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
Wednesday, September 11, 2013
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.
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