Autonomous Bootstrapping of Collective Motion Behaviours for Swarming Robots
Collective behaviours such as swarm formations of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, with the explosion in hardware platforms for autonomous vehicles, swarm robotic programming requires significant manual input for each new platform. This talk introduces two developmental approaches to evolving collective behaviours whereby the developmental process is guided by a task-non-specific value system. Two value systems will be considered: the first based on a survey of human perception of swarming and the second based on a computational model of curiosity. Unlike traditional approaches, these value systems do not need in advance the precise characteristics of the intended swarming behaviours. Rather they reward the emergence of structured collective motions, permitting the emergence of multiple collective behaviours, including aggregation and navigation behaviours. This talk will examine the performance of these value system in a series of controlled experiments on point-mass ‘boids’ and simulated robots. We will see how the value systems can recognise multiple “interesting” structured collective behaviours and distinguish them from random movement patterns. We will also see how the value systems can be used to tune random motions into structured collective motions.
Kathryn Kasmarik is a Professor of Computer Science at the University of New South Wales, Australian Defence Force Academy (UNSW Canberra). Kathryn completed a Bachelor of Computer Science and Technology at the University of Sydney, including a study exchange at the University of California, Los Angeles (UCLA). She graduated with First Class Honours and the University Medal in 2002. She completed a PhD in Computer Science through the National ICT Australia and the University of Sydney in 2007. She moved to UNSW Canberra in 2008. Kathryn’s research interests lie in the area of autonomous mental development for computers and robot.
Computational Intelligence for Disaster Planning and Mitigation
Infrastructure planning and restoration following a disaster is a complex problem requiring the integration of multiple data types and the evaluation of complex and potentially nonlinear relationships. This research uses publicly available data sets shared by the United States Geological Survey (The National Map, Digital Elevation Models, seismic data), the United States Army Corps of Engineers (River Discharge data, Lock & Dam and Levee inventory), and the U.S. National Weather Service (rainfall data, predicted weather patterns). This body of work examines evolutionary computation for infrastructure restoration planning in the aftermath of tornadoes and earthquakes. The use of neural networks is considered to predict changing water levels to capture the timing and impact of flooding and flash flooding events on transportation infrastructure. Algorithms have been used to develop plans for rerouting traffic to minimize the impact of the flooding event on the transportation system, as well as the risk to human lives. This allows emergency management and transportation engineering managers to make better decisions related to safety and the restoration of critical infrastructure elements.
Steven M. Corns an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology. He received his PhD degree in mechanical engineering from Iowa State University in 2008. Dr. Corns research interests include computational intelligence applications, the mechanics of information transfer in evolutionary algorithms, and model based approaches for complex systems design and analysis. Applications include computational biology/bioinformatics, transportation, and defense. He has applied computational intelligence techniques to disaster modeling and restoration planning for several years, including four projects involving flooding prediction and associated traffic rerouting.
Understanding the Complexity of Financial Systems of Systems
Why are the financial markets so problematical? One reason is that financial markets consist of many systems – having other complex systems as components – that interact in complicated ways. The focus of this webinar is to explain the complexity of several financial systems in terms of several properties that are characteristic of large, diverse, complex systems. We distill this framework for financial systems of systems to include properties describing interactions among other system components; properties relating to interactions within the system environment; and properties relating to interactions with time. In this webinar, we discuss how these properties impact financial systems involved with high frequency trading (connectivity vs. latency arbitrage); market fragmentation (autonomy vs. regulatory-induced feedback); and credit (model diversity vs. copula model risk).
Roy S. Freedman founded Inductive Solutions, Inc., to develop commercial AI software for genetic algorithms, neural networks, and case-based reasoning. His primary consulting interests involve solving problems associated with trading, operations, risk management, and compliance by using mathematics, statistics, and computer technology. As a consultant, he worked with stock exchanges, buy-side and sell-side firms, technology companies, startups, and government institutions. During the third term of the Bloomberg mayoral administration, he was the principal AI consultant to the Chief Information Officer of New York City Health & Human Services. As an expert witness, he testified on the federal level concerning financial and regulatory technology related to high-frequency trading. Dr. Freedman teaches various courses related to financial systems (FinTech) and regulatory systems (RegTech) as adjunct professor in the department of Finance and Risk Engineering at the New York University Tandon School of Engineering (formerly Polytechnic University).
Active Learning by Learning
Active learning is an important technique that helps reduce labeling efforts in machine learning applications. Previously, most active learning strategies are constructed based on some human-designed philosophy; that is, they reflect what human beings assume to be "good labeling questions." However, given that a single human-designed philosophy is unlikely to work on all scenarios, choosing and blending those strategies under different scenarios is an important but challenging practical task. This paper tackles this task by letting the machines adaptively "learn" from the performance of a set of given strategies on a particular data set. More specifically, we design a learning algorithm that connects active learning with the well-known multi-armed bandit problem. Further, we postulate that, given an appropriate choice for the multi-armed bandit learner, it is possible to estimate the performance of different strategies on the fly. Extensive empirical studies of the resulting algorithm confirm that it performs better than strategies that are based on human-designed philosophy. We release an open-source package libact that includes the algorithm to make active learning more realistic for general users.
Prof. Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and was promoted to an associate professor in 2012, and has been a professor since August 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier in various domains, such as digital marketing and business intelligence. Currently, he keeps growing with Appier as its Chief Data Science Consultant.
Intelligent Biomedical Image Analysis
Abstract: Medical imaging inherently entails imperfection, and is therefore an appropriate domain for involving computational intelligence. We introduce the concepts of quantitative imaging and radiomics, followed by a discussion on segmentation by clustering. Next we describe an automated and fast detection algorithm for brain tumor in MRI, and its efficient segmentation. Visual saliency is utilized for a fast localization and detection of the tumor. Use of just a single user-provided seed for an efficient delineation of the GBM tumor is also elaborated. The role of neuro-fuzzy feature selection in brain tumor detection and its classification is also detailed. Finally a discussion on Radiogenomics, and Personalized Medicine concludes the talk. Biography: Sushmita Mitra is a full professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada; Meiji University, Japan; and Aalborg University Esbjerg, Denmark. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, the CIMPA-INRIA-UNESCO Fellowship in 1996, and Fulbright-Nehru Senior Research Fellowship in 2018-2020. She was the INAE Chair Professor during 2018-2020.
Sushmita Mitra is a full professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada; Meiji University, Japan; and Aalborg University Esbjerg, Denmark. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, the CIMPA-INRIA-UNESCO Fellowship in 1996, and Fulbright-Nehru Senior Research Fellowship in 2018-2020. She was the INAE Chair Professor during 2018-2020.
Webinar Title: Convolutional Networks for Medical Image Analysis: Its Past, Future, and Issues
Webinar Speaker: Prof. Pau-Choo Chung (Julia)
Webinar Chair: Dr. Sansanee Auephanwiriyakul
Webinar Recording: https://attendee.gotowebinar.com/recording/8557093691478808080
PowerPoint Slides: https://attendee.gotowebinar.
Recent advancement of image understanding with deep learning neural networks has brought great attraction to those in image analysis into the focus of deep learning networks. While researchers on video/image analysis have jumped on the bandwagon of deep learning networks, medical image analyzers would be the coming followers. The characteristics of medical images are extremely different from those of photos and video images. The application of medical image analysis is also much more critical. For achieving the best effectiveness and feasibility of medical image analysis with deep learning approaches, several issues have to be considered. In this talk we will give a brief overview of the development of neural networks for medical image analysis in the past and the future trends with deep learning. Several issues in regard of the data preparation, techniques, and clinic applications will also be discussed.
Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received the Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and has become a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), the Vice Dean of College of Electrical Engineering and Computer Science (2011), the Director of the Center for Research of E-life Digital Technology (2005- 2008), and the Director of Electrical Laboratory (2005-2008), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as Program Director of Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Taiwan. She served asthe Director of the Department of Information and Technology Education, Ministry of Education.
Dr. Chung’s research interests include computational intelligence, medical image analysis, video analysis, and pattern recognition. She applies most of her research results to healthcare and medical applications. Dr. Chung participated in many international conferences and society activities. She served as the program committee member in many international conferences. She served as the Publicity Co-Chair of WCCI 2014, SSCI 2013, SSCI 2011, and WCCI 2010. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems(2013-2015) and IEEE Transactions on Biomedical Circuits and Systems.
Dr. Chung was the Chair of IEEE Computational Intelligence Society (CIS) (2004-2005) in Tainan Chapter, the Chair of the IEEE Life Science Systems and Applications Technical Committee (2008-2009). She was a member in BoG of CAS Society (2007-2009, 2010-2012). She served as an IEEE CAS Society Distinguished Lecturer (2005-2007) and the Chair of CIS Distinguished Lecturer Program (2012-2013). She served on two terms of ADCOM member of IEEE CIS (2009-2011, 2012-2014), the Chair of IEEE CIS Women in Engineering (2014). She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008. She also served as the Vice President for Members Activities of IEEE CIS (2015-2018).
Past Webinars: 2020
Women in Computational Intelligence: Membership Promotion and Insight on Competitions in the IEEE Computational Intelligence Society
Let us measure STEAM
STEAM is an extension of the well-known teaching approach called STEM (Science, Technology Engineering, and Mathematics). Adding the A for Arts, it becomes STEAM. Many people are working in something that became a new trend: STEM teaching. But there are very few metrics defined to evaluate the goodness of this type of activity. This webinar presents some concepts, the work of a team led by the Digital Communications Institute of the Argentine Scientific Society, and the first findings.
Introduction of STEAM, the cognitive problem in the learning process the life cycle of a STEAM project data, metrics, and findings conclusions .
Daniela López De Luise is a doctor in Computer Sciences. Se founded and lead CETI (Center for Intelligent Technologies) at National Academy of Sciences in Buenos Aires, is the main project leader of STEM/STEAM’s activities at the Scientific Society of Argentina, and the leader of Museum-Lab LINCIEVIS for STEAM activities. Director of CI2S Lab (Computational Intelligence & Information Systems Lab), since June 2013. Director of IDTI Lab (Institute of Information Technology) since March 2017. Director of project LEARNITRON at CAETI research center. Undergraduate and graduate teacher of Universidad Autónoma de Entre Ríos (UADER), Universidad Tecnológica Nacional (UTN), and Universidad Abierta Interamericana (UAI). Consultant in Intelligent Systems