Prof. Juyang Weng (IEEE Life Fellow), Brain-Mind Institute and GENISAMA, USA
Prof. Juyang Weng received a BS degree from Fudan University, in 1982, M. Sc. and PhD degrees from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively, all in computer science. He is a former faculty member of the Department of Computer Science and Engineering, faculty member of the Cognitive Science Program, and faculty member of the Neuroscience Program at Michigan State University, East Lansing. He was a visiting professor at the Computer Science School of Fudan University, Nov. 2003 - March 2014, and did sabbatical research at MIT, at Media Lab Fall 1999 – Spring 2000; and at the Department of Brain and Cognitive Science Fall 2006-Spring 2007 and taught BCS9.915/EECS6.887 Computational Cognitive and Neural Development during Spring 2007. Since the work of Cresceptron (ICCV 1993) the first deep learning neural network for a 3D world without post-selection misconduct, he expanded his research interests in biologically inspired systems to developmental learning, including perception, cognition, behaviors, motivation, machine thinking, and conscious learning models. He has published over 300 research articles on related subjects, including task muddiness, intelligence metrics, brain-mind architectures, emergent Turing machines, autonomous programming for general purposes (APFGP), Post-Selection flaws in “deep learning”, vision, audition, touch, attention, detection, recognition, autonomous navigation, and natural language understanding. He published with T. S. Huang and N. Ahuja a research monograph titled Motion and Structure from Image Sequences. He authored a book titled Natural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind. Dr. Weng is an Editor-in-Chief of the International Journal of Humanoid Robotics, the Editor-in-Chief of the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development (now Cognitive and Developmental Systems). With others’ support, he initiated the series of International Conferences on Development and Learning (ICDL), the IEEE Transactions on Autonomous Mental Development, the Brain-Mind Institute, and the startup GENISAMA LLC. He was an associate editor of the IEEE Transactions on Pattern Recognition and Machine Intelligence and the IEEE Transactions on Image Processing.
Speech Speech: Post-Selection Misconduct Protocol in Two Nobel Prizes 2024 and a Holistic Solution
Abstract: This talk exposes that the Nobel Prize for Physics 2024 and the Nobel Prize for Chemistry 2024 use a Post-Selection protocol that has flooded the AI and machine learning field. It explains why the Post-Selection is an invalid protocol for experiments that suffers from three misconducts: (1) cheating in the absence of a test; (2) hiding bad-looking data; and (3) exaggerating the prediction accuracy. Finally, the talk explains that the Post-Selection suffers from the local minima problem among the 20 million-dollar problems, and the Developmental Networks provide a holistic solution to all the 20 million-dollar problems. More details are available in the IEEE CDS Newsletter Vol. 18, No. 4, 2024. https://www.cse.msu.edu/amdtc/amdnl/CDSNL-V18-N4.pdf
Prof. Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway
Jerry Chun-Wei Lin (Senior Member, IEEE) received the Ph.D. degree from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.,He is currently a Full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences, Bergen, Norway. He is also the Project Leader of SPMF, an open-source data mining library, which is a toolkit offering multiple types of data mining algorithms. He has published more than 500 research papers in refereed journals and international conferences. His research interests include data mining, soft computing, artificial intelligence, social computing, multimedia and image processing, and privacy-preserving and security technologies.,Prof. Lin also serves as the Editor-in-Chief for the International Journal of Data Science and Pattern Recognition and an Associate Editor for several top-tier journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Dependable and Secure Computing. He is the Fellow of IET and an ACM Distinguished Scientist.
Prof. Jim Torresen, University of Oslo, Norway
Jim Torresen is a professor at the University of Oslo, where he leads the Robotics and Intelligent Systems research group. He is also a PI at the interdisciplinary Centre of Excellence for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). He received his M.Sc. and Dr.ing. (Ph.D) degrees in computer architecture and design from the Norwegian University of Science and Technology, University of Trondheim in 1991 and 1996, respectively. He has been employed as a senior hardware designer at NERA Telecommunications (1996-1998) and at Navia Aviation (1998-1999). Since 1999, he has been a professor at the Department of Informatics at the University of Oslo (associate professor 1999-2005). Jim Torresen has been a visiting researcher at Kyoto University, Japan for one year (1993-1994), four months at Electrotechnical Laboratory, Tsukuba, Japan (1997 and 2000) and a visiting professor at Cornell University, USA for one year (2010-2011).
His research interests now include artificial intelligence, ethical aspects of AI and robotics, machine learning, robotics, and applying this to complex real-world applications. Several novel methods have been proposed. He has published more than 300 peer-reviewed papers in international journals and conferences. He has given 48 invited talks/keynotes at international conferences and institutions and 21 tutorials at international conferences during the last 10 years. He is in the program committee of more than ten different international conferences, associate editor of three international scientific journals as well as a regular reviewer of a number of other international journals. He has also acted as an evaluator for proposals in EU FP7 and Horizon2020 and is currently project manager/principal investigator in three externally funded research projects/centres. He is a member of the Norwegian Academy of Technological Sciences (NTVA) and the National Committee for Research Ethics in Science and Technology (NENT), where he is a member of a working group on research ethics for AI.
More information and a list of publications can be found here: http://www.ifi.uio.no/~jimtoer
Speech Title: Techno-Ethical Considerations when Applying Machine Learning in Real-world Systems
Abstract: Computational intelligence has entered an increasing number of different domains. A growing number of people – in the general public as well as in research – have started to consider a number of potential ethical challenges and legal issues related to the development and use of such technologies. This keynote will give an overview of the most commonly expressed ethical considerations and ways being undertaken to reduce their negative impact.
Among the most important considerations are those related to privacy, fairness, transparency, safety and security. Countermeasures can be taken first at design time, second, when a user should decide where and when to apply a system and third, when a system is in use in its environment. In the latter case, there will be a need for the system by itself to perform some ethical reasoning if operating in an autonomous mode. This keynote will introduce some examples from our own and others´ work and how the challenges can be addressed both from a technical and human side with special attention to problems relevant when working with machine learning research and development. Ethical issues should not be seen only as challenges but also as new research opportunities contributing to more sustainable, socially beneficial services and systems.
Prof. Jianhua Zhang, Oslo Metropolitan University, Norway
Jianhua Zhang is has been Professor at Department of Computer Science, Oslo Metropolitan
University, Norway, since 2018. From 2007-2017 he was Professor with School of
Information Science and Engineering, East China University of Science and
Technology, Shanghai, China. From 2017 to 2018 he was Scientific Director at Vekia
(a machine learning driven IT company), Lille, France.
Dr Zhang received his PhD in electrical engineering and computer science from Ruhr
University Bochum, Germany, in 2005 and did postdoctoral research at Intelligent
Systems Research Lab, University of Sheffield, UK, from 2005 to 2006. He was a Guest Scientist at
TU Dresden, Germany, from 2002 to 2003 and Visiting Professor at TU Berlin, Germany between
2008 and 2015 and the University of Catania, Italy in 2024.
Dr Zhang has worked in the fields of AI, control systems, and signal processing since mid-1990s. His
current research interests include computational intelligence, machine learning, intelligent systems and
control, biomedical signal processing, and neurocomputing. So far he has published four books, 11
book chapters, and around 200 peer-reviewed journal and conference papers in those areas.
Dr Zhang served as Chair of IFAC (International Federation of Automatic Control) Technical
Committee on Human-Machine Systems (2017-2023) and Vice Chair of IEEE Norway Section (2019-
2023). He currently serves as Vice Chair of IFAC Technical Committee on Human-Machine Systems
(2023-) and Vice Chair of IEEE CIS (Computational Intelligence Society) Norway Chapter (2019-).
He is on editorial board of four international journals, including Frontiers in Neuroscience, Cognitive
Neurodynamics (Springer), and Cognition, Technology & Work (Springer). In addition, he was invited
to serve as keynote speaker or chair for a number of international conferences.
Dr Zhang was listed in Stanford/Elsevier's World Top 2% Scientists Rankings in 2023 and 2024.
Speech Title: Stock Price Forecasting by Means of Transformer-based Ensemble Learning
Abstract: In this talk, for the stock price forecasting problem we compare the performance of several models, including traditional time series analysis model - ARIMA and four machine learning (ML) models (Linear Regression, Long Short-Term Memory (LSTM) network, Prophet, and Transformers). Ensemble learning is proposed to reduce the prediction biases and variances of those individual models. Furthermore, in order to handle the complexity and volatility of real-world stock markets, three different hyperparameters (such as learning rate, number of layers in the network model, batch size, etc.) tuning approaches (grid search, random search, and Bayesian optimization) are compared in terms of prediction accuracy and computational cost. The real stock data analysis results showed that ensemble learning method can improve accuracy and reliability of stock time series forecasting and that the transformer model stacked with linear regression achieved the best prediction performance. The results obtained may provide insights into stock closing price dynamics modeling, stock investment decision, and portfolio management.
Prof. Sami Brandt, IT-University of Copenhagen, Denmark
Prof. Sami Brandt got his doctoral degree in 2002 in Helsinki University of Technology, Finland, on the geometric branch of computer vision applied to electron tomography. After the doctoral degree he worked for one year as a research scientist in Instrumentarium Corporation Imaging Division, Finland, a couple of years in Helsinki University of Technology, Oulu University, Finland, and Malmö University, Sweden, and Nordic Bioscience Imaging/Synarc Imaging Technologies in Denmark. He currently work as associate professor in the Image Group in University of Copenhagen, Denmark. He have been a member of the IEEE, member of the Pattern Recognition Society of Finland, member of the International Association for Pattern Recognition (IAPR), and member of the Finnish Inverse Problems Society.
Speech Title: On the non-rigid structure-from-motion problem: from independent subspace analysis, degenerate basis shapes, and tensor-based factorisation to generative adversarial networks
Abstract: This talk provides an overview of our work on the non-rigid structure and motion problem with the application of the modeling and analysis of human faces. We start by presenting the classic formulation of the problem where the goal is to estimate the non-rigid affine structure and motion from 2D point correspondences and note its known difficulties and approaches taken to tackle them. Thereafter we show how independent subspace analysis can help to achieve a solution where no prior formation, apart from the assumption of statistical independence of the basis shapes, nor camera calibration information is required. Thereafter we develop another solution to the problem, by modifying the common assumption that the non-rigid shape is a linear combination of basis shapes, by adding an additional constraint, that the basis shapes should be degenerate. By this assumption, it is then possible to derive a solution that avoids the central problems of the classic problem setting. We likewise show, how tensor based modelling of faces and non-rigid structure-motion-problem can be unified into single tensor-based modelling problem. In the last part of the talk, we show how our non-rigid structure-from-motion approaches can be extended to generative models such as StyleGAN model to achieve factorization of latent manifolds into camera geometry, pose, and non-rigid structure that opens the way of photorealistic modelling, analysis and editing of human faces and the underlying geometry via the trained generator. The future directions are also discussed.
