Reminder: conference paper deadline for IEEE CIG 2015

A reminder that the paper submission deadline for the 2015 IEEE Conference on Computational Intelligence in Games (IEEE CIG) is April 2, 2015. This conference will be held in Tainan, Taiwan, 31 August to 2 September, 2015.

Reminder: paper deadline for INNS Big Data 2015

A reminder that the deadline for submitting papers to the INNS Conference on Big Data is March 22, 2015. This conference will be held in San Francisco, USA, 8-10 August, 2015.

Database of Computational Intelligence Courses

One of the ways I contribute to the IEEE Computational Intelligence Society is by serving on the University Curricula Subcommittee of the Education Committee. Among the activities of the curricula subcommittee is maintaining a world-wide directory of courses in computational intelligence.

This directory is now available as a searchable online database, at http://ucs.ais.ac.nz/

This is a prototype of a system where educators can submit details of their courses, search existing courses, and engage in discussions with other educators about the listed courses. Anyone may access and search the database, although users must register to submit new courses or leave comments.

This system was developed as a student project by the following students:

• Pankaj Dadral
• Toshitsugu Hashimoto
• Shuxiang Wang
• Dan Mo

The project was supervised by myself and is kindly hosted by Auckland Institute of Studies, where I'm the head of the Information Technology Programme. 

Reminder: paper submission deadline: IJCNN 2015

A reminder that the paper submission deadline for the International Joint Conference on Neural Networks (IJCNN) 2015 is January 15, 2015. This conference will be held in Killarney, Ireland, July 12-17, 2015.

13th International Conference on Neuro-Computing and Evolving Intelligence 2015 (NCEI '15)

INTELLIGENT INFORMATION TECHNOLOGIES FOR BIG DATA

13th International Conference on Neuro-Computing and Evolving Intelligence 2015 (NCEI ‘15) Auckland, New Zealand, February 19-20, 2015

Venue
Auckland University of Technology
WG Sir Paul Reeves Building, level 1, room 126,
2 Governor Fitzroy Place, Auckland 1010 New Zealand

TOPICS:

• Big and Stream Data Analytics
• Spiking Neural Network Computation
• High Performance Neuromorphic System
• Novel Brain-Computer Interfaces (BCI)
• Novel Motion Data Analysis Technology
• Predictive Personalised Modelling of non-Communicable Diseases
• Predicting Response to Treatment
• Personalised Modelling in Bioinformatics
• Predictive Modelling on Ecological and Environmental Data
• Big Data in Radio-Astronomy
• Computer Vision and Image Processing for Dynamic Data Analysis
• Visualisation of Scientific Data
• Novel Human-Computer Interfaces
• Complex System Optimisation
• Collaborative and Distributed Systems Design

Selected full papers will be published after the conference in special issues of Evolving Systems and Springer Series in Bio-/Neuroinformatics.
Please visit the NCEI’15 website for more details: www.kedri.aut.ac.nz/conferences/ncei15

Special Events:

1. NZ INTERACT team discussion
2. KEDRI alumni event
3. Maori Cultural Program

IMPORTANT DATES:
Final Abstract Submission: 15 JANUARY, 2015
Acceptance Notification: 2 weeks after the submission

General Chair:
Prof. Nikola Kasabov

Organising Chair:
Joyce D’Mello
(email: jdmello@aut.ac.nz)

Web Maintenance & Tech.Support:
Elisa Capecci

Organising Committee:

• Nathan Scott (email: nascott@aut.ac.nz)
• Norhanifah Murli
• Muhaini Othman
• Paul Davidson,
• Reggio Hartono
• Fahad Alvi
• Vivienne Breen,
• Maryam Gholami
• Neelava Sengupta
• Enmei Tu
• Jin Hu

Programme Committee:

• Prof. A. Al-Jumaily
• A/Prof. D. Bailey
• Prof. M. Billinghurst
• Dr. A. Cichocki
• A/Prof. T. Clear
• Dr. A. Connor
• Prof. G. Dobbie
• Prof. V. Feigin
• A/Prof. E. Frank
• Prof. S. Furber
• Prof. S. Gulyaev
• Dr. C. Higgins
• Prof. G. Holmes
• Prof. Z. Hou
• Prof. G. Indiveri
• Prof. R. Jones
• A/Prof. F. Joseph,
• Dr. I. Khan
• Prof. R. Klette
• Dr.Y.S. Koh
• Dr. R. Krishnamurthi
• Prof. R. Kydd, Dr. D. Love
• Dr. A. Lowe
• Prof. S. MacDonell
• Dr. A. Malik
• Dr. S. Marks
• Dr. H. Nuzly
• Prof. S. Ozawa
• A/Prof. D. Parry
• A/Prof. R. Pears
• A/Prof. B. Pfahringer
• Prof. H. Regenbrecht
• Prof. A. Robins
• Dr. T. Robotham,
• Dr. B. Russell
• Dr. M. Sagar
• Prof. Z. Salcic
• Dr. S. Singamneni
• A/Prof. D. Taylor
• A/Prof. C. Walker
• Dr. G. Wang
• Dr. K. Wang
• Dr. M. Watts
• Dr. S. Weddell
• A/Prof. S. Worner
• Dr. W.Q. Yan
• Prof. J. Yang
• Prof. M. Zhang.

IEEE Transactions on Evolutionary Computation Volume 18, Number 6, December 2014

1. Parameter Optimization Algorithms for Evolving Rule Models Applied to Freshwater Ecosystems
Author(s): Cao, H. ; Recknagel, F. ; Orr, P.T.
Page(s): 793 - 806

2. An Analysis of $N!K$ Landscapes: Interaction Structure, Statistical Properties, and Expected Number of Local Optima
Author(s): Buzas, J. ; Dinitz, J.
Page(s): 807 - 818

3. The Effect of Memory Size on the Evolutionary Stability of Strategies in Iterated Prisoner's Dilemma
Author(s): Li, J. ; Kendall, G.
Page(s): 819 - 826

4. An Evolutionary Multiobjective Approach to Sparse Reconstruction
Author(s): Li, L. ; Yao, X. ; Stolkin, R. ; Gong, M. ; He, S.
Page(s): 827 - 845

5. A New Memetic Algorithm With Fitness Approximation for the Defect-Tolerant Logic Mapping in Crossbar-Based Nanoarchitectures
Author(s): Yuan, B. ; Li, B. ; Weise, T. ; Yao, X.
Page(s): 846 - 859

6. Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
Author(s): Lai, X. ; Zhou, Y. ; He, J. ; Zhang, J.
Page(s): 860 - 872

7. Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
Author(s): Barros, R.C. ; Basgalupp, M.P. ; Freitas, A.A. ; de Carvalho, A.C.P.L.F.
Page(s): 873 - 892

8. Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data
Author(s): Bhowan, U. ; Johnston, M. ; Zhang, M. ; Yao, X.
Page(s): 893 - 908

9. Stable Matching-Based Selection in Evolutionary Multiobjective Optimization
Author(s): Li, K. ; Zhang, Q. ; Kwong, S. ; Li, M. ; Wang, R.
Page(s): 909 - 923

IEEE Transactions on Fuzzy Systems, Volume 22, Number 6, December 2014

1. The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications
Author(s): Wang, J. ; Wang, C.-H. ; Chen, C.L.P.
Page(s): 1373 - 1386

2. Joint Block Structure Sparse Representation for Multi-Input–Multi-Output (MIMO) T–S Fuzzy System Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Page(s): 1387 - 1400

3. Robust $L_{bm infty}$-Gain Fuzzy Disturbance Observer-Based Control Design With Adaptive Bounding for a Hypersonic Vehicle
Author(s): Wu, H.-N. ; Liu, Z.-Y. ; Guo, L.
Page(s): 1401 - 1412

4. A Novel Model-Based Controller for Polymer Extrusion
Author(s): Abeykoon, C.
Page(s): 1413 - 1430

5. Logic Connectives for Soft Sets and Fuzzy Soft Sets
Author(s): Ali, M.I. ; Shabir, M.
Page(s): 1431 - 1442

6. A Collaborative Fuzzy Clustering Algorithm in Distributed Network Environments
Author(s): Zhou, J. ; Philip Chen, C.L. ; Chen, L. ; Li, H.-X.
Page(s): 1443 - 1456

7. Multilabel Text Categorization Based on Fuzzy Relevance Clustering
Author(s): Lee, S.-J. ; Jiang, J.-Y.
Page(s): 1457 - 1471

8. Construction of Neurofuzzy Models For Imbalanced Data Classification
Author(s): Gao, M. ; Hong, X. ; Harris, C.J.
Page(s): 1472 - 1488

9. A Banzhaf Function for a Fuzzy Game
Author(s): Tan, C. ; Jiang, Z.-Z. ; Chen, X. ; Ip, W.H.
Page(s): 1489 - 1502

10. Stability Analysis of Switched Fuzzy Systems Via Model Checking
Author(s): Ding, Z. ; Zhou, Y. ; Zhou, M.
Page(s): 1503 - 1514

11. Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic
Author(s): Melin, P. ; Gonzalez, C.I. ; Castro, J.R. ; Mendoza, O. ; Castillo, O.
Page(s): 1515 - 1525

12. Fault Tolerant Controller Design for T–S Fuzzy Systems With Time-Varying Delay and Actuator Faults: A K-Step Fault-Estimation Approach
Author(s): Huang, S.-J. ; Yang, G.-H.
Page(s): 1526 - 1540

13. A Scene Image is Nonmutually Exclusive—A Fuzzy Qualitative Scene Understanding
Author(s): Lim, C.H. ; Risnumawan, A. ; Chan, C.S.
Page(s): 1541 - 1556

14. Incremental Fuzzy Clustering With Multiple Medoids for Large Data
Author(s): Wang, Y. ; Chen, L. ; Mei, J.-P.
Page(s): 1557 - 1568

15. Multipolar Aggregation Operators in Reasoning Methods for Fuzzy Rule-Based Classification Systems
Author(s): Mesiarova-Zemankova, A.
Page(s): 1569 - 1584

16. Cluster-Centric Fuzzy Modeling
Author(s): Pedrycz, W. ; Izakian, H.
Page(s): 1585 - 1597

17. An Intelligent Second-Order Sliding-Mode Control for an Electric Power Steering System Using a Wavelet Fuzzy Neural Network
Author(s): Lin, F.-J. ; Hung, Y.-C. ; Ruan, K.-C.
Page(s): 1598 - 1611

18. Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach
Author(s): Izakian, H. ; Pedrycz, W.
Page(s): 1612 - 1624

19. Extension of the Fuzzy Integral for General Fuzzy Set-Valued Information
Author(s): Anderson, D.T. ; Havens, T.C. ; Wagner, C. ; Keller, J.M. ; Anderson, M.F. ; Wescott, D.J.
Page(s): 1625 - 1639

20. A Probabilistic Framework for Interval Type-2 Fuzzy Linguistic Summarization
Author(s): Boran, F.E. ; Akay, D. ; Yager, R.R.
Page(s): 1640 - 1653

21. On the Generalized Local Stability and Local Stabilization Conditions for Discrete-Time Takagi–Sugeno Fuzzy Systems
Author(s): Lee, D.H. ; Joo, Y.H.
Page(s): 1654 - 1668

22. Priorities of Intuitionistic Fuzzy Preference Relation Based on Multiplicative Consistency
Author(s): Liao, H. ; Xu, Z.
Page(s): 1669 - 1681

23. Backward Fuzzy Rule Interpolation
Author(s): Jin, S. ; Diao, R. ; Quek, C. ; Shen, Q.
Page(s): 1682 - 1698

Neural Networks Voume 61, Pages 1-118, January 2015

1. Neural Networks Referees in 2014
Pages: xi-xiii

2. Exciting Time for Neural Networks  
Pages: xv-xvi
Author(s): Kenji Doya, DeLiang Wang

NEURAL NETWORKS LETTERS

3. Dynamic analysis of periodic solution for high-order discrete-time Cohen–Grossberg neural networks with time delays  
Pages: 68-74
Author(s): Kaiyun Sun, Ancai Zhang, Jianlong Qiu, Xiangyong Chen, Chengdong Yang, Xiao Chen

REVIEWS

4. Trends in extreme learning machines: A review
Pages: 32-48
Author(s): Gao Huang, Guang-Bin Huang, Shiji Song, Keyou You

5. Deep learning in neural networks: An overview
Pages: 85-117
Author(s): Jürgen Schmidhuber

LEARNING SYSTEMS

6. An efficient sampling algorithm with adaptations for Bayesian variable selection
Pages: 22-31
Author(s): Takamitsu Araki, Kazushi Ikeda, Shotaro Akaho

7. A complex-valued neural dynamical optimization approach and its stability analysis
Pages: 59-67
Author(s): Songchuan Zhang, Youshen Xia, Weixing Zheng

8. Max–min distance nonnegative matrix factorization  
Pages: 75-84
Author(s): Jim Jing-Yan Wang, Xin Gao

MATHEMATICAL AND COMPUTATIONAL ANALYSIS

9. New synchronization criteria for memristor-based networks: Adaptive control and feedback control schemes
Pages: 1-9
Author(s): Ning Li, Jinde Cao

10. A one-layer recurrent neural network for constrained nonconvex optimization
Pages: 10-21
Author(s): Guocheng Li, Zheng Yan, Jun Wang

11. Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays
Pages: 49-58
Author(s): Guodong Zhang, Yi Shen, Quan Yin, Junwei Sun

IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Number 12, December 2014

1. Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis
Author(s): Liu, Z. ; Lai, G. ; Zhang, Y. ; Chen, X. ; Chen, C.L.P.
Page(s): 2129 - 2140

2. Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming
Author(s): Zhong, X. ; He, H. ; Zhang, H. ; Wang, Z.
Page(s): 2141 - 2155

3. A Novel Estimation Algorithm Based on Data and Low-Order Models for Virtual Unmodeled Dynamics
Author(s): Zhang, Y. ; Chai, T. ; Sun, J. ; Chen, X. ; Wang, H.
Page(s): 2156 - 2166

4. Structure-Constrained Low-Rank Representation
Author(s): Tang, K. ; Liu, R. ; Su, Z. ; Zhang, J.
Page(s): 2167 - 2179

5. Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems
Author(s): Wu, Z. ; Shi, P. ; Su, H. ; Chu, J.
Page(s): 2180 - 2190

6. Learning Regularized LDA by Clustering
Author(s): Pang, Y. ; Wang, S. ; Yuan, Y.
Page(s): 2191 - 2201

7. A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity
Author(s): Anguita, D. ; Ghio, A. ; Oneto, L. ; Ridella, S.
Page(s): 2202 - 2211

8. Learning Deep Hierarchical Visual Feature Coding
Author(s): Goh, H. ; Thome, N. ; Cord, M. ; Lim, J.
Page(s): 2212 - 2225

9. A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
Author(s): Cao, H. ; Tan, V.Y.F. ; Pang, J.Z.F.
Page(s): 2226 - 2239

10. Semi-supervised Domain Adaptation on Manifolds
Author(s): Cheng, L. ; Pan, S.J.
Page(s): 2240 - 2249

11. Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas
Author(s): Lee, J.H. ; Delbruck, T. ; Pfeiffer, M. ; Park, P.K.J. ; Shin, C. ; Ryu, H. ; Kang, B.C.
Page(s): 2250 - 2263

12. Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee
Author(s): Pan, Y. ; Yu, H. ; Er, M.J.
Page(s): 2264 - 2274

13. Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Author(s): Bi, W. ; Kwok, J.T.
Page(s): 2275 - 2287

14. Synchronization in an Array of Output-Coupled Boolean Networks With Time Delay
Author(s): Zhong, J. ; Lu, J. ; Liu, Y. ; Cao, J.
Page(s): 2288 - 2294

15. Hybrid Manifold Embedding
Author(s): Liu, Y. ; Liu, Y. ; Chan, K.C.C. ; Hua, K.A.
Page(s): 2295 - 2302

16. Learning Deep and Wide: A Spectral Method for Learning Deep Networks
Author(s): Shao, L. ; Wu, D. ; Li, X.
Page(s): 2303 - 2308

17. On the Additive Properties of the Fat-Shattering Dimension
Author(s): Asor, O. ; Duan, H.H. ; Kontorovich, A.
Page(s): 2309 - 2312

Reminder: paper submission deadline for CEC 2015

A reminder that the deadline for submitting papers to the IEEE Congress on Evolutionary Computation (IEEE CEC) 2015 is December 19, 2014. This conference will be held in Sendai, Japan, 25-28 May, 2015.

Reminder: conference paper deadline FUZZ-IEEE 2015

A reminder that the deadline for submitting papers to the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2015 is February 8, 2015. This conference will be held in Istanbul, Turkey, August 2-5, 2015.

IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Issue 11, November 2014

1. Online PLSA: Batch Updating Techniques Including Out-of-Vocabulary Words
Author(s): Nikoletta K. Bassiou; Constantine L. Kotropoulos
Page(s): 1953 - 1966

2. A Gaussian Process Model for Data Association and a Semidefinite Programming Solution
Author(s): Miguel Lazaro-Gredilla; Steven Van Vaerenbergh
Page(s): 1967 - 1979

3. Mahalanobis Distance on Extended Grassmann Manifolds for Variational Pattern Analysis
Author(s): Yoshikazu Washizawa; Seiji Hotta
Page(s): 1980 - 1990

4. Divisive Gaussian Processes for Nonstationary Regression
Author(s): Luis Munoz-Gonzalez; Miguel Lazaro-Gredilla; Anibal R. Figueiras-Vidal
Page(s): 1991 - 2003

5. Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
Author(s): Chenguang Yang; Zhijun Li; Rongxin Cui; Bugong Xu
Page(s): 2004 - 2016

6. Adaptive Neural Control of MIMO Nonlinear Systems With a Block-Triangular Pure-Feedback Control Structure
Author(s): Zhenfeng Chen; Shuzhi Sam Ge; Yun Zhang; Yanan Li
Page(s): 2017 - 2029

7. Single-Trial Classification of Event-Related Potentials in Rapid Serial Visual Presentation Tasks Using Supervised Spatial Filtering
Author(s): Hubert Cecotti; Miguel P. Eckstein; Barry Giesbrecht
Page(s): 2030 - 2042

8. Multiwavelet Packet Entropy and its Application in Transmission Line Fault Recognition and Classification
Author(s): Zhigang Liu; Zhiwei Han; Yang Zhang; Qiaoge Zhang
Page(s): 2043 - 2052

9. Confabulation-Inspired Association Rule Mining for Rare and Frequent Itemsets
Author(s): Azadeh Soltani; M.-R. Akbarzadeh-T.
Page(s): 2053 - 2064

10. Discriminant Locality Preserving Projections Based on L1-Norm Maximization
Author(s): Fujin Zhong; Jiashu Zhang; Defang Li
Page(s): 2065 - 2074

11. Ordinal Neural Networks Without Iterative Tuning
Author(s): Francisco Fernandez-Navarro; Annalisa Riccardi; Sante Carloni
Page(s): 2075 - 2085

12. Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy
Author(s): Cristiano Cervellera; Danilo Maccio
Page(s): 2086 - 2098

13. Passivity and Passification of Memristor-Based Recurrent Neural Networks With Time-Varying Delays
Author(s): Zhenyuan Guo; Jun Wang; Zheng Yan
Page(s): 2099 - 2109

14. Synchronization on Complex Networks of Networks
Author(s): Renquan Lu; Wenwu Yu; Jinhu Lv; Anke Xue
Page(s): 2110 - 2118

15. Real-Time Keypoint Recognition Using Restricted Boltzmann Machine
Author(s): Miaolong Yuan; Huajin Tang; Haizhou Li
Page(s): 2119 - 2126

Conference paper deadline: ICAISC 2015

The deadline for submitting papers to the 14th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2015 is November 20, 2014. This conference will be held in Zakopane, Poland, 14-18 June 2015.

Neural Networks Volume 60, Pages: 1-246, December 2014

Cognitive Science

1. How active perception and attractor dynamics shape perceptual categorization: A computational model  
Author(s): Nicola Catenacci Volpi, Jean Charles Quinton, Giovanni Pezzulo
Pages: 1-16

2. Connectionist interpretation of the association between cognitive dissonance and attention switching  
Author(s): Takao Matsumoto
Pages: 119-132

3. Neurocomputational approaches to modelling multisensory integration in the brain: A review  
Author(s): Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Pages: 141-165

4. Person-by-person prediction of intuitive economic choice  
Author(s): George Mengov
Pages: 232-245


Neuroscience

5. Global exponential almost periodicity of a delayed memristor-based neural networks  
Author(s): Jiejie Chen, Zhigang Zeng, Ping Jiang
Pages: 33-43

6. Global robust asymptotic stability of variable-time impulsive BAM neural networks  
Author(s): Mustafa Şaylı, Enes Yılmaz
Pages: 67-73

7. Noise cancellation of memristive neural networks  
Author(s): Shiping Wen, Zhigang Zeng, Tingwen Huang, Xinghuo Yu
Pages: 74-83

8. Stability and bifurcation analysis of new coupled repressilators in genetic regulatory networks with delays  
Author(s): Guang Ling, Zhi-Hong Guan, Ding-Xin He, Rui-Quan Liao, Xian-He Zhang
Pages: 222-231


Learning Systems

9. Simple randomized algorithms for online learning with kernels  
Author(s): Wenwu He, James T. Kwok
Pages: 17-24

10. New approximation method for smooth error backpropagation in a quantron network  
Author(s): Simon de Montigny
Pages: 84-95

11. Unsupervised learnable neuron model with nonlinear interaction on dendrites  
Pages: 96-103
Author(s): Yuki Todo, Hiroki Tamura, Kazuya Yamashita, Zheng Tang

12. A convolutional recursive modified Self Organizing Map for handwritten digits recognition  
Author(s): Ehsan Mohebi, Adil Bagirov
Pages: 104-118

13. Logarithmic learning for generalized classifier neural network  
Author(s): Buse Melis Ozyildirim, Mutlu Avci
Pages: 133-140

14. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)  
Author(s): Wei Huang, Sung-Kwun Oh, Witold Pedrycz
Pages: 166-181

15. On extending the complex FastICA algorithms to noisy data  
Author(s): Zongli Ruan, Liping Li, Guobing Qian
Pages: 194-202

16. Online computing of non-stationary distributions velocity fields by an accuracy controlled growing neural gas  
Author(s): Hervé Frezza-Buet
Pages: 203-221


Mathematical and Computational Analysis

17. Impulsive exponential synchronization of randomly coupled neural networks with Markovian jumping and mixed model-dependent time delays  
Author(s): Xin Wang, Chuandong Li, Tingwen Huang, Ling Chen
Pages: 25-32

18. Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal  
Author(s): M. Alfaro-Ponce, A. Argüelles, I. Chairez
Pages: 53-66


Engineering and Applications

19. Dynamic neural network-based robust observers for uncertain nonlinear systems  
Author(s): H.T. Dinh, R. Kamalapurkar, S. Bhasin, W.E. Dixon
Pages: 44-52

20. A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception  
Author(s): Johannes Mohr, Jong-Han Park, Klaus Obermayer
Pages: 182-193

Conference paper deadline: INNS Big Data 2015

The deadline for submitting papers to the INNS Conference on Big Data is March 22, 2015. This conference will be held in San Francisco, USA, 8-10 August, 2015.

Conference paper deadline: Evostar 2015

The paper submission deadline for Evostar 2015 is 15 November, 2014. This conference will be held in Copenhagen, Denmark, 8-10 April, 2015.

Reminder: paper submission deadline: IJCNN 2015

A reminder that the paper submission deadline for the International Joint Conference on Neural Networks (IJCNN) 2015 is January 15, 2015. This conference will be held in Killarney, Ireland, July 12-17, 2015.

IEEE Computational Intelligence Magazine Special Issue on "Computational Intelligence for Brain Computer Interfaces"

Aims and Scope

Brain Computer Interfaces (BCI) aims at establishing a one or two-way communication protocol between the human brain and an electronic device. The research umbrella of BCI has different names and overlaps with different research areas that evolved under the wider objective of connecting human data to an electronic device of some sort. Some of these areas include: adaptive automation, augmented cognition, brain-machine interface, human-machine symbiosis, and human-computer symbiosis.

The last decade has witnessed a rise in the number of researchers working on BCI. With the advances of sensor technologies, efficient signal processing algorithms, and parallel computing, it was possible to finally realize the dream of many researchers who talked about the concept in one form or another in the sixties and seventies including J.C.R. Licklider, R.B. Rouse, and others. Different sensor and measurement technologies are evolving rapidly from the classical functional magnetic resonance imaging (fMRI), functional near infrared (fNIR), Electroencephalography (EEG), to complex integrated psycho-physiological sensor arrays.

Researchers in Computational Intelligence have been better situated than ever to extract knowledge from these signals, transform it to actionable decisions, and designing the intelligent machine that has long been promised and is now overdue. Success has been seen in many medical applications including assisting people on wheelchairs, stroke rehabilitation, and epileptic seizures. In the non-medical domain, BCI has been used for computer games, authentication in cyber security, and air traffic control.

This special issue aims at showcasing the most exciting and recent advances in BCI and related topics. The guest editors invite submissions of previously unpublished, recent and exciting research on BCI. The special issue welcomes survey, position, and research papers

Topics of Interest include:

• Adaptive control schemes for BCI
• Applications
• Augmented cognition and adaptive aiding using BCI
• Big data for brain mining
• Collaborative multi-humans BCI environments
• Computational intelligence applications for BCI
• Data and signal processing techniques for BCI applications
• Evolutionary algorithms for BCI
• Fusion of heterogeneous psycho-physiological sensors
• Fuzzy logic for BCI
• Neuroplasticity induced by brain-computer interactions
• Neural networks for BCI
• Novel sensor technologies for BCI
• Related computational intelligence methods for BCI
• Situation awareness systems for BCI applications
• Swarm techniques for BCI
• Other closely related topics on computational intelligence for BCI

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify on the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via https://www.easychair.org/conferences/?conf=ieeecimbci2016

Important Dates (for February 2016 Issue)

15th May, 2015: Submission of Manuscripts
15th July, 2015: Notification of Review Results
15th August, 2015: Submission of Revised Manuscripts
15th September, 2015: Submission of Final Manuscripts
February 2016: Special Issue Publication

Guest Editors

Hussein A. Abbass, The University of New South Wales, School of Engineering and Information Technology, Canberra, ACT 2600, Australia.
Cuntai Guan, Institute for Infocomm Research (I2R), 1 Fusionopolis Way, Fusionopolis, 138632, Singapore.
Kay Chen Tan, National University of Singapore, Department of Electrical and Computer Engineering, 4 Engineering Drive, 117583, Singapore.

IEEE TNNLS Special Issue on "Learning in Neuromorphic Systems and Cyborg Intelligence"

Emulating brain-like learning performance has been a key challenge for research in neural networks and learning systems, including recognition, memory and perception. In the last few decades, a variety of approaches for brain-like learning and information processing have been proposed, including approaches based on sparse representations or  hierarchical/deep architectures. While capable of achieving impressive performance, these methods still perform poorly compared to biological systems under a wide variety of conditions. With the availability of neuromorphic hardware providing a fundamentally different technique for data representation, neuromorphic systems, using neural spikes to represent the outputs of sensors and for communication between computing blocks, and using spike timing based learning algorithms, have shown appealing computing characteristics.  However, current neuromorphic learning systems cannot yet achieve the performance figures comparable to what machine learning approaches can offer. Neuromorphic systems are also compatible with another framework called cyborg intelligence. Cyborg intelligence aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via brain-machine interfaces, enhancing strengths and compensating for weaknesses by combining the biological cognition capability with the machine computational capability. In cyborg intelligence, the real-time interaction and exchange of information between biological and artificial neural systems is still an important open challenge, and existing learning approaches would not be able to meet such a challenge. The goal of the special issue is to consolidate the efforts for developing a suitable learning framework for neuromorphic systems and cyborg intelligence and promote research activities in this area.

Scope of the Special Issue

We invite original contributions related to learning in neuromorphic systems and cyborg intelligence, from theories, algorithms, modelling and experiment studies to applications. Topics include but are not limited to: 

• Cognitive computing and cyborg intelligence
• Neuromorphic information/signal processing
• Brain-inspired data representation models
• Neuromorphic learning and cognitive systems
• Co-learning in bio-machine systems
• Spike-based sensing and learning
• Neuromorphic sensors and hardware systems
• Intelligence for embedded systems
• Cognition mechanisms for big data
• Embodied cognition and neuro-robotics.

Important Dates

15 Nov 2014 – Deadline for manuscript submission
15 Feb 2015 – Notification of authors
15 Apr 2015–  Deadline for submission of revised manuscripts
15 May 2015 – Final decision

Guest Editors

Zhaohui Wu, Zhejiang University, China (wzh@zju.edu.cn)
Ryad Benosman, University of Pierre and Marie Curie, France (ryad.benosman@upmc.fr)
Huajin Tang, Institute for Infocomm Research, Singapore and Sichuan University  (huajin.tang@ieee.org)
Shih-Chii Liu, Institute of Neuroinformatics, University of Zurich and ETH Zurich (shih@ini.phys.ethz.ch)

Submission Instructions

1. Read the information for Authors at http://cis.ieee.org/tnnls
2. Submit the manuscript by 15th Nov 2014 at the TNNLS webpage (http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript has been submitted to the special issue on Learning in Neuromorphic Systems and Cyborg Intelligence. Send also an email to the guest editors with subject “TNNLS special issue submission” to notify about your submission.

CFP: Special Issue IEEE Computational Intelligence Magazine on "Computational Intelligence for Changing Environments"

Aims and Scope

Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling.

To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments.

It is possible to use human-level processing power to tackle concept drift in changing enviroments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena.

However, a main outstanding issue in using computational intelligence for changing enviroments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing enviroments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments.

Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing enviroments algorithms and architectures, theoretical foundations, and representations, and their application to real-world problems. We also welcome timely surveys and review papers.

Topics of Interest include (but are not limited to):

• Computational intelligence methodologies and implementation for changing environments
• Transfer learning
• Multitask learning
• Domain adaption
• Incremental Learning architectures
• Incremental Unsupervised and semi-supervised learning architectures
• Incremental Incremental Representation learning and disentangling
• Incremental Knowledge augmentation
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering
• Incremental Neural regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

 Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via

https://www.easychair.org/conferences/?conf=ieeecimcdbil2015.

Important Dates (for August 2015 Issue)

15th November, 2014: Submission of Manuscripts
15th January, 2015: Notification of Review Results
15th Faburary, 2015: Submission of Revised Manuscripts
15th March, 2015: Submission of Final Manuscripts
August 2015: Publication

Guest Editors

Professor Amir Hussain,
University of Stirling,
Stirling FK9 4LA SCOTLAND, UK
Email: ahu@cs.stir.ac.uk

Professor Dacheng Tao,
University of Technology, Sydney
235 Jones Street, Ultimo, NSW 2007, Australia
Email: dacheng.tao@uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor, ON, Canada
Email: jwu@uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail: dongbin.zhao@gmail.com