Monday, March 16, 2015
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
Tuesday, March 10, 2015
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.
These four characteristics define the “strength” of the measurement scale. The scales, from “weakest” to “strongest” are:
Permissible statistics introduced at the ratio scale are the coefficient of variation, and permissible transformations are affine transformations, that is, y=ax.
Only affine transformations are permissible for measurements on the absolute scale.
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.
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
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.
Tuesday, March 3, 2015
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
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
Monday, March 2, 2015
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.
Thursday, February 26, 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
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
Monday, February 23, 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.
Thursday, February 5, 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
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
Wednesday, February 4, 2015
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
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
Tuesday, February 3, 2015
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
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
Sunday, February 1, 2015
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 representationPages: 234-253
Author(s): Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Neuroscience
Circuit design and exponential stabilization of memristive neural networksPages: 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 integralPages: 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 networksPages: 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 noisePages: 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
Friday, January 30, 2015
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
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
Wednesday, January 28, 2015
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
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
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
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
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=ieeecimbci2016Important Dates
15th May, 2015: Submission of Manuscripts15th 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 AbbassUniversity 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
Tuesday, January 27, 2015
CFP: Evolutionary Computation Journal (MIT Press) special issue on "Combinatorial Optimization Problems"
Special Issue in Evolutionary Computation Journal, MIT Press, on 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.
15 December 2015 authors notification
15 March 2016 authors’ revisions
1 July 2016 final notification
15 July 2016 final manuscript
Winter 2016 tentative publication
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
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 deadline15 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 Chicanoe-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
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