based on displacement sensing and control. The lack of force sensing prevents the wide application of the devices. In modern biotech industry, there are increasing needs for advanced micromanipulation equipment with microforce sensing and control capabilities. The development of force-sensing microinjectors and microgrippers enable extensive applications involving biological field with guaranteed safety and accuracy of advanced robotic manipulation. This talk reports our recent work on design and development of new force-sensing robotic micromanipulation systems dedicated to biological micromanipulation tasks. In particular, force-sensing microinjector and force-sensing microgripper are presented as typical examples. New microforce sensor design is conducted in details. Novel control schemes have been developed to fuse the position and force control to enable a safe and reliable manipulation. The effectiveness of the systems has been demonstrated by carrying out microinjection and microgripping operation of biological cells. The developed force-sensing robotic bio-micromanipulation systems have demonstrated wide applications in the fields of biomedical engineering, gene engineering and so on.

Biography:

Menchita F Dumlao is currently the Research Director of Philippine Women’s University, Philippines. Concurrently, she is an Associate Professor and Program Chair of Department of Information Technology and Technology Consultant of Imergex Information Technology, Inc. Her research interest is in the area of data science, machine learning and artificial intelligence.

Abstract:

Central Emergency Response Management system (CERM) facilitates estimation of dispatch response time and route or direction from the origin of the resource to incident site. The dispatch determined the capability required to a specific incident. Its core technology is Call Taking Management System (CTMS) which applies stochastic optimization to collaborate sub-systems and make everything else complete. They are connected end to end to create a consolidated database system leading to a criminal information system and incident command system which includes call taking and logging module, resource unit dispatch and geo-mapping module, SMS messaging services module, resource availability and dispatch. It can record audio patch from the telephone line through the audio input of the PC (client computer). The provided information on incident, incident type, location and other important information needed by the dispatcher (police). The map provides an aerial view (satellite image) of the location of an incident for the nearest equivalent search parameter within 10,000 meters from the center of crime. This technology shows the best route possible from the unit resource (of the crime) origin to the incident site. The control also shows text-based instruction, estimated time and distance.