Tuesday 22 August 2017

Webinar: Evolutionary Computation for Feature Selection and Feature Construction on Sep 25, 2017 2:00 PM NZDT

Dr. Bing Xue, School of Engineering and Computer Science at Victoria University of Wellington, New Zealand


In data mining and machine learning, many real-world problems such as bio-data classification and biomarker detection, image analysis, and text mining often involve a large number of features/attributes. However, not all the features are essential since many of them are redundant or even irrelevant, and the useful features are typically not equally important. Using all the features typically does not produce good results due to the big dimensionality and the large search space. This problem can be solved by feature selection to select a small subset of original (relevant) features or feature construction to create a smaller set of high-level features using the original low-level features. 

​E​volutionary computation techniques such as genetic algorithms, genetic programming, particle swarm optimisation, and evolutionary multi-objective optimisation have been recently used for feature selection and construction for dimensionality reduction, and achieved great success. The ​webinar will introduce the general framework within which evolutionary feature selection and construction can be studied and applied, sketching a schematic taxonomy of the field and providing examples of successful real-world applications. The application areas to be covered will include bio-data classification and biomarker detection, image analysis and pattern classification. ​


Bing Xue is currently a Senior Lecturer in School of Engineering and Computer Science at Victoria University of Wellington,New Zealand. Her research focuses mainly on evolutionary computation, feature selection, feature construction, multi-objective optimisation, data mining and machine learning.  
Dr Xue is an Associate Editor/member of Editorial Board for five international journals including IEEE Computational Intelligence Magazine, Applied Soft Computing, International Journal of Swarm Intelligence, and International Journal of Computer Information Systems and Industrial Management Applications. She is a Guest Editor for the Special Issue on Evolutionary Feature Reduction and Machine Learning for the Springer Journal of Soft Computing. She is also a Guest Editor for Evolutionary Image Analysis and Pattern Recognition in Journal of Applied Soft Computing.  She has also been a program chair, special session chair, tutorial chair, symposium chair,  and publicity chair for a number of international conferences. She is serving as a reviewer of over 20 international journals and a program committee member for over 50 international conferences.  She is currently the Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction. She is chairing the IEEE CIS Graduate Student Research Grants Committee and the Secretary of the IEEE Computational Intelligence Chapter in New Zealand.

Please register for Evolutionary Computation for Feature Selection and Feature Construction on Sep 25, 2017 2:00 PM NZDT at: 


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Thursday 17 August 2017

2nd call for participation: 4th IEEE International Conference on Data Science and Advanced Analytics (19-21 Oct)

The 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), which is also supported by ACM and ASA, will take place in Tokyo, Japan, in October 19-21, 2017. Please plan to participate. DSAA2017 will be an ideal place to learn the state-of-the-art of Data Science and Advanced Analytics, meet people to exchange ideas and find opportunities to job matching. DSAA2017 is featured with two main Tracks (Research and Application), several Special Tracks, Keynote talks, Trend & Controversies Panel, Invited Industrial talks and Tutorials. Please visit "https://www.dslab.it.aoyama.ac.jp/dsaa2017/" for the latest information. - Date: October 19-21 2-5, 2017 - Location: Shinagawa Prince Hotel in Tokyo, Japan - Registration and Accommodation: Open in early August 2017 Highlight Summary Technical presentations: About 80 technical papers will be presented. in Research track, Application track and Special Session track. Keynote speech: Four distinguished speakers will deliver the keynote. Hiroaki Kitano (The Systems Biology Institute, Japan) Nobel Turing Challenge: Grand Challenge of AI, Robotics, and Systems Biology Michael I. Jordan (University of California, Berkeley, USA) On Computational Thinking, Inferential Thinking and Data Science Dr. Katharina J. Morik (TU Dortmund, Germany) Data Analytics for Data Science One more to come. Trend & Controversies Panel: One of unique highlights of DSAA. This year's theme for discussion is "Trust". Specific focuses range from 1) the privacy and transparency issue, 2) the fairness between those who own the data and those who do not, 3) the discrimination caused by big data analysis, 4) model generalization capability, i.e. How can we trust the outcome of our mining results? How can we trust the model used, e.g. deep learning, etc.? Speakers and panelists include Dino Pedreschi (University of Pisa, Italy), Katharina Morik (TU Dortmund, Germany), Hesuan-Tien Lin (National Taiwan University and Appier, Taiwan), Ying Li (DataSpark Pte. Ltd., Singapore). Invited Industrial Talks: Financial sponsors and selected speakers from industries will give their visions on data science and data analytics. Speakers include Yanjun Ma (Baidu, China),Wenuan Dai (The 4th Paradigm Data & Technology Co., Ltd., China). Organizing Committee of DSAA2017 General Chairs Hiroshi Motoda (Osaka University, Japan) Fosca Giannotti (Inf. Sci. and Tech. Inst. of NRC at Pisa, Italy), Tomoyuki Higuchi (Inst. of Statistical Mathematics, Japan) Program Chairs Research Track Takashi Washio (Osaka University, Japan) Joao Gama (University of Porto, Portugal) Application Track Ying Li ( DataSpark Pte. Ltd., Singapore) Rajesh Parekh (Facebook. USA) Special Session Huan Liu (Arizona State University, USA) Albert Bifet (Telecom ParisTech, France) Richards D. De Veaux (Williams College, USA) T&C Panel Chairs Pau-Choo (Julia) Chung (National Cheng Kung University, Taiwan) Geoff Webb (Monash University, Australia) Philip Yu (University of Illinois at Chicago, USA) Bart Goethals (University of Antwerp, Belgium) Invited Industry Talk Chairs Hang Li (Huawei Technologies, Hong Kong) Matsuo Yutaka (University of Tokyo, Japan) Tutorial Chairs Vincent Tseng (National Chiao Tung University, Taiwan) Zhi-Hua Zhou (Nanjing University, Chian) Best Paper Award Chair Bamshad Mobasher (DePaul University, USA) Next Generation Data Scientist Award Chairs Kenji Yamanishi (University of Tokyo, Japan) Xin Wang (University of Calgary, Canada) Travel Awards Chair Zhexue Huang (Shenzhen University, China) Publicity Chairs Tu bao Ho (Japan Advanced Inst. of Sci. & Technol., Japan) Diane Cook (Washington State University, USA) Marzena Kryszkiewicz (Warsaw University of Technol., Poland) Local organizing chairs Satoshi Kurihara (University of Electro-Communications, Japan) Hiromitsu Hattori (Ritsumeikan University, Japan) Publication Chair Toshihiro Kamishima (Nat. Inst. of Advanced Indust. Sci. & Technol., Japan) Web Chair Kouzuo Ohara (Aoyama Gakuin University, Japan) Sponsorship Chairs Kiyoshi Izumi (University of Tokyo, Japan) Yoji Kiyota (LIFULL Co. Ltd., Japan) Tadashi Yanagihara (KDDI Research, Japan) Longbing Cao (University of Technology Sydney, Australia) Byeong Ho Kang (University of Tasmania, Australia)

Wednesday 16 August 2017

Webinar on "Game AI and Noisy Optimisation" on Sep 11, 2017 5:00 PM BST.

Speaker: Prof. Simon M. Lucas, University of Essex
Date: Sep 11, 2017 5:00 PM BST


Many problems in game AI can be viewed as noisy optimisation problems, where the noise or uncertainty can stem from many sources, including: games which are naturally stochastic, agents which follow stochastic policies (such us Monte Carlo Tree Search or Rolling Horizon Evolution), and noise when evaluating the quality of games. The problems arise when trying to optimise agents to play games well, or just in a particular way, when trying to optimise heuristics for Monte Carlo Tree Search, and when trying to optimise games in order to provide a particular style of player experience (such as a game where the player has to react quickly, or has to plan strategically). The webinar will show examples of how these problems arise, and describe recent research in efficient noisy optimisation that uses novel model-based optimisation algorithms to provide efficient and effective search. 


Simon M. Lucas is a professor of Artificial Intelligence and head of the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds a PhD degree (1991) in Electronics and Computer Science from the University of Southampton. His main research interests are games, evolutionary computation, and machine learning, and he has published widely in these fields with over 200 peer-reviewed papers. He is the inventor of the scanning n-tuple classifier, and is the founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games.

Please register at: 

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Tuesday 15 August 2017

Call-for-Proposals: Activity Promotion Grants (1 Dec)

IEEE Computational Intelligence Society (CIS) has strategic plans to promote member activities through chapters and set up an activity promotion grant. The CIS Chapters Subcommittee would like to invite you to submit a proposal to bid for funding for partially supporting member promotions. In the proposal (limited to no more than 2 pages), you should provide the following information for our consideration:
  • Nature and scope of the activity
  • Date of the proposed event
  • Expected number of participants (CIS and non-CIS members)
  • Requested amount (no more than USD $800)
  • How you would use the funding
  • Expected results for member promotions
  • Any other important information
We accept proposals until the December 1 of 2017 or the funding has all been used up. You should submit the proposal to the Chapters Subcommittee Chair (Albert Lam, albertlam@ieee.org) at least one month before the start date of the proposed activity. If a proposal is approved, a report has to be submitted to Albert Lam after the event. We reserve all rights to reject any proposal or decide the amount of funding granted. Should you have any question, please send your enquiry to albertlam@ieee.org.
Posted by Albert Lam (albertlam@ieee.org)

Call for submissions: IEEE CIS Student Competition 2017-Edition

    Telling a Story: How your Computational Intelligence Research Benefits Society and Humanity

    We are pleased to announce the 2016 Edition of the CIS Interactive Web Learning Systems Competition designed to encourage IEEE CIS student members to develop Web-based Applications to teach elements of fuzzy logic, neural networks, genetic algorithms or any other computational intelligence (CI) topic to young people (ages 14-16) or to non-subject-specialist professionals.
    The application should cover the teaching of any of the above aspects of CI. It may also be an interactive demo to solve a real life problem, such as the Knapsack Problem. Actually, this problem has very important "real-life" applications, as it was recently shown by the MythBusters in a recent episode on First-Person Shooters Games: so, optimizing the Knapsack problem with respect to what weapon should you carry can give you "real-life" advantage in video gaming. ☺
    Other examples can be found on the IEEE Pre-College Activities Website.
    The Student Competition is jointly organized by the IEEE CIS Student Activities and the IEEE CIS Competitions Sub-committees.

    How to register a group

    • A IEEE CIS student member has to register at this page (if not already registered)
    • After the login, you can register a group for the 2017-edition by pressing on the button Register for a competition
    • Insert the name of the other members or just press Sign in if you are the only member
    • Your group is now registered for the competition!

    Important Dates

    • Opening Date: May 1, 2017
    • Closing Date: October 1, 2017
    • Announcement of Winners: Award ceremony at IEEE SSCI 2017, in Hawai November 27-December 1,2017


    • 1st prize - 500USD
    • 2nd prize - 300USD
    • 3rd prize - 200USD

    Keeley Crockett, Chair - IEEE CIS Student Activities Subcommittee
    Professor Guilherme DeSouza ,Vice Chair - IEEE CIS Student Activities Subcommittee
    Vinit Kumar Gunjan ,Member - IEEE CIS Student Activities Subcommittee

    Friday 11 August 2017

    CFP: 10th International Conference on Advanced Computational Intelligence

    March 29-31, 2018
    Xiamen, China

    Technical cosponsor: IEEE Systems, Man and Cybernetics Society

    ICACI 2018 aims to provide a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research and applications in computational intelligence. The conference will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics. In addition, best paper awards will be given during the conference. The proceedings of ICACI 2018 will be submitted to the IEEE Xplore and EI Compendex. Moreover, selected papers will be published in special issues of related journals. The conference will favor papers representing advanced theories and innovative applications in computational intelligence.

    Call for Papers and Special Sessions
    Prospective authors are invited to contribute high-quality papers to ICACI 2018. In addition, proposals for special sessions within the technical scopes of the conference are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information on the organizers. Researchers interested in organizing special sessions are invited to submit formal proposals to the special sessions chairs (auyqli@scut.edu.cn, qqs@aber.ac.uk or shiyh@sustc.edu.cn).

    Topic Areas
    Topics areas include, but not limited to, computational neuroscience, connectionist theory and cognitive science, mathematical modeling of neural systems, neurodynamic analysis, neurodynamic optimization and adaptive dynamic programming, embedded neural systems, probabilistic and information-theoretic methods, principal and independent component analysis, hybrid intelligent systems, supervised, unsupervised and reinforcement learning, deep learning, brain imaging and neural information processing, neuroinformatics and bioinformatics, support vector machines and kernel methods, autonomous mental development, data mining, pattern recognition, time series analysis, image and signal processing, robotic and control applications, telecommunications, transportation systems, intrusion detection and fault diagnosis, hardware implementation, real-world applications, big data processing, fuzzy systems, fuzzy logic, fuzzy set theory, fuzzy decision making, fuzzy information processing, fuzzy logic control, evolutionary computation, ant colony optimization, genetic algorithms, parallel and distributed algorithms, particle swarm optimization, evolving neural networks, evolutionary fuzzy systems, evolving neuro-fuzzy systems, evolutionary games and multi-agent systems, intelligent systems applications.

    Important Dates
    Special session proposals deadline: Sep. 15, 2017
    Paper submission deadline: Nov.15, 2017
    Notification of acceptance: Dec. 15, 2017
    Camera-ready copy and author registration: Jan. 15, 2018

    For latest and additional information, please visit www.icaci2018.org

    IEEE Transactions on Emerging Topics in Computational Intelligence, Volume. 1, Issue 4, August 2017


    ***Special Issue on Emergent Topics in Artificial Immune Systems***

    Dendritic Cell Algorithm Applied to Ping Scan Investigation Revisited: Detection Quality and Performance Analysis
    Authors: Guilherme Costa Silva, Walmir Matos Caminhas and Luciano de Errico
    Page(s): 236-247

    An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots
    Authors: Grazziela P. Figueredo, Isaac Triguero, Mohammad Mesgarpour, Alexandre M. Guerra, Jonathan M. Garibaldi and Robert I. John
    Page(s): 248-258

    On the Reconstruction Method for Negative Surveys with Application to Education Surveys
    Authors: Hao Jiang, Wenjian Luo, Li Ni and Bei Hua
    Page(s): 259-269

    ***Regular Papers***

    Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components
    Authors: Sim Kuan Goh, Hussein A. Abbass, Kay Chen Tan, Abdullah Al-Mamun, Chuanchu Wang and Cuntai Guan
    Page(s): 270-279

    A Computationally Fast Convergence Measure and Implementation for Single, Multiple and Many-Objective Optimization
    Authors: Kalyanmoy Deb, Mohamed Abouhawwash and Haitham Seada
    Page(s): 280-293

    Behavior Recognition Using Multiple Depth Cameras Based on a Time-Variant Skeleton Vector Projection
    Authors: Chien-Hao Kuo, Pao-Chi Chang and Shih-Wei Sun
    Page(s): 294-304

    A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control
    Authors: Xinyi Le, Zheng Yan and Juntong Xi
    Page(s): 305-314

    Thursday 10 August 2017

    Call for Proposals for IEEE CIS Conferences in 2019 (31 Dec)

    Proposals for the organization of IEEE CIS financially sponsored conferences in 2019 must be submitted as soon as possible, and no later than December 31, 2017.
    Policies, procedures and budget worksheet for such proposals are available at http://cis.ieee.org/policies-a-procedures-for-requesting-ieee-cis-conference-support.html.
    More detailed guidelines can be obtained upon request to Bernadette Bouchon-Meunier (bernadette.bouchon-meunier@lip6.fr).
    Declarations of intention must be sent to Piero Bonissone (piero@ppb-analytics.com) and Bernadette Bouchon-Meunier (bernadette.bouchon-meunier@lip6.fr) as soon as possible.

    Posted by Bernadette Bouchon-Meunier (bernadette.bouchon-meunier@lip6.fr)

    Tuesday 8 August 2017

    IEEE Transaction on Fuzzy System, Volume 25, Issue 4, August 2017


    1. Admissibility Analysis and Control Synthesis for T–S Fuzzy Descriptor Systems
    Author(s): L. Qiao, Q. Zhang and G. Zhang
    Page(s): 729-740

    2. A Fitting Model for Feature Selection With Fuzzy Rough Sets
    Author(s): C. Wang, Y. Qi, M. Shao, Q. Hu, D. Chen, Y. Qian and Y. Lin
    Page(s): 741-753

    3. Subspace-Based Takagi–Sugeno Modeling for Improved LMI Performance
    Author(s): R. Robles, A. Sala, M. Bernal and T. González
    Page(s): 754-767

    4. Maxitive Belief Structures and Imprecise Possibility Distributions
    Author(s): R. R. Yager and N. Alajlan
    Page(s): 768-774

    4. An SOS-Based Control Lyapunov Function Design for Polynomial Fuzzy Control of Nonlinear Systems
    Author(s): R. Furqon, Y. J. Chen, M. Tanaka, K. Tanaka and H. O. Wang
    Page(s): 775-787

    5. Measuring Similarity and Ordering Based on Interval Type-2 Fuzzy Numbers
    Author(s): G. Hesamian
    Page(s): 788-798

    6. Statistical Inference in Rough Set Theory Based on Kolmogorov–Smirnov Goodness-of-Fit Test
    Author(s): D. Hu, X. Yu and J. Wang
    Page(s): 799-812

    7. Event-Triggered Control for Nonlinear Systems Under Unreliable Communication Links
    Author(s): H. Li, Z. Chen, L. Wu and H. K. Lam
    Page(s): 813-824

    8. Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets
    Author(s): Y. Yang, D. Chen and H. Wang
    Page(s): 825-838

    9. Generalized Adaptive Fuzzy Rule Interpolation
    Author(s): L. Yang, F. Chao and Q. Shen
    Page(s): 839-853

    10. State-Based Decentralized Diagnosis of Bi-Fuzzy Discrete Event Systems
    Author(s): W. Deng and D. Qiu
    Page(s): 854-867

    11. Detection of Resource Overload in Conditions of Project Ambiguity
    Author(s): M. Pelikán, H. Štiková and I. Vrana
    Page(s): 868-877

    12. Reachable Set Estimation of T–S Fuzzy Systems With Time-Varying Delay
    Author(s): Z. Feng, W. X. Zheng and L. Wu
    Page(s): 878-891, Aug. 2017.

    13. Graph Matching Using Hierarchical Fuzzy Graph Neural Networks
    Author(s): D. Krleža and K. Fertalj
    Page(s): 892-904

    14. Robust Tracking Control of MIMO Underactuated Nonlinear Systems With Dead-Zone Band and Delayed Uncertainty Using an Adaptive Fuzzy Control
    Author(s): T. S. Wu, M. Karkoub, H. Wang, H. S. Chen and T. H. Chen
    Page(s): 905-918

    15. Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation
    Author(s): A. P. García-Plaza, V. Fresno, R. M. Unanue and A. Zubiaga
    Page(s): 919-933

    16. Intelligent Decision Support System for Detection and Root Cause Analysis of Faults in Coal Mills
    Author(s): V. Agrawal, B. K. Panigrahi and P. M. V. Subbarao
    Page(s): 934-944

    17. Models of Mathematical Programming for Intuitionistic Multiplicative Preference Relations
    Author(s): Z. Zhang and W. PedryczZ. Zhang and W. Pedrycz
    Page(s): 945-957

    18. Stability and Stabilization Analysis of Positive Polynomial Fuzzy Systems With Time Delay Considering Piecewise Membership Functions
    Author(s): X. Li, H. K. Lam, F. Liu and X. Zhao
    Page(s): 958-971

    19. Reachability in Fuzzy Game Graphs
    Author(s): H. Pan, Y. Li, Y. Cao and D. Li
    Page(s): 972-984

    20. A Linear Programming Approach for Minimizing a Linear Function Subject to Fuzzy Relational Inequalities With Addition–Min Composition
    Author(s): S. M. Guu and Y. K. Wu
    Page(s): 985-992

    21. Type-2 Fuzzy Entropy Sets
    Author(s): L. Miguel, H. Santos, M. Sesma-Sara, B. Bedregal, A. Jurio, H. Bustince, and H. Hagras
    Page(s): 993-1005

    22. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
    Author(s): Y. Deng, Z. Ren, Y. Kong, F. Bao and Q. Dai
    Page(s): 1006-1012

    23. Extending Information-Theoretic Validity Indices for Fuzzy Clustering
    Author(s): Y. Lei, J. C. Bezdek, J. Chan, N. X. Vinh, S. Romano and J. Bailey
    Page(s): 1013-1018

    Message from Technical Committee on Cognitive and Developmental Systems Chair

    I am honoured to have been appointed to the role of Chair of the IEEE CIS Technical committee on Cognitive and Developmental Systems for 2017. This year the technical committee on cognitive and developmental systems has added two new research goals for our technical committee:
    • Building machines capable of life-long adaptation and interaction with the physical and social world (existing goal).
    • Building machines that can model and recognise cognitive characteristics relevant to development in their human collaborators, and act accordingly to assist human activities (new goal).
    • Using machines as tools to better understand human and animal development and cognition (existing goal).
    • Using machines to support human learning and development (new goal).
    In pursuit of these goals, this year members of our community have contributed to recent events in human-robot interaction, designing for curiosity and evolution in cognition. It is exciting to see the ongoing efforts of members of our community.

    CFP: IEEE TETCI Special Issue on Human-Machine Symbiosis (Sep 3)


    It has been 55+ years since Licklider published his seminal paper titled “Man-Machine Symbiosis”. Yet, Licklider’s argument stands still today as it was standing at that time; a vision that seems to be plausible in the near future. We particularly quote from this seminal work: “A multidisciplinary study group, examining future research and development problems of the Air Force, estimated that it would be 1980 before developments in artificial intelligence make it possible for machines alone to do much thinking or problem solving of military significance. That would leave, say, five years to develop man-computer symbiosis and 15 years to use it. The 15 may be 10 or 500, but those years should be intellectually the most creative and exciting in the history of mankind.” 

    Today, as we stand on slightly firmer ground to assert that the era of Human-Machine Symbiosis has begun, perfection in that symbiosis still remains distant. On the conceptual level, more refined concepts of Licklider’s vision have been established; these include (in a chronological order of their appearance in the literature): Biocybernetics, Brain Computer Interfaces, Adaptive Aiding, Adaptive Automation, HumanMachine Teaming, Augmented Cognition, Cognitive-Cyber Symbiosis, and Human-Autonomy Teaming. On the engineering level, most of these concepts have been implemented in one form or another. The field of Air Traffic Control has possibly been “the” testbed for almost all of these concepts; but today, autonomous systems are becoming a primary testbed for all of these concepts. 

    The above history has relied on less “intelligence” in the machine, making the symbiotic relationship more challenging. The field of Computational Intelligence (CI) is introducing some game-changing and disruptive technologies with the potential to create a leap forward in this research area. Deep learning systems are showing promise in making the machine smarter and adaptive. Fuzzy systems are offering opportunities to provide transparency to allow the human to understand what the machine does. Evolutionary computation techniques are becoming a classic technology to optimize these systems. Swarm intelligence is offering the foundations for effective teaming. Behavioral analytics using CI techniques are leading the way to synthesize low-level actions by both humans and machines into high-level meaning. Yet, the literature on this topic is spread over many scattered papers. This special issue aims to bring the elite of this literature together.


    The special issue welcomes survey, position, and research papers on the role of Computational Intelligence for one or more of the following topics:

     CI Algorithms (including hybrid algorithms) for Human Machine Symbiosis to
    - create meaning, understand, represent and model behavior from interaction data, 
    - design transparent, self-motivated, cognitive and trusted AI, 
    - facilitate natural and effective communication between humans and machines, 

     Case studies showcasing the use of CI in HumanMachine Symbiosis, for example in autonomous systems, safety critical systems including air traffic control, cyber operations, cyber-physical systems, and other interesting applications. 

     Position papers on the role of CI in architectures for trusted autonomous systems; augmented cognition systems; big data analytics for human-machine symbiosis; cognitive-cyber symbiosis, human-autonomy teaming and human-robot collaboration; role of psychophysiological and behavioral data in symbiosis; trust in human-machine interaction and teams; other related topics.


    Manuscripts should be prepared according to the “Information for Authors” section of the journal found at 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 “Human-Machine Symbiosis”, and clearly marking “Special Issue on Human-Machine Symbiosis” as comments to the Editor-in-Chief.


    Submission Deadline: 3rd September, 2017 
    First Decision: 30th October, 2017 
    Revised Manuscripts: 19th November, 2017 
    Final Decision: 8th January, 2018 
    Final Manuscripts: 28th January, 2018 


    Professor Hussein Abbass, University of New South Wales, Canberra, Northcott Drive, ACT 2600, Australia, h.abbass@adfa.edu.au 
    Dr. Gary B. Fogel, Natural Selection, Inc., 6480 Weathers Place, Suite 350, San Diego, CA 92121 USA, gfogel@naturalselection.com 
    Dr. Justin Fidock, Defence Science and Technology, Group Land Vehicles and Systems, Land Division, L81, DST Group, Justin.Fidock@dsto.defence.gov.au

    Friday 4 August 2017

    CFP: IEEE TCDS Special Issue on Neuro-Robotics Systems: Sensing, Cognition, Learning and Control (Nov 30)


    Neuro-robotics Systems (NRS) is a combined study of implementing human-like sensing, sensorimotor learning, coordination, cognition and control in autonomous robots, which can be integrated with cognitive capabilities, allowing them to imitate the way of humans and other living beings. NRS, a branch of neuroscience within robotics, is the current state-of-the-art research, as well as an important pillar in many countries’ brain projects. It assists the next generation of robots with embodied intelligence to be aware of themselves, interact with the environment and behave harmoniously with/as human beings. Therefore, it is a study to integrate recent breakthroughs in brain neuroscience, robotics and artificial intelligence in terms of new principles of understanding, modeling and developing robotic systems. This way of implementation will introduce smart and straightforward configuration of autonomous robots capable of handling complex tasks and adapting to unstructured environments. It will enable robots or robotic evices to not only do much more work, but also be smart enough to support or augment human abilities. As a bridge between neuroscience and robotics, it encourages researchers to study and understand how to define and develop the “brain” for future robots.
    This special issue aims at surveying the state of the art of the latest breakthrough technologies, new research results and developments in the area of NRS. We are particularly interested in papers that describe the formulation of various functions of NRS, including human-like sensing, fusion, cognition, learning and control, especially, the topics related to system sensing, multi-dimensional information fusion, and cognitive computation, sensorimotor learning and control technology. It provides a platform for interdisciplinary researchers to present their findings and latest developments of biomimetic mechatronics and robotics systems, covering relevant advances in engineering, computing, arts and bionic sciences. Areas of interest include, but are not limited to
    • Multi-modal perception, communication and interaction
    • Multi-modal neuromorphic computing
    • Brain-inspired end-to-end perception and control in robots
    • Knowledge representation, information acquisition, and decision making in neuro-robotics systems
    • Cognitive mechanism, and intention understanding in neuro-robotics systems
    • Affective and cognitive sciences for bio-mechatronics
    • Augmented cognitive robot systems, neuro-mechanical systems
    • Biomimetic modeling of perception and control in neuro-robotics systems
    • Brain-inspired development of rehabilitation robot systems, medical healthcare robot systems, prosthetic device systems, assistive robot systems, wearable robot systems for personal cooperative assistance
    • Sensorimotor coordination and control
    • Multi-modal intelligent learning and skill transfer system for multiple neuro-robotic systems
    • Robotic application oriented brain-inspired artificial intelligence algorithms and platforms on modeling, sensing, cognition, learning and control
    Manuscripts should be prepared according to the “Information for Authors” of the journal found at http://cis.ieee.org/publications.html and submissions should be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee and please select the category “SI: Neuro-Robotics Systems”.
    30. November 2017 – Deadline for manuscript submissions
    28. February 2018 – Notification of authors
    31. May 2018 – Deadline for revised manuscripts
    31. July 2018 – Final decisions
    February/March 2019 – Special Issue Publication
    Zhijun Li
    South China University of Technology, China
    Email: zjli@ieee.org
    Fei Chen
    Italian Institute of Technology, Italy
    Email: Fei.Chen@iit.it
    Antonio Bicchi
    University of Pisa, Italy
    Email: antonio.bicchi@unipi.it
    Yu Sun
    University of Toronto, Canada
    Toshio Fukuda
    Nagoya University/Meijo University, Japan
    Email: tofukuda@nifty.com

    CFP: IEEE TCDS Special Issue on Language Learning in Humans and Robots (Aug 31)


    Aim and Scope

    Children acquire language by interacting with their caregivers and others in their social environment. When children start to talk, their sensory-motor intelligence (visual perception, body movement, navigation, object manipulation, auditory perception and articulatory control) is already reaching a high level of competence. Importantly, communication is based on representations and skills that have started to develop much earlier and that are shaped already in first (social) interactions. These interactions are multimodal in nature and vary across contexts. The contexts vary not only across developmental time and situations within individuals, but also between individuals, socio-economic groups and cultures. Continuously, representations become further enriched in ongoing interactions and across different contexts.
    Even though there are various efforts in developmental robotics to model communication, the emergence of symbolic communication is still an unsolved problem. We are still lacking convincing theories and implementations that show how cooperation and interaction skills could emerge in long-term experiments with populations of robotic agents or how these skills develop in children. Importantly, the continuous acquisition of knowledge in different contexts and being able to further enrich the underlying representations provides a potential powerful mechanism (cross-situational learning), which is already well recognized in learning in children. Still, we need to know more about how children recognize contexts and how their language learning benefits from different language use varying across contexts.


    This special issue aims at surveying the state of the art of the emergence of communication which requires combining and integrating knowledge from diverse disciplines: developmental psychology, robotics, artificial language evolution, complex systems science, computational linguistics and machine learning. Topics relevant to this special issue include, but are not limited to
    · Psychological experiments on language learning in children
    · Corpus-based approaches to language acquisition
    · Language learning models for all stages of acquisition (gesture learning, early lexicon and grammar)
    · Representations for language learning (sensorimotor schemas, constructions, neural networks, mirror neurons)
    · Cognitive architectures and strategies for language learning
    · Cross-situational learning
    · Language acquisition and development of self-awareness
    · Role of context in language learning
    · Role of embodiment in language learning
    · Role of multimodality (gesture, gaze etc) in language learning
    · Role of social interaction and joint attention
    · Co-development of skills, e.g. motor and language skills; integration of natural language grounding into perception-action cycles
    · Connection with cultural and biological evolution of language


    Manuscripts should be prepared according to the “Information for Authors” of the journal found at http://cis.ieee.org/publications.html and submissions should be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee and please select the category “SI: Multi-Modal Integration and Development”.

    Important Dates

    31. August 2017 – Deadline for manuscript submissions
    15. October 2017 – Notification of authors
    15. December 2017 – Deadline for submission of revised manuscripts
    15. January 2017 – Final decisions
    February/March 2018 – Special Issue Publication in IEEE TCDS

    Guest Editors

    Chen Yu (Indiana University, USA)
    Katharina J. Rohlfing (Paderborn University, Germany)
    Malte Schilling (CITEC Bielefeld, Germany)
    Michael Spranger (Sony Computer Science Laboratories Inc, Japan)
    Paul Vogt (Tilburg University, the Netherlands)


    Malte Schilling: schilli at techfak dot uni-bielefeld dot de
    Michael Spranger: michael dot spranger at gmail dot com

    Call for participation: Seventh Joint IEEE International Conference on Developmental Learning and Epigenetic Robotics (ICDL-EpiRob 2017), Lisbon, Portugal (Sep 18-21)


    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 2017 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 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 psychology.

    Call for participation: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), Tokyo, Japan (Oct 19-21)

    Data driven scientific discovery is an important emerging paradigm for computing in areas including social, service, Internet of Things, sensor networks, telecommunications, biology, health-care and cloud. Under this paradigm, Data Science is the core that drives new research in many areas, from environmental to social. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. We mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation among the complex aspects to be addressed. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it the data available to enterprises, Government or on the Web.
    International Conference on Data Science and Advanced Analytics (DSAA) started in 2014 aiming to be a flagship in the data science and analytics field. It provides a premier forum that brings together researchers, industry practitioners, as well as potential users of data science and big data analytics. It covers all data science and analytics related areas, including statistical, probabilistic and mathematical methods, machine learning, data and business analytics, data mining and knowledge discovery, infrastructure, storage, retrieval and search, privacy and security, and relevant applications, practices, tools and evaluation. DSAA’2014 was not a fully IEEE supported conference, but was technically co-sponsored by IEEE Computational Intelligence Society (CIS) and ACM through SIGKDD. DSAA became a fully IEEE CIS supported conference from the second edition. The second IEEE DSAA’2015 was held in Paris in 2015 which was also very successful. The third IEEE DSAA’2016 is planned in Montreal. They continue to be technically sponsored by ACM.
    IEEE DSAA’2017 will consist of two main Tracks: Research and Application; the Research Track is aimed at collecting contributions related to theoretical foundations of Data Science and Data Analytics. The Application Track is aimed at collecting contributions related to applications of Data Science and Data Analytics in real life scenarios. DSAA’2017 solicits then both theoretical and practical works on data science and advanced analytics. It also inherits the unique features of past successful DSAA, that is the Trends & Controversies session where we invite visionary speakers to outline different insights and views about today and future of data science and advanced analytics, and the Special sessions which replace the traditional workshop and encourage submission of research on emerging topics. DSAA’2017 is also featured by a panel session where we invite internationally well recognized researchers to discuss challenges and important selected topics. Another highlight is the keynote talks. We invite four high profile keynote speakers from both academia and industry, and both domestic and abroad, to deliver insightful talks that best match the scope of data science and advanced analytics. We try to receive as much support as possible from industry to make DSAA’2017 financially secured. For this the conference must be attractive to the sponsors. We inherit the good practice of the invited industrial talk session in which the selected high profile sponsors can give talks without papers. We also plan to give them enough space to set up booths to disseminate their activities. Further, we seek for a possibility of co-locating with our domestic academic activities to attract domestic students for them to give opportunities to visit the sponsors’ booths, which gives another incentive to sponsors.