Invited Speakers
Prof. Atsushi Inoue

College of Business and Public Administration, Eastern Washington University, USA

Biography: Atsushi Inoue is specialized in Artificial Intelligence at large and Fuzzy Logic in specific. He has been affiliated with top-notch industries and institutes in several countries, including Hitachi Ltd. (Japan) and Carnegie Mellon University (USA). He is currently home at Eastern Washington University to enjoy his life with his family in the beautiful evergreen and necessary freedom.

Speech Title: Modern Information Security Management and Roles of Fuzzy Sets
Abstract: This talk suggests the roles of Fuzzy Sets for the modern Information Security Management. Especially, the automated and real-time network management based on IT policies currently set forth is centered. Opensource technologies are utilized for research and prototype. This is also viewed as a multiple agent system, a big data management, and a multiple criteria decision making setting. Each view is described with possible project proposals.
Assoc. Prof. Nick Pears

Department of Computer Science, University of York, UK

Biography: Nick Pears was awarded both a BSc in Engineering Science and a PhD in Robotics (1990) by Durham University, UK. He then worked in the Robotics Research Group, University of Oxford and the Speech, Vision and Robotics Research Group (now Machine Intelligence Lab), University of Cambridge, where he was a fellow of Girton College. More...

Speech Title: Automatic Construction of 3D Morphable Models of Shape from Large Scale Datasets
Abstract: Morphable models of 3D surface shape have many applications in medical image analysis, biometrics and creative media. Traditional model building pipelines have used manual landmarking to generate surface correspondences and initialise surface alignment procedures. However, this is extremely time-consuming and laborious for large scale datsets. Here we present a fully automatic approach and apply it to a large dataset of 3D images of the human head, thus generating the first 3D morphable model of the full craniofacial region that models both shape and texture variation. Our approach employs automatic 2D landmarking of the face, which is projected to 3D using the known 2D-to-3D registration generated by the image capture system. This facilitates normalisation of head pose to a canonical position, which matches that our template model. We then employ a hierarchical parts-based template morphing procedure, which is based on Coherent Point Drift. By morphing the same template to every 3D image in the dataset we achieve full surface vertex correspondence across the whole dataset. Generalised Procrustes Analysis is employed to place each scale-normalised template into a common alignment and subsequently Prinicipal Compenent Analysis is used to generate our statistical model. We demonstrate the ability of the model to represent a wide range of faces and we present a case study of the use of the model in the analysis of the pre- and post-operative cranial shape of a set of craniosynostosis patients.
Associate Prof. Shieu-Hong Lin

Mathematics / Computer Science Department, Biola University

Biography: Dr. Shieu-Hong Lin received his Ph.D. in Computer Science from Brown University in 1997 and is a Professor of Mathematics and Computer Science at Biola University, Los Angeles, USA. His research interests include algorithms, artificial intelligence, automatic reasoning and formal verification, combinatorial optimization, data mining and machine learning. He has published many articles in journals and conference proceedings in these research areas.

Speech Title: Automatic Verification of Event Sequences with Temporal and Causal Information
Abstract: Business workflows and industrial processes often require multi-agent collaboration in a distributed environment. When agents take actions, they trigger state-transition events in the system. We can describe the behavior of the agents in terms of the temporal relationships of their actions and the state-transition events triggered by their actions. We model such state-transition events as compact causal rules regarding the statuses of state variables before and after the events. To ensure the security of any given multi-agent distributed environment like this, we need to automatically verify the interactions of the agents to ensure they never trigger an event sequence leading to an undesirable system state. The verification task is essentially a control problem over the underlying non-deterministic discrete dynamic system. In this talk, we describe algorithms and complexity results regarding the verification task for distributed multi-agent systems in various contexts. We show that this computational task can be accomplished effectively in polynomial time in many interesting practical contexts.
Dr. Gautam Srivastava

Dept. of Computer Science, Brandon University, Canada

Biography: Dr. Gautam Srivastava was awarded a B.Sc. from Briar Cliff University in Sioux City, Iowa, U.S.A. in 2004, followed by M.Sc. and Ph.D. from the University of Victoria in Victoria, British Columbia, Canada, in the years 2006 and 2011, respectively. More...

Speech Title: Measuring Twitter User Influence Through a Discrete Event
Abstract: NASA is viewed as a prime piece of the frontier of human knowledge by several generations, and is relied upon to educate the public on astronomical matters. With the Great American Eclipse of 2017, NASA’s production was crucial to the general public’s awareness and understanding of the event. With the explosion of data mining avenues and techniques, being able to study and quantify such major events has become of utmost importance for many of the involved. Our goal with this research is to understand how the public perceived the social media coverage that NASA had provided. We accomplish this through sentiment analysis and the spotting of trends within Twitter data. Furthermore, we follow a framework of study that allows simple and cost-effective analysis of discrete events of arbitrary nature in the future.
Dr. Yoshifumi Nishida

Biography: Yoshifumi Nishida is a Team Leader of Living Intelligence Research Team at the Artificial Intelligence Research Center (AIRC) of the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. He received a PhD from the Graduate School of Mechanical Engineering, the University of Tokyo in 1998. In 1998, he joined Intelligent System Division of Electrotechnical Laboratory at AIST of the ministry of international trade and industry (MITI), Japan. In 2001, he joined Digital Human Laboratory. In 2003, he joined the Digital Human Research Center (DHRC) of AIST. He is also a Prime Senior Research Scientist at AIST from 2013. His research interests include human behavior sensing, human behavior modeling, injury prevention engineering, and social participation support. He is a member of the Robotics Society of Japan, and the Japanese Society of Artificial Intelligence. He received the best paper awards from the Robotics Society of Japan, the Japan Ergonomics Society, and the Information Processing Society of Japan.

Speech Title: Living Safety for Diversity in the Era of IoT and Artificial Intelligence
Abstract: Today it has become more necessary to address the physical and cognitive changes faced by children, elderly and disabled persons, and to ensure their safe livings and maintain active social participation levels. The society for this diversity should be redesigned into such one that is resilient to human living function changes. Recent artificial intelligence technology and internet of things technology allow us to systematically collect and analyze living data fragmented into multiple institutes and other living spaces. The Artificial Intelligence Research Center of AIST is developing the living intelligence technology that collects data from individuals with a wide variety of living functions and individualizes necessary interventions for them by networking multiple living laboratories located in local communities.