Call for participation: IEEE World Congress on Computational Intelligence (IEEE WCCI) July 8-13, 2018

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences: The 2018 International Joint Conference on Neural Networks (IJCNN 2018), the 2018 IEEE International Conference on Fuzzy Systems (FUZZ- IEEE 2018), and the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

IEEE WCCI 2018 will be held at the Windsor Barra Convention Centre, Rio de Janeiro, Brazil. Rio de Janeiro is a wonderful and cosmopolitan city, ideal for international meetings. Rio boasts fantastic weather, savory cuisine, hospitable people, and modern infrastructure. Rio is the first city to receive the Certificate of World Heritage for its Cultural Landscape, recently conferred by UNESCO.

IJCNN is the flagship conference of the IEEE Computational Intelligence Society and the International Neural Network 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.

The highlights of the Congress include:

• 5 Keynotes by top-notch researchers – Miguel Nicolelis, Klaus-Robert Müller, Dario Floreano, Enrique Ruspini and Francisco Herrera (http://www.ecomp.poli.br/~wcci2018/speakers/);
• 15 Plenary speeches by world-renown scholars on pertinent and captivating topics within the technical scope of each conference;
• A public lecture by Gary Fogel;
• A few valuable and engaging panel sessions by revered researchers within the field of computational intelligence (http://www. ecomp.poli.br/~wcci2018/panel-sessions/);
• Over 20 tutorials covering a diverse range of topics offering a unique opportunity to disseminate in-depth information on specific topics in computational intelligence (http://www.ecomp.poli.br/~wcci2018/Tutorials/);
• A number of workshops to stimulate discussions between participants on active and emerging topics of computational intelligence (http://www.ecomp.poli.br/~wcci2018/workshops/);
• 115 special sessions organized by domain experts that are encompassed within the technical scope of the conference (http:// www.ecomp.poli.br/~wcci2018/special-sessions/);
• 11 challenging and contemporary competitions offering distinctive topics in each track to cater to every participant’s needs (http://www.ecomp.poli.br/~wcci2018/competitions/).

Apart from the technical program, participants are also cordially invited to attend various social events that will include welcome

reception and conference banquet. In addition, participants are also encouraged to explore the beautiful city of Rio de Janeiro which

has an endless supply of attractions and things to see and do (http://www.ecomp.poli.br/~wcci2018/rio-de-janeiro-2/).

Deadline extension: IEEE (SMC) Conference on Evolving and Adaptive Intelligent Systems (EAIS 2018)

25-27 of May 2018
Rhodes, Greece
/http://easyconferences.eu/eais2018/

The 12th 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems is organized by the IEEE SMC.

IMPORTANT DATES:

The Paper Submission Deadline has been extended to: 18th of March, 2018
Notification of acceptance for the new submissions: 30th of March 2018
Camera Ready for the new Submissions: 10th of April 2018
Conference: 25 - 27 May, 2018

Papers must be comprised of up to 8 pages. All contributions have to be prepared according to the IEEE conference style guidelines. Templates following these guidelines can be downloaded from the IEEE website (use e.g., bare_conf.tex as an example for LaTeX based contribution).

SPECIAL ISSUES

Selected papers will be published in special issues of the following Journals:

• IJAIT (International Journal on Artificial Intelligence Tools) Impact Factor 0.67, World Scientific, USA
• EVOS Evolving Systems Journal, Edited by Springer

KEYNOTE SPEAKERS

Three distinguished Keynote speakers will be invited

1. Professor Jean-Jacques Slotine MIT, USA
Non Linear Systems Lab, Massachusetts Institute of Technology, USA
Title: "Collective computation in adaptive nonlinear networks and the grammar of evolvabilityî

2. Professor Anastasios Tefas Aristotle University of Thessaloniki, Greece
Title: ìDeep Learning and Robotics: perception, control and innovationsî

3.Professor Plamen Angelov, Lancaster University, UK†


VENUE: ALDEMAR AMILIA MARE Resort, in the exotic island of Rhodes, Kalithea, Greece http://easyconferences.eu/aiai2018/venue.html †Special cheap all-inclusive prices will be arranged.


TOPICS

Basic Methologies
* Evolving Soft Computing Techniques
* Evolving Fuzzy Systems
* Evolving Rule-Based Classifiers
* Evolving Neuro-Fuzzy Systems
* Adaptive Evolving Neural Networks
* Adaptive Evolving Fuzzy Systems
* Online Genetic and Evolutionary Algorithms
* Data Stream Mining
* Incremental and Evolving Clustering Approaches
* Adaptive Control
* Adaptive Pattern Recognition
* Computational Intelligence in Control and Estimation
* Incremental and Evolving ML Classifiers
* Adaptive Statistical Techniques
* Evolving Decision Systems
* Big Data
* Advanced Concepts

Problems and Methodologies in Data Streams
* Stability, Robustness, Convergence in Evolving Systems
* Online Feature Selection and Dimension Reduction
* Online Active and Semi-supervised Learning
* Online Complexity Reduction
* Computational Aspects
* Interpretability Issues
* Incremental Adaptive Ensemble Methods
* Online Bagging and Boosting
* Self-monitoring Evolving Systems
* Human-Machine Interaction Issues
* Hybrid Modeling
* Transfer Learning
* Reservoir Computing
* Real-world Applications

EIS (Evolving Intelligent Systems) for On-Line Modeling, System Identification, and Control†
* EIS for Time Series Prediction
* EIS for Data Stream Mining and Adaptive Knowledge Discovery
* EIS in Robotics, Intelligent Transport and Advanced Manufacturing
* EIS in Advanced Communications and Multi-Media Applications
* EIS in Bioinformatics and Medicine
* EIS in Online Quality Control and Fault Diagnosis
* EIS in Condition Monitoring Systems
* EIS in Adaptive Evolving Controller Design
* EIS in User Activities Recognition
* EIS in Huge Database and Web Mining
* EIS in Visual Inspection and Image Classification
* EIS in Image Processing
* EIS in Cloud Computing
* EIS in Multiple Sensor Networks
* EIS in Query Systems and Social Networks
* EIS in Alternative Statistical and Machine Learning Approaches

CFP: 8th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics

Tokyo, Japan

Submission deadline: April 1, 2018
Conference: September 16th – 20th, 2018
Website: http://www.icdl-epirob.org/


The past decade has seen the emergence of a new scientific field in which computational techniques
are employed to study how intelligent biological and artificial systems develop sensorimotor,
cognitive and social abilities through dynamic interactions with their physical and social environments,
with a twofold objective: to gain a better understanding of human and animal intelligence,
and to enable artificial systems with more adaptive and flexible behaviors.
The two most prominent conference series of this area, the International Conference on Development
and Learning (ICDL) and the International Conference on Epigenetic Robotics (EpiRob), are joining
forces for the seventh time and invite submissions for a joint meeting in 2018 to explore, extend,
and consolidate the interdisciplinary boundaries of this exciting research field.
In addition to the usual paper submission-selection process, the BabyBot Challenge will crown computational
models that capture core aspects of specific psychology experiments.

=== Topics ===

Topics of interest include (but are not limited to):
– general principles of development and learning;
– development of skills in biological systems and robots;
– nature VS nurture, critical periods and developmental stages;
– architectures for cognitive development and life-long learning;
– emergence of body knowledge and affordance perception;
– models for prediction, planning and problem solving;
– models of human-human and human-robot interaction;
– emergence of verbal and non-verbal communication skills;
– epistemological foundations and philosophical issues;
– models of child development from experimental psycho

=== Keynote Speakers ===

Prof. Oliver Brock (Technische Universität Berlin, Germany) 

"Proposals for a Developmental AI"

 

Prof. Kenji Doya (Okinawa Institute of Science and Technology, Japan)

"What can we learn from the brain for AI (Tentative)"

Prof. Peter J. Marshall (Temple University, U.S.A.)

"Embodiment and Human Development"

Mr. Masahiro Fujita (Sony, Japan)

"AIｘRobotics in Sony (Tentative)"

=== Submission ===

Full six-page paper submissions: Accepted papers will be included in the
conference proceedings and will be selected for either an oral presentation
or a featured poster presentation.
Two-page poster abstract submissions: To encourage discussion of late-breaking results
or for work that is not sufficiently mature for a full paper, we will accept 2-page abstracts.
Tutorials and workshops: We invite experts in different areas to organize
either a tutorial or a workshop to be held on the first day or second day of the conference.
Tutorials are meant to provide insights into specific topics through hand-on training and interactive experiences.

=== Babybot Challenge Paper Award ===

Babybot Challenge papers are expected to establish a strong link
between developmental psychology and robotics and/or computational
modeling. Submissions will be judged by the following criteria:

- How well does the computational model (e.g. an artificial system,
  which can be a robot or a software agent) represent the particular
  features of the experimental research addressed
- How closely the performance of the model replicate the experimental
  findings, and how parsimonious is the model .
- The extent of the novel insights or explanations generated by the
  model, and importantly whether the model make interesting and
  testable predictions.

We encourage the authors to tag their submission for "Babybot
Challenge" award during contributed paper submission, which would
indicate that there is significant content that puts the paper in the
spotlight of "Babybot Challenge".
The prize for the winner of the Babybot Challenge is a Titan-V (GPGPU
board) by Nvidia.

=== Workshops ===

We invite researchers to submit a one/two pages resume of their intended workshops
with indication of the invited speakers, duration (half day or full day), open to paper/poster submission and website.

=== Important Dates ===

Submission deadline: April 1st, 2018
Notification due: June 15th, 2018
Final Version due: July 15th, 2018
Conference: September 16th – 20th, 2018

=== Commitee ===

Tetsuya Ogata (Waseda University, Japan)
Angelo Cangelosi (Plymouth University, UK)
Tadahiro Taniguchi (Ritsumeikan University, Japan)
Emre Ugur (Bogazici University, Turkey)
Junko Kanero (Koç University, Turkey)
Erhan Oztop (Özyeğin University, Turkey)
Minoru Asada (Osaka University, Japan)
Giulio Sandini (Italian Institute of Technology, Italy)
Alessandra Sciutti (Italian Institute of Technology, Italy)
Philippe Gaussier (University of Cergy-Pontoise, France)
Hiroki Mori (Waseda University, Japan)
Alexandre Pitti (University of Cergy-Pontoise, France)
Umay Suanda (University of Connecticut, USA)
Shingo Shimoda (Riken, Brain Science Institute, Japan)
Tetsunari Inamura (NII, Japan)
Hiromi Mochiyama (Tsukuba University, Japan)
Takato Horii (The University of Electro-Communications, Japan)
Shingo Murata (Waseda University, Japan)

=== Contacts ===

Alex Pitti: alexandre.pitti@u-cergy.fr
Hiroki Mori: mori@idr.ias.sci.waseda.ac.jp
Umay Suanda: s.suanda@uconn.edu

CFP on IEEE CIS 2018 Summer Schools

The IEEE CIS CFP for Summer School is available http://cis.ieee.org/images/files/Documents/Education/Summer_Schools/ss_cfp_2018s.pdf

CFP: 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2018)

http://www.cibcb.org/CIBCB2018/

This conference will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineering and related fields. Computational Intelligence (CI) approaches include artificial neural networks and machine learning techniques, fuzzy logic, evolutionary algorithms and meta-heuristics, hybrid approaches and other emerging techniques.

Topics of interests include, but not limited to:

• gene expression array analysis
• structure prediction and folding
• molecular sequence alignment and analysis
• metabolic pathway analysis
• RNA and protein folding and structure prediction
• analysis and visualization of large biological data sets
• motif detection
• molecular evolution and phylogenetics
• systems and synthetic biology
• modelling, simulation and optimization of biological systems
• robustness and evolvability of biological networks
• emergent properties in complex biological systems
• ecoinformatics and applications to ecological data analysis
• medical imaging and pattern recognition
• medical image analysis
• biomedical data modelling and mining
• treatment optimisation
• biomedical model parameterisation
• brain computer interface

Important Dates

• Special sessions submissions: February 2, 2018
• Paper acceptance: February 23, 2018
• Final paper submission:TBD

Organization

Program Chair:

• Daniel Ashlock (Canada)

Technical Co-Chairs:


• Sheridan Houghten (Canada)
  Wendy Ashlock (Canada)
  
  
  General Chair:
  

  • Donald Wunsch (USA)
  Finance Chair: 
  Steven Corns (USA)
  
  Local Arrangements Chair:
  • Suzanna Long (USA)
  
  Special Session Chair:
  • Joseph Brown (Russia)
  
  Publicity Chair:
  • Sansanee Auephanwiriyakul
    (Thailand)
    Sanaz Mostaghim (Germany)
    
    
    
    
    Submission:
    
    
    Instructions for Final Submissions:
    
    Prospective authors are invited to submit papers of no more than eight (8) pages in IEEE conference format, including results, figures and references. Refer to detailed instructions and templates for preparing your manuscripts. 
    
    Manuscript Style Instructions:
    • Only papers prepared in PDF format will be accepted.
    
    
    • Paper Length: Up to 8 pages, including figures, tables & references. At maximum, two additional pages are permitted with over-length page charge of US$125/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).
    
    
    • File Size Limitation: 4.0MB.
    
    
    • No page numbering on the manuscript is allowed.
    
    
    • Note: Violations of any of the above specifications may result in rejection of your paper.
    Submission Website: 
    EasyChair is being used for paper submission to this conference. 

Proposals for IEEE CEC or FUZZ-IEEE in 2021

Proposals for the organization of IEEE CEC or FUZZ-IEEE in 2021 must be submitted as soon as possible, and no later than Mar. 15. Policies, procedures and budget worksheet for such proposals are available. More detailed guidelines can be obtained upon request to Bernadette Bouchon-Meunier.

IEEE TETCI Special Issue on New Advances in Deep-Transfer Learning (Jun 30)

I. AIM AND SCOPE

While Deep learning (DL) has achieved great success in big data applications, transfer learning (TL) is an important paradigm for small/insufficient data applications, which utilizes the data/knowledge in one task to facilitate the learning in another relevant task. How to integrate DL and TL to combine their advantages is an interesting and important research topic. Deep-Transfer Learning (DTL) is proposed to address this issue. Deep learning extracts knowledge from big data, which can then be used by TL for a new task/domain with small/insufficient data.

Computational intelligence techniques, mainly including neural networks, fuzzy logic, and evolutionary computation, can be valuable in DTL. For example:

• Neural networks (NN) are the cornerstones of DL. 
• Hierarchical/cascaded fuzzy logic systems (FLS) and fuzzy NNs may be viewed as fuzzy rule based DL models. FLSs can also capture interpretable knowledge, which may be easily transferrable to a new domain/task. Therefore, fuzzy logic is expected to play an important role in integrating DL and TL. 
• Evolutionary computation (EC) has been widely used in optimizing shallow NNs and FLSs. TL can also be viewed as an evolutionary learning strategy because it adapts the model to the changing environment. It is interesting to see novel applications of EC in DTL. 
• Other emerging forms of CI, such as (but not limited to) probabilistic computation, swarm intelligence, and artificial immune systems, can also contribute to DTL from different aspects. The aims of this special issue are: (1) present the state-of-theart research on novel CI based DTL methods and their applications, and (2) provide a forum for researchers to disseminate their views on future perspectives of the field.

II. TOPICS 

Topics of interest for this special issue include, but are not limited to: 

Theory and Methods: 

• DTL theory and algorithms 
• Fuzzy logic and fuzzy set based DTL 
• Neural networks based DTL 
• Evolutionary computation for DTL 
• Novel/emerging forms of CI (in addition to NN/FLS/EC) in DTL 
• Uncertainty theory based DTL 
• DTL for feature learning, classification, regression, and clustering 
• DTL for multi-task modeling, multi-view modeling and co-learning 

Applications: 

• CI based DTL for video analysis, text processing and natural language processing 
• CI based DTL for brain-machine interfaces and medical signal analysis

III. SUBMISSIONS 

Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-incomputational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “New Advances in Deep-Transfer Learning” and clearly marking “New Advances in Deep Transfer Learning Special Issue Paper” as comments to the Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES 

Paper submission deadline: June 31, 2018 

Notice of the first round review results: September 15, 2018 

Revision due: November 15, 2018 

Final notice of acceptance/reject: December 15, 2018 

V. GUEST EDITORS 

Zhaohong Deng, Jiangnan University, China; dengzhaohong@jiangnan.edu.cn 

Jie Lu, University of Technology Sydney, Australia; jie.lu@uts.edu.au 

Dongrui Wu, Huazhong University of Science and Technology, China; drwu@hust.edu.cn 

Kup-Sze Choi, Hong Kong Polytechnic University, Hong Kong, China; kschoi@ieee.org Shiliang Sun, East China Normal University, China; slsun@cs.ecnu.edu.cn 

Yusuke Nojima, Osaka Prefecture University, Japan; nojima@cs.osakafu-u.ac.jp

CFP: IEEE TETCI Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities (May 15)

I. AIM AND SCOPE

By 2050, more than half the world’s population is expected to live in urban regions. This rapid expansion of population in the cities of the future will lead to increasing demands on various infrastructures; the urban economics will play a major role in national economics. Cities must be competitive by providing smart functions to support high quality of life. There is thus an urgent need to develop smart cities that possess a number of smart components. Among them, smart energy is arguably the first infrastructure to be established because almost all systems require energy to operate.

Smart energy refers to energy monitoring, prediction, use or management in a smart way. In smart cities, smart energy applications include smart grids, smart mobility, and smart communications. While realizing smart energy is promising to smart cities, it involves a number of challenges.

 By using smart grid technologies, distributed power supply is replacing conventional centralized schemes, leading to regional aggregation of energy that must consider the interests of many grid participants. With the increasing penetration of electric vehicles (EVs), EV charging stations must consider many parameters and objectives to optimize the charging schedule. To make transportation or communications infrastructures go green, renewable energy sources (RESs) are often integrated into the whole system as part of power supply; a robust prediction for both power load and energy production becomes necessary for later energy management in response to intermittent power supply from RESs.

Because of the uncertainty of environments, complexity of the problem of interest, or multiplicity of objectives that must be achieved, conventional optimization methods using deterministic search algorithms cannot well address these challenges. By contrast, stochastic optimization can be useful for handling uncertainty; adaptive learning based on, for example, human behaviors, available resources, network capacity, or collected data can be a solution to complex problems; evolutionary computation can be applied to solve problems with many objectives. Computational Intelligence (CI) thus serves as a useful tool for addressing aforementioned difficulties.

II. TOPICS

This Special Issue aims to provide in-depth CI technologies that enable smart energy applications to smart cities. Topics of interest include, but are not limited to:

• Evolutionary computation for smart grids in consideration of many objectives, including energy management system, demand-side management, demand response, advanced metering infrastructure, and behind-the-meter applications.  
• Stochastic optimization for smart mobility in consideration of system uncertainty, with a primary focus on power scheduling for Internet of EVs or green public transportation.  
• Intelligent algorithms for smart communications pertaining to Internet of Things, machine-to-machine communications, vehicle-to-grid communications, and vehicle-to-infrastructure communications under the framework of green communications.  
• Machine/deep learning for renewable energy forecasting or power load forecasting.  
• Survey papers on CI for smart energy applications. 

III. IMPORTANT DATES  

Submission deadline: May 15, 2018.  

Notification due date: October 1, 2018.  

Final version due date: November 1, 2018. 

IV. SUBMISSION GUIDELINES 

Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Computational Intelligence for Smart Energy Applications to Smart Cities” and clearly marking “Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities” as comments to the Editor-in-Chief. 

V. GUEST EDITORS  

Wei-Yu Chiu, National Tsing Hua University, Taiwan wychiu@ee.nthu.edu.tw  

Hongjian Sun, Durham University, UK  

Chao Wang, Tongji University, China  

Athanasios V. Vasilakos, Lulea University of Technology, Sweden

CFP: IEEE TEVC Special Issue on Theoretical Foundations of Evolutionary Computation

I. AIM AND SCOPE

Evolutionary computation (EC) methods such as
evolutionary algorithms, ant colony optimization and artificial
immune systems have been successfully applied to a wide
range of problems. These include classical combinatorial
optimization problems and a variety of continuous, discrete
and mixed integer real-world optimization problems that are
often hard to optimize by traditional methods (e.g., because
they are non-linear, highly constrained, multi-objective, etc.).
In contrast to the successful applications, there is still a need
to understand the behaviour of these algorithms. The
achievement and development of a solid theory of bio-inspired
computation techniques is important as it provides sound
knowledge on their working principles. In particular, it
explains the success or the failure of these methods in
practical applications. Theoretical analyses lead to the
understanding of which problems are optimized (or
approximated) efficiently by a given algorithm and which ones
are not. The benefits of theoretical understanding for
practitioners are threefold. 1) Aiding algorithm design, 2)
guiding the choice of the best algorithm for the problem at
hand and 3) determining optimal parameter settings.

The aim of this special issue is to advance the theoretical
understanding of evolutionary computation methods. We
solicit novel, high quality scientific contributions on
theoretical or foundational aspects of evolutionary
computation. A successful exchange between theory and
practice in evolutionary computation is very desirable and
papers bridging theory and practice are of particular interest.
In addition to strict mathematical investigations, experimental
studies strengthening the theoretical foundations of
evolutionary computation methods are very welcome.


II. THEMES

This special issue will present novel results from different subareas
of the theory of bio-inspired algorithms. The scope of
this special issue includes (but is not limited to) the following
topics:

• Exact and approximation runtime analysis
• Black box complexity
• Self-adaptation
• Population dynamics
• Fitness landscape and problem difficulty analysis
• No free lunch theorems
• Theoretical Foundations of combining traditional optimization techniques with EC methods
• Statistical approaches for understanding the behaviour of bio-inspired heuristics
• Computational studies of a foundational nature

All classes of bio-inspired optimization algorithms will be
considered including (but not limited to) evolutionary
algorithms, ant colony optimization, artificial immune
systems, particle swarm optimization, differential evolution,
and estimation of distribution algorithms. All problem
domains will be considered including discrete and continuous
optimization, single-objective and multi-objective
optimization, constraint handling, dynamic and stochastic
optimization, co-evolution and evolutionary learning.

III. SUBMISSION

Manuscripts should be prepared according to the “Information
for Authors” section of the journal found at
http://ieee-cis.org/pubs/tec/authors/
and submissions should be made through the
journal submission website: http://mc.manuscriptcentral.com/tevc-ieee/,
by selecting the Manuscript Type of “TFoEC
Special Issue Papers” and clearly adding “TFoEC Special
Issue Paper” to the comments to the Editor-in-Chief.

Submitted papers will be reviewed by at least three different
expert reviewers. Submission of a manuscript implies that it is
the authors’ original unpublished work and is not being
submitted for possible publication elsewhere.

Each submission will contain at least one paragraph
explaining why the paper is (potentially) relevant to practice.

IV. IMPORTANT DATES

Submission open: February 1, 2018
Submission deadline: October 1, 2018
Tentative publication date: 2019

Papers will be assigned to reviewers as soon as they are
submitted. Papers will be published online as soon as they are
accepted.

For further information, please contact one of the following
Guest Editors.

V. GUEST EDITORS

Pietro S. Oliveto
Department of Computer Science
University of Sheffield
United Kingdom
p.oliveto@sheffield.ac.uk

Anne Auger
INRIA
Ecole Polytechnique Paris
France
anne.auger@jnria.fr

Francisco Chicano
Department of Languages and Computing Sciences
University of Malaga
Spain
chicano@lcc.uma.es

Carlos M. Fonseca
Department of Informatics Engineering
University of Coimbra
Portugal
cmfonsec@dei.uc.pt

CFP: IEEE CIM Special Issue on Deep Reinforcement Learning and Games (Oct 1)

Aims and Scope

Recently, there has been tremendous progress in artificial intelligence (AI) and computational intelligence (CI) and games. In 2015, Google DeepMind published a paper “Human-level control through deep reinforcement learning” in Nature, showing the power of AI&CI in learning to play Atari video games directly from the screen capture. Furthermore, in Nature 2016, it published a cover paper “Mastering the game of Go with deep neural networks and tree search” and proposed the computer Go program, AlphaGo. In March 2016, AlphaGo beat the world’s top Go player Lee Sedol by 4:1. In early 2017, the Master, a variant of AlphaGo, won 60 matches against top Go players. In late 2017, AlphaGo Zero learned only from self-play and was able to beat the original AlphaGo without any losses (Nature 2017). This becomes a new milestone in the AI&CI history, the core of which is the algorithm of deep reinforcement learning (DRL). Moreover, the achievements on DRL and games are manifest. In 2017, the AIs beat the expert in Texas Hold’em poker (Science 2017). OpenAI developed an AI to outperform the champion in the 1V1 Dota 2 game. Facebook released a huge database of StarCraft I. Blizzard and DeepMind turned StarCraft II into an AI research lab with a more open interface. In these games, DRL also plays an important role.

Needless to say, the great achievements of DRL are first obtained in the domain of games, and it is timely to report major advances in a special issue of IEEE Computational Intelligence Magazine. IEEE Trans. on Neural network and Learning Systems and IEEE Trans. on Computational Intelligence and AI in Games have organized similar ones in 2017.

DRL is able to output control signals directly based on input images, and integrates the capacity for perception of deep learning (DL) and the decision making of reinforcement learning (RL). This mechanism has many similarities to human modes of thinking. However, there is much work left to do. The theoretical analysis of DRL, e. g., the convergence, stability, and optimality, is still in early days. Learning efficiency needs to be improved by proposing new algorithms or combining with other methods. DRL algorithms still need to be demonstrated in more diverse practical settings. Therefore, the aim of this special issue is to publish the most advanced research and state-of-the-art contributions in the field of DRL and its application in games. We expect this special issue to provide a platform for international researchers to exchange ideas and to present their latest research in relevant topics. Specific topics of interest include but are not limited to:

·       Survey on DRL and games;

·       New AI&CI algorithms in games;

·       Learning forward models from experience;

·       New algorithms of DL, RL and DRL;

·       Theoretical foundation of DL, RL and DRL;

·       DRL combined with search algorithms or other learning methods;

·       Challenges of AI&CI games as limitations in strategy learning, etc.;

·       DRL or AI&CI Games based applications in realistic and complicated systems.

Important Dates

Submission Deadline: October 1st, 2018

Notification of Review Results: December 10th, 2018

Submission of Revised Manuscripts: January 31st, 2019

Submission of Final Manuscript: March 15th, 2019

Special Issue Publication: August 2019 Issue

Guest Editors

D. Zhao, Institute of Automation, Chinese Academy of Sciences, China, Dongbin.zhao@ia.ac.cn

Dr. Zhao is a professor at Institute of Automation, Chinese Academy of Sciences and also a professor with the University of Chinese Academy of Sciences, China. His current research interests are in the area of deep reinforcement learning, computational intelligence, adaptive dynamic programming, games, and robotics. Dr. Zhao is the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Computation Intelligence Magazine, etc. He is the Chair of Beijing Chapter, and the past Chair of Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society (CIS). He works as several guest editors of renowned international journals, including the leading guest editor of the IEEE Trans.on Neural Network and Learning Systems special issue on Deep Reinforcement Learning and Adaptive Dyanmic Programming.

S. Lucas, Queen Mary University of London, UK, simon.lucas@qmul.ac.uk

Dr. Lucas was a full professor of computer science, in the School of Computer Science and Electronic Engineering at the University of Essex until July 31, 2017, and now is the Professor and Head of School of Electronic Engineering and Computer Science at Queen Mary University of London. He was the Founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games, and also co-founded the IEEE Conference on Computational Intelligence and Games, first held at the University of Essex in 2005.  He is the Vice President for Education of the IEEE Computational Intelligence Society. His research has gravitated toward Game AI: games provide an ideal arena for AI research, and also make an excellent application area.

J. Togelius, New York University, USA, julian.togelius@nyu.edu.

Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Computational Intelligence and AI in Games, and a past chair of the IEEE CIS Technical Committee on Games.

Submission Instructions

1.     The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12-point font, including figures and references. Submitted manuscript must be typewritten in English in single column format. Authors of Papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission will be made via https://easychair.org/conferences/?conf=ieeecimcitbb2018.

2.     Send also an email to guest editor D. Zhao (dongbin.zhao@ia.ac.cn) with subject “IEEE CIM special issue submission” to notify about your submission.

3.      Early submissions are welcome. We will start the review process as soon as we receive your contribution.

CFP: IEEE CIM Special Issue on Computational Intelligence for Affective Computing and Sentiment Analysis (Mar 31)

http://sentic.net/ciacsa/

CIACSA @ IEEE CIM

Submissions are invited for a special issue of IEEE Computational Intelligence Magazine (IEEE CIM) on Computational Intelligence for Affective Computing and Sentiment Analysis.

RATIONALE
Emotions are intrinsically part of our mental activity and play a key role in communication and decision-making processes. Emotion is a chain of events made up of feedback loops. Feelings and behavior can affect cognition, just as cognition can influence feeling. Emotion, cognition, and action interact in feedback loops and emotion can be viewed in a structural model tied to adaptation. Besides being important for the advancement of AI, detecting and interpreting emotional information is key in multiple areas of computer science, e.g., human- agent, -computer, and -robot interaction, but also e-learning, e-health, domotics, automotive, security, user profiling and personalization.

In recent years, emotion and sentiment analysis has become increasingly popular also for processing social media data on social networks, online communities, blogs, Wikis, microblogging platforms, and other online collaborative media. The distillation of knowledge from such a big amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Most of existing approaches to affective computing and sentiment analysis are still based on the syntactic representation of text, a method that relies mainly on word co-occurrence frequencies. Such algorithms are limited by the fact that they can only process information they can 'see'. As human text processors, we do not have such limitations as every word we see activates a cascade of semantically related concepts, relevant episodes, emotions, and sensory experiences, all of which enable the completion of complex NLP tasks — such as word-sense disambiguation, textual entailment, and semantic role labeling — in a quick and effortless way. Computational intelligence can aid to mimic the way humans process and analyze text and, hence, overcome the limitations of standard approaches to affective computing and sentiment analysis.

TOPICS
Articles are thus invited in areas such as machine learning, active learning, transfer learning, deep neural networks, neural and cognitive models, fuzzy logic, evolutionary computation, natural language processing, commonsense reasoning, and big data computing. Topics include, but are not limited to:
• Context-dependent sentiment analysis
• Deep learning for personality detection
• Deep learning for sarcasm detection
• Tensor fusion networks for sentiment analysis
• Multi-level attention networks for sentiment analysis
• Affective commonsense reasoning
• Statistical learning theory for big social data analysis
• Concept-level sentiment analysis
• Social network modeling and analysis
• Multilingual emotion and sentiment analysis
• Multimodal emotion recognition and sentiment analysis
• Aspect extraction for opinion mining
• Sentic computing
• Conceptual primitives for sentiment analysis
• Affective human-agent, -computer, and -robot interaction
• User profiling and personalization
• Time-evolving sentiment tracking

TIMEFRAME
Submission Deadline: March 31st, 2018
Notification of Review Results: June 15th, 2018
Submission of Revised Manuscripts: July 15th, 2018
Submission of Final Manuscript: September 15th, 2018
Special Issue Publication: Mid-January 2019 (February 2019 Issue)

SUBMISSION AND PROCEEDINGS
The Special Issue will consist of 3 or 4 papers on novel computational intelligence techniques for mining and analyzing emotions and opinions in text, but also in other modalities. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for IEEE CIM and shall be submitted via EasyChair.

ORGANIZERS
• Erik Cambria, Nanyang Technological University (Singapore)
• Soujanya Poria, Nanyang Technological University (Singapore)
• Amir Hussain, University of Stirling (UK)
• Bing Liu, University of Illinois at Chicago (USA)

5 Minutes with Prof. Bernadette Bouchon-Meunier

IEEE CIS Student Activities Subcommittee invites you to get to know the pioneers and experts in the Computational Intelligence. This month "5 minutes with..." focuses on pioneer Prof. Bernadette Bouchon-Meunier. What is your title, full name, and place of work? Dr. Bernadette Bouchon-Meunier, Director of Research Emeritus at the CNRS (National Center for Scientific Research) - Sorbonne University, Paris, France What grade of member in CIS are you? Life Fellow How long have you been a member of CIS? I have been an IEEE member for 36 years and a CIS member since its inception. One reason why you are a member of CIS: I have been working on fuzzy and intelligent systems for more than 40 years and CIS is the best place to exchange information and meet specialists on these topics. What was your service pathway in the Computational Intelligence Society? I have been a member of the CIS Fuzzy Systems Technical Committee since 2003, chairing it in 2011-2012, I was elected a member of the CIS Adcom several times between 2004 and July 2014. I chaired the Women in Computational Intelligence Committee from 2004 to 2007 and I am still a member of this committee, happy to support women in our field. I chaired the IEEE France Section CIS chapter from 2007 to 2015. I was elected CIS Vice-President for Conferences in July 2014 and re-elected since then. Can you share with us one success story that will motivate young members and provide useful guidelines for their careers? My first research field was Information theory. In 1975, I worked on a cooperative project with a group of sociologists who were looking for a formal model for the questionnaires they used. I discovered Lotfi Zadeh’s seminal paper on fuzzy sets by chance, in the library of mathematics of the university. I was surprised to see that it matched my needs. It was the starting point of years of research based on fuzzy systems and also the first of a long list of multidisciplinary collaborations. Of course, documentary research is much easier now that we have all papers on line but it is also difficult because of the huge number of available documents. My recommendation would be not to have blinders on, to be curious, to collaborate with others. Mutidisciplinarity is enriching. Research out of your main stream may be fruitful. However, keep your own path and don’t get lost in the numerical world.