From Human Machine Interaction to Human Machine Intelligence

The levels of cooperation between HUMANS and MACHINES are in forms of Interaction, Integration, and Intelligence: Interaction can be described as stimulus-response which implies the Machines are just wait and do what Humans order; Integration implies partnership between the human and computer in which information is exchanged for physically WORKING together; Intelligence implies partnership between the human and computer in which information is exchanged for THINKING together. There is a continuum from Interaction to Integration then Intelligence. Doing research in HMI extends the level of human and machine cooperation from Interaction to Integration then Intelligence. I devote this talk in the discussion about the transformation from Interaction to Integration then Intelligence.

Assoc. Prof. Le Thanh Ha – Vietnam National University (Vietnam)

Le Thanh Ha received B.S. and M.S. degrees in Information Technology from the College of Technology, Vietnam National University, Hanoi. In 2005, he received a Korean Government Scholarship for Ph.D program at the Department of Electronics Engineering at Korea University and got Ph.D degree in 2010. After graduation, he joined the Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi as an Associate Professor. His research interests are image/video analysis and processing, satellite image processing and computer vision. He has deep experiences in teaching Digital Image Processing, Computer Vision, Multimedia Communication courses for both undergraduate and postgraduate programs. He has also been principle and main investigator of many fundamental research and technology development projects funded by both domestic and international organizations. He also makes contributions in serving many domestic and international ICT academic conferences including KSE, NICS, ATC, SoICT, ICEIC, … In addition, he is a member of the Institute of Electrical and Electronics Engineers (IEEE), The Institude of Electronics, Information and Communication Engineers (IEICE) and The Vietnamese Association for Pattern Recognition (VAPR).

Pattern recognition: feature engineering and (deep) feature learning

Feature extraction is one of the most important steps in any pattern recognition tasks. The traditional approach is to design different types of local and global function to build-up the feature map. In contrast, the new deep architecture of convolutional neural networks automatically forms the feature maps by learning convolution operators. How the two approaches are similar and different is one main topic of this tutorial. The second topic of the talk is how context information is used in recognition tasks. Example in optical character recognition will be used to characterize the difference between the traditional dictionary/language model and recently emerging long sort-term memory networks.

Duc-Dung NGUYEN received the Bachelors degree in mathematics in 1994. He received the Masters and Ph.D. degrees in knowledge science from the Japan Advanced Institute of Science and Technology, Japan, in 2003 and 2006, respectively.
He was a Research Engineer at KDDI R&D Laboratories Inc., Japan, from 2007 to 2009. He is now with the Institute of Information Technology, Vietnam Academy of Science and Technology, Ha Noi, Vietnam. His research interests include machine learning, pattern recognition, and data mining. Dr. Nguyen was awarded the Innovative Medal from the Youth Union of Vietnam in 1998 for developing the first Vietnamese optical character recognition software, and the Technical Support Achievement Award in 2008 for his contributions at KDDI R&D Laboratories.

Dialogue Engine Algorithms for Personal Artificial Intelligence (P.A.I.)

Evolutionary Multitasking – A New Paradigm

We are in era where many methods of computational problem-solving methodologies are being developed to address the diverse issues that researchers are intersted. Traditional methods for optimization, including the population-based search algorithms of Evolutionary Computation (EC). Have generally been focused on efficiently solving only a single optimization task at a time. In fact, the variety and volume of incoming information streams that must be absorbed and appropriately processed, the need to multitask are unprecedented. Recently, Multifactorial Optimization (MFO) has been developed to explore the potential for evolutionary multitasking. The pursuit of intelligent systems and algorithms that are capable of efficient multitasking is rapidly gaining importance among contemporary scientists who are faced with the increasing complexity of real-world problems. We introduce the characteristic of population-based search algorithms. Their inherent ability (much like the human mind) to handle multiple optimization tasks at once. Most notably, it shows that multi-tasking allows a person to automatically promote common ground between different optimization tasks, thus providing a significant scope for improvement in problem solving in the real world.

Assoc. Prof. Huynh Thi Thanh Binh – Hanoi University of Science and Technology (Vietnam)

Huynh Thi Thanh Binh is Associate Professor and Vice Dean of the School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST). She is Head of Modeling, Simulation and Optimization Lab (MSO).
Her current research interests include – Computational Intelligence, Artificial Intelligence, Memetic Computing, Evolutionary Multitasking. She has published more than 80 refereed academic papers/articles, 2 Books; Editor 1 Book. She is Associate Editor of the International Journal of Advances in intelligent Informatics, VNU Journal Computer Science Communication Engineering; Editor Board of Journal of Computer Science and Cybernetics,. She has served as a regular reviewer, a programme committee member of numerous prestigious academic journals and conferences, such as IEEE Transactions on Vehicular Technology, Journal of Information Science and Engineering, IES, SoICT…
She is member of IEEE Computational Intelligence Society – Women in Computational Intelligence Committe (2017, 2018); Chair of IEEE Computational Intelligent Society Vietnam Chapter (IEEE Vietnam CIS). She is member of some committee of IEEE Asia Pacific: Strategic Planning, Membership Development, Humanitarian Technology Activities.

Data science: A key in the digital transformation time

This talk consists of two parts. First to discuss the decision making in a digital economy and a digital society, as well as the relationship between AI and recent ICT breakthrough including data science. Second to illustrate the power of data science through problems and solutions in medicine, transportation, customer relationship, and more.

Prof. Ho Tu Bao – Japan Advanced Institute for Science and Technology (Japan)