Thursday, July 5, 2012
Reminder: paper submission deadline for Fuzz-IEEE 2013
A reminder that the deadline for submitting papers to the IEEE Conference on Fuzzy Systems (Fuzz-IEEE) 2013 is 5 January, 2013. This conference will be held in Hyderabad, India, 7-10 July, 2013.
Wednesday, July 4, 2012
Reminder: IEEE CIS Facebook Photo Competition
The IEEE Computational Intelligence Society are running a photo
competition on Facebook. See the flyer below to find out how to enter.
Friday, June 29, 2012
Google Brain
The so-called Google Brain has been in the news the last couple of days (see for example here, here and here, and see here for coverage from the ABC, including part of an interview I did with them on the subject).
The news coverage has focussed on how the machine learned to recognise cats, because cats are cute. Reading the original paper gives a more nuanced view of the technology. The researchers constructed a colossal neural network with one billion neurons, implemented over 1000 16-processor servers. They then presented it with ten million images taken from YouTube, and left it to train for three days before looking to see what it had learned.
The researchers knew that the three most common images on YouTube were cats, human faces, and human bodies. So, they presented images drawn from independent data sets (that is, data sets that were not involved in training the network) that were known to be of cats, faces or bodies, and saw which parts of the network activated. By examining the activations within the network, they found that there were prototypes of cats, faces and bodies within the network. That is, they showed that the network had formed its own exemplars of these objects.
There are four main technical innovations in this paper:
1) The size of the network, which had one billion artificial neurons.
2) The technique they used to reduce the interconnections between the elements of the network, to make it easier to execute in parallel across the 16 000 processors they used.
3) The number of images (ten million) used to train the network.
4) The size of the images used (200 x 200 pixels, which is larger than most).
The network did not learn to recognise cats, faces, or bodies. It still doesn't know what a cat is, or what a face is, or what a human body is. It has no concept of what the images represent. But even so, it still has potential: neural networks have finally reached the age of Big Data.
The news coverage has focussed on how the machine learned to recognise cats, because cats are cute. Reading the original paper gives a more nuanced view of the technology. The researchers constructed a colossal neural network with one billion neurons, implemented over 1000 16-processor servers. They then presented it with ten million images taken from YouTube, and left it to train for three days before looking to see what it had learned.
The researchers knew that the three most common images on YouTube were cats, human faces, and human bodies. So, they presented images drawn from independent data sets (that is, data sets that were not involved in training the network) that were known to be of cats, faces or bodies, and saw which parts of the network activated. By examining the activations within the network, they found that there were prototypes of cats, faces and bodies within the network. That is, they showed that the network had formed its own exemplars of these objects.
There are four main technical innovations in this paper:
1) The size of the network, which had one billion artificial neurons.
2) The technique they used to reduce the interconnections between the elements of the network, to make it easier to execute in parallel across the 16 000 processors they used.
3) The number of images (ten million) used to train the network.
4) The size of the images used (200 x 200 pixels, which is larger than most).
The network did not learn to recognise cats, faces, or bodies. It still doesn't know what a cat is, or what a face is, or what a human body is. It has no concept of what the images represent. But even so, it still has potential: neural networks have finally reached the age of Big Data.
Monday, June 25, 2012
Call for papers: ICANNGA 2013
The paper submission deadline for the International Conference on Adaptive and Natural Computing Algorithms (ICANNGA) 2013 is 8 October, 2013. This conference will be held in Lausanne, Switzerland, April 4-6, 2013.
Friday, June 22, 2012
Call for papers: AROB 2013
The deadline for submitting papers to the 18th International Symposium on Artificial Life and Robotics (AROB) 2013 is 1 September, 2012. This symposium will be held in Daejeon, Korea, January 30 - February 1st, 2013.
Thursday, June 21, 2012
Reminder: paper submission deadline for iFuzzy 2012
A reminder that the deadline for papers submitted to the International Conference on Fuzzy Theory and its Application 2012 is 20 August 2012. This conference will be held in Taiching, Tuiwan, 16-18 November, 2012.
Wednesday, June 20, 2012
Reminder: conference paper deadline for ICIIC 2012
A reminder that the paper submission deadline for the International Conference on Information and Intelligent Computing 2012 is 20 July 2012. This conference will be held in Chengdu, China, 8-9 December, 2012.
Tuesday, June 19, 2012
Paper submission deadline: EMO 2013
The deadline for submitting papers to the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO) 2013 is 19 August 2012. This conference will be held in Sheffield, UK, 19-22 March, 2013.
Monday, June 18, 2012
Call for papers: FOGA 2013
The deadline for submitting papers to the Foundations of Genetic Algorithms (FOGA) 2013 is 1 August 2012. This conference will be held in Adelaide, Australia, 16-20 January, 2013.
I am currently living in Adelaide, and can highly recommend a visit to this beautiful city. My only warning is that in January, the temperature often exceeds 40 degrees!
I am currently living in Adelaide, and can highly recommend a visit to this beautiful city. My only warning is that in January, the temperature often exceeds 40 degrees!
Friday, June 15, 2012
More developments in academic journals
There has been a new development in open-access academic journals (see my previous posts on this matter here, here, here, here and here). Two articles, here and here, describe PeerJ, a new approach to open-access journals. Whereas the traditional publishers charge readers for access to content, and open-access journals charge authors per-paper publication charges, PeerJ charges authors a one-off lifetime publishing fee. As long as all of the authors (or at least the first 12 authors) of a paper are subscribers, the authors can submit as many papers as they like for no further cost. The papers are peer-reviewed, and will be available for free. There are different subscriptions available, ranging from a lower-cost option that allows for a fixed number of papers per year, up to a more expensive "all you can eat" model with no restrictions.
PeerJ is starting with life sciences first: given the large number of researchers and papers coming out of the life sciences, this seems quite sensible and is more likely to give them a solid revenue stream early-on. It is interesting that they are requiring each member to review at least one paper per year, which neatly gets around the problems associated with finding enough reviewers for papers.
I suspect that the computational intelligence community does not have enough researchers to make such a model viable at the rates PeerJ are advertising. So, such a journal would probably have to charge higher subscription rates, or charge an annual or bi-annual fee.
But these are all ways for publishers to make money off of free content (submitted papers) and free labour, in the form of reviewers (who are actually paying for the privilege in the case of PeerJ). I'm not the first person to suggest this, but why not spend some of that money on reviewers? That is, when a reviewer completes an on-time review, pay them a small gratuity (like 100-200 Euros). That would motivate reviewers to do their reviews on time (if you're working for free, there is less motivation to do the work quickly). It would also be a more fair system, as those who provide the most valuable service in the publishing process would be compensated for their time and efforts. Finally, it might make it easier to find reviewers for papers: my own editorial experience has shown me how hard it can be to find reviewers for a paper. I review about a dozen papers per year, so this scheme wouldn't provide me with a living, but it would cover many of the incidental expenses that come up over the year.
Instead of a Boycott Elsevier pledge website, do we need a website where people can pledge to no longer review any papers until publishers start paying? Would anyone sign up for that?
PeerJ is starting with life sciences first: given the large number of researchers and papers coming out of the life sciences, this seems quite sensible and is more likely to give them a solid revenue stream early-on. It is interesting that they are requiring each member to review at least one paper per year, which neatly gets around the problems associated with finding enough reviewers for papers.
I suspect that the computational intelligence community does not have enough researchers to make such a model viable at the rates PeerJ are advertising. So, such a journal would probably have to charge higher subscription rates, or charge an annual or bi-annual fee.
But these are all ways for publishers to make money off of free content (submitted papers) and free labour, in the form of reviewers (who are actually paying for the privilege in the case of PeerJ). I'm not the first person to suggest this, but why not spend some of that money on reviewers? That is, when a reviewer completes an on-time review, pay them a small gratuity (like 100-200 Euros). That would motivate reviewers to do their reviews on time (if you're working for free, there is less motivation to do the work quickly). It would also be a more fair system, as those who provide the most valuable service in the publishing process would be compensated for their time and efforts. Finally, it might make it easier to find reviewers for papers: my own editorial experience has shown me how hard it can be to find reviewers for a paper. I review about a dozen papers per year, so this scheme wouldn't provide me with a living, but it would cover many of the incidental expenses that come up over the year.
Instead of a Boycott Elsevier pledge website, do we need a website where people can pledge to no longer review any papers until publishers start paying? Would anyone sign up for that?
Thursday, June 14, 2012
Publishing and perishing under gameable metrics 2
This article about the Australian Excellence in Research for Australia (ERA) initiative discusses how the process by which Australian universities and academic are assessed is flawed. It also discusses how Australian institutions have been gaming the metrics, like certain New Zealand institutions have been accused of doing.
In this previous post I described how any metric by which an institution or academic is assessed can be gamed. That is, any way in which an academic or institution is assessed can be manipulated by that institution to gain a higher score. In this post, I discussed how this has a negative effect on the teaching performance of an institution. By removing staff who do not perform well in research assessments due to a heavy teaching load, the institution can lift their research scores, but at the cost of lowering their teaching performance. As the article mentions, teaching is not assessed, so the process optimises towards a single metric at the expense of all others. This is not helpful for the long-term viability of an institution, as undergraduates will not want to attend an institution with a poor reputation for teaching.
This situation is almost certain to increase the use of contract lecturers, as contract lecturers are, as I understand it, exempt from assessment. I've already described why increasing contract lecturers is a bad idea, mostly because of a lack of job security and satisfaction for the contract lecturers, as well as a lack of continuity in teaching from the point of view of the students.
It is becoming increasingly apparent to me that assessing institutions is not as useful as assessing individuals, and that, in today's highly-mobile world, the reputation of an institution is no longer as important as the reputation of an individual researcher. This raises an interesting question:
What would happen if research performance based funding were given directly to the researchers based on their own individual performance, rather than their institutions being given extra funding based on the collective research performance of their staff?
The article linked at the start of this post does an excellent job of describing the problems with collective assessments (like what does it mean if you have one researcher ranked 1 and one ranked 5 - do they have a collective performance of 3? What does that even mean?).
Individual funding would remove a lot of the financial motivation for institutions to game the system, although it wouldn't eliminate it (institutions would still make money by charging the individual researchers over-heads, but these could be capped). Under the current Australian and New Zealand systems, individuals are assessed anyway, so it doesn't require any great changes to the current assessment process. One downside (and it could be a stonking big downside) is that early-career researchers would probably do poorly under this model. Early-career are already disadvantaged by management practices designed to game the system, and a simple weighting mechanism accounting for the length of time an individual has been doing research would go a long way to help. This would encourage researchers to start publishing early (which is essential to master the art of scientific publishing) and to publish consistently (which is essential to maintain your publishing skills). Another downside would be senior researchers taking credit for the work of junior researchers. But, again, this happens anyway, even though it is profoundly unethical. Under this system, though, it would no longer be just unethical, it would be criminal fraud.
Such a scheme could only be successful if it were paired with a scheme for assessing and rewarding teaching. While I have stated several times that an academic in a permanent position who is not publishing is not doing their job, an academic with a low (but not non-existent) research output and a strong teaching performance is an asset to an institution. Therefore, it is, in my opinion, imperative that an objective metric for teaching performance be implemented as soon as possible. That way, quality teachers, as well as quality researchers, would be recognised and rewarded. Those who do both (and this is the ideal for an academic, to teach and do research) would score even higher.
In this previous post I described how any metric by which an institution or academic is assessed can be gamed. That is, any way in which an academic or institution is assessed can be manipulated by that institution to gain a higher score. In this post, I discussed how this has a negative effect on the teaching performance of an institution. By removing staff who do not perform well in research assessments due to a heavy teaching load, the institution can lift their research scores, but at the cost of lowering their teaching performance. As the article mentions, teaching is not assessed, so the process optimises towards a single metric at the expense of all others. This is not helpful for the long-term viability of an institution, as undergraduates will not want to attend an institution with a poor reputation for teaching.
This situation is almost certain to increase the use of contract lecturers, as contract lecturers are, as I understand it, exempt from assessment. I've already described why increasing contract lecturers is a bad idea, mostly because of a lack of job security and satisfaction for the contract lecturers, as well as a lack of continuity in teaching from the point of view of the students.
It is becoming increasingly apparent to me that assessing institutions is not as useful as assessing individuals, and that, in today's highly-mobile world, the reputation of an institution is no longer as important as the reputation of an individual researcher. This raises an interesting question:
What would happen if research performance based funding were given directly to the researchers based on their own individual performance, rather than their institutions being given extra funding based on the collective research performance of their staff?
The article linked at the start of this post does an excellent job of describing the problems with collective assessments (like what does it mean if you have one researcher ranked 1 and one ranked 5 - do they have a collective performance of 3? What does that even mean?).
Individual funding would remove a lot of the financial motivation for institutions to game the system, although it wouldn't eliminate it (institutions would still make money by charging the individual researchers over-heads, but these could be capped). Under the current Australian and New Zealand systems, individuals are assessed anyway, so it doesn't require any great changes to the current assessment process. One downside (and it could be a stonking big downside) is that early-career researchers would probably do poorly under this model. Early-career are already disadvantaged by management practices designed to game the system, and a simple weighting mechanism accounting for the length of time an individual has been doing research would go a long way to help. This would encourage researchers to start publishing early (which is essential to master the art of scientific publishing) and to publish consistently (which is essential to maintain your publishing skills). Another downside would be senior researchers taking credit for the work of junior researchers. But, again, this happens anyway, even though it is profoundly unethical. Under this system, though, it would no longer be just unethical, it would be criminal fraud.
Such a scheme could only be successful if it were paired with a scheme for assessing and rewarding teaching. While I have stated several times that an academic in a permanent position who is not publishing is not doing their job, an academic with a low (but not non-existent) research output and a strong teaching performance is an asset to an institution. Therefore, it is, in my opinion, imperative that an objective metric for teaching performance be implemented as soon as possible. That way, quality teachers, as well as quality researchers, would be recognised and rewarded. Those who do both (and this is the ideal for an academic, to teach and do research) would score even higher.
Tuesday, June 12, 2012
International Neural Network Society Social Media sites
The International Neural Network Society (INNS) has established a presence on several popular social media
sites. The goals of this initiative are:
- To promote the membership and activities of the INNS
- To better bring the members of the INNS relevant information about the activities of the society
- To help facilitate networking between members
Members of the INNS and other interested people are invited join us on the following INNS social media sites:
Blog: http://innsorg.blogspot.com/
Twitter: http://www.twitter.com/#!/INNSorg
Facebook: http://www.facebook.com/pages/International-Neural-Network-Society/110873922384495
LinkedIn: http://www.linkedin.com/groups?gid=2985057
Google+: https://plus.google.com/106354210782755399208/posts
- To promote the membership and activities of the INNS
- To better bring the members of the INNS relevant information about the activities of the society
- To help facilitate networking between members
Members of the INNS and other interested people are invited join us on the following INNS social media sites:
Blog: http://innsorg.blogspot.com/
Twitter: http://www.twitter.com/#!/INNSorg
Facebook: http://www.facebook.com/pages/International-Neural-Network-Society/110873922384495
LinkedIn: http://www.linkedin.com/groups?gid=2985057
Google+: https://plus.google.com/106354210782755399208/posts
Friday, June 8, 2012
IEEE Transactions on Autonomous Mental Development, Vol.4, No.1, March 2012
1. Episodic-Like Memory for Cognitive Robots
Stachowicz, D.; Kruijff, G.M.
Page(s): 1-16
Digital Object Identifier: 10.1109/TAMD.2011.2159004
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5871687
2. A Model to Explain the Emergence of Imitation Development Based on Predictability Preference
Minato, T.; Thomas, D.; Yoshikawa, Y.; Ishiguro, H.
Page(s): 17-28
Digital Object Identifier: 10.1109/TAMD.2011.2158098
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5782935
3. Symbolic Models and Emergent Models: A Review
Juyang Weng
Page(s): 29-53
Digital Object Identifier: 10.1109/TAMD.2011.2159113
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5872008
4. A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers
Griffith, S.; Sinapov, J.; Sukhoy, V.; Stoytchev, A.
Page(s): 54-69
Digital Object Identifier: 10.1109/TAMD.2011.2157504
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5778950
5. Autonomous Learning of High-Level States and Actions in Continuous Environments
Mugan, J.; Kuipers, B.
Page(s): 70-86
Digital Object Identifier: 10.1109/TAMD.2011.2160943
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5936108
6. A Goal-Directed Visual Perception System Using Object-Based Top-Down Attention
Yuanlong Yu; Mann, G.K.I.; Gosine, R.G.
Page(s): 87-103
Digital Object Identifier: 10.1109/TAMD.2011.2163513
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5978202
Stachowicz, D.; Kruijff, G.M.
Page(s): 1-16
Digital Object Identifier: 10.1109/TAMD.2011.2159004
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5871687
2. A Model to Explain the Emergence of Imitation Development Based on Predictability Preference
Minato, T.; Thomas, D.; Yoshikawa, Y.; Ishiguro, H.
Page(s): 17-28
Digital Object Identifier: 10.1109/TAMD.2011.2158098
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5782935
3. Symbolic Models and Emergent Models: A Review
Juyang Weng
Page(s): 29-53
Digital Object Identifier: 10.1109/TAMD.2011.2159113
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5872008
4. A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers
Griffith, S.; Sinapov, J.; Sukhoy, V.; Stoytchev, A.
Page(s): 54-69
Digital Object Identifier: 10.1109/TAMD.2011.2157504
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5778950
5. Autonomous Learning of High-Level States and Actions in Continuous Environments
Mugan, J.; Kuipers, B.
Page(s): 70-86
Digital Object Identifier: 10.1109/TAMD.2011.2160943
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5936108
6. A Goal-Directed Visual Perception System Using Object-Based Top-Down Attention
Yuanlong Yu; Mann, G.K.I.; Gosine, R.G.
Page(s): 87-103
Digital Object Identifier: 10.1109/TAMD.2011.2163513
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5978202
Thursday, June 7, 2012
IEEE Transactions on Fuzzy Systems, vol. 20, issue 2, 2012
1. Human Gait Modeling Using a Genetic Fuzzy Finite State Machine
Author(s): Alvarez-Alvarez, A.; Trivino, G.; Cordon, O.
Page(s): 205 - 223
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054027
2. An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System
Author(s): Hosseini, R.; Qanadli, S.D.; Barman, S.; Mazinani, M.; Ellis, T.; Dehmeshki, J.
Page(s): 224 - 234
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054028
3. Exact Output Regulation for Nonlinear Systems Described by Takagi-Sugeno Fuzzy Models
Author(s): Meda-Campana, J.A.; Gomez-Mancilla, J.C.; Castillo-Toledo, B.
Page(s): 235 - 247
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054029
4. A Linguistic Approach to Influencing Decision Behavior
Author(s): Petry, F.E.; Yager, R.R.
Page(s): 248 - 261
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054030
5. Second-Order Sliding Fuzzy Interval Type-2 Control for an Uncertain System With Real Application
Author(s): Manceur, M.; Essounbouli, N.; Hamzaoui, A.
Page(s): 262 - 275
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6056561
6. Genetic Training Instance Selection in Multiobjective Evolutionary
Fuzzy Systems: A Coevolutionary Approach
Author(s): Antonelli, M.; Ducange, P.; Marcelloni, F.
Page(s): 276 - 290
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061952
7. Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model
Author(s): Yi-Chung Cheng; Sheng-Tun Li
Page(s): 291 - 304
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6060907
8. T¨CS Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm
Author(s): Chaoshun Li; Jianzhong Zhou; Bo Fu; Pangao Kou; Jian Xiao
Page(s): 305 - 317
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061951
9. Exponential Stabilization for a Class of Nonlinear Parabolic PDE Systems via Fuzzy Control Approach
Author(s): Huai-Ning Wu; Jun-Wei Wang; Han-Xiong Li
Page(s): 318 - 329
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061953
10. An Improved Input Delay Approach to Stabilization of Fuzzy Systems Under Variable Sampling
Author(s): Xun-Lin Zhu; Bing Chen; Dong Yue; Youyi Wang
Page(s): 330 - 341
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064888
11. Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault
Author(s): Hongyi Li; Honghai Liu; Huijun Gao; Peng Shi
Page(s): 342 - 357
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064886
12. A Fuzzy Approach for Multitype Relational Data Clustering
Author(s): Jian-Ping Mei; Lihui Chen
Page(s): 358 - 371
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6068241
13. A Fuzzy System Constructed by Rule Generation and Iterative Linear SVR
for Antecedent and Consequent Parameter Optimization
Author(s): Chia-Feng Juang; Cheng-Da Hsieh
Page(s): 372 - 384
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070980
14. A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets
Author(s): Degang Chen; Lei Zhang; Suyun Zhao; Qinghua Hu; Pengfei Zhu
Page(s): 385 - 389
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095617
15. Solving Fuzzy Relational Equations Via Semitensor Product
Author(s): Daizhan Cheng; Jun-e Feng; Hongli Lv
Page(s): 390 - 396
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064885
16. On H¡Þ Filtering for Discrete-Time Takagi-Sugeno Fuzzy Systems
Author(s): Hui Zhang; Yang Shi; Mehr, A.S.
Page(s): 396 - 401
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6081923
Author(s): Alvarez-Alvarez, A.; Trivino, G.; Cordon, O.
Page(s): 205 - 223
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054027
2. An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System
Author(s): Hosseini, R.; Qanadli, S.D.; Barman, S.; Mazinani, M.; Ellis, T.; Dehmeshki, J.
Page(s): 224 - 234
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054028
3. Exact Output Regulation for Nonlinear Systems Described by Takagi-Sugeno Fuzzy Models
Author(s): Meda-Campana, J.A.; Gomez-Mancilla, J.C.; Castillo-Toledo, B.
Page(s): 235 - 247
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054029
4. A Linguistic Approach to Influencing Decision Behavior
Author(s): Petry, F.E.; Yager, R.R.
Page(s): 248 - 261
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054030
5. Second-Order Sliding Fuzzy Interval Type-2 Control for an Uncertain System With Real Application
Author(s): Manceur, M.; Essounbouli, N.; Hamzaoui, A.
Page(s): 262 - 275
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6056561
6. Genetic Training Instance Selection in Multiobjective Evolutionary
Fuzzy Systems: A Coevolutionary Approach
Author(s): Antonelli, M.; Ducange, P.; Marcelloni, F.
Page(s): 276 - 290
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061952
7. Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model
Author(s): Yi-Chung Cheng; Sheng-Tun Li
Page(s): 291 - 304
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6060907
8. T¨CS Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm
Author(s): Chaoshun Li; Jianzhong Zhou; Bo Fu; Pangao Kou; Jian Xiao
Page(s): 305 - 317
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061951
9. Exponential Stabilization for a Class of Nonlinear Parabolic PDE Systems via Fuzzy Control Approach
Author(s): Huai-Ning Wu; Jun-Wei Wang; Han-Xiong Li
Page(s): 318 - 329
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061953
10. An Improved Input Delay Approach to Stabilization of Fuzzy Systems Under Variable Sampling
Author(s): Xun-Lin Zhu; Bing Chen; Dong Yue; Youyi Wang
Page(s): 330 - 341
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064888
11. Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault
Author(s): Hongyi Li; Honghai Liu; Huijun Gao; Peng Shi
Page(s): 342 - 357
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064886
12. A Fuzzy Approach for Multitype Relational Data Clustering
Author(s): Jian-Ping Mei; Lihui Chen
Page(s): 358 - 371
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6068241
13. A Fuzzy System Constructed by Rule Generation and Iterative Linear SVR
for Antecedent and Consequent Parameter Optimization
Author(s): Chia-Feng Juang; Cheng-Da Hsieh
Page(s): 372 - 384
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070980
14. A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets
Author(s): Degang Chen; Lei Zhang; Suyun Zhao; Qinghua Hu; Pengfei Zhu
Page(s): 385 - 389
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095617
15. Solving Fuzzy Relational Equations Via Semitensor Product
Author(s): Daizhan Cheng; Jun-e Feng; Hongli Lv
Page(s): 390 - 396
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064885
16. On H¡Þ Filtering for Discrete-Time Takagi-Sugeno Fuzzy Systems
Author(s): Hui Zhang; Yang Shi; Mehr, A.S.
Page(s): 396 - 401
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6081923
Tuesday, June 5, 2012
Final call for papers: INNS-WC 2012
The final deadline for submitting papers to the International Neural Network Society Winter Conference (INNS-WC) has been extended to 15 June 2012. This conference will be held in Bangkok, Thailand, October 3-5, 2012.
IEEE Transactions on Neural Networks and Learning Systems; Volume 23, Issue 6, June 2012
This issue published some papers on sparse representation, semi-supervised learning, spiking neural networks, high level classification, and multitask learning. We welcome you to submit your papers on these topics to IEEE TNNLS.
These articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6203443&punumber=5962385
or directly by clicking the individual paper URL below.
1. Title: Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Authors: Jin Hu; Jun Wang
Page(s): 853 - 865
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6194338
2. Title: Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects
Authors: Tingwen Huang; Chuandong Li; Shukai Duan; Janusz A. Starzyk
Page(s): 866 - 875
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6180003
3. Title: Sparse Tensor Discriminant Color Space for Face Verification
Authors: Su-Jing Wang; Jian Yang; Ming-Fang Sun; Xu-Jun Peng; Ming-Ming Sun; Chun-Guang Zhou
Page(s): 876 - 888
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6180223
4. Title: Programming Time-Multiplexed Reconfigurable Hardware Using a Scalable Neuromorphic Compiler
Authors: Kirill Minkovich; Narayan Srinivasa; Jose M. Cruz-Albrecht; Youngkwan Cho; Aleksey Nogin
Page(s): 889 - 901
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6182588
5. Title: Laplacian Embedded Regression for Scalable Manifold Regularization
Authors: Lin Chen; Ivor W. Tsang; Dong Xu
Page(s): 902 - 915
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6186826
6. Title: Neural Assembly Computing
Authors: João Ranhel
Page(s): 916 - 927
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6186825
7. Title: Extracting Representative Information to Enhance Flexible Data Queries
Authors: Jin Zhang; Guoqing Chen; Xiaohui Tang
Page(s): 928 - 941
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6189794
8. Title: Robust Synchronization for 2-D Discrete-Time Coupled Dynamical Networks
Authors: Jinling Liang; Zidong Wang; Xiaohui Liu; Panos Louvieris
Page(s): 942 - 953
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6191361
9. Title: Network-Based High Level Data Classification
Authors: Thiago Christiano Silva; Liang Zhao
Page(s): 954 - 970
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192353
10. Title: Neural Network Structure for Spatio-Temporal Long-Term Memory
Authors: Vu Anh Nguyen; Janusz A. Starzyk; Wooi-Boon Goh; Daniel Jachyra
Page(s): 971 - 983
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192329
11. Title: Feedback Optimal Control of Distributed Parameter Systems by Using Finite-Dimensional Approximation Schemes
Authors: Angelo Alessandri; Mauro Gaggero; Riccardo Zoppoli
Page(s): 984 - 996
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192328
12. Title: Generalized SMO Algorithm for SVM-Based Multitask Learning
Authors: Feng Cai; Vladimir Cherkassky
Page(s): 997 - 1003
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183517
13. Title: Complexity-Reduced Scheme for Feature Extraction With Linear Discriminant Analysis
Authors: Yuxi Hou; Iickho Song; Hwang-Ki Min; Cheol Hoon Park
Page(s): 1003 - 1009
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6191360
These articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6203443&punumber=5962385
or directly by clicking the individual paper URL below.
IEEE Transactions on Neural Networks and Learning Systems: Volume 23, Issue 6, June 2012
1. Title: Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Authors: Jin Hu; Jun Wang
Page(s): 853 - 865
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6194338
2. Title: Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects
Authors: Tingwen Huang; Chuandong Li; Shukai Duan; Janusz A. Starzyk
Page(s): 866 - 875
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6180003
3. Title: Sparse Tensor Discriminant Color Space for Face Verification
Authors: Su-Jing Wang; Jian Yang; Ming-Fang Sun; Xu-Jun Peng; Ming-Ming Sun; Chun-Guang Zhou
Page(s): 876 - 888
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6180223
4. Title: Programming Time-Multiplexed Reconfigurable Hardware Using a Scalable Neuromorphic Compiler
Authors: Kirill Minkovich; Narayan Srinivasa; Jose M. Cruz-Albrecht; Youngkwan Cho; Aleksey Nogin
Page(s): 889 - 901
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6182588
5. Title: Laplacian Embedded Regression for Scalable Manifold Regularization
Authors: Lin Chen; Ivor W. Tsang; Dong Xu
Page(s): 902 - 915
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6186826
6. Title: Neural Assembly Computing
Authors: João Ranhel
Page(s): 916 - 927
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6186825
7. Title: Extracting Representative Information to Enhance Flexible Data Queries
Authors: Jin Zhang; Guoqing Chen; Xiaohui Tang
Page(s): 928 - 941
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6189794
8. Title: Robust Synchronization for 2-D Discrete-Time Coupled Dynamical Networks
Authors: Jinling Liang; Zidong Wang; Xiaohui Liu; Panos Louvieris
Page(s): 942 - 953
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6191361
9. Title: Network-Based High Level Data Classification
Authors: Thiago Christiano Silva; Liang Zhao
Page(s): 954 - 970
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192353
10. Title: Neural Network Structure for Spatio-Temporal Long-Term Memory
Authors: Vu Anh Nguyen; Janusz A. Starzyk; Wooi-Boon Goh; Daniel Jachyra
Page(s): 971 - 983
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192329
11. Title: Feedback Optimal Control of Distributed Parameter Systems by Using Finite-Dimensional Approximation Schemes
Authors: Angelo Alessandri; Mauro Gaggero; Riccardo Zoppoli
Page(s): 984 - 996
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6192328
12. Title: Generalized SMO Algorithm for SVM-Based Multitask Learning
Authors: Feng Cai; Vladimir Cherkassky
Page(s): 997 - 1003
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183517
13. Title: Complexity-Reduced Scheme for Feature Extraction With Linear Discriminant Analysis
Authors: Yuxi Hou; Iickho Song; Hwang-Ki Min; Cheol Hoon Park
Page(s): 1003 - 1009
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6191360
Thursday, May 31, 2012
An experiment in open-source textbooks 2
To further put my money where my mouth is, in regards to my support for open source textbooks, I'm following up Monday's post by making the outline of my open source textbook, Intelligent Information Systems, available online. The outline is in PDF format, and is available at the following address:
http://mike.watts.net.nz/IIS_Outline.pdf
Readers are encouraged to comment on the outline via the comments section of this blog - I want to hear your opinions!
http://mike.watts.net.nz/IIS_Outline.pdf
Readers are encouraged to comment on the outline via the comments section of this blog - I want to hear your opinions!
Wednesday, May 30, 2012
WCCI 2012 Panel Session on Computational Intelligence in Education and University Curricula
The following panel session at WCCI 2012 is organised by the IEEE Computational Intelligence Society's Curriculum Subcommittee (which I happen to serve on), and will be held Thursday, June 14, 4:10-5:10pm.
Chairs: Robert Kozma and Jennie Si
Panelists: Haibo He, Janusz Kaczprzyk, Jim Keller, Luis Magdalena, Marios Polycarpou, Lipo Wang
Computational Intelligence is a relatively new research field. A lot of educational materials have been created in various fields of CI in the past decades. However, due to the field's relatively youth, its fundamental achievements has not been organized into a comprehensive curriculum yet. It is crucial for the development of the field to have high-quality educational materials on the state of art of CI. This allows attracting and educating talented and enthusiastic students and documenting the progress in the field. The panel will discuss various areas of CI education, including existing databases and course materials, online resources and video lectures, development of new textbooks, open-source software, and others. Various recommendations for future actions will be discussed as well.
Chairs: Robert Kozma and Jennie Si
Panelists: Haibo He, Janusz Kaczprzyk, Jim Keller, Luis Magdalena, Marios Polycarpou, Lipo Wang
Computational Intelligence is a relatively new research field. A lot of educational materials have been created in various fields of CI in the past decades. However, due to the field's relatively youth, its fundamental achievements has not been organized into a comprehensive curriculum yet. It is crucial for the development of the field to have high-quality educational materials on the state of art of CI. This allows attracting and educating talented and enthusiastic students and documenting the progress in the field. The panel will discuss various areas of CI education, including existing databases and course materials, online resources and video lectures, development of new textbooks, open-source software, and others. Various recommendations for future actions will be discussed as well.
Tuesday, May 29, 2012
Reminder: paper submission deadline for AI'12
A reminder that the deadline for submitting papers to the 25th Australasian Joint Conference on Artificial Intelligence (AI) 2012 is 29 June, 2012. This conference will be held in Sydney, Australia, 4-7 December, 2012.
Monday, May 28, 2012
An experiment in open-source textbooks
I am thinking of writing a textbook. Actually, I'm working on three at the moment, one of which is a research monograph, but the one that it most relevant to this post is tentatively titled Intelligent Information Systems, and will cover neural networks, fuzzy systems and evolutionary algorithms at an undergraduate level. I also expect it would be useful for researchers from other disciplines who want to apply methods in computational intelligence to their own research, and to software engineers who want to solve real-world problems with computational intelligence.
In line with this post, I am seriously considering making Intelligent Information Systems available as an open-source textbook. But before I do, I need some encouragement. So I'm asking you, my dear readers, to encourage me. If you think you would assign an open-source textbook on this topic to a class, or that you would buy a self-published textbook, let me know in the comments. If you could see yourself contributing some other way, let me know, too.
It's up to you good folk to push me to do this!
In line with this post, I am seriously considering making Intelligent Information Systems available as an open-source textbook. But before I do, I need some encouragement. So I'm asking you, my dear readers, to encourage me. If you think you would assign an open-source textbook on this topic to a class, or that you would buy a self-published textbook, let me know in the comments. If you could see yourself contributing some other way, let me know, too.
It's up to you good folk to push me to do this!
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