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SCT 2017 - Soft Computing Techniques: A Nature-Inspired Framework for more...

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oft Computing Techniques: A Nature-Inspired Framework for more Advanced Artificial Intelligence - SCT 2017

1 - 3 November, 2017 - Funchal, Madeira, Portugal
Within the 9th International Joint Conference on Computational Intelligence - IJCCI 2017


CHAIR

Oussama H. Hamid
University of Kurdistan Hewler
Iraq

Brief Bio
Oussama Hamid is an Assistant Professor in Computer Sciences at the University of Kurdistan Hewler (UKH) - Iraq. He is a member of the Executive Editorial Board of the UKH Journal of Science, Medicine and Engineering (UKHJSME) and a member of the Scientific Committee at the Department of Computer Science and Engineering at UKH. In 2011, Oussama received a PhD degree in Natural Sciences (Dr. rer. nat.) from the University of Magdeburg (Germany) for his research on the Role of Temporal Context in Human Reinforcement Learning. Before, he was awarded a Master's degree as a Graduate Engineer (Dipl.-Ing.) in Computational Visualistics (Germany, 2002) and Vordiplom Certificates (BSc) in Computer Science (Germany, 2000) and Mathematics (Germany, 1998). In the years between 2003 and 2017, Dr. Hamid has been investigating both theoretical and methodological aspects of Human-Machine Interaction, Neural Network Functions, Cognitive Systems and Data Analytics. Dr. Hamid has published his research findings in several European and international refereed journals and conferences.


SCOPE

Recent advances in artificial intelligence promote machines to emulate human-like abilities in a way that brings soft computing techniques into focus. What got experts excited about these developments is that one technique: ‘deep learning’, which is a modified version of artificial neural networks, has a wide range of applications. Domains in which soft computing techniques have the potential to advance artificial intelligence and affect the future of human-machine coexistence include, for example, image processing, pattern recognition, cognitive robotics, decision making, transportation, medical and financial applications.
This Special Session discusses the most recent advances in soft computing techniques from a cross-disciplinary perspective, encompassing theory, analysis, methods and applications. Contributions are solicited in all aspects of evolutionary, fuzzy, neural, and cognitive computations.


TOPICS:

Topics of interest include, but are not limited to:
- Fuzzy Controllers
- Fuzzy Systems Design
- Neuro-Fuzzy Systems
- Natural and Artificial Intelligence
- Artificial Neural Networks
- Swarm Intelligence
- Deep Learning
- Reinforcement Learning
- Statistical Learning
- Nature-Inspired Systems
- Human-Machine Interaction
- Brain-Computer Interface
- Cognitive Computing
- Business Models for Soft Computing Techniques


IMPORTANT DATES

Conference

Special Submission (Regular and Position Papers)
Paper Submission: September 4, 2017

Regular Papers
Paper Submission: June 12, 2017 (expired)
Authors Notification: July 24, 2017 (expired)
Camera Ready and Registration: September 8, 2017

Position Papers
Paper Submission: July 10, 2017 (expired)
Authors Notification: August 4, 2017 (expired)
Camera Ready and Registration: September 8, 2017

Workshops
Workshop Proposal: June 2, 2017 (expired)

Doctoral Consortium
Paper Submission: September 1, 2017
Authors Notification: September 12, 2017
Camera Ready and Registration: September 19, 2017

Special Sessions
Special Session Proposal: June 2, 2017 (expired)
Paper Submission: September 1, 2017 (extended)
Authors Notification: September 18, 2017
Camera Ready and Registration: September 25, 2017

Tutorials
Tutorial Proposal: September 13, 2017

Demos
Demo Proposal: September 13, 2017

Panels
Panel Proposal: September 13, 2017

Open Communications
Paper Submission: September 1, 2017
Authors Notification: September 12, 2017
Camera Ready and Registration: September 19, 2017

Keynote Lectures

Available soon
António Dourado, University of Coimbra, Portugal

Towards Lifelong Learning in Optimisation Algorithms
Emma Hart, Edinburgh Napier University, United Kingdom

Ambient Intelligent Systems: The Role of Non-Intrusive and Sensitive Approaches
Paulo Novais, Universidade do Minho, Portugal

Type-2 Fuzzy Systems for Human Decision Making
Jonathan Garibaldi, University of Nottingham, United Kingdom


Keynote Lecture

António Dourado
University of Coimbra
Portugal

Brief Bio
Full Professor of FCTUC/Scientific Director of Soft Computing and Automation Group of Center for Informatics and Systems of the University of Coimbra (CISUC), since 2002. Has been and is teaching courses on Systems Engineering, Automatic Control, Instrumentation, Fuzzy Systems, Neural Networks, Theory of Computation, General Management, to Engineering Students and MSc Students, in Informatics Engineering, Electrical Engineering and Biomedical Engineering. Has been and is involved in international (namely in EU FP7 ) and national projects (in collaboration with industry) , including some Networks of Excellence. He was the coordinator of the European project EPILEPSIAE- Evolving Platform for improving the Living Expectations of Patients Suffering from Ictal Events, researching algorithms for EEG-ECG processing for epileptic seizures prediction. He is author or co-author of more than 200 international publications in referred journals, book chapters and conferences. He is member of IEEE and has been co-founder of European Control Association and Portuguese Association of Automatic Control (IFAC National Member), has been active in the international scientific community, organizing conferences, integrating Technical and Program Committees, reviewing projects and papers. Main research interests: Computational Intelligence, Signal Processing, data mining for medical and industrial applications, and intelligent control.


Abstract
Available soon


Towards Lifelong Learning in Optimisation Algorithms

Emma Hart
Edinburgh Napier University
United Kingdom

Brief Bio
Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems. She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 in Natural Computation. She is active world-wide in the field of Evolutionary Computation, for example as General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She has given keynotes at EURO 2016 and UKCI 2015, as well as invited talks and tutorials at many Universities and international conferences. She is Editor-in-Chief of Evolutionary Computation (MIT Press) from January 2016 and an elected member of the ACM SIGEVO Executive Board. She is also a member of the UK Operations Research Society Research Panel.

Abstract
Optimisation is an important activity for many businesses, providing better, faster, cheaper solutions to problems in areas including scheduling of people and processes, routing of vehicles and packing of containers. Metaheuristic algorithms provide a pragmatic way to tackle optimisation, providing high-quality solutions in reasonable time. Unfortunately, selection and tuning of an appropriate algorithm can difficult, often requiring an expert to design the algorithm, a software engineer to implement it, and finally application of automated tuning processes to refine the chosen algorithm. This is not only costly, requiring significant human-effort, but also results in software which can quickly become obsolete when it no longer matches the goals of a company or if the characteristics of the optimisation problems being solved changed substantially. Unlike human-beings, optimisation software is currently unable to adapt to changing scenarios or autonomously improve its behaviour over time as it learns from experience.
To counter this, I will propose the life-long learning optimisation system (L2O) which when faced with a continual stream of problems to optimise, refines an existing set of algorithms so that they improve over time as they are exposed to more examples, and automatically generates new algorithms when faced with problem instances that are completely different from those seen before. The approach is inspired by ideas from the operation of the natural immune system, which exhibits many properties of a life-long learning system that can be exploited computationally, and uses genetic programming to automatically generate new algorithms. I will give a brief overview of the immune system, focusing on highlighting its relevant computational properties and then show how it can be used to construct a lifelong learning optimisation system. The system is shown to adapt to new problems, exhibit memory, and produce efficient and effective solutions when tested in both the bin-packing and scheduling domains, representing a paradigm shift in the way we think about optimisation.


Ambient Intelligent Systems: The Role of Non-Intrusive and Sensitive Approaches

Paulo Novais
Universidade do Minho
Portugal

Brief Bio

Paulo Novais is an Associate Professor with Habilitation of Computer Science at the Department of Informatics, in the School of Engineering of the University of Minho (Portugal) and a researcher at the ALGORITMI Centre in which he is the coordinator of the research group ISlab - Synthetic Intelligence, and the coordinator of the research line in “Ambient intelligence for well-being and Health Applications”.
He is the director of the PhD Program in Informatics and co-founder and Deputy Director of the Master in Law and Informatics at the University of Minho.
He started his career developing scientific research in the field of Intelligent Systems/Artificial Intelligence (AI), namely in Knowledge Representation and Reasoning, Machine Learning and Multi-Agent Systems.
His interest, in the last years, was absorbed by the different, yet closely related, concepts of Ambient Intelligence, Ambient Assisted Living, Intelligent Environments, Behavioural Analysis, Conflict Resolution and the incorporation of AI methods and techniques in these fields.
His main research aim is to make systems a little more smart, intelligent and also reliable.
He has led and participated in several research projects sponsored by Portuguese and European public and private Institutions and has supervised several PhD and MSc students. He is the co-author of over 250 book chapters, journal papers, conference and workshop papers and books.
He is the president of APPIA (the Portuguese Association for Artificial Intelligence) for 2016/2017, Portuguese representative at the IFIP - TC 12 - Artificial Intelligence chair of the Working Group on Intelligent Agent (WG12.3), and member of the executive committee of the IBERAMIA (IberoAmerican Society of Artificial Intelligence).
During the last years he has served as an expert/reviewer of several institutions such as EU Commission and FCT (Portuguese agency that supports science, technology and innovation).

Abstract
There is currently a significant interest in consumer electronics in applications and devices that monitor and improve the user’s well-being. This is one of the key aspects in the development of ambient intelligence systems. Nonetheless, existing approaches are generally based on physiological sensors, which are intrusive and cannot be realistically used, especially in ambient intelligence in which the transparency, pervasiveness and sensitivity are paramount. We put forward a new approach to the problem in which user behavioral cues are used as an input to assess inner state. This innovative approach has been validated by research in the last years and has characteristics that may enable the development of true unobtrusive, pervasive and sensitive ambient intelligent systems.


Type-2 Fuzzy Systems for Human Decision Making

Jonathan Garibaldi
University of Nottingham
United Kingdom

Brief Bio

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.

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 of the current research being carried out within the LUCID group at Nottingham, and wider, 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 in cyber-security contexts, and other decision support problems.


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We can also provide a certificate of completion for this event if required.

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Hotel Vila Galé Santa Cruz

Rua São Fernando, 5

9100-173 Santa Cruz

Portugal

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