IJCNN 2016 Plenary Speakers

IEEE WCCI 2016 is pleased to announce the following IJCNN Plenaries:

1. Jürgen Schmidhuber, 2016 IEEE CIS Neural Networks Pioneer Awardee
2. Bin He, IEEE Fellow, Editor-in-Chief of IEEE Transactions on Biomedical Engineering
3. Marios M. Polycarpou, IEEE Fellow, 2016 IEEE CIS Neural Networks Pioneer Awardee
4. Jun Wang, IEEE Fellow, 2014 IEEE CIS Neural Networks Pioneer Awardee
5. Haibo He, Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (Beginning in 2016)

More Plenaries will be announced soon. Visit http://wcci2016.org/speakers.php?id=ijcnn for more information.

Call for Papers WCCI / IJCNN 2016 Special Session "DISTRIBUTED LEARNING ALGORITHMS FOR NEURAL NETWORKS"

http://ispac.diet.uniroma1.it/ijcnn-2016-special-session-distributed-nn

Scope and motivations

In the era of big data and pervasive computing, it is common that datasets are distributed over multiple and geographically distinct sources of information (e.g. distributed databases). In this respect, a major challenge is designing adaptive training algorithms in a distributed fashion, with only partial or no reliance on a centralized authority. Indeed, distributed learning is an important step to handle inference within several research areas, including sensor networks, parallel and commodity computing, distributed optimization, and many others.

Based on the idea that all the aforementioned research fields share many fundamental questions and mechanisms, this special session is intended to bring forth advances on distributed training for neural networks. We are interested in papers proposing novel algorithms and protocols for distributed training under multiple constraints, analyses of their theoretical aspects, and applications for multiple source data clustering, regression and classification.

Topics

The topics of interest to be covered by this Special Session include, but are not limited to:

• Distributed algorithms for training neural networks and kernel methods
• Theoretical aspects of distributed learning (e.g. fundamental communication constraints)
• Learning on commodity computing architectures and parallel execution frameworks (e.g. MapReduce, Storm)
• Energy efficient distributed learning
• Distributed semi-supervised and active learning
• Novel results on distributed optimization for machine learning
• Cooperative and competitive multi-agent learning
• Learning in realistic wireless sensor networks
• Distributed systems with privacy concerns (e.g. healthcare systems)

   

Important dates

• Paper submission deadline: January 15, 2016
• Notification of paper acceptance: March 15, 2016
• Camera-ready deadline: April 15, 2016
• Conference: July 25-29, 2016

   

Further details

For additional details, please visit the special session's website, or contact one of the organizers:

• Massimo Panella, Sapienza University of Rome (massimo [dot] panella [at] uniroma1 [dot] it).
• Simone Scardapane, Sapienza University of Rome (simone [dot] scardapane [at] uniroma1 [dot] it).

   

Call for Papers WCCI / IJCNN 2016 Special Session "Deep Learning, Medical Imaging, and Translational Medicine"

Introduction

Deep learning has demonstrated its capability for many vision problems, such as face detection and recognition, image classification, etc. It is expected that this technique can benefit the area of medical image analysis, as well as imaging-based translational medicine. Though a few pioneering works can be found in the literature, there are still a lot of unresolved issues when applying deep learning for medical images.

Scope and Topics

Topics include, but are not limited to:

• Image descriptor and feature extraction
• Image super-resolution
• Image reconstruction
• Image registration
• Image segmentation and labeling
• Computer-assisted lesion detection
• Computer-assisted diagnosis
• Deep learning model selection
• Meta-heuristic techniques for fine-tuning parameter in deep learning-based archi-tectures
• Other related translational medical applications.

Paper Submission

• Only papers prepared in PDF format will be ac-cepted.
• Paper Length: Up to 8 pages, including figures, tables and references. At maximum, two add-tional pages are permitted with over-length page charge of US$100/page, to be paid during author registration.
• Paper Formatting: double column, single spaced, #10 point Times Roman font.
• Margins: Left, Right, and Bottom: 0.75" (19mm). The top margin must be 0.75" (19 mm), except for the title page where it must be 1" (25 mm).

Key Dates

• Paper Submission DDL 15 Jan 2016
• Paper Acceptance Notification 15 Mar 2016
• Final Paper Submission DDL 15 Apr 2016
• IEEE WCCI 2016 25-29 Jul 2016

 

Organization

• Qian Wang, Shanghai Jiao Tong Univ.
• Jun Shi, Shanghai Univ.
• Shihui Ying, Shanghai Univ.
• Manhua Liu , Shanghai Jiao Tong Univ.
• Yonghong Shi, Fudan Univ.

Call for Papers WCCI / IJCNN 2016 Special Session "Computational Intelligence for Unmanned Systems"

Overview

An unmanned system(US) is a machine or device that is equipped with necessary data processing units, sensors, automatic control, and communications systems and is capable of performing missions autonomously without human intervention. Unmanned systems include unmanned aircraft, ground robots, underwater explorers, satellites, and other unconventional structures. Computational Intelligence(CI) includes classical evolutionary computation, neural computation, fuzzy systems, swarm intelligence(Particle Swarm Optimization, Ant Colony Optimization,.etc.) and other new CI methods such as Bee colony optimization algorithms, Biogeography Based Optimization, Firefly algorithms or hybridizations of CI approaches.

Topics of Interest

This special session aims to cover all subjects of Unmanned Systems relating to the development of automatic machine systems based on CI, which include advanced technologies in unmanned hardware platforms (aerial, ground,underwater and unconventional platforms), unmanned software systems, energy systems, modeling and control, communications systems, computer vision systems, sensing and information processing, navigation and path planning and innovative application case studies.

Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to: CI methods solving technical issues underlying the development of unmanned systems.

• Biologically-inspired computing for US
• CI for motion planning of unmanned aircraft, ground robots, underwater explorers 
• CI for navigation, mapping and localization of unmanned aircraft, ground robots, underwater explorers 
• CI for image processing of unmanned aircraft, ground robots, underwater explorers
• Bio-inspired system on US 
• Artificial Neural Networks for US 
• CI on machine learning, intelligent systems design for unmanned hardware platforms (aerial, ground, underwater and unconventional platforms) 
• CI on machine learning and intelligent systems design for unmanned software systems 
• CI for energy systems of US 
• CI for modeling, control and communication systems of US 
• CI for computer vision systems of US 
• CI for sensing and information processing of US 
• Theory and applications of CI and machine learning systems for US 
• Swarm intelligence for unmanned aircraft, ground robots, underwater explorers and other unconventional structures

Submission

Please follow the IEEE CEC2016 instruction for authors and submit your paper via the IEEE CEC 2016 online submission system. Please specify that your paper is for the Special Session on Computational Intelligence for Unmanned Systems.

Important Dates

Paper Submission Deadline: 15 Jan, 2016
Notification of Acceptance: 15 Mar, 2016
Final Paper Submission Deadline: 15 Apr, 2016

Organisers

Hongwei Mo, Harbin Engineering University, China, honwei2004@126.com
Chaomin Luo, University of Detroit Mercy, Michigan, USA.

Call for Papers WCCI / IJCNN 2016 Special Session "DEEP REINFORCEMENT LEARNING"

ORGANIZED BY

ABDULRAHMAN ALTAHHAN  VASILE PALADE, JUNYU DONG, XINGHUI DONG, HUI YU AND MOHAMED CHERIET
SCHOOL OF COMPUTING, ELECTRONICS AND MATHS, FACULTY OF ENGINEERING, COVENTRY UNIVERSITY

Deep Learning has been under the focus of neural network research and industrial communities due to its proven ability to scale well into difficult problems and due to its performance breakthroughs over other architectural and learning techniques in important benchmarking problems. This was mainly in the form of improved data representation in supervised learning tasks. Reinforcement learning (RL) is considered the model of choice for problems that involve learning from interaction, where the target is to optimize a long term control strategy or to learn to formulate an optimal policy. Typically these applications involve processing a stream of data coming from different sources, ranging from central massive databases to pervasive smart sensors.

RL does not lend itself naturally to deep learning and currently there is no uniformed approach to combine deep learning with reinforcement learning despite good attempts. Examples of important open questions are: How to make the state-action learning process deep? How to make the architecture of an RL system appropriate to deep learning without compromising the interactivity of the system? Etc. Although recently there have been important advances in dealing with these issues, they are still scattered and with no overarching framework that promote them in a well-defined and natural way.

This special session will provide a unique platform for researchers from Deep Learning and Reinforcement Learning communities to share their research experience towards a uniformed Deep Reinforcement Learning (DRL) framework in order to allow this important interdisciplinary branch to take-off on solid grounds. It will focus on the potential benefits of the different approaches to combine RL and DL. The aim is to bring more focus onto the potential of infusing reinforcement learning framework with deep learning capabilities that could allow it to deal more effectively with present applications including, but not restricted to, online streamed data processing that involves actions.

SCOPE AND TOPICS

1. Novel DRL Algorithms
2. Novel DRL Neural Architectures
3. Adaptation of existing RL Techniques for Deep Learning
4. Optimization and convergence proofs for DRL algorithms
5. Deeply Hierarchical RL
6. DRL architecture and algorithms for Control
7. DRL architecture and algorithms for Robotics
8. DRL architecture and algorithms for Time Series
9. DRL architecture and algorithms for Big Streamed Data Processing
10. DRL architecture and algorithms for Optimizing Governmental Policy
11. Other DRL applications

Call for Papers WCCI / IJCNN 2016 Special Session "Deep Learning for Big Multimedia Understanding"

Conventional multimedia understanding is usually built on top of handcrafted features, which are often much restrictive in capturing complex multimedia content. Recent progress on deep learning opens an exciting new era, placing multimedia understanding on a more rigorous foundation with automatically learned representations to model the multimodal data and the cross-media interactions. Existing studies have revealed promising results that have greatly advanced the state-of-the-art performance in a series of multimedia research areas, from the multimedia content analysis, to modeling the interactions between multimodal data, to multimedia content recommendation systems, to name a few here.

Scope and Topics

This special session aims to provide a forum for the presentation of recent advancements in deep learning research that directly concerns the multimedia community. For multimedia research, it is especially important to develop deep learning methods to capture the dependencies between different genres of data, building joint deep representation for diverse modalities. The list of topics includes and is not restricted to the following:

1. Novel deep network architectures for multimodal data representation
2. Deep learning for new multimedia applications
3. Efficient training and inference methods for multimedia deep networks
4. Emerging applications of deep learning in multimedia search, retrieval and management
5. Deep learning for multimedia content analysis and recommendation
6. Deep learning for cross-media analysis, knowledge transfer and information sharing
7. Distributed computing, GPUs and new hardware for deep learning in multimedia research
8. Other deep learning topics for multimedia computing, involving at least two modalities

Submission

• Paper submission deadline is on January 15, 2016.
• All papers must be submitted through the IEEE WCCI 2016 online submission system. For special session papers, please select the respective special session title “Deep Learning for Big Multimedia Understanding” under the list of research topics in the submission system.

Organizers

Prof. Jinhui Tang, Nanjing University of Science and Technology, China
Dr. Zechao Li, Nanjing University of Science and Technology, China

Call for Papers WCCI 2016 Special Session "Ordinal Regression and Ranking"

Scope

Ordinal regression (or ordinal classification) is a relatively new learning problem, where the objective is to learn a rule to predict labels in an ordinal scale discrete labels endowed with a natural order.

Consider, for example, the case of a teacher who rates student's performance using A, B, C, D and E, and A>B>C>D>E. Such order information could be helpful for constructing more robust and fair classifiers and evaluation metrics. On the other hand, ranking generally refers to those problems where the algorithm is given a set of ordered labels, and the objective is to learn a rule to rank patterns by using this discrete set of labels. Many real problems exhibit this structure, e.g. multicriteria decision making, medicine, risk analysis, university ranking, information retrieval and filtering.

Specific solutions have been recently proposed in the machine learning and pattern recognition literature for both ordinal regression and ranking problems, resulting in a very active research field. This special session aims to cover a wide range of approaches and recent advances in ordinal regression and ranking. We hope that this session can provide a common forum for researchers and practitioners to exchange their ideas and report their latest findings in the area.

This session has been provisionally accepted for the IJCNN 2016 conference which will be held as part of the 2016 IEEE WCCI in Vancouver, Canada at July 25-29, 2016.

Topics

In particular we encourage submissions addressing the following issues:

• Extensions of standard classification methods to ordinal regression (Support Vector Machines, Gaussian Processes, Discriminant Analysis, etc.).
• Threshold models for ordinal regression.
• Decomposition methods for ordinal regression and ranking.
• Imbalanced ordinal regression problems.
• Clustering and preprocessing methods for ordinal and monotonic data (data cleaning techniques, feature selection, etc).
• Evaluation measures for ordinal regression and ranking.
• Learning to rank: ranking (pointwise, pairwise and listwise algorithms), sorting and multipartite ranking.
• Monotonic classification methods.
• Preference learning.
• Applications in medicine, information retrieval, recommendation systems, risk analysis and any other realworld problems.

Important Dates

Deadline for paper submission January 15th, 2016
Notification of acceptance March 15th, 2016
Final Paper Submission April 15h, 2016

Paper submission

If you are interested in taking part on this special session, please submit your paper directly through the WCCI website selecting the following option for the "Main research topic": "Ordinal Regression and Ranking". You can find further information related to the submission process and important dates at the conference website.

Papers submitted for special sessions are to be peer reviewed with the same criteria used for the rest of contributed papers. As a result, all accepted papers will be included in the proceedings of IJCNN2016.

Organizers

Pedro A. Gutiérrez, Dept. of Computer Science and Numerical Analysis, University of Cordoba, Spain.
pagutierrez@uco.es

María Pérez-Ortiz, Dept. of Mathematics and Engineering, Universidad Loyola Andalucía, Spain.
i82perom@uco.es

Peter Tino, School of Computer Science, University ň of Birmingham, UK.
P.Tino@cs.bham.ac.uk

Call for Papers WCCI 2016 Special Session "Reinforcement Learning and Approximate Dynamic Programming for Optimization in Dynamic Environment"

Reinforcement learning and approximate dynamic programming can be used to address learning and optimization problems in many areas of engineering and science, including artificial intelligence, control engineering, operation research, psychology, and economy. They have provided critical tools to solve some engineering and science problems in modern complex systems. However, there still exist some challenges in the applications of reinforcement learning and approximate dynamic programming to academic and industrial problems such as the curse of dimensionality and optimization in dynamic environment. At the same time, the development of new technologies such as quantum technology and deep learning provides a remarkable opportunity to revisit these challenges in reinforcement learning and approximate dynamic programming. This special session will focus on relevant topics of reinforcement learning and approximate dynamic programming, and provide a forum for idea exchange in the emerging research area.

Scope and Topics

The aim of this special session will be to provide an account of the state-of-the-art in this fast moving and cross-disciplinary field of reinforcement learning and approximate dynamic programming. It is expected to bring together the researchers in relevant areas to discuss latest progress, propose new research problems for future research. All the original papers related to reinforcement learning (RL) and approximate dynamic programming (ADP) are welcome. Topics of interest include but are not limited to:

• ADP for discrete-time systems
• ADP for continuous-time systems 
• Policy iteration algorithm
• hierarchical reinforcement learning
• multi-agent reinforcement learning
• deep reinforcement learning
• quantum reinforcement learning applications of ADP and RL to optimization in dynamic environment

Paper Submission

Perspective authors should submit their papers through the IEEE IJCNN/WCCI 2016 conference submission system at http://www.wcci2016.org/

Templates and instruction for authors will be provided on the IEEE IJCNN/WCCI webpage http://www.wcci2016.org/

Please make sure to select the Special Session IJCNN-29 “Reinforcement Learning and Approximate Dynamic Programming for Optimization in Dynamic Environment” from the “S. SPECIAL SESSION TOPICS” name in the “Main Research topic” dropdown list;

All the submissions to the special sessions will be subject to the same peer-review procedure as regular papers, and accepted papers will be published in the conference proceedings.

Further information about IEEE IJCNN/WCCI 2016 can be found at http://www.wcci2016.org/
For any question you may have about the Special Session or paper submission, feel free to contact the organizers of this Special Session.

Important Dates

Paper submission: January 15th, 2016
Paper Decision notification: March 15th, 2016
Camera-ready submission: April 15th, 2016
Conference Dates: July 25 - 29th, 2016

Organizers

Dr Daoyi Dong
Senior Lecturer, School of Engineering and Information Technology, University of New South Wales, Australia
E-mail: daoyidong@gmail.com

Prof Dongbin Zhao
Professor, Institute of Automation, Chinese Academy of Sciences, China
E-mail: dongbin.zhao@ia.ac.cn

A/Prof Qinmin Yang
Associate Professor, Department of Control Science and Engineering, Zhejiang University, China
E-mail: qmyang@iipc.zju.edu.cn

Call for Papers WCCI-IJCNN2016 Special Session on "Mind, Brain, and Cognitive Algorithms"

Organizers: Leonid Perlovsky (Harvard University and Northeastern University, MA; lperl@rcn.com), José F. Fontanari, Asim Roy, Angelo Cangelosi, Daniel Levine.
Recent progress opens new directions for modeling the mind and brain and developing cognitive algorithms for engineering applications. Cognitive algorithms solve traditional engineering problems much better than before, and new areas of engineering are opened modeling human abilities in cognition, emotion, language, art, music, cultures. Cognitive dissonances and behavioral economics is another new active area of research. A wealth of data are available about the ways humans perform various cognitive tasks (e.g., the knowledge instinct, scene and object recognition, language acquisition, interaction of cognition and language, aesthetic emotions, music cognition, cognitive dissonance) as well as about the biases involved in human judgment and decision making (e.g., the prospect theory and the fuzzy-trace theory). A wealth of data on the web can be exploited for extracting cognitive data. Explaining these laws and biases using realistic neural networks architectures, including neural modeling fields, as well as more traditional learning algorithms requires a multidisciplinary effort.

The aim of this special session is to provide a forum for the presentation of the latest data, results, and future research directions on the mathematical modeling of higher cognitive functions using neural networks, neural modeling fields, as well as cognitive algorithms exploiting web data and solving traditional and new emerging engineering problems, including genetic association studies, medical applications, Deep Learning, and Big Data.

The special session invites submissions in any of the following areas:

• Neural network models of higher cognitive function
• Neural mechanisms of emotions, cognition
• Embodied cognition modeling
• Neural modeling fields (NMF) 
• Perceptual processing
• Language learning 
• Cognitive and emotional processing
• Cognitive models of decision-making
• Models of emotional mechanisms
• Models of cognitive dissonances
• Cognitive, language, and emotional models of cultures
• Cognitive functions of art, music, and spiritual emotions.
• Emotions in cognition (affective cognition)
• Aesthetic emotions
• Cognitive dissonance, neural models
• Cognition and cultures
• Medical applications 
• Genome association studies
• Big Data

Keywords:

Cognition, Emotions, Decision-Making, Dynamic Logic, Language Acquisition, Language Emotionality, Cognitive Dissonance, Music Cognition, Models of Cultures, Neural Modeling Fields, ART Neural Network, Fuzzy-Trace Theory, Prospect Theory, Deep Learning, Genome Associations, Big Data   

Program Committee:

M. Cabanac (Canada)
A. Cangelosi (UK)
J. F. Fontanari (Brazil)
Y. R. Fu (USA)
R. Illin (USA)
B. Kovalerchuk  (USA)
R. Kozma (USA)
D. Levine (USA)
D. Marocco  (UK)
A. Minai (USA)
L. I. Perlovsky (USA)
S. Petrov (USA)
A. Roy (USA)
F. Schoeller (France)
J. Weng (USA)

WCCI 2016 Special Session on "Optimizing Neural Networks via Evolutionary Computation and Swarm Intelligence"

Organized by

IEEE CIS Task Force on Intelligent Adaptive Fault Tolerant Control, Reliability, and Optimization

Organizers:

Wei-Chang Yeh, National Tsing Hua University, Taiwan (yeh@ieee.org)

Liang Feng, College of Computer Science, Chongqing University, China. (liangf@cqu.edu.cn)

Yew-Soon Ong, School of Computer Engineering Nanyang Technological University, Singapore (ysong@ieee.org)

Scope and motivation

Today, neural networks have been widely recognized as useful frameworks to model multidimensional nonlinear relationships. It has been successfully applied in real-world applications including signal processing, robot control, classification, etc. Recently, it has also been employed to construct deep architectures for deep learning to model high-level abstractions in data, and achieved considerable success in applications such as natural language processing, music signal recognition, computer vision and automatic speech recognition, etc. Despite the success achieved by neural network, constructing multilayer neural networks involves challenging optimization problems, i.e., finding appropriate architecture and the corresponding optimal weights for some of the core applications of interest.  

Evolutionary Computation and Swarm Intelligence are natural inspired heuristic methods with global search capability that have attracted extensive attentions in the last decades. They have been successfully applied to complex optimization problems including continuous optimization, combinatorial optimization, constrained optimization, etc. The aim of this special session is to provide a forum for researchers in the field of neural network to exchange their latest advances in theories, technologies, and practice of optimizing neural networks, especially with deep and large architecture, using evolutionary computation and swarm intelligence.

Relevance for IJCNN

This Special Session on “Optimizing Neural Networks via Evolutionary Computation and Swarm Intelligence” mainly focus on the research of exploring Evolutionary Computation and Swarm Intelligence methodologies for optimizing the neural network architectures. Despite a significant amount of research have been done in neural networks, there remains many open issues and intriguing challenges in optimizing neural network architectures, especially in today's deep learning context, where neural networks usually have many layers and large number of neurons.

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

• Evolutionary Computation in Neural Networks,
• Swarm Intelligence in Neural Networks,
• Advances in Evolutionary Computation and/or Swarm Intelligence,
• Knowledge incorporation in Evolutionary Computation and/or Swarm Intelligence,
• Advances in Neural Networks
• Analytical studies that enhance our understanding on the behaviors of Evolutionary Computation and/or Swarm Intelligence in optimizing Neural Networks,
• Novel or Improved frameworks of Neural Networks,
• Others.

Program Organizers and Chair:

Professor Wei-Chang Yeh, Ph.D.
Department of Industrial Engineering and Engineering Management
National Tsing Hua University, Hsinchu, Taiwan 300
Phone: +886-3-5742443
Fax: +886-3-572-2204
Email: yeh@ieee.org
URL: http://integrationandcollaboration.org
https://sites.google.com/site/integrationcollaborationlab/
Wei-Chang Yeh has completed his Ph.D degree in 1992 at the Department of Industrial Engineering, University of Texas at Arlington, USA. He is the Professor of the Department of Industrial Engineering and Engineering Management in the National Tsing Hua University, Taiwan. He has also published more than 108 papers in reputed journals and serves as an editorial board member of repute. His research interest includes Network Reliability, Cloud Computing Management, SSO and Soft Computing and Data Mining. Prof. Yeh is an editorial board member of “Reliability Engineering and System Safety (RESS)”, “Soft Computing with Applications (SCA)” and “International Journal of management and Marketing (IJMM)”. He is most honored to be able to serve as the Chair for the IEEE Computational Intelligence Society, and looks forward to the event.

Yew-Soon Ong is currently an Associate Professor and Director of Computational Intelligence Graduate Laboratory, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems at the Nanyang Technological University, Singapore, and the Programme Principal Investigator of the Rolls-Royce@NTU Corporate Lab. He received his PhD degree on Artificial Intelligence in complex design from the Computational Engineering and Design Center, University of Southampton, United Kingdom in 2003. His current research interest in computational intelligence spans across memetic computation, evolutionary computation, machine learning, Big Data Analytics and agent-based systems.

He is the founding Technical Editor-in-Chief of Memetic Computing Journal, founding Chief Editor of the Springer book series on studies in adaptation, learning, and optimization, Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Neural Networks & Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, Soft Computing, International Journal of System Sciences and others. He has coauthored over 200 refereed publications and his research grants in the last five years amounts to a total of more than 25 million Singapore dollars. His research work on Memetic Algorithm was featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. And he is recipient of the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation. Several of his research technologies in memetic computation have been commercialized and licensed to companies and institutions worldwide. Over the last 5 years, he has been invited to deliver over 20 keynote, plenary or lecture speeches at international conferences, workshops and lecture series.
He chaired the IEEE Computational Intelligence Society Emerging Technologies Technical Committee from 2012-2013 and the IEEE Computational Intelligence Society Intelligent Systems and Applications Technical Committee from 2013-2014. Presently, he is Conference Chair of the Congress on Evolutionary Computation, World Congress on Computational Intelligence, Vancouver, Canada, 2016 and also secretary of the IEEE Transactions on Computational Intelligence and AI in Games steering committee.

Liang Feng received the PhD degree from the School of Computer Engineering, Nanyang Technological University, Singapore, in 2014. He was a Postdoctoral Research Fellow at the Computational Intelligence Graduate Lab, Nanyang Technological University, Singapore. He is currently an Assistant Professor at the College of Computer Science, Chongqing University, China. His research interests include Computational and Artificial Intelligence, Memetic Computing, Big Data Optimization and Learning, as well as Transfer Learning.

Program Committee of Potential Participants and Reviewers:

A/Professor Changseok Bae
Professor David W. Coit, Ph.D.
Professor Xiangjian He, Ph.D.
A/Professor Vera Yuk Ying Chung
A/Professor Chia-Ling Huang, Ph.D.
Dr. Gregory Levitin
Professor Ana Maria Madureira, Ph.D.
Professor Shiuhpyng Winston Shieh, Ph.D.
Professor Huaguang Zhang, Ph.D.

IJCNN/WCCI 2016 Call For Special Sessions

IJCNN/WCCI 2016
25-29, July, 2016
Vancouver, Canada
http://www.wcci2016.org

Special session proposals for are invited to the 2016 IEEE World Congress on Computational Intelligence/International Joint Conference on Neural Networks (WCCI/IJCNN). Special session proposals should include the title, aim and scope (including a list of main topics), and the names of the organizers of the special session, together with a short biography of all organizers. A list of potential contributors will be very helpful.

Special session proposals will be evaluated based on the timeliness, uniqueness of the topic and qualifications of the proposers. The proposers are expected to have a PhD degree and have a good publication track record in the proposed area. After review, a decision on whether the proposal will be provisionally accepted will be sent to the proposers within two weeks after receipt of the proposals. Provisionally accepted special sessions will be listed on the website. However, it is likely that a provisionally accepted proposal will be combined with another one to avoid multiple special sessions covering a similar topic.

Manuscripts submitted to special sessions should be done through the paper submission website of IEEE WCCI 2016. It is the responsibility of the special session organizers to make sure that papers submitted to their special session clearly indicate the name of the special session the paper belongs to. All papers submitted to special sessions will be subject to the same peer-review procedure as the regular papers. Special sessions having fewer than four accepted papers will be cancelled and the accepted papers will be moved to regular sessions.

Special sessions proposals should be submitted to ijcnn16ss@gmail.com

Call for Papers WCCI 2016 Special Session: Machine Learning Methods applied to Vision and Robotics (MLMVR)

Aims:

Over the last decades there has been an increasing interest in using machine learning methods combined with computer vision techniques to create autonomous systems that solve vision problems in different fields. This special session is designed to serve researchers and developers to publish original, innovative and state-of-the art algorithms and architectures for real time applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, neural networks, intelligent interfaces and biomimetic object-vision recognition.

This special session provides a platform for academics, developers, and industry-related researchers belonging to the vast communities of *Neural Networks*, *Computational Intelligence*, *Machine Learning*, *Biometrics*, *Vision systems*, and *Robotics *, to discuss, share experience and explore traditional and new areas of the computer vision and machine learning combined to solve a range of problems. The objective of the workshop is to integrate the growing international community of researchers working on the application of Machine Learning Methods in Vision and Robotics to a fruitful discussion on the evolution and the benefits of this technology to the society.

The Special Session topics can be identified by, but are not limited to, the following subjects:

• Artificial Vision
• Video tracking
• 3D Scene reconstruction
• 3D Tracking in Virtual Reality Environments 
• 3D Volume visualization
• Computational Intelligence
• Machine Learning
• Intelligent Interfaces (User-friendly Man Machine Interface)
• Self-adaptation and self-organisational systems
• Multi-camera and RGB-D camera systems
• Robust computer vision algorithms (operation under variable conditions, object tracking, behaviour analysis and learning, scene segmentation) 
• Multi-modal Human Pose Recovery and Behavior Analysis
• Gesture and posture analysis and recognition 
• Biometric Identification and Recognition 
• Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
• Surveillance systems
• Robotics vision
• Hardware implementation and algorithms acceleration (GPUs, FPGA,s,…)

Important dates:

Paper Submission Deadline
January 15, 2016

Paper acceptance notification date
March 15, 2016

Final paper submission deadline
April 15, 2016

Conference
July 25-29, 2016

Submission Guidelines:

Please follow the regular submission guidelines of WCCI 2016. Please notify the chairs of your submission by sending an email to: jgarcia@dtic.ua.es.

Chairs:

José García-Rodríguez -University of Alicante (Spain) (jgarcia@dtic.ua.es)
Sergio Escalera (Spain) (sergio.escalera.guerrero@gmail.com)
Alexandra Psarrou – University of Westminster (UK) (psarroa@wmin.ac.uk)
Isabelle Guyon - Coplinet (USA) (guyon@clopinet.com)
Andrew Lewis – Griffith University (Australia) (a.lewis@griffith.edu.au)

Tentative program committee:

Lourdes de Agapito - Queen Mary University of London (UK)
Anastassia Angelopoulou – University of Westminster (UK)
Antonis Argyros – FORTH (Greece) (argyros@ics.forth.gr)
Miguel Cazorla - University of Alicante (Spain)
Enrique Dominguez - University of Malaga (Spain)
Richard Duro – University of Coruña (Spain)
Robert Fisher – University of Edinburgh (UK)
Manuel Graña - Basque Country University (Spain)
Magnus Johnsson- Lund University (Sweden)
Markos Mentzelopoulos – University of Westminster (UK)
Eduardo Nebot – Australian Centre for Field Robotics (Australia)
Asim Roy (Arizona State University, USA)
Peter Roth - TU Graz (Austria)
Sergio Velastin - Kingston University (UK)

Contact:

Email: jgarcia@dtic.ua.es
Main Conference webpage: http://www.wcci2016.org
Special session webpage: http://www.dtic.ua.es/~jgarcia/IJCNN2016/

Biographies

Jose Garcia-Rodriguez received his Ph.D. degree, with specialization in Computer Vision and Neural Networks, from the University of Alicante (Spain). He is currently Associate Professor at the Department of Computer Technology of the University of Alicante. His research areas of interest include: computer vision, computational intelligence, machine learning, pattern recognition, robotics, man-machine interfaces, ambient intelligence, computational chemistry, and parallel and multicore architectures. He has authored +100 publications in journals and top conferences and revised papers for several journals like Journal of Machine Learning Research, Computational intelligence, Neurocomputing, Neural Networks, Applied Softcomputing, Image Vision and Computing, Journal of Computer Mathematics, IET on Image Processing, SPIE Optical Engineering and many others, chairing sessions in the last four editions of IJCNN and IWANN and participating in program committees of several conferences including IJCNN, ICRA, ICANN, IWANN, KES, ICDP and many others.

Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Applied Mathematics and Analysis, Universitat de Barcelona. He is a partial time professor at Universitat Oberta de Catalunya. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at Campus UAB. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is advisor and director of ChaLearn Challenges in Machine Learning. He is co-founder of PhysicalTech company. He is an active member of the Cluster de Salud Mental de Cataluña. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, and he is vice-chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 150 research papers and organized scientific events, including CCIA2004, CCIA2014, ICCV2011, and workshops at ICCV2011, ICMI2013, ECCV2014, CVPR2015, ICCV2015. He has been guest editor at JMLR, IJCV, and TPAMI. His research interests include, between others, statistical pattern recognition, visual object recognition, and HCI systems, with special interest in human pose recovery and behavior analysis from multi-modal data.

Alexandra Psarrou is Head of the Computer Science and Software Engineering Department at the University of Westminster. Psarrou received her BSc in Computer Science (1987) and MSc in Advanced Computer Science (1988) from Queen Mary University of London. Following her graduation Psarrou worked as a Knowledge Engineer on an AI assisted system (AICQS) for the support of UNISYS customer services (1988-1990) and as a Research Fellow on an SERC medical image interpretation project for the dynamic modelling of cancerous cells (1990-1992). The latter project initiated Psarrou's interest in motion-based recognition and the analysis of visual behaviour. Psarrou received her PhD from Queen Mary, London in 1996 with a thesis on the use of artificial neural networks for motion-based recognition. Since 1996 Psarrou has been working on the modelling of temporal trajectories for face, gesture and gait recognition, modelling and tracking of non-rigid objects using growing neural networks and content-based retrieval from image and video databases. Psarrou joined the University of Westminster as Lecturer in 1993. She was appointed Reader and research centre director in 1999 and Head of Department in 2003. Psarrou established the Computer Vision Laboratory at the University of Westminster and has published over 60 papers in computer vision and neural networks and a book on "Dynamic Vision: From Images to Face Recognition" with Shaogang Gong and Stephen McKenna.

Isabelle Guyon is an Independent Consultant, and specialized in statistical data analysis, pattern recognition, and machine learning. Her areas of expertise include computer vision and bioinformatics. Her recent interest is in the applications of machine learning to the discovery of causal relationships. She pioneered applications of neural networks to pen computer interfaces, and co-invented support vector machines (SVM), a machine learning technique, which has become a textbook method. She is also the primary inventor of SVM-RFE, a variable selection technique based on SVM. She organized many challenges in machine learning over the past few. She is Action Editor of the Journal of Machine Learning Research, and an Editor of the Challenges in Machine Learning book series of Microtome.

Andrew Lewis is a Senior Research Specialist in eResearch Services and an Adjunct Senior Lecturer in ICT at Griffith University. He received his BE in Computer Engineering from the University of Newcastle, Australia, and his PhD in Computer Science from Griffith University, Australia. Prior to his employment with the University, he worked in industrial applied research with BHP Billiton. His research interests include: parallel optimisation algorithms for large numerical simulations, including gradient descent, direct search methods, evolutionary programming, particle swarm and ant colony systems, multi-objective optimisation techniques for engineering design, parallel, distributed and grid computing methods, and techniques and applications of advanced visualisation. He has numerous publications across this range of topics, particularly in the area of optimisation algorithms and applications.

Repost: Call for Papers: IEEE WCCI 2016

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. The IEEE WCCI 2016 will host three conferences: The 2016 International Joint Conference on Neural Networks (IJCNN 2016), the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2016), and the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over theworld to discuss and present their research findings on computational intelligence.

IEEE WCCI 2016 will be held at the Vancouver Convention Centre, Vancouver, Canada. Vancouver is Canada’s Pacific gem, offering a winning combination of world-class hotels, meeting venues, and restaurants in a setting of spectacular beauty. Few convention cities can offer such a wide range of cosmopolitan amenities in a downtown core that is safe, clean, pedestrian friendly, and stunning in its backdrop of mountains and ocean.

IJCNN is the flagship conference of the International Neural Network Society and the IEEE Computational Intelligence Society. It covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.

FUZZ-IEEE is the foremost conference in the field of fuzzy systems. It covers all topics in fuzzy systems, from theory to applications.

IEEE CEC is a major event in the field of evolutionary computation, and covers all topics in evolutionary computation from theory to applications.

Call for Papers

Papers for IEEE WCCI 2016 should be submitted electronically through the Congress website at www.wcci2016.org, and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity.

Call for Tutorials

IEEE WCCI 2016 will feature pre-Congress tutorials, covering fundamental and advanced topics in computational intelligence. A tutorial proposal should include title, outline, expected enrollment, and presenter/organizer biography. Inquiries regarding tutorials should be addressed to Tutorials Chairs.

Call for Special Session Proposals

IEEE WCCI 2016 solicits proposals for special sessions within the technical scope of the three conferences. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Cross-fertilization of the three technical disciplines and newly emerging research areas are strongly encouraged. Inquiries regarding special sessions and proposals should be addressed to Special Sessions Chairs.

Call for Competition Proposals

IEEE WCCI 2016 will host competitions to stimulate research in computational intelligence. A competition proposal should include descriptions of the problem(s) addressed, evaluation procedures, and a biography of the organizers. Inquiries regarding competitions should be addressed to the Competitions Chair.

Important Dates

• Special Session & Workshop Proposals Deadline:  15 November 2015
• Competition & Tutorial Proposals Deadline:  15 December 2015 
• Paper Submission Deadline:  15 January 2016 
• Paper Acceptance Notification Date:  15 March 2016 
• Final Paper Submission & Early Registration Deadline:  15 April 2016
• IEEE WCCI 2016:  25-29 July 2016

For more information on the congress, please visit: www.wcci2016.org

Organizing Committee

General Co-Chairs
Kay Chen Tan, Singapore
Gary G. Yen, USA

IJCNN Conference Chair
Pablo A. Estevez, Chile

IJCNN Technical Chairs
Plamen P. Angelov, UK
Emilio Del Moral Hernandez, Brazil
Derong Liu, USA
Lipo Wang, Singapore

FUZZ-IEEE Conference Chair
Oscar Cordon, Spain

FUZZ-IEEE Technical Chairs
James M. Keller, USA
Naoyuki Kubota, Japan
Bernadette R. Bouchon Meunier, France
Nikhil R. Pal, India

CEC Conference Chair
Yew Soon Ong, Singapore

CEC Technical Chairs
Carlos A. Coello Coello, Mexico
Garrison W. Greenwood, USA
Sanaz Mostaghim, Germany
Yuhui Shi, China

Competitions Chair
Simon M. Lucas, UK

Conflict-of-Interest Paper Chairs
Cesare Alippi, Italy
Gary B. Fogel, USA
Hisao Ishibuchi, Japan

Exhibits Chair
Fakhri Karray, Canada

Finance Chair
Haibo He, USA

Local Arrangements Chair
Yifeng Li, Canada

Panel Sessions Chair
Marios M. Polycarpou, Cyprus

Paper Submissions Chair
Ke Tang, China

Plenary Sessions Chair
Chin-Teng Lin, Taiwan

Poster Sessions Chairs
Jong-Hwan Kim, South Korea
Laszlo T. Koczy, Hungary
Yi Zhang, China

Publications Chair
Hussein A. Abbass, Australia

Publicity Chairs
Rami Abielmona, Canada
Valentina E. Balas, Romania
Min Jiang, China
Xiaodong Li, Australia
Dongbin Zhao, China

Registrations Chair
Yaochu Jin, UK

Special Sessions Chairs
Uzay Kaymak, Netherlands
Mengjie Zhang, New Zealand
Zhi-Hua Zhou, China

Student Activities Chair
Dipti Srinivasan, Singapore

Tutorials Chairs
Kalyanmoy Deb, USA
Hani Hagras, UK
Nikola Kasabov, New Zealand

Workshops Chair
Piero P. Bonissone, USA

Call for Papers: IEEE WCCI 2016

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. The IEEE WCCI 2016 will host three conferences: The 2016 International Joint Conference on Neural Networks (IJCNN 2016), the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2016), and the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over theworld to discuss and present their research findings on computational intelligence.

IEEE WCCI 2016 will be held at the Vancouver Convention Centre, Vancouver, Canada. Vancouver is Canada’s Pacific gem, offering a winning combination of world-class hotels, meeting venues, and restaurants in a setting of spectacular beauty. Few convention cities can offer such a wide range of cosmopolitan amenities in a downtown core that is safe, clean, pedestrian friendly, and stunning in its backdrop of mountains and ocean.

IJCNN is the flagship conference of the International Neural Network Society and the IEEE Computational Intelligence Society. It covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.

FUZZ-IEEE is the foremost conference in the field of fuzzy systems. It covers all topics in fuzzy systems, from theory to applications.

IEEE CEC is a major event in the field of evolutionary computation, and covers all topics in evolutionary computation from theory to applications.

Call for Papers

Papers for IEEE WCCI 2016 should be submitted electronically through the Congress website at www.wcci2016.org, and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity.

Call for Tutorials

IEEE WCCI 2016 will feature pre-Congress tutorials, covering fundamental and advanced topics in computational intelligence. A tutorial proposal should include title, outline, expected enrollment, and presenter/organizer biography. Inquiries regarding tutorials should be addressed to Tutorials Chairs.

Call for Special Session Proposals

IEEE WCCI 2016 solicits proposals for special sessions within the technical scope of the three conferences. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Cross-fertilization of the three technical disciplines and newly emerging research areas are strongly encouraged. Inquiries regarding special sessions and proposals should be addressed to Special Sessions Chairs.

Call for Competition Proposals

IEEE WCCI 2016 will host competitions to stimulate research in computational intelligence. A competition proposal should include descriptions of the problem(s) addressed, evaluation procedures, and a biography of the organizers. Inquiries regarding competitions should be addressed to the Competitions Chair.

Important Dates

• Special Session & Workshop Proposals Deadline:  15 November 2015
• Competition & Tutorial Proposals Deadline:  15 December 2015 
• Paper Submission Deadline:  15 January 2016 
• Paper Acceptance Notification Date:  15 March 2016 
• Final Paper Submission & Early Registration Deadline:  15 April 2016
• IEEE WCCI 2016:  25-29 July 2016

For more information on the congress, please visit: www.wcci2016.org

Organizing Committee

General Co-Chairs
Kay Chen Tan, Singapore
Gary G. Yen, USA

IJCNN Conference Chair
Pablo A. Estevez, Chile

IJCNN Technical Chairs
Plamen P. Angelov, UK
Emilio Del Moral Hernandez, Brazil
Derong Liu, USA
Lipo Wang, Singapore

FUZZ-IEEE Conference Chair
Oscar Cordon, Spain

FUZZ-IEEE Technical Chairs
James M. Keller, USA
Naoyuki Kubota, Japan
Bernadette R. Bouchon Meunier, France
Nikhil R. Pal, India

CEC Conference Chair
Yew Soon Ong, Singapore

CEC Technical Chairs
Carlos A. Coello Coello, Mexico
Garrison W. Greenwood, USA
Sanaz Mostaghim, Germany
Yuhui Shi, China

Competitions Chair
Simon M. Lucas, UK

Conflict-of-Interest Paper Chairs
Cesare Alippi, Italy
Gary B. Fogel, USA
Hisao Ishibuchi, Japan

Exhibits Chair
Fakhri Karray, Canada

Finance Chair
Haibo He, USA

Local Arrangements Chair
Yifeng Li, Canada

Panel Sessions Chair
Marios M. Polycarpou, Cyprus

Paper Submissions Chair
Ke Tang, China

Plenary Sessions Chair
Chin-Teng Lin, Taiwan

Poster Sessions Chairs
Jong-Hwan Kim, South Korea
Laszlo T. Koczy, Hungary
Yi Zhang, China

Publications Chair
Hussein A. Abbass, Australia

Publicity Chairs
Rami Abielmona, Canada
Valentina E. Balas, Romania
Min Jiang, China
Xiaodong Li, Australia
Dongbin Zhao, China

Registrations Chair
Yaochu Jin, UK

Special Sessions Chairs
Uzay Kaymak, Netherlands
Mengjie Zhang, New Zealand
Zhi-Hua Zhou, China

Student Activities Chair
Dipti Srinivasan, Singapore

Tutorials Chairs
Kalyanmoy Deb, USA
Hani Hagras, UK
Nikola Kasabov, New Zealand

Workshops Chair
Piero P. Bonissone, USA