Tuesday, 13 February 2018

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

Monday, 12 February 2018

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.

    Wednesday, 7 February 2018

    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 MagazineIEEE 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.