Call for Papers CEC2015 Special Session "Evolutionary Computation in Dynamic and Uncertain Environments"

AIMS AND SCOPE

Many real-world optimization problems are subject to dynamism and uncertainties that are often  impossible to avoid in practice. For instance, the fitness function is uncertain or noisy  as a result of simulation/ measurement errors or approximation errors (in the case where surrogates  are used in place of the computationally expensive high fidelity fitness function). In addition,  the design variables or environmental conditions can be perturbed or they change over time.

The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible,  able to tolerate uncertainties, fast to allow reaction to changes and adaptive. Moreover, the  objective of such tools is no longer to simply locate the global optimum solution, but to  continuously track the optimum in dynamic environments, or to find a robust solution that  operates properly in the presence of uncertainties.

The last decade has witnessed increasing research efforts on handling dynamic and uncertain  optimization problems using evolutionary algorithms and other metaheuristics, and a variety of  methods have been reported across a broad range of application backgrounds.

This special session aims at bringing together researchers from both academia and industry to  review the latest advances and explore future directions in this field.

Topics of interest include but are not limited to:

• Benchmark problems and performance measures
• Dynamic single - and multi-objective optimization
• Adaptation, learning, and anticipation
• Models of uncertainty and their management
• Handling noisy fitness functions
• Using fitness approximations
• Searching for robust optimal solutions
• Algorithm comparison and benchmarking
• Hybrid approaches
• Theoretical analysis
• Real-world applications

IMPORTANT DATES:

Paper submission:        December 19, 2014
Notification:            February 20, 2015
Final paper submission:  March 13, 2015

INFORMATION FOR AUTHORS:

1. Information on the format and templates for papers can be found here: http://sites.ieee.org/cec2015/paper_submission/
2. Papers should be submitted via the CEC 2015 paper submission site: http://sites.ieee.org/cec2015/paper-submission/
3. Select SS10 in the main research topic dropdown list.
4. Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 19, 2014.

ORGANIZERS:

Dr Michalis Mavrovouniotis: De Montfort University, United Kingdom
email: mmavrovouniotis@dmu.ac.uk

Dr Changhe Li: China University of Geosciences, Wuhan, China.
email: changhe.lw@gmail.com.

Prof Shengxiang Yang: De Montfort University, United Kingdom
email: syang@dmu.ac.uk

Prof Yinan Guo: China University of Mining and Technology, China
email: guoyinan@cumt.edu.cn

Call for Papers IJCNN 2015 Special Session "Models of Cognitive-Emotional Interactions"

Special Session for IEEE IJCNN 2015. Updated submission deadline 5th February 2015

Website: http://ieee-cis.blogspot.com/2014/11/call-for-papers-ijcnn-2015-special_64.html

Scope:

Recent brain imaging studies have highlighted the interrelationship between cognitive and emotional processes.  Emotion helps the cognitive system decide allocations of attention among a large, heterogeneous, and confusing set of stimuli in the environment.  Conversely, satisfaction or dissatisfaction of the drive to understand the environment generates positive or negative emotions.  Hence, it has become increasingly difficult to categorize brain regions as primarily “cognitive” or “emotional.”  Rather, a complex network of interconnected regions including amygdala, hypothalamus, ventral and dorsal striatum, anterior cingulate cortex, insula, and several regions of prefrontal cortex is involved in the interplay of cognition and emotions in human and animal decision making.

Furthermore, a number of researchers have found it advantageous to add emotion-like capabilities to robots and other artificial neural systems.  As the work of Antonio Damasio and many others points out, emotions can be of aid in classifying objects within a robot’s environment.  In particular, emotions like joy and interest can generate approach to specific objects, whereas emotions like fear and disgust can generate avoidance of specific objects.

Topics:

We particularly encourage submissions related to the following non-exhaustive list of topics:

• Emotional influences on decision making
• Normal and abnormal affect
• Cognitive and emotional effects of the psychotherapeutic process
• Computational psychiatry
• Differences between primary biological emotions and aesthetic emotions 
• Emotional robots
• Emotional representations in artificial neural systems.

Dates and submissions:

The deadlines for submissions, author feedback, etc. are bound to the normal IJCNN 2015 deadlines (and, thus, are also subject to the same changes and extensions).

The current schedule is:

• Paper submission due UPDATED: February 5, 2015
• Paper review feedback: March 15, 2015
• Final papers due: April 15, 2015

For details on the submission process, formats, etc., please refer to the IJCNN 2015 Call for Papers ( http://www.ijcnn.org/call-for-papers ) and the IJCNN 2015 submission guidelines ( http://www.ijcnn.org/paper-submission ).
When submitting to the special session, please make sure to select the corresponding session topic during the submission process.

Session Co-Chairs:

Daniel S. Levine, University of Texas at Arlington (Levine@uta.edu)
Leonid Perlovsky, Northeastern University (lperl@rcn.com)
Abbas Edalat, Imperial College London (a.edalat@imperial.ac.uk)

Call for Papers IJCNN 2015 Special Session "Ensemble Systems and Machine Learning"

Special Session for IEEE IJCNN 2015. 

Scope

Ensembles are essential tools for classification and prediction tasks in many real-world applications.  The past decade has witnessed a vast growth of the amount of machine learning methods using ensemble, becoming a popular approach due to their capabilities in handling many real world complex problems and to the good results and analysis presented in many recently published papers. Results show that ensembles of classifiers and forecasting models can achieve better accuracy than single ones. However, ensembles face many challenges in obtaining diverse and optimized base models, as well as obtaining good fusion algorithms.

This special session of IJCNN 2015 will cover all aspects of the latest achievements in ensemble systems particularly machine learning based ensembles and their applications.

Topics for submission include, but are not limited to:

• Ensemble techniques and algorithms
• Multiple experts
• Committee of experts
• Fusion of classifiers
• Fusion of forecasting models
• Optimization techniques for multiple classifiers
• Applications of ensemble and fusion techniques

etc.

Biographic Information:

Marley Vellasco received the BSc and MSc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, in 1984 and 1987, respectively, and the PhD degree in Computer Science from the University College London (UCL) in 1992. Dr. Vellasco is currently Head of the Electrical Engineering Department of PUC-Rio and of the Computational Intelligence and Robotics Laboratory (LIRA) of PUC-Rio. She is the author of four books and more than 45 papers in professional journals, 300 papers in conference proceedings and 15 book chapters in the area of soft computing and machine learning. Her research interests include Neural Networks, Fuzzy Logic, Neuro-Fuzzy Systems, Neuro-Evolutionary models, Robotics and Intelligent Agents, applied to decision support systems, pattern classification, time-series forecasting, control, optimization and Data Mining.

Teresa B. Ludermir received the Ph.D. degree in Artificial Neural Networks in 1990 from Imperial College, University of London, UK. She is a Full Professor at the Center of Informatics, Universidade Federal de Pernambuco, Brazil. She has published over a 200 articles in scientific journals and conferences, three books in Neural Networks and organized two of the Brazilian Symposium on Neural Networks. She is one of the editors-in-Chief of the International Journal of Computation Intelligence and Applications. Her main interests are Machine Learning, Meta-Learning, Hybrid Intelligent Systems, and Time Series Forecasting.

Call for Papers CEC2015 Special Session "Computational Intelligence and Games"

Special Session for IEEE CEC 2015.

 This special session is organized in association with the IEEE Computational Intelligence Society Technical Committee on Games.

Aim

Games are an ideal domain to study computational intelligence (CI) methods because they provide affordable, competitive, dynamic, reproducible environments suitable for testing new search algorithms, pattern-based evaluation methods, or learning concepts. They are also interesting to observe, fun to play, and very attractive to students. Additionally, there is great potential for CI methods to improve the design and development of both computer games and non-digital games such as board games. This special session aims at gathering not only leading researchers, but also young researchers as well as practitioners in this field who research applications of computational intelligence methods to computer games.

Scope

In general, papers are welcome that consider all kinds of applications of CI methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.). Examples include

• Adaptation in games
• Automatic game testing 
• Coevolution in games
• Comparative studies (e.g. CI versus human-designed players)
• Dynamic difficulty in games.
• Games as test-beds for CI algorithms
• Imitating human players
• Learning to play games
• Multi-agent and multi-strategy learning
• Player/opponent modelling
• Procedural content generation
• Results of game-based CI competitions
• Results of open competitions

Submission Guidelines

Special session papers should be uploaded online through the paper submission website of IEEE CEC 2015 by December 19, 2014. Please select the corresponding special session name ("Computational Intelligence and Games") as the “main research topic” in submission.For the latest information on important dates, please refer to this page.

Organizers

Ruck Thawonmas
Professor, Dept. of Human and Computer Intelligence, Ritsumeikan University, Japan
ruck@ci.ritsumei.ac.jp

Daniel Ashlock
Professor, Dept. of Mathematics and Statistics, University of Guelph, Canada
dashlock@uoguelph.ca

Biographies

1. Dr. Ruck Thawonmas is Professor at the Department of Human and Computer Intelligence, College of Information Science and Engineering, Ritsumeikan University in Japan, where he leads the Intelligent Computer Entertainment Laboratory. His research interests include game AI, metaverse, and player-behavior analysis. His laboratory has won a number of game AI competitions with the most recent one at AIIDE 2014 StarCraft AI Competition. He also organized a fighting game AI competition at CIG 2014 and is now serving as Associate Editor for IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG).
2. Dr. Daniel Ashlock is a Professor in the Department of Mathematics and Statistics at the University of Guelph in Canada.  Dr. Ashlock's work in games includes a large number of publications in mathematical games as well as a number of papers on automatic content generation.  Dr. Ashlock is an Associate Editor of TCIAIG, the leading CI-games journal.  He is a longstanding member of the CIS Games Technical Committee, and has severed on the Organizing Committee of the Computational Intelligence in Games conference six times including serving as general chair in 2013.

Call for Papers IJCNN 2015 Special Session "Modeling and Forecasting Financial and Commodity Markets by Neural Networks"

Special Session for IEEE IJCNN 2015.Updated submission deadline: 5th February, 2015.

Aim and scope

Behaviors of stock price changes in financial and commodity markets have long been a focus of economic research for a more clear understanding of mechanism and characteristics of markets. In the empirical research, some statistical properties for the market fluctuations are uncovered by the high frequency financial time series, such as fat tails distribution of price changes, power-law of logarithmic returns and volumes, volatility clustering, multifractality of volatility, etc. The applications of neural networks in time series forecasting for financial applications have gained enormous popularity in the recent years. In fact, the analysis of financial time series is of primary importance in the economic world; by using a data driven empirical analysis, the goal is to obtain insights into the dynamics of series and out-of-sample forecasting. If one were able to forecast tomorrow’s returns on an asset with some degree of precision, one could use this information in an investment today.

The aim of this special session, which stems by the excellent success obtained during the 2014 IEEE WCCI Conference held in Beijing, is to promote research and reflect the most recent advances of neural networks, including their hybridization with evolutionary computation, fuzzy systems, metaheuristic techniques and other intelligent methods, in a series of practical problems relevant to the interactions between machine learning and financial modeling and forecasting, the main interest being finalized for searching optimal relationships in the area of financial engineering, energy commodity trading, risk management, portfolio optimization, industrial organization, auctions, searching equilibriums, financial forecasting, market simulation, agent-based computational economics, and many other areas.

Topics

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

• Financial data mining
• Time series analysis and forecasting
• Soft computing applications
• Dynamics of commodity markets
• Decision support systems
• Risk analysis and credit scoring
• Portfolio management
• Automated trading systems
• Agent-based computational economics
• Economic modeling and finance
• Stock volatility prediction
• Investment strategy
• Artificial economics
• Simulation of social processes

Important Dates

Paper submission UPDATED: February 5, 2015
Paper decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference days: July 12-17, 2015

Submission

Manuscripts submitted to special sessions should be done through the paper submission website of IJCNN 2015. All papers submitted to special sessions will be subject to the same peer-review procedure as the regular papers. If a sufficient number of papers are accepted to fill a special session, then it will be included in the final program. If not enough papers are accepted for this special session, then the accepted papers will be automatically moved to regular sessions.

The authors intended to contribute to this special session are kindly recommended to follow the manuscript style information and templates of regular IJCNN 2015 papers, as described here.

When submitting their manuscripts, authors are recommended to follow these steps:

1. select the Special Session ID and Name in the “Main research topic” dropdown list, that is SS29 - Modeling and Forecasting Financial and Commodity Markets by Neural Networks 
2. fill out the input fields, upload the PDF file and finalize the submission by January 15, 2015.

Special Session Organizer

MASSIMO PANELLA, Ph.D.
Dept. of Information Engineering, Electronics and Telecommunications
University of Rome “La Sapienza”
Via Eudossiana 18, 00184 Rome, Italy
Tel.: +39-0644585496; Skype: m.panella
E-mail: massimo.panella@uniroma1.it
Web: http://massimopanella.site.uniroma1.it
LinkedIn: http://it.linkedin.com/in/massimopanella

IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Number 12, December 2014

1. Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis
Author(s): Liu, Z. ; Lai, G. ; Zhang, Y. ; Chen, X. ; Chen, C.L.P.
Page(s): 2129 - 2140

2. Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming
Author(s): Zhong, X. ; He, H. ; Zhang, H. ; Wang, Z.
Page(s): 2141 - 2155

3. A Novel Estimation Algorithm Based on Data and Low-Order Models for Virtual Unmodeled Dynamics
Author(s): Zhang, Y. ; Chai, T. ; Sun, J. ; Chen, X. ; Wang, H.
Page(s): 2156 - 2166

4. Structure-Constrained Low-Rank Representation
Author(s): Tang, K. ; Liu, R. ; Su, Z. ; Zhang, J.
Page(s): 2167 - 2179

5. Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems
Author(s): Wu, Z. ; Shi, P. ; Su, H. ; Chu, J.
Page(s): 2180 - 2190

6. Learning Regularized LDA by Clustering
Author(s): Pang, Y. ; Wang, S. ; Yuan, Y.
Page(s): 2191 - 2201

7. A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity
Author(s): Anguita, D. ; Ghio, A. ; Oneto, L. ; Ridella, S.
Page(s): 2202 - 2211

8. Learning Deep Hierarchical Visual Feature Coding
Author(s): Goh, H. ; Thome, N. ; Cord, M. ; Lim, J.
Page(s): 2212 - 2225

9. A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
Author(s): Cao, H. ; Tan, V.Y.F. ; Pang, J.Z.F.
Page(s): 2226 - 2239

10. Semi-supervised Domain Adaptation on Manifolds
Author(s): Cheng, L. ; Pan, S.J.
Page(s): 2240 - 2249

11. Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas
Author(s): Lee, J.H. ; Delbruck, T. ; Pfeiffer, M. ; Park, P.K.J. ; Shin, C. ; Ryu, H. ; Kang, B.C.
Page(s): 2250 - 2263

12. Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee
Author(s): Pan, Y. ; Yu, H. ; Er, M.J.
Page(s): 2264 - 2274

13. Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Author(s): Bi, W. ; Kwok, J.T.
Page(s): 2275 - 2287

14. Synchronization in an Array of Output-Coupled Boolean Networks With Time Delay
Author(s): Zhong, J. ; Lu, J. ; Liu, Y. ; Cao, J.
Page(s): 2288 - 2294

15. Hybrid Manifold Embedding
Author(s): Liu, Y. ; Liu, Y. ; Chan, K.C.C. ; Hua, K.A.
Page(s): 2295 - 2302

16. Learning Deep and Wide: A Spectral Method for Learning Deep Networks
Author(s): Shao, L. ; Wu, D. ; Li, X.
Page(s): 2303 - 2308

17. On the Additive Properties of the Fat-Shattering Dimension
Author(s): Asor, O. ; Duan, H.H. ; Kontorovich, A.
Page(s): 2309 - 2312

Call for Papers CEC2015 Special Session "Evolutionary Computer Vision"

Computer vision is a major unsolved problem in computer science and engineering. Over the last decade there has been increasing interest in using evolutionary computation approaches to solve vision problems. Computer vision provides a range of problems of varying difficulty for the development and testing of evolutionary algorithms.

The theme proposed special session is the use of evolutionary computation for solving computer vision and image processing problems. This special session aims to bring together theories and applications of evolutionary computation to computer vision and image processing problems. Authors are invited to submit their original and unpublished work to this Special Session. Topics of interest include but are not limited to

New theories and methods in different EC paradigms to computer vision and image processing including

• Evolutionary algorithms such as Genetic algorithms, genetic programming, evolutionary strategy and evolutionary programming;
• Swarm Intelligence such as particle swarm optimisation, ant colony optimisation, and differential evolution; and
• Other approaches such as learning classifier systems, harmony search, and artificial immune systems.  Cross-fertilization of evolutionary computation and other techniques such as neural networks and fuzzy systems is also encouraged.

Applications in computer vision and image processing including

• Edge detection in noisy images
• Image segmentation in biological images
• Automatic feature extraction, construction and selection in complex images
• Object identification and scene analysis for medical applications
• Object detection and classification in security scenarios
• Handwritten digit recognition and detection
• Vehicle plate detection
• Face detection and recognition
• Texture image analysis
• Automatic target recognition in military services
• Gesture identification and recognition
• Robot vision

Important dates:

•  Paper submission: 19 Dec 2014
• Acceptance notification: 20 Feb 2015
• Final paper submission: 13 Mar 2015

Special Session Organizers:

Mengjie Zhang, School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. 
Email: mengjie.zhang@ecs.vuw.ac.nz
Homepage: http://homepages.ecs.vuw.ac.nz/~mengjie/

Mengjie Zhang is Professor of computer science at Victoria University of Wellington, New Zealand, where he is heading the interdisciplinary Evolutionary Computation Research Group. He has been working in the area of evolutionary computer vision and signal processing for over 10 years. He has over 300 publications in international conferences and journals including over 100 in evolutionary computer vision and has been supervising over 50 research students in this area. He is the Chair of IEEE CIS Evolutionary Computation Technical Committee, a member of IEEE CIS Intelligent Systems and Application Technical Committee, an Associate Editor or Editorial Board for five international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer). He is also a Vice-Chair of the Task Force on Evolutionary computer vision and image processing (IEEE CIS EC Technical Committee) and the founding Chair of the IEEE Chapter on Computational Intelligence in New Zealand (Central Section).


Vic Ciesielski, School of Computer Science and Information Technology, RMIT University, City Campus, GPO 2476V, VIC, Australia.
Email: vic.ciesielski@cs.rmit.edu.au
Homepage: http://www.cs.rmit.edu.au/~vc/

Vic Ciesielski is an Associate Professor of computer science at RMIT University. He has been working in the area of evolutionary computer vision for over 10 years. He has over 100 publications in international conferences and journals including more than 40 on various aspects of evolutionary computer vision. He has supervised six PhD students in this area. He is also a member of the Task Force on Evolutionary Computer Vision and Image Processing (IEEE CIS EC Technical Committee).


Mario Koeppen, Graduate School of Creative Informatics, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502 JAPAN.
Email: mkoeppen@ieee.org
Homepage: http://science.mkoeppen.com/

Mario Köppen is a professor at Kyuhsu Institute of Technology, Japan. He has also been working in the field of applied image processing within the scope of industrial projects for more than ten years. His research is focused on the use of soft computing technologies, esp. evolutionary computation, neural networks and fuzzy fusion, for the design of image processing applications. He has over 150 publications in international conferences, journals and book chapters, including a large number of publications that are strongly related to the topic of this proposal.  He is also a Vice-Chair of the Task Force on Evolutionary computer vision and image processing (IEEE CIS EC Technical Committee).

Call for Papers IJCNN 2015 Special Session "Unsupervised Neural Network Clustering"

Special Session for IEEE IJCNN 2015.

IMPORTANT DATES

Paper submission UPDATED:  February 5, 2015
Paper Decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference: July 12-17, 2015

SCOPE AND MOTIVATION

Generally, unsupervised learning or self-organized learning finds regularities in the data represented by the examples. Clustering methods such as model-based, density based and user guided methods are often applied for data reduction such as summarization like preprocessing of classification; compression like vector quantization; and finding the nearest neighbors. Specifically, a feed-forward neural network is a software version of the human brain and have their roots in Hebbian and competitive learning such as Kohonen’s self-organizing map and growing neural gas. In this network, data processing has only one forward direction from the input layer to the output layer without any cycle or backward movement; and generally exhibits several advantages such as an inherent distributed parallel processing architectures, as well as capabilities to adjust the interconnection weights to learn and describe suitable clusters, process vector quantization prototypes and distribute similar data without class labels to describe the clusters, control noisy data, cluster unknown data, and learn the types of input values on the basis of their weights and properties. The current online dynamic unsupervised feed-forward neural network clustering methods such as evolving self-organizing map and dynamic self-organizing map inherit some of the advantages and disadvantages of static unsupervised feed-forward neural network clustering methods; which are suitable to be applied in different research areas such as email logs, networks, credit card transactions, astronomy and satellite communications. Generally, the critical issues of clustering are data losing, definition of clustering principles, number and Unsupervised clustering is a valuable subject to research, however, their critical issues are data losing, definition of clustering principles, number and densities of clusters. Specially, the main problems in dynamic feed-forward neural network clustering are low speed, high memory usage and memory complexity through using random weights and parameters, and relearning. The goal of this research is an investigation of current unsupervised clustering and identify their limitations and problems through a literature review and experience.

TOPICS

The topics of the special session include, but are not limited to:

• Learning and Neural Network
• Unsupervised Feed Forward Neural Network clustering
• Static Unsupervised Neural Network clustering
• Dynamic Unsupervised Neural Network clustering
• Semi-supervised Neural Network clustering

ORGANIZERS

Roya Asadi, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Selangor, 50603, Malaysia, (royaasadi@siswa.um.edu.my).

Sameem Abdul Kareem, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Selangor, 50603, Malaysia. (sameem@um.edu.my).

Shokoofeh Asadi, Department of Agreecultural Management Engineering, Faculty of Ebne-Sina, University of Sciece and Research Branch, Tehran, 930277941, Iran, (shokoofeh.ame@gmail.com).

SUBMISSION

All papers are to be submitted through the conference website: http://www.ijcnn.org/

Call for Papers IJCNN 2015 Special Session "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 different cognitive tasks (e.g., scene and object recognition, language acquisition, interaction of cognition and language, 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)
• Cognitive dissonance, neural models
• Cognition and cultures
• Medical applications 
• Genome association studies
• Big Data

Keywords:

Cognition, Emotions, Decision-Making, 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)
R. Illin (USA)
B. Kovalerchuk  (USA)
R. Kozma (USA)
D. Levine (USA)
D. Marocco  (UK)
A. Minai
L. I. Perlovsky (USA)
S. Petrov (USA)
A. Roy
J. Weng

Call for Papers CEC2015 Special Session "Evolutionary Computation for Music, Art, and Creativity"

Special Session for IEEE CEC 2015.


http://cilab.cs.ccu.edu.tw/ci-tf/ECMAC2015.html

This special session extends our previous events at IEEE CIS conferences:

• Special Session on Evolutionary Computation for Creative Intelligence at CEC 2013
• IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC 2013) at SSCI 2013
• Special Session on Evolutionary Computation for Creative Intelligence at CEC 2012
• Workshop in Evolutionary Music at CEC 2011

Organizers:

This special session is organized by the co-chairs of IEEE CIS ETTC Task Force on Creative Intelligence.

Francisco Fernández de Vega   

University of Extremadura, Spain    
Francisco Fernández is Associate Professor at the University of Extremadura. He received his BS from the University of Seville 1993, MS from the University of Seville 1997, and Ph. D from the University of Extremadura 2001. His research interests include Parallel and Distributed Evolutionary Algorithms and their applications to multiple aspects of art and design. He's been guest editor with Soft Computing, Parallel Computing, Journal of Parallel and Distributed, Natural Computing and edited the books Parallel and Distributed Computational Intelligence and Parallel Architectures and Bioinspired Algorithms, with Springer. He is cochair of EvoPar, part of Evo* Conference. He has published more than 200 papers in conferences and journals.  His work was recently awarded with the 2013 ACM GECCO Art, Design and Creativity Competition.

Palle Dahlstedt   

University of Gothenburg, Sweden   
Palle Dahlstedt is active both as a researcher in the field of computational creativity, and as an internationally recognized composer and improviser. He is associate professor in computer-aided creativity at the Dept. of Applied Information Technology, University of Gothenburg & Chalmers University of Technology, Sweden, and main lecturer in electronic and computer music and artistic director of the Lindblad Studios at the Academy of Music and Drama, University of Gothenburg. His music has been performed on six continents, and received prizes such as the prestigeous Gaudeamus Prize 2001. He has published extensively within the field, and is currently directing a major research project around technology-based creativity in musical performance.

Chuan-Kang Ting   

National Chung Cheng University, Taiwan   
Chuan-Kang Ting (S’01–M’06¬–SM’13) received the B.S. degree from National Chiao Tung University, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Taiwan, in 1996, and the Ph.D. degree from the University of Paderborn, Germany, in 2005. He is currently an Associate Professor at the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in computer networks, bioinformatics, music and games.

Introduction to the special session:

Evolutionary computation (EC) techniques, including genetic algorithm, evolution strategies, genetic programming, particle swarm optimization, ant colony optimization, differential evolution, and memetic algorithms, have shown to be effective for search and optimization problems. Recently, EC gained several promising results and becomes an important tool in computational creativity, such as in music, visual art, literature, architecture, and industrial design.

The aim of this special session is to reflect the most recent advances of EC for Music, Art, and Creativity, with the goal to enhance autonomous creative systems as well as human creativity. This session will allow researchers to share experiences and present their new ways for taking advantage of EC techniques in computational creativity. Topics of interest include, but are not limited to, EC technologies in the following aspects:

• Generation of music, visual art, literature, architecture, and industrial design
• Algorithmic design in creative intelligence
• Optimization in creativity
• Development of hardware and software for creative systems
• Evaluation methodologies
• Assistance of human creativity
• Computational aesthetics
• Emotion response
• Human-machine creativity

Keywords

Evolutionary computation, computational creativity, music, visual art, creative intelligence, emotion response, and aesthetics

Call for Papers CEC2015 Special Session "When Evolutionary Computation Meets Data Mining"

Special Session for IEEE CEC 2015.
 

Introduction:

Many of the tasks carried out in data mining and machine learning, such as feature subset selection, associate rule mining, model building, etc., can be transformed as optimization problems.  Thus it is very natural that Evolutionary Computation (EC),  has been widely applied to these tasks in the fields of data mining (DM) and machine learning (ML),  as an optimization technique.

On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC.  A plethora of successful applications have been reported, including the creation of new optimization paradigm such as Estimation of Distribution Algorithm,  the adaptation of parameters or operators in an algorithm, mining the external archive for promising search regions, etc.  
However, there remain many open issues and opportunities that are continually emerging as intriguing challenges for bridging the gaps between EC and DM. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.

We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:

• EC Enhanced by Data Mining and Machine Learning Concepts and/or Method
• Data Mining and Machine Learning Based on EC Techniques    
• Data Mining and Machine Learning Enhanced Multi-Objective Optimization     
• Data Mining and Machine Learning Enhanced Constrained Optimization
• Data Mining and Machine Learning Enhanced Memetic Computation
• Multi-Objective Optimization and Rule Mining Problems
• Knowledge Discovery in Data Mining via Evolutionary Algorithm
• Genetic Programming in Data Mining
• Multi-Agent Data Mining using Evolutionary Computation
• Medical Data Mining with Evolutionary Computation
• Evolutionary Computation in Intelligent Network Management
• Evolutionary Clustering in Noisy Data Sets
• Big Data Projects with Evolutionary Computation
• Real World Applications   

Co-Chairs

Zhun Fan

Department of Electronic Engineering, Shantou University, Shantou, China
E-mail:  zfan@stu.edu.cn
Zhun Fan received his Ph.D. (Electrical and Computer Engineering) in 2004 from the Michigan State University. He received the B.S. degree in 1995 and M.S degree in 2000, both from Huazhong University of Science and Technology, China. From 2004 to 2011, he was employed as an Assistant Professor and Associate Professor at the Technical University of Denmark. He has also been working at the BEACON Center for Study of Evolution in Action at Michigan State University. He is currently a Professor and Head of Department of Electronic and Informatics Engineering at the Shantou University, China.  He is also the Director of the Guangdong Provincial Key Laboratory of Digital Signal and Image Processing.  His major research interests include applying evolutionary computation and computational intelligence in design automation and optimization of mechatronic systems, computational intelligence, wireless communication networks, MEMS, intelligent control and robotic systems, robot vision etc

Xinye Cai

Nanjing University of Aeronautics and Astronautics, Nanjing, China
E-Mail: xinye@nauu.edu.cn
Xinye Cai received his BEng. Degree in Electronic&Information Engineering Department from Huazhong Univeristy of Science&Technology, China in 2004, and a Msc. degree in Electronic Department University of York, UK in 2006. Later, he received his PhD degree in Electrical&Computer Engineering Department in Kansas State University in 2009. Currently, he is an Associate Professor with the College of Computer Science and Technology, Nanjing University of Aeronautics&Astronautics, China. His main research interests include evolutionary computation, multi-objective optimization, constrained optimization and relevant real-world application.

Chuan-Kang Ting

National Chung Cheng University, Chiayi, Taiwan
E-Mail: ckting@cs.ccu.edu.tw
Chuan-Kang Ting (S’01–M’06–SM’13) received the B.S. degree from National Chiao Tung University, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Taiwan, in 1996, and the Ph.D. degree from the University of Paderborn, Germany, in 2005. He is currently an Associate Professor at the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in computer networks, data mining, bioinformatics, music and games.

Jun Zhang

Sun Yat-Sen University, Guangzhou, China.
E-Mail: issai@mail.sysu.edu.cn
Jun Zhang (M’02–SM’08) received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2002. From 2003 to 2004, he was a Brain Korean 21 Post-Doctoral Fellow with the Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Korea. Since 2004, he has been with Sun Yat-Sen University, Guangzhou, China, where he is currently a Cheung Kong Professor with the Department of Computer Science. He has authored seven research books and book chapters, and over 100 technical papers in his research areas. His current research interests include computational intelligence, cloud computing, high performance computing, data mining, wireless sensor networks, operations research, and power electronic circuits. Dr. Zhang was a recipient of the China National Funds for Distinguished Young Scientists from the National Natural Science Foundation of China in 2011 and the First-Grade Award in Natural Science Research from the Ministry of Education, China, in 2009. He is currently an Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Industrial Electronics, the IEEE Transactions on Cybernetics, and the IEEE Computational Intelligence Magazine. He is the Founding and Current Chair of the IEEE Guangzhou Subsection and IEEE Beijing (Guangzhou) Section Computational Intelligence Society Chapters.

K. C. Tan

Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Mail: eletankc@nus.edu.sg
TAN Kay Chen received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. Dr Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009). Dr Tan has been invited to be an invited keynote/plenary speaker for over 30 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 40 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society since 2011. Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Computational  Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling etc. Dr Tan is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research.

Qingfu Zhang

School of Computer Science & Electronic Engineering, University of Essex, Essex, UK
E-Mail: qzhang@essex.ac.uk
Qingfu Zhang is currently a Professor with the School of Computer Science and Electronic Engineering, University of Essex, UK. His is also a Changjiang Visiting Chair Professor in Xidian University, China. From 1994 to 2000, he was with the National Laboratory of Parallel Processing and Computing, National University of Defence Science and Technology, China, Hong Kong Polytechnic University, Hong Kong, the German National Research Centre for Information Technology (now Fraunhofer-Gesellschaft, Germany), and the University of Manchester Institute of Science and Technology, Manchester, U.K. He holds two patents and is the author of many research publications. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Systems, Man, and Cybernetics–Part B. He is also an Editorial Board Member of three other international journals.  MOEA/D, a multobjevitve optimization algorithm developed in his group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the Congress of Evolutionary Computation 2009, and was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award.

Call for Papers CEC2015 Special Session "Intelligent Network Systems"

Aim and Scope:

The impact of optimization in network environments, such as communication networks and transportation networks, on the modern economy and society has been growing steadily over the last few decades. The worldwide division of labor, the connection of distributed centers, and the increased mobility of individuals and devices lead to an increased demand for efficient solutions to solve optimization problems in network systems. With the advent of computer systems, computational intelligence approaches have been developed for systematic design, optimization, and improvement of different network systems.

The aim of the special session is to promote research and reflect the most recent advances of computational intelligence, including evolutionary computation, neural network, fuzzy systems, metaheuristic techniques and other intelligent methods, in the solution of problems in network systems.

Topics of interest include, but are not limited to:

• Communication network systems: telecommunications; mobile, satellite, optical, and voice communications; personal communication systems; switching and routing; transmission systems; communication systems simulation; station and antenna design; information and speech processing; intrusion detection; error control coding; compression and cryptography; propagation and channel modeling, protocol design, etc.
• Transportation and logistics network systems: transportation and supply networks; logistics; supply chain management; freight and passenger services; tracking and tracing; fleet and order management; modeling and traffic management; traffic simulation; individual and public transportation; inventory optimization; routing and scheduling, etc.
• Social network systems: action policies; networking strategies; network and friendship management; identification of interests; advertisement of interests; hierarchical networks; distributed games; behavior analysis; inter-personal communication; group communication, etc.
• Financial and economic network systems: system modeling; modeling payment system, market modeling; forecasting market prices; price tracking; invest strategies; portfolio strategies; measuring systemic importance of the financial system though the network topology, etc.
• General network problems: parallel and distributed systems; networks and graph problems; unconstrained and constrained network design problems; structural and computational complexity; adaptability to environmental variations; robustness to network changes and failures; effectiveness and scalability of performance; location and link design; reliability and failure; corporate network design; location placement; network physical and software architecture; network hardware and software technologies; operations, maintenance, and management; signaling and control; active networks; network services and applications, etc.

 

Organizers:

This special session is organized by IEEE CIS ISATC Task Force on Intelligent Network Systems (TF-INS).

Hui Cheng   

Senior Lecturer, Liverpool John Moores University, UK, Email: H.Cheng@ljmu.ac.uk   
Hui Cheng received the BSc and the MSc degrees in Computer Science from Northeastern University, China in 2001 and 2004, and the Ph.D degree in Computer Science from The Hong Kong Polytechnic University, Hong Kong in 2007. From January 2008 to July 2010, he was employed as a Research Associate at University of Leicester, UK. He joined Department of Computer Science and Technology at University of Bedfordshire as a Lecturer in October 2010. He is currently a Senior Lecturer in Liverpool John Moores University. His research interests include artificial intelligence, dynamic optimization, cloud computing, optical networks, mobile ad hoc networks, and QoS routing.

Shengxiang Yang   

Professor, Director of Centre for Computational Intelligence, De Montfort University, UK, Email: syang@dmu.ac.uk   
Shengxiang Yang (M'00-SM'14) received the PhD degree in systems engineering from Northeastern University, China, in 1999. From October 1999 to June 2012, he worked as a Post-Doctoral Research Associate, a Lecturer, and a Senior Lecturer with King's College London, University of Leicester, and Brunel University, respectively. He joined De Montfort University, UK, as a Professor in Computational Intelligence in July 2012. He is now Director of the Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University.  He has over 180 publications. His current research interests include evolutionary computation (EC), swarm intelligence, meta-heuristics, artificial neural networks, evolutionary multi-objective optimization, computational intelligence in dynamic and uncertain environments, and relevant real-world applications. He is the Chair of IEEE Computational Intelligence Society (CIS) ECTC Task Force on EC in Dynamic and Uncertain Environments, and the Founding Chair of IEEE CIS ISATC Task Force on Intelligent Network Systems (TF-INS). He has given invited keynote speeches in several international conferences and co-organized over 20 workshops and special sessions in conferences. He was the founding Co-Chair of the IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. He serves as an Area Editor, an Associate Editor, or an Editorial Board Member for four international journals. He has co-edited several books and conference proceedings and co-guest-edited several journal special issues.

Chuan-Kang Ting   

Associate Professor, National Chung Cheng University, Taiwan, Email: ckting@cs.ccu.edu.tw
Chuan-Kang Ting (S’01–M’06¬–SM’13) received the B.S. degree from National Chiao Tung University, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Taiwan, in 1996, and the Ph.D. degree from the University of Paderborn, Germany, in 2005. He is currently an Associate Professor at Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in communication and transportation networks, bioinformatics, music and games. He is an associate editor of IEEE Computational Intelligence Magazine and an editorial board member of Soft Computing and Memetic Computing journals. He chaired the AI Forum 2012 and co-chaired the 2013 IEEE Symposium on Computational Intelligence for Creativity and Affective Computing.

Call for Papers IJCNN 2015 Special Session "Big Data in Smart Industry"

Submissions are invited for IJCNN 2015 Special Session on “Big Data in Smart Industry”

Updated submission deadline 5th February, 2015.

Webpage: http://www.cpdee.ufmg.br/~apbraga/Paginas2014/SSIJCNN2015.html

ABSTRACT

Industry is experiencing worldwide the beginning of a new era of innovation and change. Sensors, actuators, supervision and control elements are increasingly endowed with autonomy, flexibility, communication capability and interoperability. The new generation of devices, which is capable of data collection and processing, has been gradually incorporated into several levels of the industrial production chain. The synergy of these physical and computational elements forms the base for a profound transformation of the global industry with a perspective of a dramatic increase of productivity and reliability, and significant benefits for the society. Within this new scenario, the availability of a large amount of high dimensional data reveals itself as a dilemma for the induction of data models. On the one hand, there is an expectation that a greater ability of sampling might improve performance and reliability. Nevertheless, the reality is that most current methods and models are not able to deal with problems of such high dimension and volume. Many current problems involve terabytes of data with hundreds of variables and dimensions that tend to rise continuously if the expected industrial growth rate in the sector is maintained. Prognostics are for an exponential growth of the data storage capacity in the worldwide network of devices during the next years. In addition to the increased internal connectivity of the industry, an improved integration and enhanced synergy with consumer markets and inputs through networking appears to be an inevitable path. Current trend suggests worldwide industry will highly demand data models and processing capabilities to handle time-varying, massive and high dimensional data. This is where Big Data in Smart Industry problems become relevant, and it is crucial that academia and industry are prepared from scientific and technological points of view to face the new challenges.

TOPICS:

We would like to encourage the submission of papers within the general scope of the Special Session (Big data in Smart Industry) in the following topics:

• Modeling of large datasets
• Dimensionality reduction of very large datasets
• Learning from large industrial datasets
• Data analysis and visualization
• Online modeling, optimization, and autonomous control of industrial processes
• Embedded intelligence in cyber-physical systems
• Computational intelligence for smart energy management
• Data stream processing for water, transportation, agriculture, and sustainability
• Internet of things and smart resources management
• Data-driven optimization and control of dynamical systems
• Cyber-physical system units (CPSU) with embedded autonomy
• Data acquisition and storage in distributed industrial environments

ORGANIZERS:

Antônio Pádua Braga
Federal University of Minas Gerais, Brazil
apbraga@ufmg.br
http://www.ppgee.ufmg.br/~apbraga

Fernando Gomide
University of Campinas, Brazil
gomide@dca.fee.unicamp.br
http://www.dca.fee.unicamp.br/~gomide

SUBMISSION & IMPORTANT DATES

Special session papers will undergo the same review process as regular papers and follow the same schedule of the conference. Paper submission should be done directly to IJCNN submissions page found at http://ijcnn.org. Be sure to set the "Main research topic" to your special session. The special sessions are found at the bottom of the list.

Paper submission deadline : February 5, 2015
Paper decision notification: March 15, 2015
Camera-ready submission: April 15, 2015

Call for Papers IJCNN 2015 Special Session "Autonomous Learning from Big Data"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

Organizers: 

P. Angelov (www.lancs.ac.uk/staff/angelov)
A. Roy (ASIM.ROY@asu.edu)

The aim of the special session is to present latest results in this fast expanding area of Autonomous Learning Systems and Big Data Analytics and to give a forum to discuss the challenges for the future.
It is organised by the new Special Interest Group on Autonomous Learning Systems and the Section on Big Data Analytics within INNS and by the Technical Committee on Evolving Intelligent Systems, SMC Society, IEEE and aims to be a focal point of the latest research in this emerging area.

One of the important research challenges today is to cope effectively and efficiently with the ever growing amount of data that is being exponentially produced by sensors, Internet activity, nature and society. To deal with this ocean of zeta-bytes of data, data streams and navigate to the small islands of human-interpretable knowledge and information requires new types of analytics approaches and autonomous learning systems and processes.

Traditionally, for decades or even centuries machine learning, AI, cognitive science were developed with the assumption that the data available to test and validate the hypotheses is a small, finite volume and can be processed iteratively and offline. The realities of dynamically evolving big data streams and big data sets (e.g. pentabytes of data from retail industry, high frequency trading, genomics or other areas) become more prominent only during the last decade or so. This poses new challenges and requires new, revolutionary approaches.

Topics of interest 

(include but not limited to):

Methodology

• Autonomous, online, incremental learning – theory, algorithms and applications in big data
• High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
• Scalable algorithms for big data
• Learning algorithms for high-velocity streaming data
• Kernel methods and statistical learning theory
• Big data streams analytics
• Deep neural network learning
• Machine vision and big data
• Brain-machine interfaces and big data
• Cognitive modeling and big data
• Embodied robotics and big data
• Fuzzy systems and big data
• Evolutionary systems and big data
• Evolving systems for big data analytics
• Neuromorphic hardware for scalable machine learning
• Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
• New Adaptive and Evolving Learning Methods
• Autonomous Learning Systems
• Stability, Robustness, Unlearning Effects
• Structure Flexibility and Robustness in Evolving Systems
• Evolving in Dynamic Environments
• Drift and Shift in Data Streams
• Self-monitoring Evolving Systems
• Evolving Decision Systems
• Evolving Perceptions
• Self-organising Systems
• Neural Networks with Evolving Structure
• Non-stationary Time Series Prediction with Evolving Systems
• Automatic Novelty Detection in Evolving Systems
• On-Line Identification of Fuzzy Systems
• Evolving Neuro-fuzzy Systems
• Evolving Clustering Methods
• Evolving Fuzzy Rule-based Classifiers
• Evolving Regression-based Classifiers
• Evolving Intelligent Systems for Time Series Prediction
• Evolving Intelligent System State Monitoring and Prognostics Methods
• Evolving Intelligent Controllers
• Evolving Fuzzy Decision Support Systems
• Evolving Probabilistic Models
• Big data and collective intelligence/collaborative learning
• Big data and hybrid systems
• Big data and self-aware systems
• Big Data and infrastructure
• Big data analytics and healthcare/medical applications
• Big data analytics and energy systems/smart grids
• Big data analytics and transportation systems
• Big data analytics in large sensor networks
• Big data and machine learning in computational biology, bioinformatics
• Recommendation systems/collaborative filtering for big data
• Big data visualization
• Online multimedia/ stream/ text analytics
• Link and graph mining
• Big data and cloud computing, large scale stream processing on the cloud

Real-life applications

• Robotics
• Defence
• Intelligent Transport
• Bio-Informatics
• Industrial Applications
• Data Mining and Knowledge Discovery
• Control Systems
• Evolving Consumer Behaviour
• Evolving Activities Recognition
• Evolving Self-localisation Systems

Dates:

• Send Title & Abstract to p.angelov@lancaster.ac.uk or ASIM.ROY@asu.edu as soon as possible
• Deadline for Paper Submission Updated 5 February, 2015
• Notification of Acceptance 15 March, 2015
• Final Paper Submission 15 April, 2015

Selected authors will be invited to submit extended papers for a special issue of the Springer journal Evolving Systems

Call for Papers IJCNN 2015 Special Session "Digital Audio Applications"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

THEME AND SCOPE

Neural Networks (NN) based techniques, and Computational Intelligence (CI) ones from a wider perspective, are largely used to face complex modelling, prediction, and recognition tasks in different research fields. One of these is represented by Digital Audio, which finds application in contexts like entertainment, security, and health. Scientists and technicians worldwide actively cooperate to develop new services and products, and they typically employ advanced NN and CI techniques, in combination with suitable Digital Signal Processing algorithms.

In particular, this is typically accomplished with the aim of extracting and manipulating useful information from the audio stream to pilot the execution of automatized services, also in an interactive fashion. Several are the Digital Audio topics touched by such a paradigm, involving different kinds of audible signals. In the “music” case study we have the music information retrieval with many diverse sub-topics therein; for “speech” we can mention speech/speaker recognition, speaker diarization, speaker localization; in the case of “sound”, acoustic monitoring and sound detection and identification have lately registered a big interest among the scientists working in the field. Moreover, also cross-domain approaches to exploit the information contained in diverse signals in the acoustic range have been also recently developed. In many applicative contexts, this happens in conjunction with data coming from other media, like textual and visual, for which specific fusion techniques are required. 

In dealing with the problems correlated to these topics, the adoption of data-driven learning systems is often a ``must'', and the recent success encountered by deep neural architectures comes just in confirmation of that. This is not, however, immune to technological issues, due to the presence of non-stationary operating conditions and hard real-time constraints (made often harder by the big amount of data to process).

It is indeed of great interest for the scientific community to understand how and to what extent novel CI based techniques (with special attention to the NN ones) can be efficiently employed in Digital Audio, in the light of all aforementioned aspects. The aim of the session is therefore to focus on the most recent advancements in the CI field and on their applicability to Digital Audio problems.

TOPICS

Potential topics include, but are not limited to:

• Computational Audio Analysis
• Deep Learning algorithms in Digital Audio
• Neural Architectures for Audio Processing
• Music Information Retrieval
• Speech/Speaker Analysis and Classification
• Sound Detection and Identification
• Acoustic Source Separation
• Brain inspired auditory scene analysis
• Cross-domain Audio Analysis
• Speech and Audio Forensics
• Audio-based Security Systems
• Intelligent Audio Interfaces

IMPORTANT DATES

• UPDATED February 5, 2015: Paper submission deadline
• March 15, 2015: Notification of paper acceptance
• April 15, 2015: Camera-ready deadline
• July 11-16, 2015: Conference days

ORGANIZERS

Stefano Squartini, Università Politecnica delle Marche, Italy, s.squartini@univpm.it      
Aurelio Uncini, Università La Sapienza, Italy, aurel@ieee.org
Björn Schuller, University of Passau, Germany/Imperial College London, UK, schuller@ieee.org
Francesco Piazza, Università Politecnica delle Marche, Italy, f.piazza@univpm.it

http://a3lab.dii.univpm.it/news/ijcnn2015-special-session

Call for Papers IJCNN 2015 Special Session "Intelligence for cyber-physical, embedded and pervasive systems"

Special Session for IEEE IJCNN 2015.

Overview

The emergence of nontrivial embedded units mounting a rich sensor and actuator platform, sensor networks, the Internet of Things (IoT), pervasive and cyber-physical systems has made possible the design of sophisticated applications where large amounts of real-time data are collected to constitute a “big data” picture as time passes. Intelligence, adaptation through learning, neural and neuromorphic systems, cognitive fault tolerance and healing abilities constitute some key mechanisms needed to boost the future generation of intelligent systems and derived applications.

Topics

The special session aims at gathering scholars and practitioners addressing those research topics and applied problems that the interaction of intelligent systems with the real world is requesting.

Papers must present original work, applications or review the state-of-the-art in the following non-exhaustive list of topics:

• Intelligent devices and solutions for the Internet of things
• Cognitive Sensor Networks
• Neural and neuromorphic Systems
• Intelligent Measurement Systems
• Incremental learning in cyber-physical and embedded systems
• Intelligent diagnosis and healing mechanisms 
• Low level learning mechanisms for time invariant environments
• Intelligent sensors and robotics
• Intelligent systems for embedded applications

Relevance of the topic

Although we experience an increasingly pervasive presence of embedded applications in our everyday life, such that by 2020 it is predicted that each person will possess between 5 and 10 embedded systems, the presence of intelligence in current versions of embedded devices is generally very basic, mostly confined to passive adaptation.  Learning and cognitive mechanisms play a key role here to boost the next generation of embedded systems and applications.

Workshop Organizers

Cesare Alippi (Italy)

CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), USI (CH). Alippi is an IEEE Fellow, Vice-President education of the IEEE Computational Intelligence Society (CIS), Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Trans Instrumentation and Measurements, IEEE-Trans. Neural Networks.

In 2004 he received the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2011 has been awarded Knight of the Order of Merit of the Italian Republic; in 2013 he received the IBM Faculty Award.Current research activity addresses adaptation and learning in non-stationary environments and Intelligent embedded systems. He holds 5 patents and has published one monograph book published by Springer on "Intelligence for embedded systems", 6 edited books and about 200 papers in international journals and conference proceedings.

Manuel Roveri (Italy)

MANUEL ROVERI received the Dr.Eng. degree in Computer Science Engineering from the Politecnico di Milano (Milano, Italy) in June 2003, the MS in Computer Science from the University of Illinois at Chicago (Chicago, Illinois, U.S.A.) in December 2003 and the Ph.D. degree in Computer Engineering from Politecnico di Milano (Milano, Italy) in May 2007.

Currently, he is an assistant professor at the Departent of Electroncis and Information of the Politecnico di Milano. His research interests include intelligent embedded systems, computational intelligence and adaptive algorithms. Manuel Roveri is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems and served as chair and member in many IEEE subcommittees.

Keywords

• Embedded systems
• Cyber-physical systems
• Intelligent applications
• Intelligent sensors
• Intelligence for sensor networks
• Neuromorphic systems
• Internet of Things
• Approximate computing

Further information and Paper submission can be found at the conference homepage:  http://www.ijcnn.org/
Please make sure you select the correct Special Session in the submission process.

Call for Papers IJCNN 2015 Special Session "Emerging trends in CI methods for Biomedicine and Healthcare"

Special Session for IEEE IJCNN 2015.


http://www.cs.upc.edu/~avellido/research/conferences/IJCNN15-HealthBiomed.html

Beyond basic research, biology in general and biomedicine in particular are increasingly and rapidly becoming data-based sciences, an evolution driven by technological advances in image and signal non-invasive data acquisition (perfectly exemplified by the 2014 Nobel Prize in Chemistry for the development of super-resolved fluorescence microscopy), or high-throughput genomics, to name just a few. In the Biomedical field, the large amount of data generated from a wide range of devices and patients is creating challenging scenarios for researchers, related to storing, processing and even just transferring information in its electronic form, all these compounded by privacy and anonymity legal issues. The situation is not different in healthcare, where electronic health records are becoming commonplace and new possibilities such as remote home monitoring, or wearable medical devices are likely to make an impact as part as the ambitious p-Health, or 4-P (Predictive, personalized, preventive, participatory) paradigm for medicine. Data-based healthcare finds a paramount example in the current Institute for Systems Biology (ISB, Seattle) "Hundred Person Wellness Project"(*), a pilot in which 100 healthy individuals are intensively monitored on a daily basis. New data requirements require new approaches to data analysis, some of the most interesting ones are currently stemming from the Computational Intelligence (CI) and Machine Learning (ML) community. This session is particularly interested in the proposal of novel CI and ML approaches to problems in the biomedical and healthcare domains, with a non-exclusive focus on methods that overcome the "black-box syndrome" by making models interpretable and thus fulfil the usability requirements of most real medical applications.

Topics that are of interest to this session include (but are not necessarily limited to):

• Novel applications of existing CI and ML methods to biomedicine and healthcare
• Novel CI and ML techniques for biomedicine and healthcare.
• CI and ML-based methods to improve model interpretability in biomedicalproblems, including data/model visualization techniques.
• Novel CI and ML techniques for dealing with non-structured and heterogeneousdata formats.
• Development of user-friendly interactive exploratory interfaces and subjectspecificmodels.

(*) Medicine Gets Up Close and Personal, Nature, 506(7487), 144-145

Organizers:

• Alfredo Vellido, Computer Science Department, Universitat Politècnica de Catalunya BarcelonaTECH, Spain
• Paulo J.G. Lisboa and Sandra Ortega-Martorell, School of Computing and Mathematical Sciences, Liverpool John Moores University, United Kingdom
• José D. Martín, Intelligent Data Analysis Laboratory (IDAL), Department of Electronic Engineering, University of Valencia, Spain

Call for Papers IJCNN 2015 Special Session "Clustering and Co-clustering"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

Description

Everyday, huge amounts of data are generated by users via the web, social networks, etc. Clustering/Coclustering techniques are a tool of choice to help organize the huge collections of data that increasingly beset us. Clustering/Coclustering is an unsupervised learning approach that allows one to discover global structures in the data (i.e. clusters). Given a dataset, it identifies different data subsets which are hopefully meaningful. The discovered clusters are deemed interesting if they are while instances within each (co)cluster share similar features. This (co)clustering problem has motivated a huge body of work and has resulted in a large number of algorithms. (Co)Clustering has thus been used in numerous real-life application domains such as marketing, city planning, and so forth.

Clustering algorithms are a tool of choice to explore these high-dimensional data sets. However numerous questions remain open as:

• What are the last advances in
• supervised clustering that combines the main characteristics of both traditional clustering and supervised classification tasks?
• quality criteria?
• clustering for big data?
• evolving clustering?
• clustering events or time series?

This special session offers a meeting opportunity for academics and industry researchers belonging to the communities of Computational Intelligence, Machine Learning, Experimental Design, and Data Mining to discuss new areas of (co)clustering, One goal of this special session will be two-fold: On the one hand, to look for new algorithms and techniques proposals based on (co)clustering. On the other hand, to look for new application domains, real problems, where the application of (co)clustering have demonstrated an outstanding performance or interpretation abilities against other traditional approaches.

Publication opportunities: Papers should be submitted to IJCNN. We encourage papers that describe new algorithm and applications of (co)clustering in real-world. In the industrial context, the main difficulties met and the original solutions developed, have to be described.

Paper acceptance and publication will be judged on the basis of their quality and relevance to the special session themes, clarity of presentation, originality and accuracy of results and proposed solutions.

The set of proposed topics includes, but is not limited to::

• Clustering, Coclustering
• Supervised Clustering (Coclustering)
• Semi-Supervised Clustering (Coclustering)
• Quality Criteria for Clustering (Coclustering)
• Measure of Variable Importance for a Clustering (Coclustering)
• Automatic tuning of Cluster Number (Cocluster Number)
• Clustering for Big Data
• Method to assess the evolution of a Clustering (Coclustering)
• Constrainted Clustering (Coclustering)

The list of application domain is includes, but it is not limited to:

• Evolving textual information analysis
• Evolving social network analysis
• Dynamic process control and tracking
• Intrusion and anomaly detection
• Genomics and DNA micro-array data analysis
• Adaptive recommender and filtering systems

A list of Applicative domains could be found in
P. Berkhin « Survey of clustering data mining techniques », Accrue Software, San Jose, CA, 2002.

Organizers:

Vincent Lemaire
ORANGE-LABS, Lannion, France
2 avenue Pierre Marzin
22300 Lannion
vincent.lemaire@orange-ftgroup.com
http://www.vincentlemaire-labs.fr

Pascal Cuxac
INIST-CNRS,
Recherche Développement
2 allée du Parc de Brabois
CS 10310
54519 Vandoeuvre les Nancy Cedex
pascal.cuxac@inist.fr,
https://sites.google.com/site/pascalcuxac/

Jean-Charles Lamirel
TALARIS (ex-TALARIS) -LORIA,
Campus Scientifique
BP 239
54506 Vandoeuvre-lès-Nancy
jean-charles.lamirel@loria.fr

Organizing committee 

(tentative, to be confirmed):

• Abou-Nasr Mahmoud Ford Motor Company USA
• Al Shehabi Shadi Allepo University Syria
• Albatineh Ahmed N. Dept of Biostatistics Florida Int. U. Miami USA
• Alippi Cesare Politecnico di Milano Italia
• Arredondo Tomas U.T.F.S.M. Valparaíso Chile
• Bennani Younes LIPN, Paris France
• Bifet Albert University of Waikato, Hamilton New Zealand
• Bondu Alexis EDF R&D France
• Cabanes Guenael LIPN, Paris France
• Candillier Laurent (Expertiselcandillier.free.fr)
• Chawla Nitesh Notre Dame University, Indiana USA
• Chen Chaomei Drexel University, Philadelphia USA
• Cleuziou Guillaume (LIFO) France
• Cornuéjols Antoine (AgroParisTech) France
• Cuxac Pascal INIST-CNRS, Vandoeuvre-les Nancy France
• Diallo Abdoulaye B. UQAM Montreal Canada
• El Haddadi Anass IRIT France
• Escalante Hugo Jair Mexico
• García-Rodríguez José University of Alicante Spain
• Glanzel Wolfgang KU Leuven, Leuven Belgia
• Guigoures Romain (Zalando - German)
• Grozavu Nistor LIPN, Paris France
• Hammer Barbara University of Bielefeld Germany
• Kumova Bora I. Izmir University Turkey
• Kuntz-Cosperec Pascale Polytech'Nantes France
• Labroche Nicolas (Université de Tours) France
• Lallich Stephane University of Lyon 2 France
• Lamirel Jean-Charles TALARIS- LORIA, Nancy France
• Lebbah Mustapha LIPN, Paris France
• Lemaire Vincent Orange Labs, Lannion France
• Lenca Philippe Telecom Bretagne France
• Li Bin UTS, Sydney Australia
• Nuggent Rebecca Carnegie Mellon University, Pittsburgh USA
• Popescu Florin Fraunhofer Institute, Berlin Germany
• Roveri Manuel Politecnicodi Milano Italia
• Sublemontier Jacques-henri (CEALIST), France
• Tamir Dan Texas State University, San Marcos USA
• Torre Fabien University of Lille3 France
• Urvoy Tanguy Orange Labs, Lannion France
• Vrain Christel (LIFO) France
• Zhou Zhi-Hua Nanjing University China
• Zhu Xingquan Florida Atlantic University USA

Important dates:

• Paper submission UPDATED: February 5, 2015
• Paper decision notification: March 15, 2015
• Camera-ready submission: April 15, 2015

Organizers:

Vincent Lemaire (Orange Labs, France, vincent.lemaire@orange.com) was born in 1968 and he obtained his undergraduate degree from the University of Paris 12 in signal processing and was in the same period an Electronic Teacher. He obtained a PhD in Computer Science from the University of Paris 6 in 1999. He thereafter joined the R&D Division of France Telecom where he became a senior expert in data-mining. His research interests are the application of machine learning in various areas for telecommunication companies with an actual main application in data mining for business intelligence. He developed exploratory data analysis and classification interpretation tools. Active learning and data-space exploration are now his main research interests. He obtained his Research Accreditation (HDR) in Computer Science from the University of Paris-Sud 11 (Orsay) in 2008.

Previous workshop or special session organizations:

• Atelier - Clustering and Co-clustering (CluCo) at EGC 2015
• Incremental classification, concept drift and novelty detection (IClaNov) - ICDM 2014 
• Workshop of the Discovery Challenge - ECML 2014 
• Incremental learning and novelty detection methods and their applications - ESANN 2014 
• Atelier - Clustering and Co-clustering (CluCo) at EGC 2014 
• Incremental clustering, concept drift and novelty detection (IClaNov) - ICDM 2013 
• Active Learning and Experimental Design (ALED) - IJCNN 2013 
• Incremental classification and novelty detection (CIDN) - EGC 2013 
• Active Learning in Real-World Applications - (ALRA) - ECML 2012 
• Active and Incremental Learning (AIL) - ECAI 2012 
• Incremental classification and novelty detection (CIDN) - EGC 2012 
• Active, Incremental and Autonomous Learning: Algorithms and Applications - IJCNN 2012 
• Workshop on Unsupervised and Transfer Learning (UTL) - ICML 2011 
• Autonomous and Incremental Learning (AIL) - IJCNN 2011 
• Active and Autonomous Learning (AAL) - IJCNN 2010 
• Fast scoring on a Large Database - KDD 2009

Jean-Charles Lamirel (LORIA - INRIA, France, lamirel@loria.fr) is Lecturer since 1997. He obtained his PhD in Computer Science in 1995 and his Research Accreditation in the same domain in 2010. He is currently teaching Information Science and Computer Science at the University of Strasbourg and achieving his research at the INRIA laboratory of Nancy. He was a research member of the INRIA-CORTEX project whose scope is Neural Networks and Biological Systems. He has recently integrated the INRIA-TALARIS project whose main concern is automatic language and text processing. Jean-Charles Lamirel main domains of research are textual data mining based on neural networks, multiple viewpoints data analysis paradigms, data mining auto-evaluation methods and evolving data mining. He is board member of the international Webometrics journal: "Collnet Journal of Scientometrics and Information Management" and was taking part in the committee of ICTAI 2011-2012 and WSOM 2012 conference. He is member of the IEE task force on "Evaluation and quality issues in data mining" within the Data Mining Technical Committee and committee member of the corresponding PAKDD-QIMIE 2013 workshop.

Previous workshop or special session organizations:

• Clustering and co-clustering – CluCo - workshop EGC 2014
• Incremental clustering, concept drift and novelty detection (IClaNov), Workshop ICDM 2013
• Incremental classification and novelty detection - CIDN - workshop EGC 2013
• Intelligent analysis of time varying information and concept drift management – IEA/AIE 2012
• Incremental classification and novelty detection - CIDN 2012
• Incremental clustering and novelty detection techniques and their application to intelligent analysis of time varying information - IEA/AIE 2011
• Incremental clustering and novelty detection - CIDN 2011
• International Conference on Webometrics, Informetrics and Scientometrics & 7th COLLNET Meeting in conjunction with the Extra Session on Information Visualization for Webometrics, Informetrics and Scientometrics, Nancy, France, 10-12 May, 2006

Pascal Cuxac (INIST - CNRS, France pascal.cuxac@inist.fr) is Research Engineer at the INIST/CNRS (Institute for Scientific & Technical Information / National Center for Scientific Research) in Nancy, France. He obtained his PhD in Geological and Mining Engineering from the Nancy School of Geology in 1991 and he was working on mechanical behavior of anisotropic rock. In 1993, he joined the CNRS as Research Engineer. Currently, in INIST Research & Development Engineering Service, he takes part in a research program on classification methods for bibliographic corpora, in particular in the development of an incremental unsupervised clustering algorithm.

Previous workshop or special session organizations:

• Clustering and co-clustering – CluCo - workshop EGC 2014
• Incremental clustering, concept drift and novelty detection (IClaNov), Workshop ICDM 2013
• Incremental classification and novelty detection - CIDN - workshop EGC 2013
• Incremental classification and novelty detection - CIDN 2012
• Incremental clustering and novelty detection techniques and their application to intelligent analysis of time varying information - IEA/AIE 2011
• Incremental clustering and novelty detection - CIDN 2011

Call for Papers IJCNN 2015 Special Session "Nature Inspired Deep Learning"

Special Session for IEEE IJCNN 2015.

IMPORTANT DATES

Paper submission UPDATED:  February 5, 2015
Paper Decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference: July 12-17, 2015

AIM

Deep learning has recently emerged as a prominent CI discipline. However, applications of the nature-inspired methods such as particle swarm optimisation and evolutionary optimisation to deep learning are still very limited. Training deep neural networks is a challenging task due to the inherent high dimensionality. The aim of this special session is to discuss the existing nature-inspired approaches to deep learning, to identify problems that arise, and to encourage research in this new and exciting field of computational intelligence.

SCOPE

The topics of the special session include, but are not limited to:

• Applications of evolutionary algorithms to deep learning
• Applications of swarm-based algorithms to deep learning
• Hybrid approaches to deep learning
• Theoretical and empirical analysis of the nature-inspired deep learning algorithms
• Identifying and understanding the limitations of nature-inspired deep learning
• Real-world applications of nature-inspired deep learning
• Training Restricted Boltzmann Machines with nature-inspired algorithms
• Training Deep Belief Networks with nature-inspired algorithms
• Training autoencoders and stacked autoencoders with nature-inspired algorithms
• Training convolutional neural networks with nature-inspired algorithms
• Weight pretraining with nature-inspired algorithms
• High-performance implementations of nature-inspired deep learning
• Analysis of overfitting and generalization of deep networks training using nature-inspired algorithms
• Training of deep networks in dynamic environments

ORGANIZERS

Prof Andries Engelbrecht, Department of Computer Science, University of Pretoria, Pretoria, South Africa (engel@cs.up.ac.za)
Ms Anna Rakitianskaia, Department of Computer Science, University of Pretoria, Pretoria, South Africa (annar@cs.up.ac.za)

SUBMISSION

All papers are to be submitted through the conference website: http://www.ijcnn.org/

More details on the special session can be found at: https://sites.google.com/site/ijcnn2015deepnature/

Call for Papers IJCNN 2015 Special Session "Autonomous Machine Learning for Cyber-Physical Systems"

Special Session for IEEE IJCNN 2015.

Updated submission deadline 5th February, 2015.

Scope and Motivation

Recent development in ICT and sensor devices brings us a new form of intelligent systems called Cyber-Physical System (CPS). In CPS, physical entities such as humans, robots, cars, factories, houses interact and communicate with other entities in both physical- and cyber-worlds. The information processed in cyber-physical worlds are video images, voice/sounds, texts (e.g. documents, tweets, e-mails), control signals, sensor data, etc., and such data are continuously generated as “big stream data”. In general, such data are composed not only of explicit information on physical entities (e.g. location, translation, acceleration), but also of implicit information such as health conditions, emotion, and behaviors, which should be extracted from original sensor data. To acquire knowledge from the latter type of implicit information, autonomous machine learning and data mining methods that can learn from high-dimensional stream data are solicited for CPS.

The purpose of this special session is to share new ideas to develop autonomous machine learning and data mining methods for big stream data that are generated not only by connecting cyber-physical worlds but also within either of cyber- and physical- worlds.

Topics

A wide range of autonomous machine learning/data mining methods and applications for cyber-physical systems is covered, including but not limited to the followings:
Theoretical approaches to machine learning /data mining methods for cyber-physical systems

• Supervised/Unsupervised Learning
• Online/Incremental Learning
• Online Feature Selection/Extraction
• Online Clustering
• Active Learning
• Stream Data Mining
• Text Mining
• Time-Series Analysis

Applications of cyber-physical systems such as

• Human-Robot Interactions
• Smart Life Technologies (Smart Grids, Smart City, Smart Home, Smart Car, Smart Agriculture, etc.)
• Social Network Analysis (e.g. sentimental analysis, user profiling, etc.)
• Cybersecurity
• Opinion Mining
• Emotion/Behavior Mining
• Person Attitude Mining
• Realty Mining

Important Dates

• February 5, 2015: Paper submission deadline
• March 15, 2015: Notification of paper acceptance
• April 15, 2015: Camera-ready deadline
• July 12-17, 2015: Conference days

Submission

1. Manuscripts submitted to special sessions should be done through the paper submission website of IJCNN 2015 as regular submissions. Please follow the instructions below:Go to http://ieee-cis.org/conferences/ijcnn2015/upload.php to submit a paper to IJCNN 2015.
2. Be sure to set the "Main research topic" to “SS32: Autonomous Machine Learning for Cyber-Physical Systems”. (The special sessions are found at the bottom of the list.)

All papers submitted to special sessions will be subject to the same peer-review review procedure as the regular papers. Accepted papers will be part of the regular conference proceedings.

For more information, please contact the Special Session organizers:

• Seiichi Ozawa (ozawasei@kobe-u.ac.jp), Kobe University, Japan
• Nistor Grozavu (nistor@lipn.univ-paris13.fr), LIPN, Paris 13 University, Villetaneuse, France
• Nicoleta Rogovschi (nicoleta.rogovschi@parisdescartes.fr), LIPADE, Paris Descartes University, Paris, France
• Shogo Okada (okada@ntt.dis.titech.ac.jp), Tokyo Institute of Technology, Japan