1. Error Surface of Recurrent Neural Networks
Author(s): Phan, M.C. ; Hagan, M.T.
Pages: 1709-1721
2. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram
Author(s): Tengtrairat, N. ; Gao, B. ; Woo, W.L. ; Dlay, S.S.
Pages: 1722-1735
3. On the SVMpath Singularity
Author(s): Dai, J. ; Chang, C. ; Mai, F. ; Zhao, D. ; Xu, W.
Pages: 1736-1748
4. Multistability of Two Kinds of Recurrent Neural Networks With Activation Functions Symmetrical About the Origin on the Phase Plane
Author(s): Zeng, Z. ; Zheng, W.X.
Pages: 1749-1762
5. Safety-Aware Semi-Supervised Classification
Author(s): Wang, Y. ; Chen, S.
Pages: 1763-1772
6. Neural Network Approaches for Noisy Language Modeling
Author(s): Li, J. ; Ouazzane, K. ; Kazemian, H.B. ; Afzal, M.S.
Pages: 1773-1784
7. A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction
Author(s): Zhang, L. ; Li, K. ; He, H. ; Irwin, G.W.
Pages: 1785-1798
8. Finding Potential Support Vectors in Separable Classification Problems
Author(s): Varagnolo, D. ; Del Favero, S. ; Dinuzzo, F. ; Schenato, L. ; Pillonetto, G.
Pages: 1799-1813
9. Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks
Author(s): Fu, Z.-J. ; Xie, W.-F. ; Han, X. ; Luo, W.-D.
Pages: 1814-1823
10. Hierarchical Similarity Transformations Between Gaussian Mixtures
Author(s): Rigas, G. ; Nikou, C. ; Goletsis, Y. ; Fotiadis, D.I.
Pages: 1824-1835
11. Negative Correlation Ensemble Learning for Ordinal Regression
Author(s): Fernandez-Navarro, F. ; Gutierrez, P.A. ; Hervas-Martinez, C. ; Yao, X.
Pages: 1836-1849
12. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Author(s): Fan, W. ; Bouguila, N.
Pages: 1850-1862
13. Transfer Ordinal Label Learning
Author(s): Seah, C.-W. ; Tsang, I.W. ; Ong, Y.-S.
Pages: 1863-1876
14. Pseudo-Orthogonalization of Memory Patterns for Associative Memory
Author(s): Oku, M. ; Makino, T. ; Aihara, K.
Pages: 1877-1887
15. Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
Author(s): Ferng, C.-S. ; Lin, H.-T.
Pages: 1888-1900
16. Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation
Author(s): Gonzalez, P. ; Alvarez, E. ; Barranquero, J. ; Diez, J. ; Gonzalez-Quiros, R. ; Nogueira, E. ; Lopez-Urrutia, A. ; del Coz, J.J.
Pages: 1901-1905
17. Corrections to: “Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay”
Author(s): Zhang, D. ; Yu, L. ; Wang, Q.-G. ; Ong, C.
Pages: 1906
Friday, November 1, 2013
Tuesday, October 29, 2013
Prioritizing insect pests with Kohonen SOM
My research interests and activities are split between two fields: computational intelligence (obviously) and ecological modelling. Although I got into ecological modelling via computational intelligence, many of my recent publications in ecological modelling haven't had anything to do with computational intelligence. An exception to this is a recently published paper in the journal Neobiota that I am a coauthor of: "Prioritizing the risk of plant pests by clustering methods: self-organising maps, k-means and hierarchical clustering".
The problem is this: given the species that are known to exist in various geo-political regions of the world, what is the likelihood of one of those species establishing in a region where it is not already present? Species presences and absences are represented by binary vectors, where each region has a vector, a one represents a presence of a particular species in that region, and a zero represents an absence in that region. By clustering the assemblage vectors using a SOM, it is possible to infer which species pose the greatest threat to any particular region.
The rationale behind this approach is that regions that have similar species assemblages are likely to have similar environments. So if several assemblages end up in the same cluster, and a species is present in many of those regions but absent in others, then that species is likely to become established in the regions from which it is absent.
In this work the SOM were used as data clustering algorithms, with the vector quantisation abilities of the SOM being largely underutilized. My own contribution to the work was the realisation that the SOM were being used to cluster data, and hence to test the approach against the much-faster k-means clustering algorithm. I found that k-means is just as effective at producing good clusters as the SOM, and is much faster.
There are some problems with this work as well: it is virtually impossible to determine which approach is better without testing data. Which means that if you are clustering a set of species assemblages, you also need some more up-to-date data to validate the predictions. I do have some thoughts on getting around this, which I am currently investigating.
The problem is this: given the species that are known to exist in various geo-political regions of the world, what is the likelihood of one of those species establishing in a region where it is not already present? Species presences and absences are represented by binary vectors, where each region has a vector, a one represents a presence of a particular species in that region, and a zero represents an absence in that region. By clustering the assemblage vectors using a SOM, it is possible to infer which species pose the greatest threat to any particular region.
The rationale behind this approach is that regions that have similar species assemblages are likely to have similar environments. So if several assemblages end up in the same cluster, and a species is present in many of those regions but absent in others, then that species is likely to become established in the regions from which it is absent.
In this work the SOM were used as data clustering algorithms, with the vector quantisation abilities of the SOM being largely underutilized. My own contribution to the work was the realisation that the SOM were being used to cluster data, and hence to test the approach against the much-faster k-means clustering algorithm. I found that k-means is just as effective at producing good clusters as the SOM, and is much faster.
There are some problems with this work as well: it is virtually impossible to determine which approach is better without testing data. Which means that if you are clustering a set of species assemblages, you also need some more up-to-date data to validate the predictions. I do have some thoughts on getting around this, which I am currently investigating.
Friday, October 25, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Wednesday, October 23, 2013
Conference paper deadline: KES 2014
The deadline for submitting papers to the 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2014 is 15 March, 2014. This conference will be held in Gdynia, Poland, 15-17 September, 2014.
Tuesday, October 22, 2013
Conference paper deadline: EAIS 2014
The deadline for submitting papers to the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) 2014 is January 15, 2014. This conference will be held in Linz, Austria, June 2-4, 2014.
Monday, October 21, 2013
Reminder: paper submission deadline: ICAISC 2014
A reminder that the deadline for submitting papers to the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2014 is November 20, 2013 (beware the front page linked to has embedded folk-music playing). This conference will be held in Zakopane, Poland, June 1-5 2014.
Friday, October 18, 2013
Video: How to publish your research in IEEE CIS publications
A panel discussion on how to publish in the IEEE Computational Intelligence Society's publications. This discussion wraps up the individual talks by Professors Xin Yao, Kay Chen Tan, Derong Liu, Garrison Greenwood, Chin-Teng Lin and Simon M. Lucas . This panel session was part of the CEC 2013 conference.
Thursday, October 17, 2013
How to publish your research: Video of Professor Simon M. Lucas
Professor Simon M. Lucas talks about how to publish research in IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games. This talk was part of a panel session at CEC 2013 and mostly talks about what kind of papers are published in TCIAIG.
Wednesday, October 16, 2013
How to publish your research: Video of Professor Chin-Teng Lin
Professor Chin-Teng Lin, who is the editor-in-chief of IEEE Transactions on Fuzzy Systems, speaks about publishing in that journal. This talk was part of a panel discussion at the CEC 2013 conference. Some of the points he makes in this talk are applicable to publishing in most journals:
- Read existing papers, know the field
- Present an issue of significance
- Choose a journal that fits well with your research
- Use the correct format for that journal
- Have focus & vision, don't be too ambitious with your paper
- Write clearly
- Get pre-review, ask your colleagues to check your paper before submission
- Proofread!
- Be patient, reviews take time
Tuesday, October 15, 2013
How to publish your research: Video of Professor Garrison Greenwood
Professor Garrison Greenwood talks about how to publish your research in the IEEE Transactions on Evolutionary Computation. This talk was part of a panel session at the CEC 2013 conference.
The main points of this talk are:
The main points of this talk are:
- Establish the context of your research in the Related Research section following the introduction (as a side-note, in other fields this is the introduction: why does computer science insist of separating them?
- Use enough citations, and no more
- Write enough detail for a competent researcher to replicate your work, don't try to write a tutorial
- Read the instructions for authors
- Use good grammar and spelling
Monday, October 14, 2013
How to publish your research: Video of Professor Derong Liu
Professor Derong Liu talks about how to publish your research in IEEE Transactions on Neural Networks and Learning Systems at a panel session at the CEC 2013 conference. This talk gives a lot of information about the editorial and review process, as well as how to increase your chances of having a paper accepted and even how to get on the editorial board of the journal.
Friday, October 11, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Thursday, October 10, 2013
How to publish your research: Video of Professor Kay Chen Tan
A video of a talk by Professor Kay Chen Tan as part of a panel session at the CEC 2013 conference. In this video he describes the IEEE CIS Computational Intelligence Magazine.
Wednesday, October 9, 2013
How to publish your research: Video of Professor Xin Yao
This is a video of a talk given by Professor Xin Yao as part of a panel session at the CEC 2013 conference. A couple of the most salient points that I noticed:
1) If you want to publish your research, you must first do good research
2) Contact the editors of the journals you want to publish in before submitting
1) If you want to publish your research, you must first do good research
2) Contact the editors of the journals you want to publish in before submitting
Tuesday, October 8, 2013
IEEE Transactions on Computational Intelligence and AI in Games: Volume 5, Issue 3, September 2013
1. Monte Carlo Search Algorithm Discovery for Single-Player Games
Author(s): F. Maes, D. L. St-Pierre, and D. Ernst
Pages: 201-213
2. On Scalability, Generalization, and Hybridization of Coevolutionary Learning: A Case Study for Othello
Author(s): M. Szubert, W. Jaśkowski, and K. Krawiec
Pages: 214-226
3. Database-Driven Real-Time Heuristic Search in Video-Game Pathfinding
Author(s): R. Lawrence and V. Bulitko
Pages: 227-241
4. Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
Author(s): J. Li, G. Kendall, and A. V. Vasilakos
Pages: 242-250
5. An Efficient Approach to Solving Nonograms
Author(s): I.-C. Wu, D.-J. Sun, L.-P. Chen, K.-Y. Chen, C.-H. Kuo, H.-H. Kang, and H.-H. Lin
Pages: 251-264
6. Applicability of GPGPU Computing to Real-Time AI Solutions in Games
Author(s): W. Blewitt, G. Ushaw, and G. Morgan
Pages: 265-275
7. Crowdsourcing the Aesthetics of Platform Games
Author(s): N. Shaker, G. N. Yannakakis, and J. Togelius
Pages: 276-290
Author(s): F. Maes, D. L. St-Pierre, and D. Ernst
Pages: 201-213
2. On Scalability, Generalization, and Hybridization of Coevolutionary Learning: A Case Study for Othello
Author(s): M. Szubert, W. Jaśkowski, and K. Krawiec
Pages: 214-226
3. Database-Driven Real-Time Heuristic Search in Video-Game Pathfinding
Author(s): R. Lawrence and V. Bulitko
Pages: 227-241
4. Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
Author(s): J. Li, G. Kendall, and A. V. Vasilakos
Pages: 242-250
5. An Efficient Approach to Solving Nonograms
Author(s): I.-C. Wu, D.-J. Sun, L.-P. Chen, K.-Y. Chen, C.-H. Kuo, H.-H. Kang, and H.-H. Lin
Pages: 251-264
6. Applicability of GPGPU Computing to Real-Time AI Solutions in Games
Author(s): W. Blewitt, G. Ushaw, and G. Morgan
Pages: 265-275
7. Crowdsourcing the Aesthetics of Platform Games
Author(s): N. Shaker, G. N. Yannakakis, and J. Togelius
Pages: 276-290
Monday, October 7, 2013
IEEE Transactions on Autonomous Mental Development: Volume 5, Issue 3, 2013
1. GUEST EDITORIAL: Microdynamics of Interaction: Capturing and Modeling Infants’ Social Learning
Author(s): K. J. Rohlfing and G. O. Deák
Pages: 189-191
2. Mothers’ infant-directed gaze during object demonstration highlights action boundaries and goals
Author(s): R. J. Brand, E. Hollenbeck, and J. F. Kominsky
Pages: 192-201
3. From Action to Interaction: Infant Object Exploration and Mothers’ Contingent Responsiveness
Author(s): C. S. Tamis-LeMonda, Y. Kuchirko, and L. Tafuro
Pages: 202-209
4. Young Children’s Dialogical Actions: The Beginnings of Purposeful Intersubjectivity
Author(s): J. Rączaszek-Leonardi, I. Nomikou, and K.J.Rohlfing
Pages: 210-221
5. From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
Author(s): T. Cederborg and P.-Y. Oudeyer
Pages: 222-239
6. Supporting Early Vocabulary Development: What Sort of Responsiveness Matters?
Author(s): M. L. McGillion, J. S. Herbert, J. M. Pine, T. Keren-Portnoy, M. M. Vihman, and D. E. Matthews
Pages: 240-248
7. SEED Framework of Early Language Development: The Dynamic Coupling of Infant–Caregiver Perceiving and Acting Forms a Continuous Loop during Interaction
Author(s): P. Zukow-Goldring and N. d. V. Rader
Pages: 249-257
8. Methodological Considerations For Investigating the Microdynamics of Social Interaction Development
Author(s): K. de Barbaro, C. M. Johnson, D. Forster, and G. O. Deák
Pages: 258
Author(s): K. J. Rohlfing and G. O. Deák
Pages: 189-191
2. Mothers’ infant-directed gaze during object demonstration highlights action boundaries and goals
Author(s): R. J. Brand, E. Hollenbeck, and J. F. Kominsky
Pages: 192-201
3. From Action to Interaction: Infant Object Exploration and Mothers’ Contingent Responsiveness
Author(s): C. S. Tamis-LeMonda, Y. Kuchirko, and L. Tafuro
Pages: 202-209
4. Young Children’s Dialogical Actions: The Beginnings of Purposeful Intersubjectivity
Author(s): J. Rączaszek-Leonardi, I. Nomikou, and K.J.Rohlfing
Pages: 210-221
5. From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
Author(s): T. Cederborg and P.-Y. Oudeyer
Pages: 222-239
6. Supporting Early Vocabulary Development: What Sort of Responsiveness Matters?
Author(s): M. L. McGillion, J. S. Herbert, J. M. Pine, T. Keren-Portnoy, M. M. Vihman, and D. E. Matthews
Pages: 240-248
7. SEED Framework of Early Language Development: The Dynamic Coupling of Infant–Caregiver Perceiving and Acting Forms a Continuous Loop during Interaction
Author(s): P. Zukow-Goldring and N. d. V. Rader
Pages: 249-257
8. Methodological Considerations For Investigating the Microdynamics of Social Interaction Development
Author(s): K. de Barbaro, C. M. Johnson, D. Forster, and G. O. Deák
Pages: 258
Friday, October 4, 2013
IEEE Transactions on Evolutionary Computation: Volume 17, Issue 5, October 2013
1. On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization
Author(s): A. Y. S. Lam, V. O. K. Li, and J. Xu
Pages: 605-620
2. A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
Author(s): S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan
Pages: 621-639
3. An Evolutionary Negative-Correlation Framework for Robust Analog-Circuit Design Under Uncertain Faults
Author(s): M. Liu and J. He
Pages: 640-665
4. Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection
Author(s): A. Basak, S. Das, and K. C. Tan
Pages: 666-685
5. Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers
Author(s): A. R. Baig, W. Shahzad, and S. Khan
Pages: 686-704
6. An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
Author(s): M. Hu, T. Wu, and J. D. Weir
Pages: 705-720
7. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): S. Yang, M. Li, X. Liu, and J. Zheng
Pages: 721
Author(s): A. Y. S. Lam, V. O. K. Li, and J. Xu
Pages: 605-620
2. A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
Author(s): S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan
Pages: 621-639
3. An Evolutionary Negative-Correlation Framework for Robust Analog-Circuit Design Under Uncertain Faults
Author(s): M. Liu and J. He
Pages: 640-665
4. Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection
Author(s): A. Basak, S. Das, and K. C. Tan
Pages: 666-685
5. Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers
Author(s): A. R. Baig, W. Shahzad, and S. Khan
Pages: 686-704
6. An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
Author(s): M. Hu, T. Wu, and J. D. Weir
Pages: 705-720
7. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): S. Yang, M. Li, X. Liu, and J. Zheng
Pages: 721
Thursday, October 3, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 10, October 2013
1. Adaptive Optimal Control of Unknown Constrained-Input Systems Using Policy Iteration and Neural Networks
Author(s): H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani
Pages: 1513-1525
2. Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition
Author(s): V. G. Kaburlasos, S. E. Papadakis, and G. A. Papakostas
Pages: 1526-1538
3. Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons
Author(s): Q. Yu, H. Tang, K. C. Tan, and H. Li
Pages: 1539-1552
4. Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data
Author(s): R. Jenssen
Pages: 1553-1564
5. RBF-Based Technique for Statistical Demodulation of Pathological Tremor
Author(s): F. Gianfelici
Pages: 1565-1574
6. Automated Induction of Heterogeneous Proximity Measures for Supervised Spectral Embedding
Author(s): E. Rodriguez-Martinez, T. Mu, J. Jiang, and J. Y. Goulermas
Pages: 1575-1587
7. Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies
Author(s): J. Qin and C. Yu
Pages: 1588-1597
8. Learning Capability of Relaxed Greedy Algorithms
Author(s): S. Lin, Y. Rong, X. Sun, and Z. Xu
Pages: 1598-1608
9. Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection
Author(s): M. Tan, I. W. Tsang, and L. Wang
Pages: 1609-1622
10. Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
Author(s): R. Coop, A. Mishtal, and I. Arel
Pages: 1623-1634
11. SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems
Author(s): X.-X. Zhang, Y. Jiang, H.-X. Li, and S.-Y. Li
Pages: 1635-1647
12. Single Image Super-Resolution With Multiscale Similarity Learning
Author(s): K. Zhang, X. Gao, D. Tao, and X. Li
Pages: 1648-1659
13. Tracking Algorithms for Multiagent Systems
Author(s): D. Meng, Y. Jia, J. Du, and F. Yu
Pages: 1660-1676
14. A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Author(s): D. Wang and S. Tapan
Pages: 1677-1688
15. EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
Author(s): C.-T. Lin, S.-F. Tsai, and L.-W. Ko
Pages: 1689-1700
16. New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays
Author(s): G. Zhang and Y. Shen
Pages: 1701
Author(s): H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani
Pages: 1513-1525
2. Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition
Author(s): V. G. Kaburlasos, S. E. Papadakis, and G. A. Papakostas
Pages: 1526-1538
3. Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons
Author(s): Q. Yu, H. Tang, K. C. Tan, and H. Li
Pages: 1539-1552
4. Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data
Author(s): R. Jenssen
Pages: 1553-1564
5. RBF-Based Technique for Statistical Demodulation of Pathological Tremor
Author(s): F. Gianfelici
Pages: 1565-1574
6. Automated Induction of Heterogeneous Proximity Measures for Supervised Spectral Embedding
Author(s): E. Rodriguez-Martinez, T. Mu, J. Jiang, and J. Y. Goulermas
Pages: 1575-1587
7. Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies
Author(s): J. Qin and C. Yu
Pages: 1588-1597
8. Learning Capability of Relaxed Greedy Algorithms
Author(s): S. Lin, Y. Rong, X. Sun, and Z. Xu
Pages: 1598-1608
9. Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection
Author(s): M. Tan, I. W. Tsang, and L. Wang
Pages: 1609-1622
10. Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
Author(s): R. Coop, A. Mishtal, and I. Arel
Pages: 1623-1634
11. SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems
Author(s): X.-X. Zhang, Y. Jiang, H.-X. Li, and S.-Y. Li
Pages: 1635-1647
12. Single Image Super-Resolution With Multiscale Similarity Learning
Author(s): K. Zhang, X. Gao, D. Tao, and X. Li
Pages: 1648-1659
13. Tracking Algorithms for Multiagent Systems
Author(s): D. Meng, Y. Jia, J. Du, and F. Yu
Pages: 1660-1676
14. A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Author(s): D. Wang and S. Tapan
Pages: 1677-1688
15. EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
Author(s): C.-T. Lin, S.-F. Tsai, and L.-W. Ko
Pages: 1689-1700
16. New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays
Author(s): G. Zhang and Y. Shen
Pages: 1701
Tuesday, October 1, 2013
Reminder: paper submission deadline for IEEE CIFEr 2014
A reminder that the deadline for submitting papers to the IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr) 2014 is November 1, 2013. This conference will be held in London, UK, 27-28 March, 2014.
Tuesday, September 24, 2013
On the importance of a good supervisor
One day, a fox was walking through the forest when he met a rabbit sitting outside a rabbit hole reading a pile of papers. "What are you doing?" the fox asked the rabbit. The rabbit looked up at the fox and replied "I'm doing the literature review for my thesis. It's on the superiority of rabbits over foxes. Would you like to come inside and discuss it?". The fox hungrily licked his lips, followed the rabbit into the rabbit hole, and was never seen again.
Some time later, a wolf was walking through the forest and saw the rabbit sitting outside of his rabbit hole making notes on a thick pile of paper with a big, red, pen. "What are you doing?" the wolf asked the rabbit. The rabbit looked up and replied "I'm revising my thesis". The wolf asked the rabbit "What's your thesis about?" and the rabbit said "It's on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The wolf hungrily licked his lips, followed the rabbit down the rabbit hole, and was never seen again.
Some time later, a hare was walking through the forest when he saw the rabbit sitting in the sun with a big, satisfied grin on this face. "Why are you looking so happy?" the hare asked the rabbit. The rabbit looked at the hare and said "I've just been awarded my PhD. My thesis was on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The hare, curious about such a topic, followed the rabbit down the rabbit hole into the warren. In one corner of the rabbit's room was a pile of fox bones. In another corner was a pile of wolf bones. Sitting between the two piles of bones was a lion.
So you see, it doesn't matter what your thesis is on, as long as your supervisor is a lion.
A newly published article (discussed in more detail by one of the authors here) has examined the influence of several factors that may determine how successful a scientist is in their career, where success is measured by the number of publications the scientist (biologists in this case) has. While factors such as gender and language had some slight effect, the factor that was most influential was the number of publications a scientist had before completing their PhD.
In other words, someone who has learned to produce papers before they finish their PhD is more likely to be able to continue producing papers after they have finished their PhD. To me this seems analagous to saying that someone who has learned how to drive can drive. Apparently, stating the blindingly obvious is original research as long as it uses statistics.
Who does a pre-PhD learn this paper-production skill from? Most of the time, from their supervisor. A supervisor who produces a lot of papers, and includes their students in the process of doing so, will produce PhD graduates who have the skills to produce papers post-PhD. If the supervisor doesn't teach the student how to publish, where else will they get this skill?
The most disturbing implication of this is that if a student chooses the wrong supervisor, they will have little chance of a successful career. The article linked to above states that the institution that the PhD graduates from has no influence on success and the influence of other factors is weak. As an aside, this reinforces something I've been saying for a while - that the reputation of an institution is good for marketing, but says little about the quality of the staff there.
The sentiment behind the story at the top of this post, is that as long as your supervisor is a good supervisor, you will be successful. This makes choosing the right supervisor probably the most critical decision an aspiring academic can ever make, yet they must make it when they have little knowledge and no experience on which to draw to make that decision. This is a huge problem - how many perfectly capable researchers have had their careers destroyed, before they have even begun, by a bad choice of supervisor? More importantly, how do those of us who are post-PhD stop it from happening in the future?
I really wish I had an answer to that question.
Some time later, a wolf was walking through the forest and saw the rabbit sitting outside of his rabbit hole making notes on a thick pile of paper with a big, red, pen. "What are you doing?" the wolf asked the rabbit. The rabbit looked up and replied "I'm revising my thesis". The wolf asked the rabbit "What's your thesis about?" and the rabbit said "It's on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The wolf hungrily licked his lips, followed the rabbit down the rabbit hole, and was never seen again.
Some time later, a hare was walking through the forest when he saw the rabbit sitting in the sun with a big, satisfied grin on this face. "Why are you looking so happy?" the hare asked the rabbit. The rabbit looked at the hare and said "I've just been awarded my PhD. My thesis was on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The hare, curious about such a topic, followed the rabbit down the rabbit hole into the warren. In one corner of the rabbit's room was a pile of fox bones. In another corner was a pile of wolf bones. Sitting between the two piles of bones was a lion.
So you see, it doesn't matter what your thesis is on, as long as your supervisor is a lion.
A newly published article (discussed in more detail by one of the authors here) has examined the influence of several factors that may determine how successful a scientist is in their career, where success is measured by the number of publications the scientist (biologists in this case) has. While factors such as gender and language had some slight effect, the factor that was most influential was the number of publications a scientist had before completing their PhD.
In other words, someone who has learned to produce papers before they finish their PhD is more likely to be able to continue producing papers after they have finished their PhD. To me this seems analagous to saying that someone who has learned how to drive can drive. Apparently, stating the blindingly obvious is original research as long as it uses statistics.
Who does a pre-PhD learn this paper-production skill from? Most of the time, from their supervisor. A supervisor who produces a lot of papers, and includes their students in the process of doing so, will produce PhD graduates who have the skills to produce papers post-PhD. If the supervisor doesn't teach the student how to publish, where else will they get this skill?
The most disturbing implication of this is that if a student chooses the wrong supervisor, they will have little chance of a successful career. The article linked to above states that the institution that the PhD graduates from has no influence on success and the influence of other factors is weak. As an aside, this reinforces something I've been saying for a while - that the reputation of an institution is good for marketing, but says little about the quality of the staff there.
The sentiment behind the story at the top of this post, is that as long as your supervisor is a good supervisor, you will be successful. This makes choosing the right supervisor probably the most critical decision an aspiring academic can ever make, yet they must make it when they have little knowledge and no experience on which to draw to make that decision. This is a huge problem - how many perfectly capable researchers have had their careers destroyed, before they have even begun, by a bad choice of supervisor? More importantly, how do those of us who are post-PhD stop it from happening in the future?
I really wish I had an answer to that question.
Tuesday, September 17, 2013
Neural Networks Volume 47 Pages 1-150
1. Computation in the Cerebellum
Author(s): Dieter Jaeger, Henrik Jorntell, Mitsuo Kawato
Pages: 1-2
2. The importance of stochastic signaling processes in the induction of long-term synaptic plasticity
Author(s): Erik De Schutter
Pages: 3-10
3. Dendritic calcium signaling in cerebellar Purkinje cell
Author(s): Kazuo Kitamura, Masanobu Kano
Pages: 11-17
4. Bistability in Purkinje neurons: Ups and downs in cerebellar research
Author(s): Jordan D.T. Engbers, Fernando R. Fernandez, Ray W. Turner
Pages: 18-31
5. Mechanisms producing time course of cerebellar long-term depression
Author(s): Taegon Kim, Keiko Tanaka-Yamamoto
Pages: 32-35
6. Cerebellar LTD vs. motor learning—Lessons learned from studying GluD2
Author(s): Michisuke Yuzaki
Pages: 36-41
7. Adaptive coupling of inferior olive neurons in cerebellar learning
Author(s): Isao T. Tokuda, Huu Hoang, Nicolas Schweighofer, Mitsuo Kawato
Pages: 42-50
8. Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation
Author(s): Miho Onizuka, Huu Hoang, Mitsuo Kawato, Isao T. Tokuda, Nicolas Schweighofer, Yuichi Katori, Kazuyuki Aihara, Eric J. Lang, Keisuke Toyama
Pages: 51-63
9. Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control
Author(s): Ivan Herreros, Paul F.M.J. Verschure
Pages: 64-71
10. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: A model study
Author(s): Soichi Nagao, Takeru Honda, Tadashi Yamazaki
Pages: 72-80
11. Classical conditioning of motor responses: What is the learning mechanism?
Author(s): Germund Hesslow, Dan-Anders Jirenhed, Anders Rasmussen, Fredrik Johansson
Pages: 81-87
12. Cross-correlations between pairs of neurons in cerebellar cortex in vivo
Author(s): Fredrik Bengtsson, Pontus Geborek, Henrik Jörntell
Pages: 88-94
13. Using a million cell simulation of the cerebellum: Network scaling and task generality
Author(s): Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, Michael D. Mauk
Pages: 95-102
14. Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit
Author(s): Tadashi Yamazaki, Jun Igarashi
Pages: 103-111
15. Modeling the generation of output by the cerebellar nuclei
Author(s): Volker Steuber, Dieter Jaeger
Pages: 112-119
16. Modeling cancelation of periodic inputs with burst-STDP and feedback
Author(s): K. Bol, G. Marsat, J.F. Mejias, L. Maler, A. Longtin
Pages: 120-133
17. Adaptive filters and internal models: Multilevel description of cerebellar function
Author(s): John Porrill, Paul Dean, Sean R. Anderson
Pages: 134-149
Author(s): Dieter Jaeger, Henrik Jorntell, Mitsuo Kawato
Pages: 1-2
2. The importance of stochastic signaling processes in the induction of long-term synaptic plasticity
Author(s): Erik De Schutter
Pages: 3-10
3. Dendritic calcium signaling in cerebellar Purkinje cell
Author(s): Kazuo Kitamura, Masanobu Kano
Pages: 11-17
4. Bistability in Purkinje neurons: Ups and downs in cerebellar research
Author(s): Jordan D.T. Engbers, Fernando R. Fernandez, Ray W. Turner
Pages: 18-31
5. Mechanisms producing time course of cerebellar long-term depression
Author(s): Taegon Kim, Keiko Tanaka-Yamamoto
Pages: 32-35
6. Cerebellar LTD vs. motor learning—Lessons learned from studying GluD2
Author(s): Michisuke Yuzaki
Pages: 36-41
7. Adaptive coupling of inferior olive neurons in cerebellar learning
Author(s): Isao T. Tokuda, Huu Hoang, Nicolas Schweighofer, Mitsuo Kawato
Pages: 42-50
8. Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation
Author(s): Miho Onizuka, Huu Hoang, Mitsuo Kawato, Isao T. Tokuda, Nicolas Schweighofer, Yuichi Katori, Kazuyuki Aihara, Eric J. Lang, Keisuke Toyama
Pages: 51-63
9. Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control
Author(s): Ivan Herreros, Paul F.M.J. Verschure
Pages: 64-71
10. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: A model study
Author(s): Soichi Nagao, Takeru Honda, Tadashi Yamazaki
Pages: 72-80
11. Classical conditioning of motor responses: What is the learning mechanism?
Author(s): Germund Hesslow, Dan-Anders Jirenhed, Anders Rasmussen, Fredrik Johansson
Pages: 81-87
12. Cross-correlations between pairs of neurons in cerebellar cortex in vivo
Author(s): Fredrik Bengtsson, Pontus Geborek, Henrik Jörntell
Pages: 88-94
13. Using a million cell simulation of the cerebellum: Network scaling and task generality
Author(s): Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, Michael D. Mauk
Pages: 95-102
14. Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit
Author(s): Tadashi Yamazaki, Jun Igarashi
Pages: 103-111
15. Modeling the generation of output by the cerebellar nuclei
Author(s): Volker Steuber, Dieter Jaeger
Pages: 112-119
16. Modeling cancelation of periodic inputs with burst-STDP and feedback
Author(s): K. Bol, G. Marsat, J.F. Mejias, L. Maler, A. Longtin
Pages: 120-133
17. Adaptive filters and internal models: Multilevel description of cerebellar function
Author(s): John Porrill, Paul Dean, Sean R. Anderson
Pages: 134-149
Wednesday, September 11, 2013
Neural Networks: 3-9 September 2013
1. Dynamical behaviors for discontinuous and delayed neural networks in the framework of Filippov differential inclusions
Authors: Lihong Huang, Zuowei Cai, Lingling Zhang, Lian Duan
2. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays
Authors: Zhenyuan Guo, Jun Wang, Zheng Yan
3. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning
Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi
4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Authors: He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian
Authors: Lihong Huang, Zuowei Cai, Lingling Zhang, Lian Duan
2. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays
Authors: Zhenyuan Guo, Jun Wang, Zheng Yan
3. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning
Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi
4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Authors: He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian
Friday, September 6, 2013
Reminder: conference submission deadline for SIAM SDM 2014
A reminder that the deadline for submitting abstracts to the 2014 SIAM International Conference on Data Mining (SIAM SDM) is 6 October 2013. The deadline for submitting full papers is 13 October 2013. This conference will be held in Philadelphia, USA, 24-26 April, 2014.
Thursday, September 5, 2013
Rules for success
Adam Savage of Mythbusters fame has come up with a list of ten rules for success. Surprisingly enough, I think that most of them apply to success in academia as well as life in general, or at least life as a maker who blows things up on television. The rules (shamelessly copy-and-pasted from Boing Boing) are:
In my opinion, these rules apply to success in research and academia as well. Looking at them one-by-one:
1. Get good at something
While there is something to be said about the value of a generalist, everyone in research is a specialist at something. This is what the process of getting a PhD is about: becoming an expert, or specialist, in one particular topic. Of course, it's better to be good at several things, which is why I've been able to publish about computational intelligence and ecology as well as developing software. But I was only able to get into ecology because I'm good at computational intelligence, especially neural networks, and I was only able to get into neural networks because I'm a good programmer. So, being good at one thing can lead to being good at another thing.
2. Getting good at stuff takes practice
When I was an undergrad I was always programming - it was what I did to relax. But I got really good at it, which led me to neural networks and research. I've also written a lot of papers: the early ones were pretty bad, but after enough practice I got to be good at that as well. Even my experimental design has improved through practice. It's been said that mastering any skill takes 10,000 hours of practice, which doesn't seem too far off the mark to me.
3. Get OBSESSED
Obsession can be dangerous, it can keep you from your family, ruin your health and drive away your friends. But obsession also drives you to find the last bug in your code, to run just one more experiment, to refine your writing just that little bit more. Obsession leads to great results and great research.
4. Doing something well and thoroughly is its OWN reward
This is really close to the heart of research. Academics don't get paid for the journal articles they publish (despite the huge profits the journal publishers make, the content is provided for free). For an academic, doing your work well enough to get published is its own reward, and only research done well and thoroughly gets published.
5. Show and Tell
If you're an academic or a scientist, you should have something to say to the world about your work. That is why we publish our research, which is just the grown-up, scientist way of showing and telling the world about you've done.
6. If you want something, ASK
Some bosses, the good ones, will want to develop their staff. Development means pursuing something that you are interested in, something that you can do well and something that will help you do your job better. Even if it's only peripherally related to your job, it's still worth asking for support.
7. Have GOALS
Everyone in academia should have goals. Everyone in academia with goals should know that you're probably going to end up with something that is completely different to your goal, but just as good. Two years ago my goal was to get a permanent lecturer / senior lecturer position at a university. Now I'm the head of department at a private college. A different role to what I was aiming for, but just as good, if not better.
8. Be nice. To EVERYONE
My best friend likes to say that good things happen to good people, and it's true. Not because of any mystical, karmic nonsense, but because people who are nice to others make more friends and are the kind of people that others like to help out. Treating people badly might achieve short term goals, but long term, it's a self-defeating strategy.
9. FAIL
You learn more from your failures than you do from your successes. There are certainly people who don't fail early in their careers, and become professors in their early thirties, but they also unfortunately tend to be insufferably arrogant people. Failure teaches you humility, and it teaches you persistence. If my nine-year-old daughter is trying to learn how to do something, and is doing it wrongly, I don't stop her because she needs to learn through failure, and she needs to learn persistence. An academic is the same: you need to fail to learn what doesn't work.
10. WORK YOUR ASS OFF
The people who are most successful are the ones who work the hardest. Which is why I'm sitting at my dining table typing on a laptop at 11:15pm instead of dozing happily next to my wife.
1. Get good at something.
Really good. Get good at as many things as you can. Being good at one thing makes it easier to get good at other things.
2. Getting good at stuff takes practice.
Lots and lots of practice.
3. Get OBSESSED.
Everyone at the top of their field is obsessed with what they're doing.
4. Doing something well and thoroughly is its OWN reward.
5. Show and Tell.
If you do something well and you're happy with it, for FSM's sake, tell EVERYONE.
6. If you want something, ASK.
If something piques your interest, tell someone. If you want to learn something, ask someone, like your BOSS. As an employer, I can tell you, people who want to learn new skills are people I want to keep employed.
7. Have GOALS.
Make up goals. Set goals. Regularly assess where you are and where you want to be in terms of them. This is a kind of prayer that works, and works well. Allow for the fact that things will NEVER turn out like you think they will, and you must be prepared to end up miles from where you intended.
8. Be nice. To EVERYONE.
Life is way too short to be an asshole. If you are an asshole, apologize.
9. FAIL.
You will fail. It's one of our jobs in life. Keep failing. When you fail, admit it. When you don't, don't get cocky. 'Cause you're just about to fail again.
10. WORK YOUR ASS OFF.
Work like your life depends on it...
In my opinion, these rules apply to success in research and academia as well. Looking at them one-by-one:
1. Get good at something
While there is something to be said about the value of a generalist, everyone in research is a specialist at something. This is what the process of getting a PhD is about: becoming an expert, or specialist, in one particular topic. Of course, it's better to be good at several things, which is why I've been able to publish about computational intelligence and ecology as well as developing software. But I was only able to get into ecology because I'm good at computational intelligence, especially neural networks, and I was only able to get into neural networks because I'm a good programmer. So, being good at one thing can lead to being good at another thing.
2. Getting good at stuff takes practice
When I was an undergrad I was always programming - it was what I did to relax. But I got really good at it, which led me to neural networks and research. I've also written a lot of papers: the early ones were pretty bad, but after enough practice I got to be good at that as well. Even my experimental design has improved through practice. It's been said that mastering any skill takes 10,000 hours of practice, which doesn't seem too far off the mark to me.
3. Get OBSESSED
Obsession can be dangerous, it can keep you from your family, ruin your health and drive away your friends. But obsession also drives you to find the last bug in your code, to run just one more experiment, to refine your writing just that little bit more. Obsession leads to great results and great research.
4. Doing something well and thoroughly is its OWN reward
This is really close to the heart of research. Academics don't get paid for the journal articles they publish (despite the huge profits the journal publishers make, the content is provided for free). For an academic, doing your work well enough to get published is its own reward, and only research done well and thoroughly gets published.
5. Show and Tell
If you're an academic or a scientist, you should have something to say to the world about your work. That is why we publish our research, which is just the grown-up, scientist way of showing and telling the world about you've done.
6. If you want something, ASK
Some bosses, the good ones, will want to develop their staff. Development means pursuing something that you are interested in, something that you can do well and something that will help you do your job better. Even if it's only peripherally related to your job, it's still worth asking for support.
7. Have GOALS
Everyone in academia should have goals. Everyone in academia with goals should know that you're probably going to end up with something that is completely different to your goal, but just as good. Two years ago my goal was to get a permanent lecturer / senior lecturer position at a university. Now I'm the head of department at a private college. A different role to what I was aiming for, but just as good, if not better.
8. Be nice. To EVERYONE
My best friend likes to say that good things happen to good people, and it's true. Not because of any mystical, karmic nonsense, but because people who are nice to others make more friends and are the kind of people that others like to help out. Treating people badly might achieve short term goals, but long term, it's a self-defeating strategy.
9. FAIL
You learn more from your failures than you do from your successes. There are certainly people who don't fail early in their careers, and become professors in their early thirties, but they also unfortunately tend to be insufferably arrogant people. Failure teaches you humility, and it teaches you persistence. If my nine-year-old daughter is trying to learn how to do something, and is doing it wrongly, I don't stop her because she needs to learn through failure, and she needs to learn persistence. An academic is the same: you need to fail to learn what doesn't work.
10. WORK YOUR ASS OFF
The people who are most successful are the ones who work the hardest. Which is why I'm sitting at my dining table typing on a laptop at 11:15pm instead of dozing happily next to my wife.
Wednesday, September 4, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 9, September 2013
1. Study of the Convergence Behavior of the Complex Kernel Least Mean Square Algorithm
Author(s): Paul, T.K. ; Ogunfunmi, T.
Pages: 1349-1363
2. Transductive Face Sketch-Photo Synthesis
Author(s): Wang, N. ; Tao, D. ; Gao, X. ; Li, X. ; Li, J.
Pages: 1364-1376
3. Learning Sparse Kernel Classifiers for Multi-Instance Classification
Author(s): Fu, Z. ; Lu, G. ; Ting, K.M. ; Zhang, D.
Pages: 1377-1389
4. FPGA-Based Distributed Computing Microarchitecture for Complex Physical Dynamics Investigation
Author(s): Borgese, G. ; Pace, C. ; Pantano, P. ; Bilotta, E.
Pages: 1390-1399
5. Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties
Author(s): Li, Z. ; Su, C.-Y.
Pages: 1400-1413
6. Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data
Author(s): Lim, S.P. ; Haron, H.
Pages: 1414-1424
7. Real-Time Model Predictive Control Using a Self-Organizing Neural Network
Author(s): Han, H.-G. ; Wu, X.-L. ; Qiao, J.-F.
Pages: 1425-1436
8. Memory Models of Adaptive Behavior
Author(s): Traversa, F.L. ; Pershin, Y.V. ; Di Ventra, M.
Pages: 1437-1448
9. Model of an Excitatory Synapse Based on Stochastic Processes
Author(s): L'Esperance, P.-Y. ; Labib, R.
Pages: 1449-1458
10. Combined Convex Technique on Delay-Dependent Stability for Delayed Neural Networks
Author(s): Li, T. ; Wang, T. ; Song, A. ; Fei, S.
Pages: 1459-1465
11. Low-Temperature Fabrication of Spiking Soma Circuits Using Nanocrystalline-Silicon TFTs
Author(s): Subramaniam, A. ; Cantley, K.D. ; Stiegler, H.J. ; Chapman, R.A. ; Vogel, E.M.
Pages: 1466-1471
12. Effect of Input Noise and Output Node Stochastic on Wang's kWTA
Author(s): Sum, J. ; Leung, C.-S. ; Ho, K.
Pages: 1472-1477
13. Controllability and Observability of Boolean Control Networks With Time-Variant Delays in States
Author(s): Zhang, L. ; Zhang, K.
Pages: 1478-1483
14. Quantized Kernel Recursive Least Squares Algorithm
Author(s): Chen, B. ; Zhao, S. ; Zhu, P. ; Principe, J.C.
Pages: 1484-1490
15. On the Optimal Class Representation in Linear Discriminant Analysis
Author(s): Iosifidis, A. ; Tefas, A. ; Pitas, I.
Pages: 1491-1496
16. L\infty Analysis and State-Feedback Control of Hopfield Networks
Author(s): Stoica, A.-M. ; Yaesh, I.
Pages: 1497-1502
17. Sequential Blind Identification of Underdetermined Mixtures Using a Novel Deflation Scheme
Author(s): Zhang, M. ; Yu, S. ; Wei, G.
Pages: 1503-1509
Author(s): Paul, T.K. ; Ogunfunmi, T.
Pages: 1349-1363
2. Transductive Face Sketch-Photo Synthesis
Author(s): Wang, N. ; Tao, D. ; Gao, X. ; Li, X. ; Li, J.
Pages: 1364-1376
3. Learning Sparse Kernel Classifiers for Multi-Instance Classification
Author(s): Fu, Z. ; Lu, G. ; Ting, K.M. ; Zhang, D.
Pages: 1377-1389
4. FPGA-Based Distributed Computing Microarchitecture for Complex Physical Dynamics Investigation
Author(s): Borgese, G. ; Pace, C. ; Pantano, P. ; Bilotta, E.
Pages: 1390-1399
5. Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties
Author(s): Li, Z. ; Su, C.-Y.
Pages: 1400-1413
6. Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data
Author(s): Lim, S.P. ; Haron, H.
Pages: 1414-1424
7. Real-Time Model Predictive Control Using a Self-Organizing Neural Network
Author(s): Han, H.-G. ; Wu, X.-L. ; Qiao, J.-F.
Pages: 1425-1436
8. Memory Models of Adaptive Behavior
Author(s): Traversa, F.L. ; Pershin, Y.V. ; Di Ventra, M.
Pages: 1437-1448
9. Model of an Excitatory Synapse Based on Stochastic Processes
Author(s): L'Esperance, P.-Y. ; Labib, R.
Pages: 1449-1458
10. Combined Convex Technique on Delay-Dependent Stability for Delayed Neural Networks
Author(s): Li, T. ; Wang, T. ; Song, A. ; Fei, S.
Pages: 1459-1465
11. Low-Temperature Fabrication of Spiking Soma Circuits Using Nanocrystalline-Silicon TFTs
Author(s): Subramaniam, A. ; Cantley, K.D. ; Stiegler, H.J. ; Chapman, R.A. ; Vogel, E.M.
Pages: 1466-1471
12. Effect of Input Noise and Output Node Stochastic on Wang's kWTA
Author(s): Sum, J. ; Leung, C.-S. ; Ho, K.
Pages: 1472-1477
13. Controllability and Observability of Boolean Control Networks With Time-Variant Delays in States
Author(s): Zhang, L. ; Zhang, K.
Pages: 1478-1483
14. Quantized Kernel Recursive Least Squares Algorithm
Author(s): Chen, B. ; Zhao, S. ; Zhu, P. ; Principe, J.C.
Pages: 1484-1490
15. On the Optimal Class Representation in Linear Discriminant Analysis
Author(s): Iosifidis, A. ; Tefas, A. ; Pitas, I.
Pages: 1491-1496
16. L\infty Analysis and State-Feedback Control of Hopfield Networks
Author(s): Stoica, A.-M. ; Yaesh, I.
Pages: 1497-1502
17. Sequential Blind Identification of Underdetermined Mixtures Using a Novel Deflation Scheme
Author(s): Zhang, M. ; Yu, S. ; Wei, G.
Pages: 1503-1509
Tuesday, September 3, 2013
Webinar: So you want to be an Academic? Some Tips and Tricks
Professor Bob John of University of Nottingham, United Kingdom, will give a live webinar to our IEEE CIS members and friends. The information of the webinar is shown below:
Webinar arrangement
Topic:
Webinar arrangement
Topic:
So you want to be an Academic? Some Tips and Tricks
Date and time:
Date and time:
2:00 PM, Oct 23, 2013, BST (London Time)
9:00 AM, Oct 23, 2013, EDT (New York Time)
9:00 PM, Oct 23, 2013, HKT (Hong Kong Time)
Webinar ID:
Webinar ID:
112-059-947
Registration
Registration
The webinar is free-of-charge. We only have limited seats. First come first served.
Please register for "So you want to be an Academic? Some Tips and Tricks" on Oct 23, 2013 2:00 PM BST at:
https://attendee.gotowebinar.
Webinar information
https://attendee.gotowebinar.
Webinar information
Speaker:
Professor Bob John, Automated Scheduling, Optimisation and Planning Group (ASAP), University of Nottingham, United Kingdom
Abstract:
Abstract:
This webinar will be given by Bob John who has led two highly successful computational intelligence research groups (www.cci.dmu.ac.uk and www.asap.ac.uk)
for more than 12 years. He will give his personal views, based on his
experiences, of what's needed to become an academic. Although based in
the United Kingdom the points he makes will broadly translate to other
countries. He will discuss your best strategies for producing
publications, the role of networking, looking for funding and generally
how to get on the academic career ladder.
Speaker's Biography:
Speaker's Biography:
Bob has a BSc Mathematics, a MSc in Statistics and a PhD in Fuzzy Logic. He worked in industry for 10 years as a mathematician and knowledge engineer developing knowledge based systems for British Gas and the financial services industry. Bob spent 24 years at De Montfort University in various roles including Head of Department, Head of School and Deputy Dean. He led the Centre for Computational Intelligence research group from 2001 until 2012. He has over 150 research publications of which about 50 are in international journals. Bob joined the University of Nottingham this year where he heads up the Automated Scheduling, Optimisation and Planning (ASAP) research group in the School of Computer Science. The ASAP research group carries out multi-disciplinary research into mathematical models and algorithms for a variety of real-world optimisation problems. ASAP has 8 academic staff, 9 researchers and over 30 PhD students.
Only limited seats are available. Please register as soon as possible.
After registering, you will receive a confirmation email containing information about joining the webinar.
Only limited seats are available. Please register as soon as possible.
After registering, you will receive a confirmation email containing information about joining the webinar.
Monday, September 2, 2013
Reminder: paper submission deadline for SysInt 2014
A reminder that the deadline for submitting abstracts to the 2nd International Conference on System-Integrated Intelligence (SysInt) 2014 is October 1, 2013. The deadline for submitting full papers is February 1, 2014. This conference will be held in Bremen, Germany, 2-4 July, 2014.
Friday, August 30, 2013
Reminder: paper submission deadline for ICC 2014
A reminder that the deadline for submitting papers to the International Conference on Intelligent Cloud Computing (ICC) 2014 is September 30, 2013. This conference will be held 24-26 February in Muscat, Oman.
Monday, August 26, 2013
On management 2
The Argentine guerrilla Che Guevara wrote in his book on guerrilla warfare:
"There is nothing more important than information. Moreover, it should be in perfect order, and done well by capable personnel".
I have found, as a manager, that this is very true. Management is about making decisions, and you cannot make good decisions if you do not have good information. This being the case, the most valuable staff you can have as a manager are the staff who will tell you what they think rather than telling you what you want to hear. Getting a forthright, unfiltered opinion is essential to any manager, and the staff who will give you this are the ones you must value the most.
Some managers find those sort of people hard to manage, but I never have. I think that's because, if someone is forthright in their opinion, then it is easier to make them happy. People who keep their thoughts to themselves are harder to manage because you don't always know how to make them happy, and if someone isn't happy in their work, they won't do their job well.
It is tempting to dismiss this as "touchy-feely stuff" that doesn't have anything to do with research, but that's not true. Managing research certainly requires a good knowledge of research, and a good research background - the best managers are leaders, and leaders should lead from the front. But managing research is not really about doing research, it's about managing people. And that is where so many academic labs fall down: they are headed by someone who is very good at research, but doesn't know how to deal with people. These labs are marked by dissatisfied staff and a high staff-turnover, as people arrive, get rapidly disillusioned, and leave. The lucky ones will find a better job somewhere else, while the unlucky ones end up with their careers in ruins. As a person of conscience, I do everything I can to avoid that happening to the people I manage - to the people for whom I am responsible.
"There is nothing more important than information. Moreover, it should be in perfect order, and done well by capable personnel".
I have found, as a manager, that this is very true. Management is about making decisions, and you cannot make good decisions if you do not have good information. This being the case, the most valuable staff you can have as a manager are the staff who will tell you what they think rather than telling you what you want to hear. Getting a forthright, unfiltered opinion is essential to any manager, and the staff who will give you this are the ones you must value the most.
Some managers find those sort of people hard to manage, but I never have. I think that's because, if someone is forthright in their opinion, then it is easier to make them happy. People who keep their thoughts to themselves are harder to manage because you don't always know how to make them happy, and if someone isn't happy in their work, they won't do their job well.
It is tempting to dismiss this as "touchy-feely stuff" that doesn't have anything to do with research, but that's not true. Managing research certainly requires a good knowledge of research, and a good research background - the best managers are leaders, and leaders should lead from the front. But managing research is not really about doing research, it's about managing people. And that is where so many academic labs fall down: they are headed by someone who is very good at research, but doesn't know how to deal with people. These labs are marked by dissatisfied staff and a high staff-turnover, as people arrive, get rapidly disillusioned, and leave. The lucky ones will find a better job somewhere else, while the unlucky ones end up with their careers in ruins. As a person of conscience, I do everything I can to avoid that happening to the people I manage - to the people for whom I am responsible.
Thursday, August 22, 2013
Evolving Systems Vol 4, Issue 3, August 2013
1. Sliding mode control of fractional order nonlinear differential inclusion systems
Author(s): Saeed Balochian
Abstract Full text HTML Full text PDF
2. Solving the task assignment problem using Harmony Search algorithm
Author(s): Ayed Salman , Imtiaz Ahmad , Hanaa AL-Rushood & Suha Hamdan
Abstract Full text HTML Full text PDF
3. A fuzzy logic model based Markov random field for medical image segmentation
Author(s): Thanh Minh Nguyen & Q. M. Jonathan Wu
Abstract Full text HTML Full text PDF
4. The transformation method between tree and lattice for file management system
Author(s): Kazuhito Sawase & Hajime Nobuhara
Abstract Full text HTML Full text PDF
5. Application of neural network and fuzzy model to grinding process control
Author(s): A. O. Odior
Abstract Full text HTML Full text PDF
6. Iris data encryption based on Aztec Symbology
Author(s): Shrinivasrao B. Kulkarni , Ravindra S. Hegadi & Umakant P. Kulkarni
Abstract Full text HTML Full text PDF
Author(s): Saeed Balochian
Abstract Full text HTML Full text PDF
2. Solving the task assignment problem using Harmony Search algorithm
Author(s): Ayed Salman , Imtiaz Ahmad , Hanaa AL-Rushood & Suha Hamdan
Abstract Full text HTML Full text PDF
3. A fuzzy logic model based Markov random field for medical image segmentation
Author(s): Thanh Minh Nguyen & Q. M. Jonathan Wu
Abstract Full text HTML Full text PDF
4. The transformation method between tree and lattice for file management system
Author(s): Kazuhito Sawase & Hajime Nobuhara
Abstract Full text HTML Full text PDF
5. Application of neural network and fuzzy model to grinding process control
Author(s): A. O. Odior
Abstract Full text HTML Full text PDF
6. Iris data encryption based on Aztec Symbology
Author(s): Shrinivasrao B. Kulkarni , Ravindra S. Hegadi & Umakant P. Kulkarni
Abstract Full text HTML Full text PDF
Wednesday, August 21, 2013
Second Annual CIS IEEE Video Competition
Following on from the first video competition, which produced two outstanding winning videos about fuzzy logic, we invite you to produce an introductory 3-minute video about one of the following Computational Intelligence fields of interest:
● Neural Networks
● Evolutionary Computation
● Hybrid Intelligent Systems
The winners of the previous competition can be seen at:
● www.youtube.com/watch?v=J_Q5X0nTmrA
● www.youtube.com/watch?v=P8wY6mi1vV8
The aim of the video is to answer the question “what is...?”, for a Computational Intelligence field of interest, to an audience of high school students and the general public, with limited mathematical background. The video should also explain an example application of how the method can be used in everyday life. The video should be suitable for posting on YouTube and Facebook. The video competition is an exciting opportunity for all IEEE members to work together in conveying technical topics to nonexperts.
Prizes for entrants:
1st prize: 600USD + iPad* + CIS membership (1 year)
2nd prize: 500USD + CIS membership (1 year)
3rd prize: 400USD + CIS membership (1 year)
Prize for everyone: An iPad* will be raffled to anyone who “likes” a video on our www.facebook.com/IEEE.CIS page (excluding competition entrants and organisers).
Teams consist of 1 to 5 people. The team leader must be an IEEE member.
Registration deadline is 2nd September 2013.
Video submission deadline is 14th October 2013.
Winners will be notified on 1st December 2013.
Rules and submission details can be found at cis.ieee.org/videocomp. Please contact cis.ieee@gmail.com for specific queries.
*Disclaimer: The iPad prize does not apply to countries officially embargoed by the Office of Foreign Assets Control (OFAC) of the U.S. Department of the Treasury. In accordance with U.S. law, IEEE is unable to provide such goods to OFAC embargoed countries. In the case where the first place winner comes from an OFAC embargoed country, no iPad 2 will be awarded.
● Neural Networks
● Evolutionary Computation
● Hybrid Intelligent Systems
The winners of the previous competition can be seen at:
● www.youtube.com/watch?v=J_Q5X0nTmrA
● www.youtube.com/watch?v=P8wY6mi1vV8
The aim of the video is to answer the question “what is...?”, for a Computational Intelligence field of interest, to an audience of high school students and the general public, with limited mathematical background. The video should also explain an example application of how the method can be used in everyday life. The video should be suitable for posting on YouTube and Facebook. The video competition is an exciting opportunity for all IEEE members to work together in conveying technical topics to nonexperts.
Prizes for entrants:
1st prize: 600USD + iPad* + CIS membership (1 year)
2nd prize: 500USD + CIS membership (1 year)
3rd prize: 400USD + CIS membership (1 year)
Prize for everyone: An iPad* will be raffled to anyone who “likes” a video on our www.facebook.com/IEEE.CIS page (excluding competition entrants and organisers).
Teams consist of 1 to 5 people. The team leader must be an IEEE member.
Registration deadline is 2nd September 2013.
Video submission deadline is 14th October 2013.
Winners will be notified on 1st December 2013.
Rules and submission details can be found at cis.ieee.org/videocomp. Please contact cis.ieee@gmail.com for specific queries.
*Disclaimer: The iPad prize does not apply to countries officially embargoed by the Office of Foreign Assets Control (OFAC) of the U.S. Department of the Treasury. In accordance with U.S. law, IEEE is unable to provide such goods to OFAC embargoed countries. In the case where the first place winner comes from an OFAC embargoed country, no iPad 2 will be awarded.
Friday, August 16, 2013
Reminder: paper submission deadline for PAKDD 2014
A reminder that the deadline for the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014 is 16 September 2013. This conference will be held in Tainan, Taiwan, 13-16 May 2014.
Thursday, August 15, 2013
Reminder: paper submission deadline for SCDM 2014
A reminder that the deadline for submitting papers to the First International Conference on Data Mining (SCDM) 2014 is 15 December 2014. This conference will be held in Kuala Lumpur, Malaysia, 16-18 June, 2014.
Wednesday, August 14, 2013
IEEE Transactions on evolutionary Computation: Volume 17, Issue 4, August 2013
1. Multiobjective Metaheuristics for Traffic Grooming in Optical Networks
Author(s): Rubio-Largo, A. ; Vega-Rodriguez, M.A. ; Gomez-Pulido, J.A. ; Sanchez-Perez, J.M.
2. Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
Author(s): Wang, R. ; Purshouse, R.C. ; Fleming, P.J.
3. A Hybrid Framework for Evolutionary Multi-Objective Optimization
Author(s): Sindhya, K. ; Miettinen, K. ; Deb, K.
4. A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem
Author(s): Zhong, J.-H. ; Shen, M. ; Zhang, J. ; Chung, H.S.-H. ; Shi, Y.-H. ; Li, Y.
5. Evolutionary Foundation of Bounded Rationality in a Financial Market
Author(s): Kinoshita, K. ; Suzuki, K. ; Shimokawa, T.
6. Self-Adaptive Evolution Toward New Parameter Free Image Registration Methods
Author(s): Santamaria, J. ; Damas, S. ; Cordon, O. ; Escamez, A.
7. Clustered Memetic Algorithm With Local Heuristics for Ab Initio Protein Structure Prediction
Author(s): Islam, M.K. ; Chetty, M.
8. Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization
Author(s): Palafox, L. ; Noman, N. ; Iba, H.
9. A Memetic Algorithm for Matching Spatial Configurations With the Histograms of Forces
Author(s): Buck, A.R. ; Keller, J.M. ; Skubic, M.
Author(s): Rubio-Largo, A. ; Vega-Rodriguez, M.A. ; Gomez-Pulido, J.A. ; Sanchez-Perez, J.M.
2. Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
Author(s): Wang, R. ; Purshouse, R.C. ; Fleming, P.J.
3. A Hybrid Framework for Evolutionary Multi-Objective Optimization
Author(s): Sindhya, K. ; Miettinen, K. ; Deb, K.
4. A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem
Author(s): Zhong, J.-H. ; Shen, M. ; Zhang, J. ; Chung, H.S.-H. ; Shi, Y.-H. ; Li, Y.
5. Evolutionary Foundation of Bounded Rationality in a Financial Market
Author(s): Kinoshita, K. ; Suzuki, K. ; Shimokawa, T.
6. Self-Adaptive Evolution Toward New Parameter Free Image Registration Methods
Author(s): Santamaria, J. ; Damas, S. ; Cordon, O. ; Escamez, A.
7. Clustered Memetic Algorithm With Local Heuristics for Ab Initio Protein Structure Prediction
Author(s): Islam, M.K. ; Chetty, M.
8. Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization
Author(s): Palafox, L. ; Noman, N. ; Iba, H.
9. A Memetic Algorithm for Matching Spatial Configurations With the Histograms of Forces
Author(s): Buck, A.R. ; Keller, J.M. ; Skubic, M.
Tuesday, August 13, 2013
IEEE Transactions on Fuzzy Systems: Volume 21, Issue 4, August 2013
1. Knowledge-Leverage-Based Fuzzy System and Its Modeling
Author(s): Deng, Z. ; Jiang, Y. ; Chung, F.-L. ; Ishibuchi, H. ; Wang, S.
2. Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays
Author(s): Li, Z. ; Ding, L. ; Gao, H. ; Duan, G. ; Su, C.-Y.
3. Reliable All-Pairs Evolving Fuzzy Classifiers
Author(s): Lughofer, E. ; Buchtala, O.
4. Priority Weight Intervals Derived From Intuitionistic Multiplicative Preference Relations
Author(s): Xu, Z.
5. A Novel Control Design on Discrete-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delays
Author(s): Su, X. ; Shi, P. ; Wu, L. ; Song, Y.-D.
6. Looking for Like-Minded Individuals in Social Networks Using Tagging and E Fuzzy Sets
Author(s): Yager, R.R. ; Reformat, M.Z.
7. Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network
Author(s): Wai, R.-J. ; Lin, Y.-W.
8. A Fuzzy Logic-Based System for Indoor Localization Using WiFi in Ambient Intelligent Environments
Author(s): Garcia-Valverde, T. ; Garcia-Sola, A. ; Hagras, H. ; Dooley, J.A. ; Callaghan, V. ; Botia, J.A.
9. Extending the Contraposition Property of Propositional Logic for Fuzzy Abduction
Author(s): Chakraborty, A. ; Konar, A. ; Pal, N.R. ; Jain, L.C.
10. A Behavioral Distance for Fuzzy-Transition Systems
Author(s): Cao, Y. ; Sun, S.X. ; Wang, H. ; Chen, G.
11. New Stability Conditions Based on Piecewise Fuzzy Lyapunov Functions and Tensor Product Transformations
Author(s): Campos, V.C.S. ; Souza, F.O. ; Torres, L.A.B. ; Palhares, R.M.
12. Relaxation Technique for a T–S Fuzzy Control Design Based on a Continuous-Time Fuzzy Weighting-Dependent Lyapunov Function
Author(s): Kim, S.H.
13. Comparing Fuzzy, Probabilistic, and Possibilistic Partitions Using the Earth Mover’s Distance
Author(s): Anderson, D.T. ; Zare, A. ; Price, S.
14. Parallel Algorithms for Fuzzy Ontology Reasoning
Author(s): Bobillo, F. ; Delgado, M. ; Sanchez-Sanchez, J.C.
15. A Matrix Approach to Latticized Linear Programming With Fuzzy-Relation Inequality Constraints
Author(s): Li, H. ; Wang, Y.
Author(s): Deng, Z. ; Jiang, Y. ; Chung, F.-L. ; Ishibuchi, H. ; Wang, S.
2. Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays
Author(s): Li, Z. ; Ding, L. ; Gao, H. ; Duan, G. ; Su, C.-Y.
3. Reliable All-Pairs Evolving Fuzzy Classifiers
Author(s): Lughofer, E. ; Buchtala, O.
4. Priority Weight Intervals Derived From Intuitionistic Multiplicative Preference Relations
Author(s): Xu, Z.
5. A Novel Control Design on Discrete-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delays
Author(s): Su, X. ; Shi, P. ; Wu, L. ; Song, Y.-D.
6. Looking for Like-Minded Individuals in Social Networks Using Tagging and E Fuzzy Sets
Author(s): Yager, R.R. ; Reformat, M.Z.
7. Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network
Author(s): Wai, R.-J. ; Lin, Y.-W.
8. A Fuzzy Logic-Based System for Indoor Localization Using WiFi in Ambient Intelligent Environments
Author(s): Garcia-Valverde, T. ; Garcia-Sola, A. ; Hagras, H. ; Dooley, J.A. ; Callaghan, V. ; Botia, J.A.
9. Extending the Contraposition Property of Propositional Logic for Fuzzy Abduction
Author(s): Chakraborty, A. ; Konar, A. ; Pal, N.R. ; Jain, L.C.
10. A Behavioral Distance for Fuzzy-Transition Systems
Author(s): Cao, Y. ; Sun, S.X. ; Wang, H. ; Chen, G.
11. New Stability Conditions Based on Piecewise Fuzzy Lyapunov Functions and Tensor Product Transformations
Author(s): Campos, V.C.S. ; Souza, F.O. ; Torres, L.A.B. ; Palhares, R.M.
12. Relaxation Technique for a T–S Fuzzy Control Design Based on a Continuous-Time Fuzzy Weighting-Dependent Lyapunov Function
Author(s): Kim, S.H.
13. Comparing Fuzzy, Probabilistic, and Possibilistic Partitions Using the Earth Mover’s Distance
Author(s): Anderson, D.T. ; Zare, A. ; Price, S.
14. Parallel Algorithms for Fuzzy Ontology Reasoning
Author(s): Bobillo, F. ; Delgado, M. ; Sanchez-Sanchez, J.C.
15. A Matrix Approach to Latticized Linear Programming With Fuzzy-Relation Inequality Constraints
Author(s): Li, H. ; Wang, Y.
Monday, August 12, 2013
Paper submission deadline: ICAISC 2014
The deadline for submitting papers to the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2014 is November 20, 2013 (beware the front page linked to has embedded folk-music playing). This conference will be held in Zakopane, Poland, June 1-5 2014.
Thursday, August 8, 2013
Paper submission deadline: IEEE CIFEr 2014
The deadline for submitting papers to the IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr) 2014 is November 1, 2013. This conference will be held in London, UK, 27-28 March, 2014.
Wednesday, August 7, 2013
Conference paper deadline: ICPRAM 2014
The deadline for submitting papers to the 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2014 is 25 September 2013. This conference will be held in Angers, Loire Valley, France, 6-8 March, 2014.
Tuesday, August 6, 2013
Nominations for IEEE CIS Distinguished Lecturers
This is cross-posted from the IEEE CIS blog.
The Committee of the Distinguished Lecturer Program (DLP) of the IEEE CIS Society invites all Society’s Technical Committees Chairs, Chapter Chairs, EiCs, and AdCom/ExCom members to nominate Distinguished Lecturers. Five to seven exceptionally outstanding IEEE CIS members will be selected among the nominees to serve as the DLs for the three year term of 2014-2016. The nominations should be received after the nomination opens July 1 2013 and no later than August 30, 2013.
The nomination form, procedure, policies and criteria for the Selection of DL Lecturers are available at the following URL: http://cis.ieee.org/distinguished-lecturers-program.html
The Committee of the Distinguished Lecturer Program (DLP) of the IEEE CIS Society invites all Society’s Technical Committees Chairs, Chapter Chairs, EiCs, and AdCom/ExCom members to nominate Distinguished Lecturers. Five to seven exceptionally outstanding IEEE CIS members will be selected among the nominees to serve as the DLs for the three year term of 2014-2016. The nominations should be received after the nomination opens July 1 2013 and no later than August 30, 2013.
The nomination form, procedure, policies and criteria for the Selection of DL Lecturers are available at the following URL: http://cis.ieee.org/distinguished-lecturers-program.html
Tuesday, July 30, 2013
Conference paper deadline: SysInt 2014
The deadline for submitting abstracts to the 2nd International Conference on System-Integrated Intelligence (SysInt) 2014 is October 1, 2013. The deadline for submitting full papers is February 1, 2014. This conference will be held in Bremen, Germany, 2-4 July, 2014.
Monday, July 29, 2013
Conference submission deadline: SIAM SDM 2014
The deadline for submitting abstracts to the 2014 SIAM International Conference on Data Mining (SIAM SDM) is 6 October 2013. The deadline for submitting full papers is 13 October 2013. This conference will be held in Philadelphia, USA, 24-26 April, 2014.
Friday, July 26, 2013
Conference paper deadline: IEEE SSCI 2014
The deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (SSCI) 2014 is 15 June 2014. This group of symposia will be held in Orlando, Florida, 9-12 December, 2014.
Thursday, July 25, 2013
Conference paper deadline: RAICS 2013
The deadline for submitting papers to the Recent Advances in Intelligent Computational Systems (RAICS) 2013 conference is 1 August 2013. This conference will be held in Trivandrum, India, December 19-21, 2013.
Wednesday, July 24, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 9, August 2013
1. Sampled-Data Exponential Synchronization of Complex Dynamical Networks With Time-Varying Coupling Delay
Author(s): Zheng-Guang Wu; Peng Shi; Hongye Su; Jian Chu
Page(s): 1177 - 1187
2. Dictionary Learning-Based Subspace Structure Identification in Spectral Clustering
Author(s): Liping Jing; Michael K. Ng; Tieyong Zeng
Page(s): 1188 - 1199
3. Knowledge-Leverage-Based TSK Fuzzy System Modeling
Author(s): Zhaohong Deng; Yizhang Jiang; Kup-Sze Choi; Fu-Lai Chung; Shitong Wang
Page(s): 1200 - 1212
4. A Cognitive Fault Diagnosis System for Distributed Sensor Networks
Author(s): Cesare Alippi; Stavros Ntalampiras; Manuel Roveri
Page(s): 1213 - 1226
5. Boundedness and Complete Stability of Complex-Valued Neural Networks With Time Delay
Author(s): Bo Zhou; Qiankun Song
Page(s): 1227 - 1238
6. Fast Neuromimetic Object Recognition Using FPGA Outperforms GPU Implementations
Author(s): Garrick Orchard; Jacob G. Martin; R. Jacob Vogelstein; Ralph Etienne-Cummings
Page(s): 1239 - 1252
7. Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance
Author(s): Ezequiel Lopez-Rubio
Page(s): 1253 - 1265
8. Quantum-Based Algorithm for Optimizing Artificial Neural Networks
Author(s): Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
Page(s): 1266 - 1278
9. Hinging Hyperplanes for Time-Series Segmentation
Author(s): Xiaolin Huang; Marin Matijas; Johan A. K. Suykens
Page(s): 1279 - 1291
10. Ranking Graph Embedding for Learning to Rerank
Author(s): Yanwei Pang; Zhong Ji; Peiguang Jing; Xuelong Li
Page(s): 1292 - 1303
11. Feasibility and Finite Convergence Analysis for Accurate On-Line $nu$-Support Vector Machine
Author(s): Bin Gu; Victor S. Sheng
Page(s): 1304 - 1315
12. Exponential Synchronization of Coupled Switched Neural Networks With Mode-Dependent Impulsive Effects
Author(s): Wenbing Zhang; Yang Tang; Qingying Miao; Wei Du
Page(s): 1316 - 1326
13. Analysis of Boundedness and Convergence of Online Gradient Method for Two-Layer Feedforward Neural Networks
Author(s): Lu Xu; Jinshu Chen; Defeng Huang; Jianhua Lu; Licai Fang
Page(s): 1327 - 1338
14. Phase-Noise-Induced Resonance in Arrays of Coupled Excitable Neural Models
Author(s): Xiaoming Liang; Liang Zhao
Page(s): 1339 - 1345
Author(s): Zheng-Guang Wu; Peng Shi; Hongye Su; Jian Chu
Page(s): 1177 - 1187
2. Dictionary Learning-Based Subspace Structure Identification in Spectral Clustering
Author(s): Liping Jing; Michael K. Ng; Tieyong Zeng
Page(s): 1188 - 1199
3. Knowledge-Leverage-Based TSK Fuzzy System Modeling
Author(s): Zhaohong Deng; Yizhang Jiang; Kup-Sze Choi; Fu-Lai Chung; Shitong Wang
Page(s): 1200 - 1212
4. A Cognitive Fault Diagnosis System for Distributed Sensor Networks
Author(s): Cesare Alippi; Stavros Ntalampiras; Manuel Roveri
Page(s): 1213 - 1226
5. Boundedness and Complete Stability of Complex-Valued Neural Networks With Time Delay
Author(s): Bo Zhou; Qiankun Song
Page(s): 1227 - 1238
6. Fast Neuromimetic Object Recognition Using FPGA Outperforms GPU Implementations
Author(s): Garrick Orchard; Jacob G. Martin; R. Jacob Vogelstein; Ralph Etienne-Cummings
Page(s): 1239 - 1252
7. Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance
Author(s): Ezequiel Lopez-Rubio
Page(s): 1253 - 1265
8. Quantum-Based Algorithm for Optimizing Artificial Neural Networks
Author(s): Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
Page(s): 1266 - 1278
9. Hinging Hyperplanes for Time-Series Segmentation
Author(s): Xiaolin Huang; Marin Matijas; Johan A. K. Suykens
Page(s): 1279 - 1291
10. Ranking Graph Embedding for Learning to Rerank
Author(s): Yanwei Pang; Zhong Ji; Peiguang Jing; Xuelong Li
Page(s): 1292 - 1303
11. Feasibility and Finite Convergence Analysis for Accurate On-Line $nu$-Support Vector Machine
Author(s): Bin Gu; Victor S. Sheng
Page(s): 1304 - 1315
12. Exponential Synchronization of Coupled Switched Neural Networks With Mode-Dependent Impulsive Effects
Author(s): Wenbing Zhang; Yang Tang; Qingying Miao; Wei Du
Page(s): 1316 - 1326
13. Analysis of Boundedness and Convergence of Online Gradient Method for Two-Layer Feedforward Neural Networks
Author(s): Lu Xu; Jinshu Chen; Defeng Huang; Jianhua Lu; Licai Fang
Page(s): 1327 - 1338
14. Phase-Noise-Induced Resonance in Arrays of Coupled Excitable Neural Models
Author(s): Xiaoming Liang; Liang Zhao
Page(s): 1339 - 1345
Tuesday, July 23, 2013
Conference paper deadline: PAKDD 2014
The deadline for the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014 is 16 September 2013. This conference will be held in Tainan, Taiwan, 13-16 May 2014.
Monday, July 22, 2013
Conference paper deadline: SCDM 2014
The deadline for submitting papers to the First International Conference on Data Mining (SCDM) 2014 is 15 December 2014. This conference will be held in Kuala Lumpur, Malaysia, 16-18 June, 2014.
Friday, July 19, 2013
Conference paper deadline: TAAI 2013
The deadline for submitting papers to the 2013 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) is 23 August 2013. This conference will be held in Taipei, Taiwan, 6-8 December, 2013.
Thursday, July 18, 2013
Conference paper deadline: ICMLC 2014
The deadline for submitting papers to the 6th International Conference on Machine Learning and Computing (ICMLC) 2014 is 20 August 2013. This conference will be held in Toronto, Canada, 10-12 January, 2014.
Wednesday, July 17, 2013
Neural Networks: 9-15 July 2013
1. Is mutual information adequate for feature selection in regression?
Author(s): Benoît Frénay, Gauthier Doquire, Michel Verleysen
2. An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion
Author(s): Yuanyuan Liu, L.C. Jiao, Fanhua Shang, Fei Yin, F. Liu
Author(s): Benoît Frénay, Gauthier Doquire, Michel Verleysen
2. An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion
Author(s): Yuanyuan Liu, L.C. Jiao, Fanhua Shang, Fei Yin, F. Liu
Tuesday, July 16, 2013
Paper submission deadline: ICC 2014
The deadline for submitting papers to the International Conference on Intelligent Cloud Computing (ICC) 2014 is September 30, 2013. This conference will be held 24-26 February in Muscat, Oman.
Monday, July 15, 2013
Conference paper deadline: WSDM 2014
The deadline for submitting abstracts to the 7th International Conference on Web Search and Data Mining (WSDM) 2014 is 19 August 2013. The deadline for submitting full papers is 26 August 2013. This conference will be held in New York City 24-28 February, 2014.
Tuesday, July 9, 2013
Neural Networks: 2-8 July, 2013
1. A model of task-specific focal dystonia
Author(s): Eckart Altenmüller, Dieter Müller
2. Universal approximation by radial basis function networks of Delsarte translates
Author(s): Cristian Arteaga, Isabel Marrero
3. Neural architecture design based on extreme learning machine
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez
4. Stochastic stability of discrete-time Markovian jump delay neural networks with impulses and incomplete information on transition probability
Author(s): Yu Zhang
Author(s): Eckart Altenmüller, Dieter Müller
2. Universal approximation by radial basis function networks of Delsarte translates
Author(s): Cristian Arteaga, Isabel Marrero
3. Neural architecture design based on extreme learning machine
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez
4. Stochastic stability of discrete-time Markovian jump delay neural networks with impulses and incomplete information on transition probability
Author(s): Yu Zhang
Monday, July 8, 2013
IEEE CIS Second Annual Video Competition
This is cross-posted from the IEEE Computational Intelligence Society blog.
The IEEE Computational Intelligence Society is running its second annual video competition. The competition is to create a video on one of the following subjects:
More details can be found here.
The IEEE Computational Intelligence Society is running its second annual video competition. The competition is to create a video on one of the following subjects:
- Neural Networks
- Evolutionary Computation
- Hybrid Intelligent Systems
More details can be found here.
Friday, July 5, 2013
IEEE Transactions on Autonomous Mental Development: Volume 5, Issue 2, 2013
1. Brain-Like Emergent Temporal Processing: Emergent Open States
Author(s): Juyang Weng ; Luciw, M.D. ; Qi Zhang
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6504480
2. A Simple Ontology of Manipulation Actions Based on Hand-Object Relations
Author(s): Worgotter, F. ; Aksoy, E.E. ; Kruger, N. ; Piater, J. ; Ude, A. ; Tamosiunaite, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493410
3. An Autonomous Social Robot in Fear
Author(s): Castro-Gonzalez, A. ; Malfaz, M. ; Salichs, M.A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493412
4. Adaptability of Tacit Learning in Bipedal Locomotion
Author(s): Shimoda, S. ; Yoshihara, Y. ; Kimura, H.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6491448
5. Reaching for the Unreachable: Reorganization of Reaching with Walking
Author(s): Grzyb, B.J. ; Smith, L.B. ; del Pobil, A.P.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493413
6. Redundant Neural Vision Systems—Competing for Collision Recognition Roles
Author(s): Shigang Yue ; Rind, F.C.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6491449
Author(s): Juyang Weng ; Luciw, M.D. ; Qi Zhang
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6504480
2. A Simple Ontology of Manipulation Actions Based on Hand-Object Relations
Author(s): Worgotter, F. ; Aksoy, E.E. ; Kruger, N. ; Piater, J. ; Ude, A. ; Tamosiunaite, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493410
3. An Autonomous Social Robot in Fear
Author(s): Castro-Gonzalez, A. ; Malfaz, M. ; Salichs, M.A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493412
4. Adaptability of Tacit Learning in Bipedal Locomotion
Author(s): Shimoda, S. ; Yoshihara, Y. ; Kimura, H.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6491448
5. Reaching for the Unreachable: Reorganization of Reaching with Walking
Author(s): Grzyb, B.J. ; Smith, L.B. ; del Pobil, A.P.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6493413
6. Redundant Neural Vision Systems—Competing for Collision Recognition Roles
Author(s): Shigang Yue ; Rind, F.C.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6491449
Thursday, July 4, 2013
IEEE Transactions on Computational Intelligence and AI in Games: Volume 5, Issue 2, 2013
1. Guest Editorial: Brain/neuronal - Computer game interfaces and interaction
Author(s): Coyle, D. ; Principe, J. ; Lotte, F. ; Nijholt, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6528021
2. Games, Gameplay, and BCI: The State of the Art
Author(s): Marshall, D. ; Coyle, D. ; Wilson, S. ; Callaghan, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6518141
3. Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device
Author(s): Chumerin, N. ; Manyakov, N.V. ; van Vliet, M. ; Robben, A. ; Combaz, A. ; Van Hulle, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6334432
4.Toward Contextual SSVEP-Based BCI Controller: Smart Activation of Stimuli and Control Weighting
Author(s): Legeny, J. ; Viciana-Abad, R. ; Lecuyer, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6509428
5. Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game
Author(s): Leeb, R. ; Lancelle, M. ; Kaiser, V. ; Fellner, D.W. ; Pfurtscheller, G.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6418003
6. Controlling a Tactile ERP–BCI in a Dual Task
Author(s): Thurlings, M.E. ; Van Erp, J.B.F. ; Brouwer, A.-M. ; Werkhoven, P.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6461930
7. Adapting the P300-Based Brain–Computer Interface for Gaming: A Review
Author(s): Kaplan, A. ; Shishkin, S. ; Ganin, I. ; Basyul, I. ; Zhigalov, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6401177
8. Evaluation and Comparison of a Multimodal Combination of BCI Paradigms and Eye Tracking With Affordable Consumer-Grade Hardware in a Gaming Context
Author(s): Kos'myna, N. ; Tarpin-Bernard, F.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6451193
9. On the Use of Games for Noninvasive EEG-Based Functional Brain Mapping
Author(s): Scherer, R. ; Moitzi, G. ; Daly, I. ; Muller-Putz, G.R.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6472046
10. Electroencephalogram and Physiological Signal Analysis for Assessing Flow in Games
Author(s): Berta, R. ; Bellotti, F. ; De Gloria, A. ; Pranantha, D. ; Schatten, C.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6509413
11. Experiencing BCI Control in a Popular Computer Game
Author(s): van de Laar, B. ; Gurkok, H. ; Plass-Oude Bos, D. ; Poel, M. ; Nijholt, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6484110
12. Two Brains, One Game: Design and Evaluation of a Multiuser BCI Video Game Based on Motor Imagery
Author(s): Bonnet, L. ; Lotte, F. ; Lecuyer, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6400237
Author(s): Coyle, D. ; Principe, J. ; Lotte, F. ; Nijholt, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6528021
2. Games, Gameplay, and BCI: The State of the Art
Author(s): Marshall, D. ; Coyle, D. ; Wilson, S. ; Callaghan, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6518141
3. Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device
Author(s): Chumerin, N. ; Manyakov, N.V. ; van Vliet, M. ; Robben, A. ; Combaz, A. ; Van Hulle, M.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6334432
4.Toward Contextual SSVEP-Based BCI Controller: Smart Activation of Stimuli and Control Weighting
Author(s): Legeny, J. ; Viciana-Abad, R. ; Lecuyer, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6509428
5. Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game
Author(s): Leeb, R. ; Lancelle, M. ; Kaiser, V. ; Fellner, D.W. ; Pfurtscheller, G.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6418003
6. Controlling a Tactile ERP–BCI in a Dual Task
Author(s): Thurlings, M.E. ; Van Erp, J.B.F. ; Brouwer, A.-M. ; Werkhoven, P.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6461930
7. Adapting the P300-Based Brain–Computer Interface for Gaming: A Review
Author(s): Kaplan, A. ; Shishkin, S. ; Ganin, I. ; Basyul, I. ; Zhigalov, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6401177
8. Evaluation and Comparison of a Multimodal Combination of BCI Paradigms and Eye Tracking With Affordable Consumer-Grade Hardware in a Gaming Context
Author(s): Kos'myna, N. ; Tarpin-Bernard, F.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6451193
9. On the Use of Games for Noninvasive EEG-Based Functional Brain Mapping
Author(s): Scherer, R. ; Moitzi, G. ; Daly, I. ; Muller-Putz, G.R.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6472046
10. Electroencephalogram and Physiological Signal Analysis for Assessing Flow in Games
Author(s): Berta, R. ; Bellotti, F. ; De Gloria, A. ; Pranantha, D. ; Schatten, C.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6509413
11. Experiencing BCI Control in a Popular Computer Game
Author(s): van de Laar, B. ; Gurkok, H. ; Plass-Oude Bos, D. ; Poel, M. ; Nijholt, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6484110
12. Two Brains, One Game: Design and Evaluation of a Multiuser BCI Video Game Based on Motor Imagery
Author(s): Bonnet, L. ; Lotte, F. ; Lecuyer, A.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6400237
Monday, July 1, 2013
Reminder: paper submission deadline for TAAI 2013
A reminder that the deadline for submitting paper to the Conference on Technologies and Applications of Artificial Intelligence 2013 is 1 August 2013. This conference will be held in Taipei, Taiwan, 6-8 December 2013.
Monday, June 10, 2013
IJCNN Early registration deadline
The deadline for early registration at the International Joint Conference on Neural Networks (IJCNN) 2013 is rapidly approaching: it is the 15th of June.
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