To disseminate advanced knowledge on hot topics in computational intelligence seminars will be organized and distributed through the web free of charge to our members. Past webinars can also be accessed by members free of charge.
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The IEEE CIS Webinars Subcommittee has sponsored the following webinars:
Webinar Speaker: Dr. Leandro L. Minku
Webinar Chair: Keeley Crockett
Webinar Title: A Novel Data Augmentation Technique for Regression Problems and Its Application to Software Effort Estimation
Date and Time: 12th November 2018. 17.00 (GMT)
Abstract Despite the large amount of data being produced by various real world applications, supervised learning problems still frequently face training data scarcity due to the high cost of collecting labelled data. This can potentially lead to poor predictive models. One way to deal with such scarcity is to use data augmentation techniques. Even though there are several data augmentation techniques for classification problems, very few studies have investigated data augmentation for regression problems. In this talk, I will present a novel data augmentation technique for regression problems. The technique generates synthetic data by perturbing the input and output features of existing training examples. Experiments using datasets from the context of software effort estimation are performed to (1) evaluate the effectiveness of the proposed technique and (2) to understand when and why it works well. The experiments show that the proposed technique is able to significantly improve the predictive performance of baseline models especially when the training data is insufficient and when the baseline models are global rather than local learning algorithms. When the proposed technique cannot significantly improve predictive performance, it is not detrimental either. Besides, our technique performs similarly or better than an existing data augmentation technique for software effort estimation. Therefore, our proposed technique is useful to tackle training data scarcity in regression problems.
Registration URL: https://attendee.gotowebinar.com/register/7450936597671121922
Webinar ID: 753-679-603
Biography: Dr. Leandro L. Minku is a Lecturer in Intelligent Systems at the School of Computer Science, University of Birmingham (UK). Prior to that, he was a Lecturer in Computer Science at the University of Leicester (UK). He received the PhD degree in Computer Science from the University of Birmingham (UK) in 2010. During his PhD, he was the recipient of the Overseas Research Students Award (ORSAS) from the British government and was invited to a 6-month internship at Google. Dr. Minku's main research interests are machine learning in non-stationary environments / data stream mining, online class imbalance learning, ensembles of learning machines and computational intelligence for software engineering. His work has been published in internationally renowned journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. Among other roles, Dr. Minku was a co-chair for the IJCAI'17 Workshop on Learning in the Presence of Class Imbalance and Concept Drift and a guest editor for the Neurocomputing Special Issue on this topic, and is a steering committee member for the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), an Associate Editor for the Journal of Systems and Software, and a conference correspondent for IEEE Software.
Webinar Speaker: Professor Jon Garibaldi
Webinar Chair: Keeley Crockett
Webinar Title: Developments in Type-2 Fuzzy Logic
Date and Time: 10th December 2018. 16.00 (GMT)
Abstract Type-2 fuzzy sets and systems, including both interval and general type-2 sets, are now firmly established as tools for the fuzzy researcher that may be deployed on a wide range of applications and in a wide set of contexts. However, in many situations, the output of type-2 systems are type-reduced and then defuzzified to an interval centroid, which are then often even simply averaged to obtain a single crisp output. Many successful applications of type-2 have been in control contexts, often focussing on reducing the RMSE. This is not taking full advantage of the extra modelling capabilities inherent in type-2 fuzzy sets. In this talk, I will present some recent research being carried out within the LUCID group at Nottingham into type-2 for modelling human reasoning. I will cover approaches and methodologies which make more use of type-2 capabilities, illustrating these with reference to practical applications such as classification of breast cancer tumours, modelling expert variability, and other decision support problems.
Registration URL: https://attendee.gotowebinar.com/register/1090820383071903235
Webinar ID: 879-280-491
Biography: Professor Jon Garibaldi received the BSc degree in Physics from University of Bristol, UK, in 1984, and MSc degree and PhD degree from the University of Plymouth, UK, in 1990 and 1997, respectively. Prof. Garibaldi is currently Head of School of Computer Science, University of Nottingham, Head of the Intelligent Modelling and Analysis (IMA) Research Group, Member of the Lab for Uncertainty in Data and Decision Making (LUCID) and joint Director of the Advanced Data Analysis Centre (ADAC). His main research interests include modelling uncertainty and variation in human reasoning, and in modelling and interpreting complex data to enable better decision making, particularly in medical domains. Prof. Garibaldi is the current Editor-in-Chief of IEEE Transactions on Fuzzy Systems. He has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN.
Webinar Speaker: Professor María Daniela López De Luise
Webinar Chair: Keeley Crockett
Webinar Title: Theories for Modelling Reasoning
Date and Time: December 19th, 2018. 13.00 (GMT) and 09:00 in Argentina
Abstract: There are many approaches to model the different abilities of the brain. In this short webinar, three of them will be briefly introduced: Morphosyntactic Linguistic Wavelets, Fuzzy Harmonic Systems, and Bacteria Reasoning. Some of their current and potential applications will also be presented
Registration URL: https://attendee.gotowebinar.com/register/6817907071459647745
Webinar ID: 705-024-091
Biography: Professor María Daniela López De Luise has a master’s degree in System Analysis (Buenos Aires University, 1989), Expert System Engineering (INSTITUTO TECNOLOGICO DE BUENOS AIRES, 2000), Ph.D. Computer Sciences (Universidad Nacional de La Plata, 2008). Director, founder, and researcher at CI2S Labs, director of IDTI Lab (UADER, Entre Ríos Argentina), director of graduated Career in Computer Sciences, Lecturer for the local IEEE Lecturer Program, founder and vice-chair of the IEEE CIS Argentina, director of the IEEE GTC Argentina, and IEEE WCI. Declared Eminent Engineer of Region 9 (IEEE). Among other prizes: Banco Rio, Sadosky, CIITI, TRIC. Currently researching in the heuristic prospection of natural language production.
Past Webinars: 2018
Title: Knowledge discovery with Genetic Programming based Symbolic Regression
Speaker: Professor Qi Chen
Chair: Bing Xue
Date and Time: 11th June 2018 at 09:00 BST. This is 8am GMT time (due to British summer time)
Genetic Programming (GP) based symbolic regression, as a kind of regression analysis, is to find the relationship between the input data and the output data and express this relationship in a mathematical model for some given data of the unknown process. GP based symbolic regression provides a way to getting a good insight into the data generating systems. It is extremely useful when we do not have any domain knowledge of the data generating process. At the same time, by not requiring any specific model and letting the patterns in the data itself reveal the appropriate models, GP based symbolic regression is not affected by human bias. It is clear that the importance of GP based symbolic regression will increase as the complexity of the solved problems are increasing in science and industry. In this webinar, we will discuss the background and basic mechanism of GP based symbolic regression, and enhancements that have improved symbolic regression.
Registration link: https://attendee.gotowebinar.com/register/8540749429968458754
Webinar ID: 208-996-579
Qi Chen received the B.E. degree in automation from the University of South China, Hunan, China, in 2005, the M.E. degree in software engineering from the Beijing Institute of Technology, Beijing, China, in 200, and the Ph.D. degree in Computer Science from Victoria University of Wellington (VUW), Wellington, New Zealand. Since 2014, she has been with the Evolutionary Computation Research Group, VUW. Her current research interests include genetic programming for symbolic regression, machine learning, evolutionary computation, feature selection, feature construction, transfer learning, domain adaptation, and statistical learning theory. Ms Chen serves as a Reviewer of international conferences, including the IEEE Congress on Evolutionary Computation, and international journals, including the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Knowledge-based Systems and the Journal of Heuristics.
Title: Challenging the stigma surrounding the role of women in technology, a journey from combinatorial optimization to IBM
Speaker: Dr Amy Khalfay, Early Career Researcher, IBM
Date and Time: 8th June 2018 at: 15:00 BST
This session will cover the role of females within the technology and wider STEM sector. Many people feel that you must have studied a certain degree, know a programming language, or prefer to work alone to be able to have a career in technology. This is not the case, these careers are open to everyone, from any background. During this session we will be exploring some of the misconceptions about careers within STEM, discovering the many types of roles and doing some myth busting. We will also discuss my personal journey to becoming a graduate technology consultant for IBM, my background of research and my commitment to ensure more females enter STEM careers. My PhD, titled "Optimization heuristics for solving technician and task scheduling problems", focused on solving NP hard combinatorial optimization problems that arise in the real world and was sponsored by industry. The project enabled me to enhance my soft skills, write academically, learn to code and develop a deeper understanding of real world business problems and innovative ways to solve them.
Registration link: https://attendee.gotowebinar.com/register/41855499930987777
Webinar ID: 938-415-115
Dr Amy Khalfay is currently a graduate technology consultant for IBM, joining in October 2017. Prior to this Amy completed a BSc in Mathematics (2014) and a PhD in Operational Research (2017). Amy is also a committee member of IEEE Women in Engineering. Amy’s research area is combinatorial optimisation, solving NP hard scheduling problems. Areas of skill include Java, Statistical Analysis, Mathematical Modelling and Algorithm Design and Development.
Title: The Social and Ethical Implications Of Computational Intelligence
Speaker: Dr Matt Garratt, University of New South Wales
Date and Time: 17th May 2018 at 09:00 BST.
Computational Intelligence (CI) is a term encompassing a basket of soft-computing methodologies used to solve problems that are not suited to solution by mathematical or other traditional methods. CI techniques include technologies such as fuzzy logic, artificial neural networks, deep learning, evolutionary computation and cognitive and developmental systems. Today, CI techniques are embodied within many technologies ranging from autonomous driverless cars to automated decision making on the stock exchange. These technologies already have a significant positive effect on the global economy and when used properly can greatly enhance the lives of many people. There are however risks with misuse of CI. In this webinar, we will discuss what the moral principles should be that govern the behaviour of CI technology, as well as the designer. These principles cover balancing the ecological footprint of technologies against the economic benefits, managing the impact of automation on the workforce, ensuring privacy is not adversely affected and dealing with the legal implications of embodying CI technologies in autonomous systems.
Registration link: https://attendee.gotowebinar.com/register/2071141647237678337
Matthew A Garratt received a BE degree in Aeronautical Engineering from Sydney University, Australia, a graduate diploma in applied computer science from Central Queensland University and a PhD in the field of biologically inspired robotics from the Australian National University in 2008. Prior to entering academia, Matt worked as an engineer in the Royal Australian Navy for a decade. Some of his research successes include demonstration of terrain following using vision for an unmanned helicopter, landing an unmanned helicopter onto a moving deck simulator and control of helicopters using neural networks. Some of his current research projects include achieving autonomous flight in cluttered environments using monocular cameras and range sensors, landing UAVs on moving platforms, adaptive flight control for flapping wing and rotary wing vehicles, trusted human-autonomy teaming in teleoperations and self-organising Unmanned Systems in contested RF environments. He is an Associate professor with the School of Engineering and Information Technology (SEIT) at the University of New South Wales, Canberra. Matt is currently the Deputy Head of School (Research) in SEIT and is the chair of the Computational Intelligence Society task force on the Ethics and Social Implications of CI. His research interests include sensing, guidance and control for autonomous systems with particular emphasis on biologically inspired and Computational Intelligence approaches.
Title: Type-2 Fuzzy Logic Systems and Their Applications
Speaker: Prof. Hani Hagras
Date and Time: 6pm (BST), April 30th, 2018
This topic will present a brief overview on theoretical and practical coverage of the area of type-2 fuzzy logic systems and their applications. The talks will cover the following topics
- Introduction to Type-2 Fuzzy Logic Sets and systems and their theoretical basis
- Practical Implementation of Interval Type-2 Fuzz Logic Systems and their various applications
- Emerging areas of type-2 fuzzy logic systems
Registration link: https://attendee.gotowebinar.com/register/7173930031155852545
Prof. Hani Hagras is a Professor of Computational Intelligence, Director of Research. Director of the Computational Intelligence Centre, Head of the Fuzzy Systems Research Group and Head of the Intelligent Environments Research Group in the University of Essex, UK. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE) and he is also a Fellow of the Institution of Engineering and Technology (IET). and Principal Fellow of the UK Higher Education Academy. His major research interests are in computational intelligence, notably type-2 fuzzy systems, fuzzy logic, neural networks, genetic algorithms, and evolutionary computation. His research interests also include ambient intelligence, pervasive computing and intelligent buildings. He is also interested in embedded agents, robotics and intelligent control. He has authored more than 300 papers in international journals, conferences and books. His work has received funding that totalled to about £5 Million in the last five years from the European Union, the UK Technology Strategy Board (TSB), the UK Department of Trade and Industry (DTI), the UK Engineering and Physical Sciences Research Council (EPSRC), the UK Economic and Social Sciences Research Council (ESRC) as well as several industrial companies including. He has also Five industrial patents in the field of computational intelligence and intelligent control. His research has won numerous prestigious international awards where most recently he was awarded by the IEEE Computational Intelligence Society (CIS), the 2013 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems and also he has won the 2004 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems. He was also awarded the 2015 Global Telecommunications Business award for his joint project with British Telecom. In 2016, he was elected as Distinguished Lecturer by the IEEE Computational Intelligence Society. He was also the Chair of the IEEE CIS Chapter that won the 2011 IEEE CIS Outstanding Chapter award. His work with IP4 Ltd has won the 2009 Lord Stafford Award for Achievement in Innovation for East of England. His work has also won the 2011 Best Knowledge Transfer Partnership Project for London and the Eastern Region. His work has also won best paper awards in several conferences including the 2014 and 2006 IEEE International Conference on Fuzzy Systems and the 2012 UK Workshop on Computational Intelligence. He served as the Chair of IEEE Computational Intelligence Society (CIS) Senior Members Sub-Committee. He served also as the chair of the IEEE CIS Task Force on Intelligent Agents. He is currently the Chair of the IEEE CIS Task Force on Extensions to Type-1 Fuzzy Sets. He is also a Vice Chair of the IEEE CIS Technical Committee on Emergent Technologies. He is a member of the IEEE Computational Intelligence Society (CIS) Fuzzy Systems Technical Committee. He served also as a member of the IEEE CIS Fellows Committee. He serves also as a member of the IEEE CIS conferences committee. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems. He is also an Associate Editor of the International Journal of Robotics and Automation. Prof. Hagras chaired several international conferences where he will act as the Programme Chair of the 2017 IEEE International Conference on Fuzzy Systems.
Webinars Committee Chair
Manchester Metropolitan University
Webinars Committee Vice Chair
Queen Mary University of London
Webinars Committee Member
Chiang Mai University
INESC-ID / Instituto Superior Técnico, Universidade de Lisboa
University of Science and Technology of China
University of Missouri
The University of Arizona
University of New South Wales
University of Birmingham
Otto von Guericke University of Magdeburg
Victoria University of Wellington
Webinars Subcommittee Liaison
University of Science and Technology of China