Friday, 31 July 2015

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 8, August 2015

1. Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment
Author(s): Gao, Y. ; Wang, X. ; Cheng, Y. ; Wang, Z.J.
Page(s): 1582 - 1593

2. Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial Locomotion
Author(s): Araujo, A.F.R. ; Santana, O.V.
Page(s): 1594 - 1607

3. Two-Stage Orthogonal Least Squares Methods for Neural Network Construction
Author(s): Zhang, L. ; Li, K. ; Bai, E. ; Irwin, G.W.
Page(s): 1608 - 1621

4. Learning a Probabilistic Topology Discovering Model for Scene Categorization
Author(s): Zhang, L. ; Ji, R. ; Xia, Y. ; Zhang, Y. ; Li, X.
Page(s): 1622 - 1634

5. A Convex Geometry-Based Blind Source Separation Method for Separating Nonnegative Sources
Author(s): Yang, Z. ; Xiang, Y. ; Rong, Y. ; Xie, K.
Page(s): 1635 - 1644

6. Approximate N -Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System
Author(s): Johnson, M. ; Kamalapurkar, R. ; Bhasin, S. ; Dixon, W.E.
Page(s): 1645 - 1658

7. A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning
Author(s): Wu, X. ; Rozycki, P. ; Wilamowski, B.M.
Page(s): 1659 - 1668

8. Two-Stage Regularized Linear Discriminant Analysis for 2-D Data
Author(s): Zhao, J. ; Shi, L. ; Zhu, J.
Page(s): 1669 - 1681

9. Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN
Author(s): Chen, Y. ; Ma, Y. ; Kim, D.H. ; Park, S.
Page(s): 1682 - 1697

10. Graph Theory-Based Approach for Stability Analysis of Stochastic Coupled Systems With Lévy Noise on Networks
Author(s): Zhang, C. ; Li, W. ; Wang, K.
Page(s): 1698 - 1709

11. Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking
Author(s): Lagorce, X. ; Meyer, C. ; Ieng, S. ; Filliat, D. ; Benosman, R.
Page(s): 1710 - 1720

12. An Experimentation Platform for On-Chip Integration of Analog Neural Networks: A Pathway to Trusted and Robust Analog/RF ICs
Author(s): Maliuk, D. ; Makris, Y.
Page(s): 1721 - 1734

13. Opportunistic Behavior in Motivated Learning Agents
Author(s): Graham, J. ; Starzyk, J.A. ; Jachyra, D.
Page(s): 1735 - 1746

14. Adaptive Batch Mode Active Learning
Author(s): Chakraborty, S. ; Balasubramanian, V. ; Panchanathan, S.
Page(s): 1747 - 1760

15. Incremental Generalized Discriminative Common Vectors for Image Classification
Author(s): Diaz-Chito, K. ; Ferri, F.J. ; Diaz-Villanueva, W.
Page(s): 1761 - 1775

16. Finite-Horizon Near-Optimal Output Feedback Neural Network Control of Quantized Nonlinear Discrete-Time Systems With Input Constraint
Author(s): Xu, H. ; Zhao, Q. ; Jagannathan, S.
Page(s): 1776 - 1788

17. Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity
Author(s): Liu, Z. ; Lai, G. ; Zhang, Y. ; Chen, C.L.P.
Page(s): 1789 - 1802

18. Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization
Author(s): Li, Z. ; Xia, Y. ; Su, C. ; Deng, J. ; Fu, J. ; He, W.
Page(s): 1803 - 1809

19. Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method
Author(s): Khosravi, A. ; Nahavandi, S. ; Srinivasan, D. ; Khosravi, R.
Page(s): 1810 - 1815

20. Robust Exemplar Extraction Using Structured Sparse Coding
Author(s): Liu, H. ; Liu, Y. ; Sun, F.
Page(s): 1816 - 1821

21. Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities
Author(s): Yang, Z. ; Yang, Q. ; Sun, Y.
Page(s): 1822 - 1827

22. Comparison of \ell _{1} -Norm SVR and Sparse Coding Algorithms for Linear Regression
Author(s): Zhang, Q. ; Hu, X. ; Zhang, B.
Page(s): 1828 - 1833

23. Model-Free Dual Heuristic Dynamic Programming
Author(s): Ni, Z. ; He, H. ; Zhong, X. ; Prokhorov, D.V.
Page(s): 1834 - 1839


Wednesday, 29 July 2015

WCCI 2016 Keynote

The 2016 IEEE Frank Rosenblatt Award winner, Professor Ronald R. Yager, will receive his Award and deliver a Keynote at IEEE WCCI 2016.

2016 IEEE Congress on Evolutionary Computation (IEEE CEC) Special Session on "Evolutionary Computation and Big Data"

Overview

Nowadays, big data has been attracting increasing attention from academia, industry and government. Big data is defined as the dataset whose size is beyond the processing ability of typical databases or computers. Big data analytics is to automatically extract knowledge from large amounts of data. It can be seen as mining or processing of massive data, and “useful” information can be retrieved from large dataset. Big data analytics can be characterized by several properties, such as large volume, variety of different sources, and fast increasing speed (velocity). It is of great interest to investigate the role of evolutionary computing (EC) techniques, including evolutionary algorithms and swarm intelligence algorithms for the optimization and learning involving big data, in particular, the ability of EC techniques to solve large scale, dynamic, and sometimes multi-objective big data analytics problems.

Topics of Interest

This special session aims at presenting the latest developments of EC techniques for big data problems, as well as exchanging new ideas and discussing the future directions of EC for big data. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of big data analytics are very welcome for this Special Session. Potential topics include, but are not limited to:
  1. High-dimensional and many-objective evolutionary optimization
  2. Big data driven optimization of complex engineering systems
  3. Integrative analytics of diverse, structured and unstructured data
  4. Extracting new understanding from real-time, distributed, diverse and large-scale data resources
  5. Big data visualization and visual data analytics
  6. Scalable, incremental learning and understanding of big data
  7. Scalable learning techniques for big data
  8. Big data driven optimization of complex systems
  9. Human-computer interaction and collaboration in big data
  10. Big data and cloud computing
  11. Cross-connections of big data analysis and hardware
  12. Big data techniques for business intelligence, finance, healthcare, bioinformatics, intelligent transportation, smart city, smart sensor networks, cyber security and other critical application areas
  13. MapReduce implementations combined with evolutionary computation or swarm intelligence approaches

Submission

Please follow the IEEE CEC2016 instruction for authors and submit your paper via the IEEE CEC 2016 online submission system. Please specify that your paper is for the Special Session on Evolutionary Computation and Big Data.

Important Dates

Paper Submission Deadline:    15 Jan 2016
Notification of Acceptance:    15 Mar 2016
Final Paper Submission Deadline:    15 Apr 2016

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng@nottingham.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi@xjtlu.edu.cn
Yaochu Jin, University of Surrey, Guildford, United Kingdom, yaochu.jin@surrey.ac.uk
Bin Li, University of Science and Technology of China, Hefei, China, binli@ustc.edu.cn

Committee Member

Simone Ludwig, North Dakota State University, USA, simone.ludwig@ndsu.edu
Yinan Guo, China University of Mining and Technology, Xuzhou,  China, guoyinan@cumt.edu.cn
Junfeng Chen, Hohai University, Changzhou, China, chen-1997@163.com

Biography of the Proposers

Shi Cheng received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently a research fellow with Division of Computer Science, University of Nottingham Ningbo, China. He has published more than 30 research articles in peer-reviewed journals and international conferences. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.

Yuhui Shi received the PhD degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a Professor in the Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China. Before joining Xi'an Jiaotong-Liverpool University, he was with Electronic Data Systems Corporation, Indianapolis, IN. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

Yaochu Jin is currently a Professor of Computational Intelligence with the Department of Computing, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor awarded by Academy of Finland. His main research interests include computational intelligence, computational neuroscience and computational systems biology, with applications to complex engineering optimization, bioengineering, swarm robotics, and autonomous systems. His current research is funded by EU FP7, UK EPSRC and industries, including Intellas UK, Santander, Aero Optimal, Bosch UK and Honda. He has delivered 20 invited keynote speeches at international conferences. Dr Jin is the founding chair of the IEEE Symposium on Computational Intelligence in Big Data and Guest Editor of the IEEE Computational Intelligence Magazine special issue on Big Data. He is an Associate Editor of several international journals including IEEE TRANSACTIONS ON CYBERNETICS, IEEE TRANSACTIONS ON NANOBIOSCIENCE, and IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE and BioSystems. He is currently Vice President for Technical Activities, and IEEE Distinguished Lecturer. He was the recipient of the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is a Fellow of BCS and Senior Member of IEEE.

Bin Li received the B.S. degree from HeFei University of Technology, Hefei, China, in 1992, the M.Sc degree from Institute of Plasma Physics, China Academy of Science, Hefei, China, in 1995, and the Ph.D degree from University of Science and Technology of China, China in 2001. He is currently a professor with the School of Information Science and Technology, University of Science and Technology of China, Hefei, China. He has published more than 50 refereed publications. His major research interests include evolutionary computation, pattern recognition, and real-world applications. Dr. Li is the Founding Chair of IEEE CIS Hefei Chapter, and the founding Counselor of IEEE USTC Student Branch.

Simone Ludwig received the PhD degree from Brunel University, UK, in 2004. She is currently an associate professor at North Dakota State University, USA, conducting research in the area of computational intelligence. In particular, developing novel algorithms to address different optimization problems in the area of data mining (large data) and distributed computing. She has published around 90 peer-reviewed articles both in journals and conference proceedings. Dr. Ludwig has served as a co-chair, track chair, and tutorial chair for different conferences, as well as served on numerous conference program committees. In addition, she currently serves on the editorial board of 3 journals.

Yinan Guo received the PhD degree in control theories and their applications from China University of Mining and Technology, China, in 2003. From September 2000, she worked as a Lecturer, Associate professor and Professor in China University of Mining and Technology respectively. In 2012, she done cooperative research on computational intelligence as an Academic visitor in CERCIA, School of Computer Science, Birmingham University, UK. Her current research interests include interactive evolutionary algorithms, knowledge-inducing cultural algorithm, evolutionary multi-objective optimization, evolutionary dynamic optimizations and relevant real-world applications. Dr Guo has over 70 publications and six research projects in the above domains.

Junfeng Chen received the PhD degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2011. Currently, she is an associate professor in the College of IOT Engineering, Hohai University, Changzhou, China. Her research interests include swarm intelligence, artificial intelligence with uncertainty and big data analytics. She has published over 20 papers in international journals and conference proceedings.

2016 IEEE Congress on Evolutionary Computation (IEEE CEC) Special Session on "Brain Storm Optimization Algorithms"

Overview

Swarm intelligence algorithm should have two kinds of ability: capability learning and capacity developing. The capacity developing focuses on moving the algorithm’s search to the area(s) where higher search potential may be obtained, while the capability learning focuses on its actually search from the current solution for single point based optimization algorithms and from the current population for population-based swarm intelligence algorithms.  The swarm intelligence algorithms with both capability learning and capacity developing can be called as developmental swarm intelligence algorithms.

The capacity developing is a top-level learning or macro-level learning methodology. The capacity developing describes the learning ability of an algorithm to adaptively change its parameters, structures, and/or its learning potential according to the search states of the problem to be solved. In other words, the capacity developing is the search strength possessed by an algorithm. The capability learning is a bottom-level learning or micro-level learning. The capability learning describes the ability for an algorithm to find better solution(s) from current solution(s) with the learning capacity it possesses.

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence, which is based on the collective behaviour of human being, that is, the brainstorming process. It is natural to expect that an optimization algorithm based on human collective behaviour could be a better optimization algorithm than existing swarm intelligence algorithms which are based on collective behaviour of simple insects, because human beings are social animals and are the most intelligent animals in the world. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

The BSO algorithm is a good example of developmental swarm intelligence algorithm. A "good enough" optimum could be obtained through solution divergence and convergence in the search space. In the BSO algorithm, the solutions are clustered into several categories, and the new solutions are generated by the mutation of cluster or existing solutions. The capacity developing, i.e., the adaptation during the search, is another common feature of the BSO algorithms. 

The BSO algorithm can be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Topics of Interest

This special session aims at presenting the latest developments of BSO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special Session. Potential topics include, but are not limited to:
  • Analysis and control of BSO parameters
  • Parallelized and distributed realizations of BSO algorithms
  • BSO for Multi-objective optimization
  • BSO for Constrained optimization
  • BSO for Discrete optimization
  • BSO algorithm with data mining techniques
  • BSO in uncertain environments
  • Theoretical aspects of BSO algorithm
  • BSO for Real-world applications

Submission

Please follow the IEEE CEC2016 instruction for authors and submit your paper via the IEEE CEC 2016 online submission system. Please specify that your paper is for the Special Session on Brain Storm Optimization Algorithms.

Important Dates

Paper Submission Deadline:    15 Jan 2016
Notification of Acceptance:    15 Mar 2016
Final Paper Submission Deadline:    15 Apr 2016

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng@nottingham.edu.cn
Quande Qin, Shenzhen University, Shenzhen China, qdqin@szu.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi@xjtlu.edu.cn
Simone Ludwig, North Dakota State University, USA, simone.ludwig@ndsu.edu

Committee Member

Shangce Gao, University of Toyama, Gofuku, Japan, gaosc@eng.u-toyama.ac.jp
Xingquan Zuo, Beijing University of Posts and Telecommunications, Beijing, China, zuoxq@bupt.edu.cn

Biography of the Proposers

Shi Cheng received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently a research fellow with Division of Computer Science, University of Nottingham Ningbo, China. He has published more than 30 research articles in peer-reviewed journals and international conferences. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.

Quande Qin received PhD degree in Management Science and Engineering from School of Business Administration, South China University of Technology, Guangzhou, China. Currently, he is a lecturer in the College of Management, Shenzhen University, Shenzhen, China. His current research interests include swarm intelligence, evolutionary optimization and their applications in management and economics.

Yuhui Shi received the PhD degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a Professor in the Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China. Before joining Xi'an Jiaotong-Liverpool University, he was with Electronic Data Systems Corporation, Indianapolis, IN. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

Simone Ludwig received the PhD degree from Brunel University, UK, in 2004. She is currently an associate professor at North Dakota State University, USA, conducting research in the area of computational intelligence. In particular, developing novel algorithms to address different optimization problems in the area of data mining (large data) and distributed computing. She has published around 90 peer-reviewed articles both in journals and conference proceedings. Dr. Ludwig has served as a co-chair, track chair, and tutorial chair for different conferences, as well as served on numerous conference program committees. In addition, she currently serves on the editorial board of 3 journals.

Shangce Gao received the B.S. degree from Southeast University, Nanjing, China in 2005, and the M.S. and Ph. D. degrees in intellectual information systems and innovative life science from University of Toyama, Toyama, Japan in 2008 and 2011, respectively. He is currently an Associate Professor with the Faculty of Engineering, University of Toyama, Toyama, Japan. From 2011 to 2012, he was an associate research fellow with the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China. From 2012 to 2014, he was an associate professor with the College of Information Sciences and Technology, Donghua University, Shanghai, China. His main research interests include computational intelligence, nature-inspired technologies, swarm intelligence, and neural networks for optimization and real-world applications. He was a recipient of the Shanghai Rising-Star Scientist award, the Chen-Guang Scholar of Shanghai award, the Outstanding Academic Performance Award of IEICE, the Outstanding Self-financed Students Abroad Award of Chinese Government, and the Outstanding Academic Achievement Award of IPSJ.

Xingquan Zuo received the Ph.D. degree in Control Theory and Control Engineering from Harbin Institute of Technology, Harbin, China, in 2004. He is currently an Associate Professor in Computer School, Beijing University of Posts and Telecommunications, Beijing, China. From 2012 to 2013, he was a Visiting Scholar in Industrial and System Engineering Department, Auburn University, AL, USA. His research interests are in intelligent optimization and scheduling, evolutionary computation, data mining with applications. He has published more than 70 research papers in journals and conferences, two books and several book chapters. He is a senior member of IEEE and served in program committee of many conferences. 

Friday, 24 July 2015

Call for Papers IEEE CIM Special Issue "Model Complexity, Regularization and Sparsity"

IEEE Computational Intelligence Magazine Special Issue on “Model Complexity, Regularization and Sparsity” http://home.deib.polimi.it/boracchi/events/ModelComplexity.html

 

Aims and Scope

The effective management of solution complexity is one of the most important issues in addressing Computational Intelligence problems. Regularization techniques control model complexity by taking advantage of some prior information regarding the problem at hand, represented as penalty expressions that impose these properties on the solution. Over the past few years, one of the most prominent and successful types of regularization has been based on the sparsity prior, which promotes solutions that can be expressed as a linear combination of a few atoms belonging to a dictionary. Sparsity can in some sense be considered a "measure of simplicity" and, as such, is compatible with many nature-inspired principles of Computational Intelligence. Nowadays, sparsity has become one of the leading approaches for learning adaptive representations for both descriptive and discriminative tasks, and has been shown to be particularly effective when dealing with structured, complex and high-dimensional data.

Regularization, including sparsity and other priors to control the model complexity, is often the key ingredient in the successful solution of difficult problems; it is therefore not surprising that these aspects have also recently gained a lot of attention in big-data processing, due to unprecedented challenges associated with the need to handle massive datastreams that are possibly high-dimensional and organized in complex structures.

This special issue aims at presenting the most relevant regularization techniques and approaches to control model complexity in Computational Intelligence. Submissions of papers presenting regularization methods for Neural Networks, Evolutionary Computation or Fuzzy Systems, are welcome. Submissions of papers presenting advanced regularization techniques in specific, but relevant, application fields such as data/datastream-mining, classification, big-data analytics, image/signal analysis, natural-language processing, are also encouraged.

Topics of Interest

  • Regularization methods for big and high-dimensional data;
  • Regularization methods for supervised and unsupervised learning;
  • Regularization methods for ill-posed problems in Computational Intelligence;
  • Techniques to control model complexity;
  • Sparse representations in Computational Intelligence;
  • Managing model complexity in data analytics;
  • Effective priors for solving Computational Intelligence problems;
  • Multiple prior integration;
  • Regularization in kernel methods and support vector machines.

Important Dates

  • 22nd January, 2016: Submission of Manuscripts
  • 30th March, 2016: Notification of Review Results
  • 30th April, 2016: Submission of Revised Manuscripts
  • 15th June, 2016: Submission of Final Manuscripts
  • November, 2016: Special Issue Publication

Submission Process

The maximum length for the manuscript is typically 20 pages in single column with double-spacing, including figures and references. Authors of papers should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website.
Submission should be made via at https://easychair.org/conferences/?conf=ieeecim1116

 

Guest Editors

Prof. Cesare Alippi,
Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
via Ponzio 34/5, Milano, 20133, Italy
email: cesare.alippi@polimi.it

Dr. Giacomo Boracchi,
Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
via Ponzio 34/5, Milano, 20133, Italy
email: giacomo.boracchi@polimi.it

Dr. Brendt Wohlberg,
Theoretical Division, Los Alamos National Laboratory,
Los Alamos NM 87545, USA
email: brendt@lanl.gov

Thursday, 23 July 2015

IEEE 2015 International Conference on Data Science and Advanced Analytics

IEEE 2015 International Conference on Data Science and Advanced Analytics  (IEEE DSAA'2015)

IEEE DSAA'2015 Highlights


DSAA2015 will feature
  • DSAA'2015 appreciates our confirmed sponsors at platinum to bronze levels, including Booz|Allen|Hamilton, Infosys, IEEE Big Data Initiative, Technicolor, Baidu, Telecom ParisTech, Google, Prof Ram Kumar Memorial Foundation, Internet Memory Foundation and Lianing Technical University.
  • DSAA'2015 main track has received a large number of submissions from about 50 countries and regions, which makes DSAA a very competitive conference in data science and analytics,
  • 4 keynote speeches to be delivered by prestigious academic and industry leaders, 
  • 4-8 invited talks about the trends and controversies of data science and big data analytics, 
  • 9 special sessions on specific interesting data science and analytics topics, 
  • an industry session and an application track to highlight the best practices,
  • a panel jointly held with IEEE Big Data Initiative will include high profile panelists from data science, big data, computational intelligence, data mining, machine learning, statistics etc.
  • DSAA'2015 receives contributions from major sponsors and supporters in different regions in North America, Europe and Asia.

 

IEEE DSAA Introduction

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 researches 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. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. 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 data available to enterprises, Government or on the Web.

IEEE DSAA aims to be a flagship data science and analytics conference in its kind, and provides a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.

DSAA is fully sponsored by IEEE and technically sponsored by ACM. DSAA is the only IEEE/ACM jointly sponsored conference truly in data science, big data and advanced analytics.

All accepted papers will be published by IEEE and included in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and publication in the special issues of some international journals, including International Journal of Data Science and Analytics and IEEE Intelligent Systems.





Monday, 20 July 2015

2015 Webinar Competition for Students and Young Professionals-Real World Applications and Emerging Topics in Computational Intelligence








We invite students and young professionals who are members of IEEE CIS to submit their entries to the 2015 webinars competition and get the chance to win prizes of up to 500 USD! We welcome submissions on the topics of real world applications and emerging topics in computational intelligence. The deadline for abstracts submission is 10th August 2015. Please find the call for submissions here:

http://cis.ieee.org/images/files/Documents/GOLD/WebinarCompetition_CFS_v2.pdf


Sunday, 12 July 2015

IEEE ICDL-EPIROB 2015 Registration Open

The Fifth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics
Brown University, Providence, Rhode Island, USA 
August 13-16, 2015 

Registration is now open at: http://www.tech.plym.ac.uk/SoCCE/CRNS/icdl-epirob//2015/registration.html

In addition to the main conference, we will have both a preconference workshop and tutorial:
Morning: "Introduction to Brains as Emergent Turing Machines" (Juyang Weng)
Afternoon: "Mechanisms of learning in social contexts" (Michael Goldstein, Chen Yu)

Conference description

The past decade has seen the emergence of a new scientific field that studies how intelligent biological and artificial systems develop sensorimotor, cognitive, emotional and social abilities, over extended periods of time, through dynamic interactions with their physical and social environments. This field lies at the intersection of a number of scientific and engineering disciplines including Neuroscience, Developmental Psychology, Developmental Linguistics, Cognitive Science, Computational Neuroscience, Artificial Intelligence, Machine Learning, and Robotics. Various terms have been associated with this new field such as Autonomous Mental Development, Epigenetic Robotics, Developmental Robotics, etc., and several scientific meetings have been 
established. The two most prominent conference series of this field, the International Conference on 
Development and Learning (ICDL) and the International Conference on Epigenetic Robotics (EpiRob), are now joining forces for the fifth time and invite submissions for a joint conference in 2015, to explore and extend the interdisciplinary boundaries of this field.

BABYBOT CHALLENGE

We are excited to announce a new ICDL-EpiRob conference feature: the BABYBOT CHALLENGE. The goal of the challenge is to use the tools of developmental robotics to replicate and extend the key findings from one of three selected human-infant studies. These will be presented during a special session. 

Keynote speakers

Prof. Dare Baldwin, Dept. of Psychology, University of Oregon, USA
Prof. Kerstin Dautenhahn, School of Computer Science, University of Hertfordshire, UK
Prof. Asif Ghazanfar, Dept. of Psychology, Princeton University, USA

Program committee

General Chairs:
Matthew Schlesinger (Southern Illinois Univ.)
Dima Amso (Brown University)

Bridge Chairs:
Jeffrey Krichmar (UC Irvine)
Bertram Malle (Brown University)

Program Chairs:
Anne Warlaumont (UC Merced)
Clemént Moulin-Frier (INRIA)

Publications Chairs:
Lisa Meeden (Swarthmore College)

Publicity Chairs:
Lola Cañamero (Univ. of Hertfordshire)
Matthias Rolf (Osaka University)
Benjamin Rosman (CSIR)

Local chairs:
David Sobel (Brown University)
Thomas Serre (Brown University)

Finance chairs:
Clayton Morrison (University of Arizona)

Thursday, 2 July 2015

IEEE Transactions on Neural Networks and Learning Systems; Volume 26, Issue 7, July 2015

1. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time
Author(s): Lijun Long; Jun Zhao
Page(s): 1350 - 1362

2. A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals With Globally Converged Solution Approximating to  l_{0} Minimization
Author(s): Chengan Guo; Qingshan Yang
Page(s): 1363 - 1374

3. Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition
Author(s): Djamel Bouchaffra
Page(s): 1375 - 1387

4. FREL: A Stable Feature Selection Algorithm
Author(s): Yun Li; Jennie Si; Guojing Zhou; Shasha Huang; Songcan Chen
Page(s): 1388 - 1402

5. Incremental Support Vector Learning for Ordinal Regression
Author(s): Bin Gu; Victor S. Sheng; Keng Yeow Tay; Walter Romano; Shuo Li
Page(s): 1403 - 1416

6. On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation
Author(s): Shen Yuong Wong; Keem Siah Yap; Hwa Jen Yap; Shing Chiang Tan; Siow Wee Chang
Page(s): 1417 - 1430

7. Exponential Stabilization of Memristor-based Chaotic Neural Networks with Time-Varying Delays via Intermittent Control
Author(s): Guodong Zhang; Yi Shen
Page(s): 1431 - 1441

8. An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination
Author(s): Chin-Teng Lin; Nikhil R. Pal; Shang-Lin Wu; Yu-Ting Liu; Yang-Yin Lin
Page(s): 1442 - 1455

9. Finite-Horizon Approximate Optimal Guaranteed Cost Control of Uncertain Nonlinear Systems With Application to Mars Entry Guidance
Author(s): Huai-Ning Wu; Mao-Mao Li; Lei Guo
Page(s): 1456 - 1467

10. Multitask Classification Hypothesis Space With Improved Generalization Bounds
Author(s): Cong Li; Michael Georgiopoulos; Georgios C. Anagnostopoulos
Page(s): 1468 - 1479

11. Stability Analysis of Distributed Delay Neural Networks Based on Relaxed Lyapunov–Krasovskii Functionals
Author(s): Baoyong Zhang; James Lam; Shengyuan Xu
Page(s): 1480 - 1492

12. Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption
Author(s): Shiping Wen; Zhigang Zeng; Tingwen Huang; Qinggang Meng; Wei Yao
Page(s): 1493 - 1502

13. Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution
Author(s): Qin Zhang
Page(s): 1503 - 1517

14. The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS
Author(s): Aleksandr Y. Aravkin; Bradley M. Bell; James V. Burke; Gianluigi Pillonetto
Page(s): 1518 - 1524

15. Discrete-Time Zhang Neural Network for Online Time-Varying Nonlinear Optimization With Application to Manipulator Motion Generation
Author(s): Long Jin; Yunong Zhang
Page(s): 1525 - 1531

16. Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators
Author(s): Jianjun Ma; Shuzhi Sam Ge; Zhiqiang Zheng; Dewen Hu
Page(s): 1532 - 1538

17. Phase Oscillatory Network and Visual Pattern Recognition
Author(s): Rosangela Follmann; Elbert E. N. Macau; Epaminondas Rosa, Jr.; Jose R. C. Piqueira
Page(s): 1539 - 1544

18. Optoelectronic Systems Trained With Backpropagation Through Time
Author(s): Michiel Hermans; Joni Dambre; Peter Bienstman
Page(s): 1545 - 1550

19. Ordinal Distance Metric Learning for Image Ranking
Author(s): Changsheng Li; Qingshan Liu; Jing Liu; Hanqing Lu
Page(s): 1551 - 1559

20. Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique
Author(s): Wen Yu
Page(s): 1560 - 1566

21. A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study
Author(s): Francisco Naveros; Niceto R. Luque; Jesus A. Garrido; Richard R. Carrillo; Mancia Anguita; Eduardo Ros
Page(s): 1567 - 1574

22. A Deterministic Analysis of an Online Convex Mixture of Experts Algorithm
Author(s): Huseyin Ozkan; Mehmet A. Donmez; Sait Tunc; Suleyman S. Kozat
Page(s): 1575 - 1580

IEEE Women in Engineering Newsletter, June 2015

The June 2015 issue of the IEEE Women in Engineering Newsletter is now available:

http://www.ieee.org/membership_services/membership/women/newsletter/wie_june_2015.pdf