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IDEAL 2013: Submission Deadline Extended to 10 June 2013

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  • Thomas Weise
    [Apologies for multiple copies of this e-mail]   Dear colleague,   Due to numerous requests, the submission deadline has been extended. We cordially invite
    Message 1 of 1 , May 24, 2013
      [Apologies for multiple copies of this e-mail]
      Dear colleague,
      Due to numerous requests, the submission deadline has
      been extended.
      We cordially invite you to submit papers for IDEAL 2013,
      attend the
      conference and visit Hefei, China.  IDEAL 2013 is technically
      cosponsored by several other institutions such as the
      Computational Intelligence Society (CIS).
      EXTENDED Deadline for full papers (8 pages): June 10,
      ======= Call for Papers: IDEAL'13, October 2013, Hefei,
      China =======
      The 14th International Conference on Intelligent Data
      Engineering and
                         Automated Learning (IDEAL'2013)
                    October 20-23, 2013, Hefei, Anhui, China
      The International Conference on Intelligent Data
      Engineering and
      Automated Learning (IDEAL) is an annual international
      dedicated to emerging and challenging topics in
      intelligent data
      analysis, data mining and their associated learning
      systems and
      paradigms. Its core themes include: Big Data challenges,
      Learning, Data Mining, Information Retrieval and
      Management, Bio- and
      Neuro-Informatics, Bio-Inspired Models (including Neural
      Evolutionary Computation and Swarm Intelligence), Agents
      and Hybrid
      Intelligent Systems, and Real-world Applications of
      Techniques. Other related and emerging themes and topics
      are also
      The conference provides a unique opportunity and
      stimulating forum for
      presenting and discussing the latest theoretical advances
      and real-
      world applications in Computational Intelligence and
      Intelligent Data
      Analysis. It also features a panel discussion on Big Data
      chaired by
      Prof. Zhi-Hua Zhou. Authors and researchers are warmly
      invited to
      submit their latest findings and research work to the
      A number of leader experts in the field will give plenary
      speeches at
      the conference. More details can be found or will appear
      on the
      conference website http://nical.ustc.edu.cn/ideal13/
      Instructions for Authors
      Authors are invited to submit their manuscripts (in pdf
      written in English via the conference online submission
      (http://nical.ustc.edu.cn/ideal13/submission.html). All
      will be refereed by experts in the field based on
      significance, quality and clarity. All contributions must
      be original,
      must not have been published elsewhere and must not be
      elsewhere during the review period. Papers should not
      exceed 8 pages
      and must comply with the format of Springer LNCS/LNAI
      Accepted papers presented at the conference will be
      included in the
      Proceedings of IDEAL 2013, to be published by Springer in
      its LNCS
      series, which is indexed in EI. In addition, selected
      papers will be
      invited for special issues in the folloing leading
      journals in the field:
       - International
      Journal of Neural Systems
       - Connection
      Important Dates:
        Paper Submission
      Deadline:  10 June    2013 (extended)
        Notification of
      Acceptance: 05 July    2013
        Camera-Ready Copy
      Due:      26 July    2013
      Registration:         26 July    2013
      Presentation:    20-23 October 2013
      Website:         http://nical.ustc.edu.cn/ideal13/
      Conference History
      In recent years, the IDEAL conference has been held in
      many countries
      or continents such as Brazil (2012), England (2011),
      Scotland (2010),
      Spain (2009), and Korea (2008). The 14th International
      IDEAL 2014, will set foot to Mainland China and will be
      held from 20th
      to 23rd October 2013, in Hefei, China, hosted by the
      Joint Research Institute of Intelligent Computation and
      Applications (UBRI, http://ubri.ustc.edu.cn).
      IDEAL'13 will be held at the Empark Grand Hotel, Anhui in
      China. Hefei, the capital of the Anhui Province, is a
      fine historical
      city characterized by a green environment and both modern
      areas and
      historical sights. The city is located centrally in China
      and about
      100 miles (160 km) from Nanjing, or 300 miles (500 km)
      from Shanghai.
      Hefei has its own airport and an excellent railway
      connection to many
      cities of China. It is also easy to reach via airplane
      from Beijing.
      Hefei, well known as a historic site famous from the
      Three Kingdoms
      Period and the home town of Lord Bao, is a city with a
      history of more
      than 2500 years. The city of Hefei is also a well-known
      "Green City"
      across the nation. It is a fast developing city which
      still preserves
      historical sights and has many local attractions.
      ========================== Special Sessions
      We are happy to announce that the following special
      sessions have been
      approved for IDEAL'13:
      Special Session on Adaptive and Learning Multi-Agent
      Systems (MAS) have grown into an interdisciplinary field
        that includes
      various tracks and embraces many previously
      research areas. More and more MASs are situated in open
        and dynamic
      environments. The changes of environments that may be
      uncontrollable and evolving typically affect the MAS.
      adaptive MAS and MAS learning have become important sub-
        areas in the
      literature of MAS. Particularly, both of them
        investigate how
      multiple intelligent computational agents can work
        together to
      achieve high- level goals by adjusting themselves and
        obtaining more
      information. Various approaches have been applied to
        improve the
      adaptive and learning ability of MAS. MAS are still
        facing challenges
      of scaling to large numbers of entities and real-
        world tasks.
        This special
      session on adaptive and learning multi-agent systems
        will provide a
      forum for researchers and practitioners interested in
        adaptation and
      learning for multi-agent systems, and report their
        latest findings.
        For more
      information, see the special session web site
        Organizers: Dong,
      Hongbin. Harbin Engineering University, China
      Jun. Aberystwyth University, UK
      Xinjun. National University of Defense Tech., China
      Xiangrong. Yantai University, China
      Special Session on Big Data
        Recent years have
      witnessed the unprecedented prevalence of "Big
        Data". Big
      Data is transforming science, engineering, medicine,
      finance, business, and ultimately, the society itself.
        This year
      IDEAL'2013 is pleased to introduce a Special Session on
        Big Data. We wish
      to encourage researcher to submit high-quality
        original papers
      (including significant work-in-progress) in any
        aspect of Big
      Data with emphasis on 5Vs (Volume, Velocity, Variety,
        Value and
      Veracity): big data science and foundations, big data
      big data management, big data searching and mining,
        big data
      privacy/security, and big data applications.
        For more
      information, see the special session web site
        Oranizers: Hui
      Xiong. Rutgers University, USA
      Zhou. University of Tennessee, USA
      Special Session on Soft-Computing Algorithms in Renewable
        In the current
      context of world economic crisis, Renewable Energies
        are of crucial
      importance towards a cleaner and more sustainable
        future. Several
      factors have recently pushed Renewable Energies,
        such as recent
      proofs of the direct connection between global
        warming and CO2
      emissions from fossil fuels, the intended reduction
        of greenhouse
      gasses thanks to the Kyoto protocol or the growing of
        the risk
      perception after the nuclear accident in Japan in December
        2011, among
        Nevertheless, the
      establishment and maximum exploitation of
        Renewable Energy
      still need a lot of work and research effort. Many
        of the problems
      that arise in Renewable Energy are so difficult,
        that traditional
      mathematical methods do not obtain good results.
        The design of new
      renewable energy facilities (wind farms, solar
        plants, smart and
      micro-grids with renewable generation, or stand-
        alone systems,
      etc.), the correct estimation of the renewable energy
        resource (wind,
      radiation, reservoir levels) or the optimization of
        technologies to
      obtain more productive systems (wind turbine design,
        solar panels
      design), are just some examples of these hard problems
        related to
      renewable energy.
        In these
      problems, the use of Soft-Computing approaches has been
        massive in the
      last few years, as powerful computational methods
        that obtain good
      results, with moderate computational effort. This
        Special Session
      is focused on Soft-Computing approaches in Renewable
        Energy problems,
      in a broad sense. We consider all Renewable Energy
      where Soft-Computing approaches can be used to improve
        the final
      systems. Real problems and case studies are particularly
        For more
      information, see the special session web site
      Sancho Salcedo Sanz. Universidad de Alcalá, Spain.
      Antonio Portilla-Figueras. Univ. de Alcalá, Spain.
      Special Session on Swarm Intelligence and Data Mining
      (SIDM 2013)
      intelligence is a recent trend in computational intelligence
        and popular for
      the simplicity of its realizations, such as particle
      optimization (PSO), ant colony optimization (ACO), bee colony
      (BCO), and the like. As optimization techniques,
        methods in swarm
      intelligence have been applied to many aspects in
        the fields of
      data engineering and automated learning. For example,
        as reported in
      the literature, PSO has been adopted to handle data
        clustering, and
      ACO has been employed to solve the problem of
      On the other hand, advances in data mining, an
        important section
      in data engineering and automated learning, also
      optimization algorithm designers to develop better methods.
        For instance, Apriori
      algorithm has been utilized for finding the
      among decision variables for optimizers. In order to
        bridge the
      concepts and methodologies from the two ends, this
        special session
      concentrates on the related topics of integrating
        and utilizing
      algorithms in swarm intelligence and data mining. It
        provides the
      opportunity for practitioners handling their data
        mining issues by
      using swarm intelligence methodologies and for
      investigating swarm intelligence with data mining
        approaches to
      share findings and look into future directions.
        For more
      information, see the special session web site
        http://sidm2013.nclab.tw (or under
        Organizers: Jing
      Liang, Zhengzhou University, China
                    Chuan-Kang Ting, National Chung Cheng Univ., Taiwan
                    Ying-ping Chen, National Chiao Tung Univ., Taiwan
      Special Session on Text Data Learning
        Tremendous efforts
      have been devoted to developing and applying
        different machine
      learning technologies to natural language text
        data, greatly
      expanding the fields of information retrieval and
        natural language
      processing, creating new areas of research.
        However, many
      challenges remain, such as:
          o how we can
      successfully process different natural language
            related tasks
      with machine learning: ranking documents,
      text, clustering, summarizing, analyzing, extracting
      and so on?
          o how we can
      circumvent the barrier of lacking enough annotated
            data, despite
      the vast quantities of unannotated data?
          o how we can
      adapt machine learning solutions across domains,
            genres, and
          o how we can
      make full use of the characteristics of text data in
      machine learning based solutions?
          o how we can
      create text learning systems to process Big Data in
      and parallel environments?
        This special
      session within IDEAL2013 on text data learning will
        provide a forum
      for researchers and practitioners interested in
      retrieval and natural language processing to exchange
        and report their
      latest findings in applying machine learning to
        understanding and
      mining natural language text data.
        For more
      information, see the special session web site
        Organizers: Baoli
      Li. Henan University of Technology, China
      Vogel. Trinity College Dublin, Ireland
      Special Session on Coevolution
      methodologies that are based on the natural
      process have been applied successfully to solve a
        variety of
      machine learning problems. In particular, competitive
        coevolution is used
      to solve difficult adversarial problems such as
        games whereby the
      target functions are unknown and that training
        samples are
      unavailable for supervised learning methods. Competitive
        coevolution seeks
      to solve these problems naturally with one
      consisting of candidate solutions (e.g. game strategies)
        and another
      population consisting of test cases (e.g. test
        strategies) that
      interact and undergo adaptation in a manner that
        promotes the
      search for problem solutions while using typically a
        small number of
      representative test cases that are discovered. Other
        research studies
      have been made in the framework of cooperative
        coevolution and
      its novel use to solve complex real-world learning
        problems that are
      amenable to divide-and-conquer approaches.
        Examples include
      ensemble learning for classification tasks and data
        mining through
      Bayesian networks. Furthermore, recent theoretical
        studies have been
      made for coevolutionary learning. These include
      performance analysis of coevolutionary algorithms
        through the
      generalization framework from machine learning, which
        provide the means
      for in-depth analysis how specific designs of
        components (e.g.,
      selection and variation operators) can affect the
        performance of
      coevolutionary learning. This special session aims to
        bring together
      researchers in theoretical aspects and practitioners
        in the real-world
      problem solving applications of coevolution.
        For more
      information, see the special session web site
        Organizers: Siang
      Yew Chong, University of Nottingham, Malaysia
                    Zhenyu Yang, National University of Defense Tech., China
                    Xiaodong Li, Royal Melbourne Inst. of Techn., Australia
      Special Session on Combining Learning and Optimisation
      for Intelligent
      Data Engineering
        Techniques of
      Machine Learning and Optimisation are workhorses in
        intelligent data
      engineering and in today's emerging data science.
        Finding ways to
      combine learning with optimisation has tremendous
        potential to
      provide powerful computational intelligence techniques.
        In fact,
      optimisation is a key in many machine learning and data
      algorithms; at the same time optimisation methods that
        incorporate some
      form of learning strategy have an added level of
      and ability to explore large search spaces.
        This special
      session aims at exploring new synergies and multi-
      perspectives between optimisation and machine learning
        in the context of
      intelligent data engineering and large scale data
        mining problems.
        For more
      information, see the special session web site
        Organizer:  Ata Kaban, The University of Birmingham, UK
      ================== Organizing Committee and Contact
      Programme Chair: Hujun Yin
      School of Electrical and Electronic Engineering,
      The University of Manchester,
      Manchester, M13 9PL, UK.
        Tel: +44 161 306
      Email: h.yin@...
      Programme Co-Chair: Ke Tang
      USTC-Birmingham Joint Research Institute of Intelligent
      Computation and
      Its Applications (UBRI), School of Computer Science and
      University of Science and Technology of China,
      Hefei, Anhui, China, 230027
        Tel: +86 551 3600
      Email: ketang@...
      Conference Chairs and Organizers
      o General Chair:    Xin Yao   (X.Yao@...)
      o Programme Chair:  Hujun Yin (h.yin@...)
      o Programme Co-Chairs:
        - Ke Tang         (ketang@...)
        - Yang Gao        (gaoy@...)
        - Frank
      Klawonn   (f.klawonn@...)
        - Min-ho Lee      (mholee@...)
      o Publicity Co-Chairs:
        - Emilio Corchado
        - Jose A.
      Costa   (jafcosta@...)
        - Thomas
      Weise    (tweise@...)
      o Organizing Committee:
        - Bin Li
      (Chair)  (binli@...)
        - Kaiming
      Chen    (chenkm@...)
        - Jinlong Li      (jlli@...)
        - Thomas
      Weise    (tweise@...)
        - Rui Xu          (rxu@...)
      o International Liaisons:
      China/Visa:  Jinlong Li        (jlli@...)
        - Europe:      David Camacho     (david.camacho@...)
        - America:     Guilherme Barreto (guilherme@...)
        - Australasia:
      Brijesh Verma     (b.verma@...)
      o International Advisory Committee
        - Lei Xu
      (Chair)              - Yaser Abu-Mostafa
        - Shun-ichi
      Amari             - Michael Dempster
        - Nick
      Jennings               - Soo-Young Lee
        - Erkki Oja                   - Latit M. Patnaik
        - Burkhard
      Rost               - Xin Yao
      o Steering Committee
        - Hujun Yin
      (Co-chair)        - Laiwan Chan
        - Guilherme
      Barreto           - Yiu-ming Cheung
        - Emilio
      Corchado             - Jose A. Costa
        - Colin Fyfe                  - Marc van Hulle
        - Samuel
      Kaski                - John Keane
        - Jimmy Lee                   - Malik Magdon-Ismail
        - Vic
      Rayward-Smith           - Peter Tino
        - Zheng Rong
      Yang             - Ning Zhong
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