Neural Networks Volume 66, Pages 1-138, June 2015

1) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks
Pages: 1-10
Author(s): Huaqing Li, Xiaofeng Liao, Guo Chen, David J. Hill, Zhaoyang Dong, Tingwen Huang

2) Multilingual part-of-speech tagging with weightless neural networks 
Pages: 11-21
Author(s): Hugo C.C. Carneiro, Felipe M.G. Franca, Priscila M.V. Lima

3) Hierarchical neural networks perform both serial and parallel processing
Pages: 22-35
Author(s): Elena Agliari, Adriano Barra, Andrea Galluzzi, Francesco Guerra, Daniele Tantari, Flavia Tavani

4) Diversifying customer review rankings
Pages: 36-45
Author(s): Ralf Krestel, Nima Dokoohaki

5) Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays  
Pages: 46-63
Author(s): R. Rakkiyappan, N. Sakthivel, Jinde Cao

6) Multi-frame image super resolution based on sparse coding  
Pages: 64-78
Author(s): Toshiyuki Kato, Hideitsu Hino, Noboru Murata

7) A digital implementation of neuron–astrocyte interaction for neuromorphic applications 
Pages: 79-90
Author(s): Soheila Nazari, Karim Faez, Mahmood Amiri, Ehsan Karami

8) Asynchronous event-based corner detection and matching  
Pages: 91-106
Author(s): Xavier Clady, Sio-Hoi Ieng, Ryad Benosman

9) Phase synchronization of coupled bursting neurons and the generalized Kuramoto model  
Pages: 107-118
Author(s): F.A.S. Ferrari, R.L. Viana, S.R. Lopes, R. Stoop

10) Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays  
Pages: 119-130
Author(s): Jin Hu, Jun Wang

11) A new delay-independent condition for global robust stability of neural networks with time delays
Pages: 131-137
Author(s): Ruya Samli

Reminder: paper submission deadline for ICSI3 2015

A reminder that the deadline for submitting papers to the International Congress on Systems Immunology, Immunoinformatics & Immune-Computation (ICSI3) 2015 is 28 April, 2015. This conference will be held in Taormina, Italy, 17-18 July, 2015.

Paper submission deadline: ISNN 2015

The paper submission deadline for the 12th International Symposium on Neural Networks (ISNN) 2015 is May 15, 2015. This conference will be held in Jeju, Korea, 15-18 October, 2015.

Conference paper deadline: TAAI 2015

The deadline for submitting papers to the 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2015) is July 12, 2015. This conference will be held in Tainan, Taiwan, November 20-22, 2015.

Conference paper deadline: BESC 2015

The paper submission deadline for Behavioral, Economic and Socio-Cultural Computing (BESC) 2015 is 17 June, 2015. This conference will be held in Nanjing, China, 30 October - 1 November, 2015.

Paper submission deadline: IEEE SSCI 2015

The deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (IEEE SSCI) 2015 is June 14, 2015. This conference will be held in Cape Town, South Africa, December 7-10, 2015.

Conference submission deadline SMAP 2015

The paper submission deadline for the 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) 2015 is June 12, 2015. This conference will be held in  Trento, Italy, November 5-6, 2015.

Conference paper deadline: DSAA'15

The paper submission deadline for the IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015 is 18 May, 2015. This conference will be held in Paris, France, 19-21 October, 2015.

Conference paper deadline: ICSI3 2015

The deadline for submitting papers to the International Congress on Systems Immunology, Immunoinformatics & Immune-Computation (ICSI3) 2015 is 28 April, 2015. This conference will be held in Taormina, Italy, 17-18 July, 2015.

Conference paper deadline: ICICIP 2015

The deadline for submitting paper to the Sixth International Conference on Intelligent Control and Information Processing (ICICIP) 2015 is June 15, 2015. This conference will be held in Wuhan, China, November 26-28, 2015.

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 4, April 2015

1. Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach
Authors: Yi-Fei Pu; Ji-Liu Zhou; Yi Zhang; Ni Zhang; Guo Huang; Patrick Siarry
Page(s): 653 - 662

2. Quaternion-Valued Echo State Networks
Authors: Yili Xia; Cyrus Jahanchahi; Danilo P. Mandic
Page(s): 663 - 673

3. Successive Overrelaxation for Laplacian Support Vector Machine
Authors: Zhiquan Qi; Yingjie Tian; Yong Shi
Page(s): 674 - 683

4. Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming
Authors: Biao Luo; Huai-Ning Wu; Han-Xiong Li
Page(s): 684 - 696

5. Convolutive Bounded Component Analysis Algorithms for Independent and Dependent Source Separation
Authors: Huseyin A. Inan; Alper T. Erdogan
Page(s): 697 - 708

6. Gaussian Kernel Width Optimization for Sparse Bayesian Learning
Authors: Yalda Mohsenzadeh; Hamid Sheikhzadeh
Page(s): 709 - 719

7. Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering
Authors: Siamak Mehrkanoon; Carlos Alzate; Raghvendra Mall; Rocco Langone; Johan A. K. Suykens
Page(s): 720 - 733

8. Impulsive Stabilization and Impulsive Synchronization of Discrete-Time Delayed Neural Networks
Authors: Wu-Hua Chen; Xiaomei Lu; Wei Xing Zheng
Page(s): 734 - 748

9. A Universal Concept Based on Cellular Neural Networks for Ultrafast and Flexible Solving of Differential Equations
Authors: Jean Chamberlain Chedjou; Kyandoghere Kyamakya
Page(s): 749 - 762

10. On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures
Authors: Eric Aislan Antonelo; Benjamin Schrauwen
Page(s): 763 - 780

11. Coupled Attribute Similarity Learning on Categorical Data
Authors: Can Wang; Xiangjun Dong; Fei Zhou; Longbing Cao; Chi-Hung Chi
Page(s): 781 - 797

12. Topology-Based Clustering Using Polar Self-Organizing Map
Authors: Lu Xu; Tommy W. S. Chow; Eden W. M. Ma
Page(s): 798 - 808

13. Robust Consensus Tracking Control for Multiagent Systems With Initial State Shifts, Disturbances, and Switching Topologies
Authors: Deyuan Meng; Yingmin Jia; Junping Du
Page(s): 809 - 824

14. $L_{1}$ -Norm Low-Rank Matrix Factorization by Variational Bayesian Method
Authors: Qian Zhao; Deyu Meng; Zongben Xu; Wangmeng Zuo; Yan Yan
Page(s): 825 - 839

15. Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization
Authors: Zheng Yan; Jun Wang
Page(s): 840 - 850

16. Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data
Authors: Ruizhuo Song; Frank Lewis; Qinglai Wei; Hua-Guang Zhang; Zhong-Ping Jiang; Dan Levine
Page(s): 851 - 865

17. Infinite Horizon Self-Learning Optimal Control of Nonaffine Discrete-Time Nonlinear Systems
Authors: Qinglai Wei; Derong Liu; Xiong Yang
Page(s): 866 - 879

18. A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance
Authors: Gregory Ditzler; Robi Polikar; Gail Rosen
Page(s): 880 - 886

IEEE Transactions on Autonomous Mental Development, Volume 7, Number 1, March 2015

1. Editorial IEEE Transactions on Autonomous Mental Development
Author(s): Cangelosi, A
Page(s): 1 - 2

2. Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot
Author(s): Ognibene, D. ; Baldassare, G.
Page(s): 3 - 25

3. A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control
Author(s): Shim, V.A. ; Ranjit, C.S.N. ; Bo Tian ; Miaolong Yuan ; Huajin Tang
Page(s): 26 - 38

4. Mental States, EEG Manifestations, and Mentally Emulated Digital Circuits for Brain-Robot Interaction
Author(s): Bozinovski, S. ; Bozinovski, A.
Page(s): 39 - 51

5. Can Real-Time, Adaptive Human–Robot Motor Coordination Improve Humans’ Overall Perception of a Robot?
Author(s): Qiming Shen ; Dautenhahn, K. ; Saunders, J. ; Kose, H.
Page(s): 52 - 64

IEEE Transactions on Evolutionary Computation, Volume 19, Number 2, April 2015

1. Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming
Author(s): Ni, J. ; Rockett, P.
Page(s): 157 - 166

2. Parameter Control in Evolutionary Algorithms: Trends and Challenges
Author(s): Karafotias, G. ; Hoogendoorn, M. ; Eiben, A.E.
Page(s): 167 - 187

3. Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance
Author(s): Wang, P. ; Emmerich, M. ; Li, R. ; Tang, K. ; Back, T. ; Yao, X.
Page(s): 188 - 200

4. An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
Author(s): Zhang, X. ; Tian, Y. ; Cheng, R. ; Jin, Y.
Page(s): 201 - 213

5. Probabilistic Dominance in Multiobjective Reliability-Based Optimization: Theory and Implementation
Author(s): Filomeno Coelho, R.
Page(s): 214 - 224

6. Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method
Author(s): Tusar, T. ; Filipic, B.
Page(s): 225 - 245

7. Inducing Niching Behavior in Differential Evolution Through Local Information Sharing
Author(s): Biswas, S. ; Kundu, S. ; Das, S.
Page(s): 246 - 263

8. Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
Author(s): Ishibuchi, H. ; Akedo, N. ; Nojima, Y.
Page(s): 264 - 283

9. Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Author(s): Preen, R.J. ; Bull, L.
Page(s): 284 - 294

10. On the Easiest and Hardest Fitness Functions
Author(s): He, J. ; Chen, T. ; Yao, X.
Page(s): 295 - 305

IEEE Transactions on Computational Intelligence and AI in Games, Volume 7, Number 1, March 2015

Editorial
1. Editorial: IEEE Transactions on Computational Intelligence and AI in Games
Page(s): 1 - 2

Regular Papers
2. On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing
Author(s): Yang Gao ; Yan Chen ; Liu, K.J.R.
Page(s): 3 - 15

3. An Enhanced Solver for the Game of Amazons
Author(s): Jiaxing Song ; Muller, M.
Page(s): 16 - 27

4. Job-Level Alpha-Beta Search
Author(s): Jr-Chang Chen ; I-Chen Wu ; Wen-Jie Tseng ; Bo-Han Lin ; Chia-Hui Chang
Page(s): 28 - 38

5. Suspenser: A Story Generation System for Suspense
Author(s): Yun-Gyung Cheong ; Young, R.M.
Page(s): 39 - 52

6. Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents
Author(s): Ashlock, D. ; Brown, J.A. ; Hingston, P.
Page(s): 53 - 65

7. Design and Implementation of Chinese Dark Chess Programs
Author(s): Shi-Jim Yen ; Cheng-Wei Chou ; Jr-Chang Chen ; I-Chen Wu ; Kuo-Yuan Kao
Page(s): 66 - 74

8. Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning
Author(s): Ersen, M. ; Sariel, S.
Page(s): 75 - 87

9. Learning-Based Procedural Content Generation
Author(s): Roberts, J. ; Ke Chen
Page(s): 88 - 101

Short Papers
10. Sequential Halving Applied to Trees
Author(s): Cazenave, T.
Page(s): 102 - 105

Neural Networks, Volume 65 , Pages 1-126, May 2015

1. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning  
Pages: 1-17
Author(s): Yong Peng, Bao-Liang Lu, Suhang Wang

2. Computational cognitive models of spatial memory in navigation space: A review 
Pages: 18-43
Author(s): Tamas Madl, Ke Chen, Daniela Montaldi, Robert Trappl

3. Attention modeled as information in learning multisensory integration  
Pages: 44-52
Author(s): Johannes Bauer, Sven Magg, Stefan Wermter

4. A new class of multi-stable neural networks: Stability analysis and learning process  
Pages: 53-64
Author(s): E. Bavafaye Haghighi, G. Palm, M. Rahmati, M.J. Yazdanpanah

5. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays  
Pages: 65-79
Author(s): Xiaobing Nie, Wei Xing Zheng

6. Pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control  
Pages: 80-91
Author(s): Xinsong Yang, Jinde Cao, Jianlong Qiu

7. Robust L1-norm two-dimensional linear discriminant analysis  
Pages: 92-104
Author(s): Chun-Na Li, Yuan-Hai Shao, Nai-Yang Deng

8. New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations  
Pages: 105-114
Author(s): Zuowei Cai, Lihong Huang, Lingling Zhang

9. Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples 
Pages: 115-125
Author(s): Luca Oneto, Alessandro Ghio, Sandro Ridella, Davide Anguita

Measurement Theory

Just because something is expressed as a number, doesn't mean you can do arithmetic with it. Let's say I give you three numbers: 1,2,3. What's the mean of these numbers? (1+2+3)/3=2, right? Well, what if I told you that 1=apple, 2=pear and 3=banana? What's the mean of an apple, a pear and a banana? Plainly, the question is ridiculous. Yet there is still a substantial number of people in science, and in computational intelligence, who fall into this trap, especially when presenting data to models like neural networks.

Neural networks aren't magic: they can't tell what a number submitted to their input layers mean, they just multiply them by their connection weights, sum the products and apply a transformation function to them. So if the numbers you are submitting to them represent classes, rather than measurements, what are they really modelling?

Measurement is the most fundamental part of data collection, as all natural data originates as measurements of properties of events. Measurements should represent reality and relationships between measurements should reflect the relationships between attributes. This is most important is consideration is given to the principle that data represents reality: as the source of data, measurements must yield and adequate representation of reality.

Measurement theory, as originated by Stevens, helps us achieve this. By specifying and formalising what exactly measurement is, we can better use measurement to gather data. By understanding exactly what the numbers mean, we can better analyse and transform the data into information and knowledge, while avoiding such traps as making meaningless statements about the numbers or performing a meaningless transformation on the data. A crucial point to bear to in mind is that measurements represent reality but are not the same as reality.

Measurement Scales

At the heart of Steven’s measurement theory is the concept of measurement scales. Four such scales are defined (although other have been added since) where each scale is distinguished according to four characteristics:

• Distinctiveness: individuals are assigned different values if the property being measure is different.
• Ordering in magnitude: larger numbers represent greater quantities of the property being measured;
• Equal intervals: a difference in measurement represents the same difference in the property.
• Absolute zero: a measurement of zero represents an absence of the property being measured.

These four characteristics define the “strength” of the measurement scale. The scales, from “weakest” to “strongest” are:

• Nominal
• Ordinal
• Interval
• Ratio
• Absolute

Also associated with each of the measurement scales are specific, permissible statistics and transformations. The term permissible is slightly misleading: if a statistic is not permissible for a certain scale, it is not forbidden. Rather, the results of that statistic or transformation are not reliable, with the unreliability of the result determined by the way in which the measurements were made. Permissible statistics and transformations are simply those statistics and transformations that yield reliable results. A permissible statistic tells us something meaningful about the data, while a permissible transformation maintains the properties of the data as appropriate for the particular scale. A statistic may still be applied to data from a scale, for which that statistic is impermissible, and it may yield useful results, but these results need to be treated with caution, and interpreted within the context of the original measurements. Note also that permissible statistics and transformations are cumulative across scales, that is, all statistics and transformation permissible for a lower scale are permissible for a higher scale.

Nominal Scale

The nominal scale is the weakest of the measurement scales. It possesses only the characteristic of distinctiveness. In other words, if the same attribute of two individuals are assigned the same number, then the attributes are identical. No other conclusions may be drawn from those numbers, as they are simply arbitrary numeric labels. For example, the colours Red, Green, and Blue can be placed on the nominal scale with the measurements Red=1, Green=2, Blue=3. However, two reds do not make a green. They could just as easily be labelled Green=1, Blue=2, Red=3, or any other permutation, without altering their distinctiveness. The only permissible statistics for nominal scale measurements are the number of cases and the mode. Permissible transformations are permutations and one-to-one substitutions.

Ordinal Scale

Measurements on the ordinal scale have the properties of distinctiveness and ordering in magnitude. In other words, objects are ordered in the scale according to some pair-wise comparison. That is, measurements on this scale can be compared to one another with the equality, greater than or less than operators. However, while we can say that one measurement is greater than or less than another, we cannot say how different they are. Numbers in this scale are categories; they do not have the arithmetic properties of numbers. An example of an ordinal scale measurement is teaching evaluations: a teacher’s performance is evaluated by students over several variables, with the performance being rated from one to five, with one being “Poor” and five being “Excellent”. While it is meaningful to draw the conclusion that a score of four is better than a score of two, it is not meaningful to draw the conclusion that a score of four is twice as good as a score of two, nor is it meaningful to say that a score of five is the same “distance” from a score of three, as a score of three is from one. Permissible statistics introduced at the ordinal scale are medians and percentiles. Permissible transformations introduced are monotonic increasing functions, that is, any transformation that will maintain the order of the individuals.

Interval Scale

Measurements on the interval scale have the characteristics of distinctiveness, ordering in magnitude and equal intervals. In this scale, objects are placed in order on a number line with an arbitrary zero point and an arbitrary interval between objects. While the numerical values have no significance other than as labels, differences between the values do have meaning. An example of an interval scale is the date in years. The common era (CE) scale has an arbitrary zero point (set at the putative time of the birth of Christ) and equally sized intervals (the length of a year does not vary, excepting leap years, which actually make up for errors caused by the slight mismatch between the arbitrary length of the year set at 365 days and the actual length of the Earth’s orbit).  It is meaningful to say that 1973 is later than 1928, and that the difference between 1999 and 1973 is twice the difference between 1986 and 1973. It is not meaningful, however, to say that 2004 is twice the year that 1002 was. Permissible statistics introduced at the interval scale are the mean, standard deviation, rank-order correlation and product-moment correlation. Permissible transformations introduced are linear transformations of the format y=ax+b, where x is the measurement, and the constant a cannot be zero. In other words, permissible transformations are those transformations that preserve the order of the objects, and the relative intervals between them.

Ratio Scale

Measurements on the ratio scale have the characteristics of distinctiveness, ordering in magnitude, equal intervals and absolute zero. In this scale, objects are placed in order on a number line with equally sized intervals and a true zero point. A measurement of zero on the ratio scale indicates the absence of the property being measured. A ratio scale can also be defined as the differences between two interval measures: a difference of zero between two interval measurements indicates an absence of difference. In the ratio scale, the values themselves have significance, as do the differences and ratios of those values. Many properties in physics are ratio scale measurements. An example of this is speed. An object with a speed of zero isn’t moving, that is, it has no speed, while an object moving at fifty metres per second is twice as fast as an object moving at twenty-five metres per second.

Permissible statistics introduced at the ratio scale are the coefficient of variation, and permissible transformations are affine transformations, that is, y=ax.

Absolute Scale

Whereas measurements on the ratio scale have an absolute zero point, measurements on the absolute scale have and absolute zero and an absolute upper bound. The classical example of this is probabilities: the probability of an event can range from zero (the event will never happen) to one (the event will always happen). A probability of less than zero or greater than one is meaningless.

Only affine transformations are permissible for measurements on the absolute scale.

Transforming Between Scales

It is possible to transform a measurement made on a particular measurement scale to a weaker scale only. This transformation will involve a loss of information, and cannot be reversed. In other words, it is not possible to transform to a higher measurement scale. For example, consider the heights, in metres, of a group of three people. One person is 1.4 metres tall, the second is 1.8 metres tall, and the third is 2 metres tall. If we say that a person’s height is 1 if they are short, 2 if they are average and 3 if they are tall, then it is possible to transform these ratio scale measurements into the ordinal scale, by assigning the first person’s height a value of 1, the second a value of 2 and the third a value of 3. However, if we know only that a persons height is 2 on this scale, we cannot determine exactly what their true height is.

Summary

The major implication of this is that data must be collected with great care. Once a measurement is made on a particular measurement scale, it cannot be transformed into a higher scale. Once the measurement is made, no further information can be associated with it.

You must know which scale the measurements belong to, as they will determine what you can meaningfully do with the data. They will also inform as to how you represent the data for presentation to your models. A working knowledge of measurement theory, therefore, is essential for any serious practitioner in computational intelligence.

IEEE Transactions on Neural Networks and Learning Systems: Volume 26, Issue 3, March 2015

1. An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification
Authors: Mohammed Falah Mohammed; Chee Peng Lim
Page(s): 417 - 429

2. ML-TREE: A Tree-Structure-Based Approach to Multilabel Learning
Authors: Qingyao Wu; Yunming Ye; Haijun Zhang; Tommy W. S. Chow; Shen-Shyang Ho
Page(s): 430 - 443

3. Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Authors: Kai Zhang; Liang Lan; James T. Kwok; Slobodan Vucetic; Bahram Parvin
Page(s): 444 - 457

4. Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering
Authors: Hansenclever F. Bassani; Aluizio F. R. Araujo
Page(s): 458 - 471

5. Neural Network-Based Finite Horizon Stochastic Optimal Control Design for Nonlinear Networked Control Systems
Authors: Hao Xu; Sarangapani Jagannathan
Page(s): 472 - 485

6. Neural Network-Based Finite-Horizon Optimal Control of Uncertain Affine Nonlinear Discrete-Time Systems
Authors: Qiming Zhao; Hao Xu; Sarangapani Jagannathan
Page(s): 486 - 499

7. Maintaining the Integrity of Sources in Complex Learning Systems: Intraference and the Correlation Preserving Transform
Authors: Clive Cheong Took; Scott C. Douglas; Danilo P. Mandic
Page(s): 500 - 509

8. Exponential Synchronization of Complex Networks of Linear Systems and Nonlinear Oscillators: A Unified Analysis
Authors: Jiahu Qin; Huijun Gao; Wei Xing Zheng
Page(s): 510 - 521

9. Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes
Authors: Harold Soh; Yiannis Demiris
Page(s): 522 - 536

10. Bayesian Nonparametric Adaptive Control Using Gaussian Processes
Authors: Girish Chowdhary; Hassan A. Kingravi; Jonathan P. How; Patricio A. Vela
Page(s): 537 - 550

11. Generalization Performance of Radial Basis Function Networks
Authors: Yunwen Lei; Lixin Ding; Wensheng Zhang
Page(s): 551 - 564

12. Second-Order Global Consensus in Multiagent Networks With Random Directional Link Failure
Authors: Huaqing Li; Xiaofeng Liao; Tingwen Huang; Wei Zhu; Yanbing Liu
Page(s): 565 - 575

13. Novelty Detection Using Level Set Methods
Authors: Xuemei Ding; Yuhua Li; Ammar Belatreche; Liam P. Maguire
Page(s): 576 - 588

14. A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems
Authors: Achilles Theodorakopoulos; George A. Rovithakis
Page(s): 589 - 600

15. Convergence Analysis of the FOCUSS Algorithm
Authors: Kan Xie; Zhaoshui He; Andrzej Cichocki
Page(s): 601 - 613

16. GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming
Authors: Zhen Ni; Haibo He; Dongbin Zhao; Xin Xu; Danil V. Prokhorov
Page(s): 614 - 627

17. The Generalization Ability of Online SVM Classification Based on Markov Sampling
Authors: Jie Xu; Yuan Yan Tang; Bin Zou; Zongben Xu; Luoqing Li; Yang Lu
Page(s): 628 - 639

18. Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors
Authors: Jinpeng Yu; Peng Shi; Wenjie Dong; Bing Chen; Chong Lin
Page(s): 640 - 645

19. A Unified Approach to Universal Prediction: Generalized Upper and Lower Bounds
Authors: Nuri Denizcan Vanli; Suleyman S. Kozat
Page(s): 646 - 651

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.

Evolving Systems, Vol. 6, Issue 1

1. Evolving personalized modeling system for integrated feature, neighborhood and parameter optimization utilizing gravitational search algorithm
Author(s): Wen Liang, Yingjie Hu & Nikola Kasabov
Pages: 1-14

2. Comparative study of existing personalized approaches for identifying important gene markers and for risk estimation in Type2 Diabetes in Italian population
Author(s): Anju Verma, Maurizio Fiasché, Maria Cuzzola & Giuseppe Irrera
Pages: 15-22

3. Self-* programming: run-time parallel control search for reflection box
Author(s): Olga Brukman, Shlomi Dolev, Moshe Weinstock & Gera Weiss
Pages: 23-40

4. Immune optimization approach solving multi-objective chance-constrained programming
Author(s): Zhuhong Zhang, Lei Wang & Fei Long
Pages: 41-53

5. Enabling public verifiability and availability for secure data storage in cloud computing
Author(s): Rashmi M. Jogdand, R. H. Goudar, Gazal Begum Sayed & Pratik B. Dhamanekar
Pages: 55-65

6. Fuzzy hypotheses testing using fuzzy data and confidence interval in radar decision criteria
Author(s): Ahmed K. Elsherif, Chunming Tang & Lei Zhang
Pages: 67-74

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.

Neural Networks Volume 64, Pages 1-64, April 2015

Special Issue on “Deep Learning of Representations”
Edited by Yoshua Bengio and Honglak Lee

1. Editorial introduction to the Neural Networks special issue on Deep Learning of Representations  
Pages: 1-3
Author(s): Yoshua Bengio, Honglak Lee

2. Two-layer contractive encodings for learning stable nonlinear features 
Pages: 4-11
Author(s): Hannes Schulz, Kyunghyun Cho, Tapani Raiko, Sven Behnke

3. Measuring the usefulness of hidden units in Boltzmann machines with mutual information  
Pages: 12-18
Author(s): Mathias Berglund, Tapani Raiko, Kyunghyun Cho

4. Deep learning of support vector machines with class probability output networks 
Pages: 19-28
Author(s): Sangwook Kim, Zhibin Yu, Rhee Man Kil, Minho Lee

5. Expected energy-based restricted Boltzmann machine for classification  
Pages: 29-38
Author(s): S. Elfwing, E. Uchibe, K. Doya

6. Deep Convolutional Neural Networks for Large-scale Speech Tasks  
Pages: 39-48
Author(s): Tara N. Sainath, Brian Kingsbury, George Saon, Hagen Soltau, Abdel-rahman Mohamed, George Dahl, Bhuvana Ramabhadran

7. Frame-by-frame language identification in short utterances using deep neural networks  
Pages: 49-58
Author(s): Javier Gonzalez-Dominguez, Ignacio Lopez-Moreno, Pedro J. Moreno, Joaquin Gonzalez-Rodriguez

8. Challenges in representation learning: A report on three machine learning contests  
Pages: 59-63
Author(s): Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio

IEEE Transactions on Fuzzy Systems, Volume 23, Number 1, February 2015

1. Guest Editorial Web-Based Intelligent Support Systems—Preface to the Special Section
Author(s): Pedrycz, W.; Lu, J.
Page(s): 1-2

2. Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets
Author(s): Yao, J.; Azam, N.
Page(s): 3-15

3. A Web-based Decision Support Center for Electrical Energy Companies
Author(s): Kokshenev, I.; Parreiras, R.O.; Ekel, P.Y.; Alves, G.B.; Menicucci, S.V.
Page(s): 16 - 28

4. A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services
Author(s): Wu, D.; Zhang, G.; Lu, J.
Page(s): 29-43

5. Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data
Author(s): Ramos-Soto, A.; Bugarin, A.J.; Barro, S.; Taboada, J.
Page(s): 44-57

6. An Improved Direct Adaptive Fuzzy Controller of an Uncertain PMSM for Web-Based E-Service Systems
Author(s): Zhou, C.; Quach, D.; Xiong, N.; Huang, S.; Zhang, Q.; Yin, Q.; Vasilakos, A.V.
Page(s): 58-71

7. Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map
Author(s): Wei, X.; Luo, X.; Li, Q.; Zhang, J.; Xu, Z.
Page(s): 72-84

8. Observer-Biased Fuzzy Clustering
Author(s): Fazendeiro, P.; de Oliveira, J.V.
Page(s): 85-97

9. Theory of Generalized Fuzzy Discrete-Event Systems
Author(s): Nie, M.; Tan, W.W.
Page(s): 98-110

10. Fuzzy Rating Scale-Based Questionnaires and Their Statistical Analysis
Author(s): de la Rosa de Saa, S.; Gil, M.A.; Gonzalez-Rodriguez, G.; Lopez, M.T.; Lubiano, M.A.
Page(s): 111-126

11. Cauchy-Like Functional Equation Based on Continuous T-Conorms and Representable Uninorms
Author(s): Qin, F.
Page(s): 127-138

12. Fault Detection and Isolation for a Class of Uncertain State-Feedback Fuzzy Control Systems
Author(s): Yang, G.-H.; Wang, H.
Page(s): 139-151

13. Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming
Author(s): Zhang, H.; Zhang, J.; Yang, G.-H.; Luo, Y.
Page(s): 152-163

14. Fuzzy Logic for Adaptive Instruction in an E-learning Environment for Computer Programming
Author(s): Chrysafiadi, K.; Virvou, M.
Page(s): 164 - 177

15. Supervisory Control of Fuzzy Discrete-Event Systems for Simulation Equivalence
Author(s): Deng, W.; Qiu, D.
Page(s): 178-192

16. Adaptive Tracking Control for A Class of Nonlinear Systems With a Fuzzy Dead-Zone Input
Author(s): Liu, Z.; Wang, F.; Zhang, Y.; Chen, X.; Chen, C.L.P.
Page(s): 193-204

17. Adaptive Indirect Fuzzy Sliding Mode Controller for Networked Control Systems Subject to Time-Varying Network-Induced Time Delay
Author(s): Khanesar, M.A.; Kaynak, O.; Yin, S.; Gao, H.
Page(s): 205-214

18. Error-Compensated Marginal Linearization Method for Modeling a Fuzzy System
Author(s): Wang, D.-G.; Chen, C.L.P.; Song, W.-Y.; Li, H.-X.
Page(s): 215-222

19. A Novel Observer-Based Output Feedback Controller Design for Discrete-Time Fuzzy Systems
Author(s): Zhang, J.; Shi, P.; Qiu, J.; Nguang, S.K.
Page(s): 223-229

20. Comments on "Exact Output Regulation for Nonlinear Systems Described by Takagi–Sugeno Fuzzy Models"
Author(s): Robles, R.; Bernal, M.
Page(s): 230-233

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 2, February 2015

1. Targeting Accurate Object Extraction From an Image: A Comprehensive Study of Natural Image Matting
Author(s): Qingsong Zhu; Ling Shao; Xuelong Li; Lei Wang
Page(s): 185 - 207

2. Kernel Association for Classification and Prediction: A Survey
Author(s): Yuichi Motai
Page(s): 208 - 223

3. Modified Neural Dynamic Surface Approach to Output Feedback of MIMO Nonlinear Systems
Author(s): Guofa Sun; Dongwu Li; Xuemei Ren
Page(s): 224 - 236

4. Efficient $l_{1}$-Norm-Based Low-Rank Matrix Approximations for Large-Scale Problems Using Alternating Rectified Gradient Method
Author(s): Eunwoo Kim; Minsik Lee; Chong-Ho Choi; Nojun Kwak; Songhwai Oh
Page(s): 237 - 251

5. Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition Constraints
Author(s): Yahong Han; Yi Yang; Yan Yan; Zhigang Ma; Nicu Sebe; Xiaofang Zhou
Page(s): 252 - 264

6. A Scalable Projective Scaling Algorithm for $l_{p}$ Loss With Convex Penalizations
Author(s): Hongbo Zhou; Qiang Cheng
Page(s): 265 - 276

7. Scatter Balance: An Angle-Based Supervised Dimensionality Reduction
Author(s): Shenglan Liu; Lin Feng; Hong Qiao
Page(s): 277 - 289

8. Consensus in Continuous-Time Multiagent Systems Under Discontinuous Nonlinear Protocols
Author(s): Bo Liu; Wenlian Lu; Tianping Chen
Page(s): 290 - 301

9. Backstepping Fuzzy-Neural-Network Control Design for Hybrid Maglev Transportation System
Author(s): Rong-Jong Wai; Jing-Xiang Yao; Jeng-Dao Lee
Page(s): 302 - 317

10. Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection
Author(s): Yi Cao; Yuhua Li; Sonya Coleman; Ammar Belatreche; Thomas Martin McGinnity
Page(s): 318 - 330

11. Consensus-Based Distributed Cooperative Learning From Closed-Loop Neural Control Systems
Author(s): Weisheng Chen; Shaoyong Hua; Huaguang Zhang
Page(s): 331 - 345

12. MEC—A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems
Author(s): Dongbin Zhao; Yuanheng Zhu
Page(s): 346 - 356

13. Passivity of Switched Recurrent Neural Networks With Time-Varying Delays
Author(s): Jie Lian; Jun Wang
Page(s): 357 - 366

14. A Parametric Classification Rule Based on the Exponentially Embedded Family
Author(s): Bo Tang; Haibo He; Quan Ding; Steven Kay
Page(s): 367 - 377

15. VC-Dimension of Univariate Decision Trees
Author(s): Olcay Taner Yildiz
Page(s): 378 - 387

16. Delay-Based Reservoir Computing: Noise Effects in a Combined Analog and Digital Implementation
Author(s): Miguel C. Soriano; Silvia Ortin; Lars Keuninckx; Lennert Appeltant; Jan Danckaert; Luis Pesquera; Guy Van der Sande
Page(s): 388 - 393

17. Non-Divergence of Stochastic Discrete Time Algorithms for PCA Neural Networks
Author(s): Jian Cheng Lv; Zhang Yi; Yunxia Li
Page(s): 394 - 399

18. Asymmetric Mixture Model With Simultaneous Feature Selection and Model Detection
Author(s): Thanh Minh Nguyen; Q. M. Jonathan Wu; Hui Zhang
Page(s): 400 - 408

19. Optimal Codesign of Nonlinear Control Systems Based on a Modified Policy Iteration Method
Author(s): Yu Jiang; Yebin Wang; Scott A. Bortoff; Zhong-Ping Jiang
Page(s): 409 - 414

Neural Networks, Volume 63, Pages 1-292, March 2015

Cognitive Science

A neural network for learning the meaning of objects and words from a featural representation  
Pages: 234-253
Author(s): Mauro Ursino, Cristiano Cuppini, Elisa Magosso

Neuroscience

Circuit design and exponential stabilization of memristive neural networks  
Pages: 48-56
Author(s): Wen, Tingwen Huang, Zhigang Zeng, Yiran Chen, Peng Li

Learning Systems

Performance improvement of classifier fusion for batch samples based on upper integral  
Pages: 87-93
Author(s): Hui-Min Feng, Xi-Zhao Wang

Convex nonnegative matrix factorization with manifold regularization  
Pages: 94-103
Author(s): Wenjun Hu, Kup-Sze Choi, Peiliang Wang, Yunliang Jiang, Shitong Wang

Towards an intelligent framework for multimodal affective data analysis  
Pages: 104-116
Author(s): Soujanya Poria, Erik Cambria, Amir Hussain, Guang-Bin Huang

Approximate kernel competitive learning  
Pages: 117-132
Author(s): Jian-Sheng Wu, Wei-Shi Zheng, Jian-Huang Lai

A vector reconstruction based clustering algorithm particularly for large-scale text collection  
Pages: 141-155
Author(s): Ming Liu, Chong Wu, Lei Chen

Active learning for semi-supervised clustering based on locally linear propagation reconstruction  
Pages: 170-184
Author(s): Chin-Chun Chang, Po-Yi Lin

Adaptive learning rate of SpikeProp based on weight convergence analysis  
Pages: 185-198
Author(s): Sumit Bam Shrestha, Qing Song

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise  
Pages: 199-207
Author(s): Randa Herzallah

Self-organizing maps based on limit cycle attractors  
Pages: 208-222
Author(s): Di-Wei Huang, Rodolphe J. Gentili, James A. Reggia

Mathematical and Computational Analysis

Projective synchronization of fractional-order memristor-based neural networks  
Pages: 1-9
Author(s): Hai-Bo Bao, Jin-De Cao

Estimates on compressed neural networks regression  
Pages: 10-17
Author(s): Yongquan Zhang, Youmei Li, Jianyong Sun, Jiabing Ji

Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability  
Pages: 18-30
Author(s): Yonggui Kao, Lei Shi, Jing Xie, Hamid Reza Karimi

Jackson-type inequalities for spherical neural networks with doubling weights  
Pages: 57-65
Author(s): Shaobo Lin, Jinshan Zeng, Lin Xu, Zongben Xu

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction  
Pages: 66-78
Author(s): Vidya L., Vivekanand V., Shyamkumar U., Deepak Mishra

Finite-time synchronization control of a class of memristor-based recurrent neural networks  
Pages: 133-140
Author(s): Minghui Jiang, Shuangtao Wang, Jun Mei, Yanjun Shen

Dynamics of neural networks over undirected graphs  
Pages: 156-169
Author(s): Eric Goles, Gonzalo A. Ruz

Convergence and attractivity of memristor-based cellular neural networks with time delays  
Pages: 223-233
Author(s): Sitian Qin, Jun Wang, Xiaoping Xue

Massively parallel neural circuits for stereoscopic color vision: Encoding, decoding and identification  
Pages: 254-271
Author(s): Aurel A. Lazar, Yevgeniy B. Slutskiy, Yiyin Zhou

Neural network for constrained nonsmooth optimization using Tikhonov regularization  
Pages: 272-281
Author(s): Sitian Qin, Dejun Fan, Guangxi Wu, Lijun Zhao

Engineering and Applications

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise  
Pages: 31-47
Author(s): Najdan Vuković, Zoran Miljković

Fast Clustered Radial Basis Function Network as an adaptive predictive controller  
Pages: 79-86
Author(s): Dino Kosic

Designing a deep brain stimulator to suppress pathological neuronal synchrony  
Pages: 282-292
Author(s): Ghazal Montaseri, Mohammad Javad Yazdanpanah, Fariba Bahrami

IEEE Transactions on Evolutionary Computation, Volume 19, Number 1, February 2015

1. Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System
Author(s): Hu, W. ; Yen, G.G.
Page(s): 1 - 18

2. Characterization of the Performance of Memetic Algorithms for the Automation of Bone Tracking With Fluoroscopy
Author(s): Tersi, L. ; Fantozzi, S. ; Stagni, R.
Page(s): 19 - 30

3. Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator
Author(s): Guo, S. ; Yang, C.
Page(s): 31 - 49

4. A New Local Search-Based Multiobjective Optimization Algorithm
Author(s): Chen, B. ; Zeng, W. ; Lin, Y. ; Zhang, D.
Page(s): 50 - 73

5. Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content
Author(s): Munoz, M.A. ; Kirley, M. ; Halgamuge, S.K.
Page(s): 74 - 87

6. Learning Value Functions in Interactive Evolutionary Multiobjective Optimization
Author(s): Branke, J. ; Greco, S. ; Slowinski, R. ; Zielniewicz, P.
Page(s): 88 - 102

7. The Rolling Tide Evolutionary Algorithm: A Multiobjective Optimizer for Noisy Optimization Problems
Author(s): Fieldsend, J.E. ; Everson, R.M.
Page(s): 103 - 117

8. Optimizing Existing Software With Genetic Programming
Author(s): Langdon, W.B. ; Harman, M.
Page(s): 118 - 135

9. History-Based Topological Speciation for Multimodal Optimization
Author(s): Li, L. ; Tang, K.
Page(s): 136 - 150

10. Intelligent Bandwidth Estimation for Variable Bit Rate Traffic
Author(s): Khan, G.M. ; Arshad, R. ; Mahmud, S.A. ; Ullah, F.
Page(s): 151 - 155

CFP: IEEE CIM special issue on "Computational Intelligence for Brain Computer Interfaces"

IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence for Brain Computer Interfaces
http://www.husseinabbass.net/ieeecimbci2016.html
Hussein A. Abbass, Cuntai Guan, Kay Chen Tan

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

• 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

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

Professor Hussein Abbass
University of New South Wales
Canberra, Northcott Drive, ACT 2600, Australia
Email: h.abbass@adfa.edu.au

Professor Cuntai Guan
Institute for Infocomm Research (I2R),
1 Fusionopolis Way, Fusionopolis, Singapore 138632
Email: ctguan@i2r.a-star.edu.sg

Professor Kay Chen Tan
National University of Singapore
4 Engineering Drive, Singapore 117583
Email: eletankc@nus.edu.sg

CFP: Evolutionary Computation Journal (MIT Press) special issue on "Combinatorial Optimization Problems"

Special Issue in Evolutionary Computation Journal, MIT Press, on Combinatorial Optimization Problems

DESCRIPTION

Combinatorial Optimization Problems consist in finding an optimal solution (according to some objective function) from a finite search space. These problems arise in Industry and Academia and, unfortunately, most of them cannot be solved efficiently, that is, they are NP-hard and no polynomial time algorithm is known to solve them. For this reason, in the last decades researches have investigated the use of stochastic search algorithms to find near optimal solutions to these problems. In particular, a great research effort has been devoted to the development of metaheuristic algorithms to solve combinatorial optimization problems.

Successfully solved problems include scheduling, timetabling, network design, transportation and distribution problems, vehicle routing, travelling salesman, graph problems, satisfiability, energy optimization problems, packing problems and planning problems.

Prominent examples of metaheuristics include evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, ant colony optimization, particle swarm optimization, variable neighbourhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, hyperheuristics and hybrid algorithms.

We encourage authors to submit original high-quality research on the application of metaheuristic algorithms to combinatorial optimization problems or theoretical aspects of this application.

SUBMISSION GUIDELINES

All submissions have to be prepared according to the "guidelines for authors" as published in the journal website at http://ecj.fhv.at. Authors should submit their manuscripts to the Evolutionary Computation Editorial Manager at http://ecj.fhv.at . When submitting a paper, please send at the same time an email to Francisco Chicano (chicano@lcc.uma.es) and a copy to ecj@fhv.at mentioning the special issue, the paper title, and author list to inform about the submission.

TENTATIVE SCHEDULE

15 August 2015       submission deadline
15 December 2015     authors notification
15 March 2016        authors’ revisions
1 July 2016          final notification
15 July 2016         final manuscript
Winter 2016          tentative publication

GUEST EDITORS

Francisco Chicano
e-mail: chicano@lcc.uma.es
University of Malaga, Spain

Christian Blum
e-mail: christian.blum@ehu.es
University of the Basque Country, Spain

Gabriela Ochoa
e-mail: gabriela.ochoa@cs.stir.ac.uk
University of Stirling, Scotland, UK

CFP: IEEE TFS Special Issue on "Fuzzy Techniques in Financial Modelling and Simulation"

I. AIMS AND SCOPE

Computational intelligence has attracted a significant and increasing interest from the financial engineering and economics communities in recent years.  Computational systems capturing sentiments, preferences, behaviour and beliefs, are becoming indispensable in virtually all financial applications, from portfolio selection to proprietary trading, algorithmic trading, and risk management.  The bar has been raised with the revision of regulations, and the required compliance and risk management.  The new rules should be implemented through new processes and supported by developing new computational tools.

The fuzzy systems domain provides an armoury of techniques to address the challenges currently encountered in the financial engineering area.  Fuzzy logic can be used to effectively describe and incorporate financial experts’ and market participants’ intuition and behaviour, reaching beyond the capabilities of probabilistic models traditionally used in financial modelling.  In addition, fuzzy techniques can be used in conjunction with probabilistic models or with other machine learning techniques, such as evolutionary optimisation methods or neural networks, in order to better address the challenges raised in this area.

The objective of this special issue is to bring together the most recent advances in the design and application of fuzzy approaches to real problems in financial engineering.  A focus of interest is simulating scenarios at different level of granularity, as well as developing test environments for new financial and banking regulation, while accommodating behavioural aspects.

II. TOPICS COVERED

This special issue solicits original contributions on theoretical developments for financial modelling and simulations based on the following paradigms:

• fuzzy time series         
• fuzzy data mining
• fuzzy intelligent          
• fuzzy optimisation
• decision-making           
• fuzzy systems
• fuzzy granular             
• fuzzy-rough approaches
• computing                   
• evolving fuzzy systems
• neuro-fuzzy systems     
• support vector machines

Application papers of these paradigms to the following financial engineering areas are welcome:

• agent-based artificial financial markets
• financial-regulation test environments
• financial sentiment analysis, emotion mining
• financial scenarios modelling and simulation
• algorithmic trading       
• instruments pricing
• financial forecasting     
• risk management
• contagion analysis        
• systemic risk modelling
• portfolio optimization   
• trading strategies
• behavioural finance      
• finance big data analytics

III. IMPORTANT DATES

July 1, 2015: Submission deadline
Oct. 1, 2015: Notification of the first-round review
Nov. 1, 2015: Revised submission due
Dec. 15, 2015: Final notice of acceptance/reject

IV. SUBMISSION GUIDELINES

Manuscripts should be prepared according to the instruction of the “Information for Authors” section of the journal found and submission should be done through the IEEE TFS journal website: http://mc.manuscriptcentral.com/tfs-ieee Clearly mark “Special Issue on Fuzzy Techniques in Financial Modelling and Simulation” in your cover letter to the Editor-in-Chief. All submitted manuscripts will be reviewed using the standard procedure that is followed for regular submissions.

V. GUEST EDITORS

Ronald Yager
Machine Intelligence Institute
Iona College, USA
ryager@iona.edu

Antoaneta Serguieva
Financial Computing and Analytics Group
University College London, UK
a.serguieva@ucl.ac.uk

Vasile Palade
Faculty of Engineering and Computing
Coventry University, UK
vasile.palade@coventry.ac.uk

Hisao Ishibuchi
Computer Science and Intelligent Systems
Osaka Prefecture University, Japan
hisaoi@cs.osakafu-u.ac.jp

Neural Networks, Volume 62, Pages 1-118, February 2015

Communication and Brain  
Author(s): Yutaka Sakaguchi, Takeshi Aihara, Peter Ford Dominey, Ichiro Tsuda
Pages: 1-2

Mathematical Theory and Model

Mathematical modeling for evolution of heterogeneous modules in the brain  
Author(s): Yutaka Yamaguti, Ichiro Tsuda
Pages: 3-10

Self-organization of a recurrent network under ongoing synaptic plasticity  
Author(s): Takaaki Aoki
Pages: 11-19

Hodge–Kodaira decomposition of evolving neural networks  
Author(s): Keiji Miura, Takaaki Aoki
Pages: 20-24

Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs
Author(s): Tomoki Kurikawa, Kunihiko Kaneko
Pages: 25-31

Multistate network model for the pathfinding problem with a self-recovery property
Author(s): Kei-Ichi Ueda, Masaaki Yadome, Yasumasa Nishiura
Pages: 32-38

Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model  
Author(s): Alexander Woodward, Tom Froese, Takashi Ikegami
Pages: 39-46

Physiology, Neuroscience and Model

Phase shifts in alpha-frequency rhythm detected in electroencephalograms influence reaction time  
Author(s): Yasushi Naruse, Ken Takiyama, Masato Okada, Hiroaki Umehara, Yutaka Sakaguchi
Pages: 47-51

Spatial consistency of neural firing regulates long-range local field potential synchronization: A computational study  
Author(s): Naoyuki Sato
Pages: 52-61

Arm-use dependent lateralization of gamma and beta oscillations in primate medial motor areas  
Author(s): Ryosuke Hosaka, Toshi Nakajima, Kazuyuki Aihara, Yoko Yamaguchi, Hajime Mushiake
Pages: 62-66

Spatiotemporal patterns of current source density in the prefrontal cortex of a behaving monkey  
Author(s): Kazuhiro Sakamoto, Norihiko Kawaguchi, Kohei Yagi, Hajime Mushiake
Pages: 67-72

Computational model of visual hallucination in dementia with Lewy bodies  
Author(s): Hiromichi Tsukada, Hiroshi Fujii, Kazuyuki Aihara, Ichiro Tsuda
Pages: 73-82

Behavioral and System model

Immediate return preference emerged from a synaptic learning rule for return maximization  
Author(s): Yoshiya Yamaguchi, Takeshi Aihara, Yutaka Sakai
Pages: 83-90

A wavelet-based method for extracting intermittent discontinuities observed in human motor behavior  
Author(s): Yasuyuki Inoue, Yutaka Sakaguchi
Pages: 91-101

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation  
Author(s): Sylvain Mahé, Raphaël Braud, Philippe Gaussier, Mathias Quoy, Alexandre Pitti
Pages: 102-111

Communication, concepts and grounding  
Author(s): Frank van der Velde
Pages: 112-117

Call for Papers: IEEE 2015 International Conference on Data Science and Advanced Analytics

Preliminary Call for Papers: IEEE 2015 International Conference on Data Science and Advanced Analytics (DSAA 2015)

19-21 October, 2015, Paris, France

Website: http://dsaa2015.lip6.fr/

Important Dates

Paper Submission deadline: 18 May, 2015
Notification of acceptance: 6 July, 2015
Final Camera-ready papers due: 28 August, 2015

Publications

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 IEEE Intelligent Systems and WWWJ.

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.

Following the first successful edition held in 2014 in Shanghai, the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015) aims to provide 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 also technically sponsored by ACM through SIGKDD.

DSAA'2015 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 solicits then both theoretical and practical works on data science and advanced analytics.

Topics of Interest

General areas of interest to DSAA'2015 include but are not limited to:
1. Foundations

• New mathematical, probabilistic and statistical models and theories
• New machine learning theories, models and systems
• New knowledge discovery theories, models and systems
• Manifold and metric learning, deep learning
• Scalable analysis and learning
• Non-iidness learning
• Heterogeneous data/information integration
• Data pre-processing, sampling and reduction
• High dimensional data, feature selection and feature transformation
• Large scale optimization
• High performance computing for data analytics
• Architecture, management and process for data science

2. Data analytics, machine learning and knowledge discovery

• Learning for streaming data
• Learning for structured and relational data
• Intent and insight learning
• Mining multi-source and mixed-source information
• Mixed-type and structure data analytics
• Cross-media data analytics
• Big data visualization, modeling and analytics
• Multimedia/stream/text/visual analytics
• Relation, coupling, link and graph mining
• Personalization analytics and learning
• Web/online/social/network mining and learning
• Structure/group/community/network mining
• Cloud computing and service data analysis

3. Storage, retrieval and search

• Data warehouses, cloud architectures
• Large-scale databases
• Information and knowledge retrieval
• Information and knowledge retrieval
• Web/social/databases query and search
• Personalized search and recommendation
• Human-machine interaction and interfaces
• Crowdsourcing and collective intelligence

4. Privacy and security

• Security, trust and risk in big data
• Data integrity, matching and sharing
• Privacy and protection standards and policies
• Privacy preserving big data access/analytics
• Social impact

5. Applications, practices, tools and evaluation

• Best practices and lessons
• Data-intensive organizations, business and economy
• Domain-specific applications
• Business/government analytics
• Online/social/living/environment data analysis
• Mobile analytics for hand-held devices
• Quality assessment and interestingness metrics
• Complexity, efficiency and scalability
• Anomaly/fraud/exception/change/event/crisis analysis
• Large-scale recommender and search systems
• Big data representation and visualization
• Large scale application case studies

Organizing Committee

Honorary Chair
Usama Fayyad, Barclays Bank, UK

General Chairs
Longbing Cao, University of Technology Sydney, Australia
Eric Gaussier, University Joseph Fourier, France

Conference Chairs
Olivier Capp, Telecom Paristech, CNRS, France
Wei Wang, University of California at Los Angeles, USA

Research Track Chairs
Patrick Gallinari, University Pierre & Marie Curie, France
James Kwok, Hong Kong University of Science and Technology, China

Application Track Chairs
Gabriella Pasi, Universita degli Studi di Milano Biccoca, Italy
Osmar Zaiane, Univ. of Alberta, Canada

Call for Papers: 12th International Symposium on Neural Networks (ISNN2015)

Call for papers: 12th International Symposium on Neural Networks (ISNN2015)， October 15-18, 2015, Jeju, Korea

Sponsors and co-sponsor: The Chinese University of Hong Kong, Pusan National University

Technical co-sponsors: Asia Pacific Neural Network Assembly (pending), IEEE Computational Intelligence Society (pending), International Neural Network Society, and Korean Institute of Intelligent Systems

Website: http://isnn.mae.cuhk.edu.hk

Important Dates

Special session proposals deadline April 15, 2015
Paper submission deadline May 15, 2015
Notification of acceptance June 15, 2015
Camera-ready copy and author registration July 15, 2015

Following the successes of previous events, Twelfth International Symposium on Neural Networks (ISNN 2015) will be held in Jeju, Korea. Jeju's temperate climate, natural scenery, and beaches make it a popular tourist destination for South Koreans as well as visitors from other parts of East Asia. There are numerous popular tourist spots on the island, such as Cheonjeyeon Waterfalls, Mount Halla, Hyeobje Cave, and Hyeongje Island. In particular, Jeju Volcanic Island and Lava Tubes was listed by UNESCO as a World Natural Heritage. ISNN 2015 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.

Call for Papers and Special Sessions

Prospective authors are invited to contribute high-quality papers to ISNN 2015. In addition, proposals for special sessions within the technical scopes of the symposium are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Researchers interested in organizing special sessions are invited to submit formal proposals to ISNN 2015. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information of the organizers.

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.

Paper Submission

Authors are invited to submit full-length papers (10 pages maximum) by the submission deadline through the online submission system. Potential organizers are also invited to enlist five or more papers with cohesive topics to form special sessions. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality, and clarity. The authors of accepted papers will have an opportunity to revise their papers and take consideration of the referees' comments and suggestions. Papers presented at ISNN 2015 will be published in the EI-indexed proceedings in the Springer LNCS series and selected good papers will be included in special issues of several SCI journals.

Organizers:

General Chair
Jun Wang, The Chinese University of Hong Kong, Hong Kong

Steering Chair
Derong Liu, Chinese Academy of Sciences, Beijing, China and University of Illinois - Chicago, USA

Organizing Committee Chairs
Chengan Guo, Dalian University of Technology, Dalian, China
Sungshin Kim, Pusan National University, Busan, Korea
Zhigang Zeng, Huazhong University of Science and Technology, Wuhan, China

Program Chairs
Xiaolin Hu, Tsinghua University, Beijing, China
Yousheng Xia, Fuzhou University, Fuzhou, China
Yunong Zhang, Sun Yet-sen University, Guangzhou, China
Dongbin Zhao, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Special Sessions Chairs
Sanqing Hu, Hangzhou Dianzi University, Hangzhou, China
Kwang Baek Kim, Silla University, Busan, Korea
Tieshan Li, Dalian Maritime University, Dalian, China

Publicity Chairs
Yuanqing Li, South China University of Technology, Guangzhou, China
Yi Shen, Huazhong University of Science and Technology, Wuhan, China
Zhang Yi, Sichuan University, Chengdu, China

Publications Chairs
Jianchao Fan, National Marine Environmental Monitoring Center, Dalian, China
Jin Hu, Chongqing Jiaotong University, Chongqing, China
Zheng Yan, Huawei Shannon Laboratory, Beijing, China

Registration Chairs
Shenshen Gu, Shanghai University, Shanghai, China
Qingshan Liu, Huazhong University of Science and Technology, Wuhan, China

Secretariat
Xinyi Le, The Chinese University of Hong Kong, Hong Kong

Webmaster
Shaofu Yang, The Chinese University of Hong Kong, Hong Kong