Next week my family and I will be moving away from Adelaide and leaving Australia permanently. We are moving so that I can take up a new, permanent, academic position. Since this move will involve a fair amount of chaos and interrupted Internet access (at least until we find a house and get the Internet connected again), posting to this blog will be quite sporadic.
Monday, August 20, 2012
Friday, August 17, 2012
IEEE Transactions on Neural Networks and Learning Systems: Volume 23, Issue 9, September 2012
1. Adaptive Pinning Control of Deteriorated Nonlinear Coupling Networks With Circuit Realization
Xiao-Zheng Jin; Guang-Hong Yang; Wei-Wei Che
Page(s): 1345 - 1355
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222060
2. Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines
Siamak Mehrkanoon; Tillmann Falck; Johan A. K. Suykens
Page(s): 1356 - 1367
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6224185
3. Exponential Synchronization of Neural Networks With Discrete and Distributed Delays Under Time-Varying Sampling
Zheng-Guang Wu; Peng Shi; Hongye Su; Jian Chu
Page(s): 1368 - 1376
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227362
4. Convergence and Rate Analysis of Neural Networks for Sparse Approximation
Aurèle Balavoine; Justin Romberg; Christopher J. Rozell
Page(s): 1377 - 1389
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227360
5. In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines
Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella
Page(s): 1390 - 1406
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228541
6. Robust Exponential Stability of Uncertain Stochastic Neural Networks With Distributed Delays and Reaction-Diffusions
Jianping Zhou; Shengyuan Xu; Baoyong Zhang; Yun Zou; Hao Shen
Page(s): 1407 - 1416
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228542
7. Online Kernel-Based Learning for Task-Space Tracking Robot Control
Duy Nguyen-Tuong; Jan Peters
Page(s): 1417 - 1425
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6230657
8. Memristor Bridge Synapse-Based Neural Network and Its Learning
Shyam Prasad Adhikari; Changju Yang; Hyongsuk Kim; Leon O. Chua
Page(s): 1426 - 1435
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232461
9. Efficient Sparse Modeling With Automatic Feature Grouping
Leon Wenliang Zhong; James T. Kwok
Page(s): 1436 - 1447
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238378
10. Hierarchical Approach for Multiscale Support Vector Regression
Francesco Bellocchio; Stefano Ferrari; Vincenzo Piuri; Nunzio Alberto Borghese
Page(s): 1448 - 1460
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238377
11. Discretized-Vapnik-Chervonenkis Dimension for Analyzing Complexity of Real Function Classes
Chao Zhang; Wei Bian; Dacheng Tao; Weisi Lin
Page(s): 1461 - 1472
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6239601
12. Limit Set Dichotomy and Multistability for a Class of Cooperative Neural Networks With Delays
Mauro Di Marco; Mauro Forti; Massimo Grazzini; Luca Pancioni
Page(s): 1473 - 1485
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6241435
13. Adaptive Visual and Auditory Map Alignment in Barn Owl Superior Colliculus and Its Neuromorphic Implementation
Juan Huo; Alan Murray; Dongqing Wei
Page(s): 1486 - 1497
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6255791
14. Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
Yimin Yang; Yaonan Wang; Xiaofang Yuan
Page(s): 1498 - 1505
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222007
15. Enhancing Weak Signal Transmission Through a Feedforward Network
Xiaoming Liang; Liang Zhao; Zonghua Liu
Page(s): 1506 - 1512
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227359
Xiao-Zheng Jin; Guang-Hong Yang; Wei-Wei Che
Page(s): 1345 - 1355
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222060
2. Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines
Siamak Mehrkanoon; Tillmann Falck; Johan A. K. Suykens
Page(s): 1356 - 1367
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6224185
3. Exponential Synchronization of Neural Networks With Discrete and Distributed Delays Under Time-Varying Sampling
Zheng-Guang Wu; Peng Shi; Hongye Su; Jian Chu
Page(s): 1368 - 1376
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227362
4. Convergence and Rate Analysis of Neural Networks for Sparse Approximation
Aurèle Balavoine; Justin Romberg; Christopher J. Rozell
Page(s): 1377 - 1389
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227360
5. In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines
Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella
Page(s): 1390 - 1406
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228541
6. Robust Exponential Stability of Uncertain Stochastic Neural Networks With Distributed Delays and Reaction-Diffusions
Jianping Zhou; Shengyuan Xu; Baoyong Zhang; Yun Zou; Hao Shen
Page(s): 1407 - 1416
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228542
7. Online Kernel-Based Learning for Task-Space Tracking Robot Control
Duy Nguyen-Tuong; Jan Peters
Page(s): 1417 - 1425
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6230657
8. Memristor Bridge Synapse-Based Neural Network and Its Learning
Shyam Prasad Adhikari; Changju Yang; Hyongsuk Kim; Leon O. Chua
Page(s): 1426 - 1435
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232461
9. Efficient Sparse Modeling With Automatic Feature Grouping
Leon Wenliang Zhong; James T. Kwok
Page(s): 1436 - 1447
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238378
10. Hierarchical Approach for Multiscale Support Vector Regression
Francesco Bellocchio; Stefano Ferrari; Vincenzo Piuri; Nunzio Alberto Borghese
Page(s): 1448 - 1460
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238377
11. Discretized-Vapnik-Chervonenkis Dimension for Analyzing Complexity of Real Function Classes
Chao Zhang; Wei Bian; Dacheng Tao; Weisi Lin
Page(s): 1461 - 1472
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6239601
12. Limit Set Dichotomy and Multistability for a Class of Cooperative Neural Networks With Delays
Mauro Di Marco; Mauro Forti; Massimo Grazzini; Luca Pancioni
Page(s): 1473 - 1485
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6241435
13. Adaptive Visual and Auditory Map Alignment in Barn Owl Superior Colliculus and Its Neuromorphic Implementation
Juan Huo; Alan Murray; Dongqing Wei
Page(s): 1486 - 1497
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6255791
14. Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
Yimin Yang; Yaonan Wang; Xiaofang Yuan
Page(s): 1498 - 1505
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222007
15. Enhancing Weak Signal Transmission Through a Feedforward Network
Xiaoming Liang; Liang Zhao; Zonghua Liu
Page(s): 1506 - 1512
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227359
Thursday, August 16, 2012
ECoS toolbox API?
Is anyone interested in an ECoS DLL / API? I'm thinking of wrapping the functionality in the command-line ECoS Toolbox up in a DLL and providing an API so that people could include EFuNN and SECoS in their own programs. Is this a worthwhile use of my time? Would anyone use it?
Wednesday, August 15, 2012
Reminder: paper submission deadline for CEC 2013
A reminder that the deadline for submitting papers to the IEEE Congress on Evolutionary Computation (CEC) 2013 is February 15, 2013. This conference will be held in Cancun, Mexico, June 20-23, 2013.
Tuesday, August 14, 2012
Identifying bats with ANN
An interesting paper has just come out in the Journal of Applied Ecology, which describes how ANN were used to identify thirty-four European species of bats based on their echolocation calls. This is a challenging problem, because the calls within any bat species can vary quite a lot, depending on what the bat is doing. For example, the calls that a bat uses while hunting are different to the calls that a bat uses while commuting to a hunting ground. The work described in this paper has several good features.
Firstly, they used a hierarchy of MLP ensembles to identify the species. First a level of MLP identified the geographic region (out of six) that the bat came from. Then a second level was used to identify the genus (out of seven) of the bat. Finally, an ensemble of species-specific MLP identified the species itself.
Secondly, they used a large data set to train the MLP, and performed a thorough data analysis to identify the significant features. Rather than just cramming every acoustic feature through the MLP and hoping for the best, they only used the most significant twenty-four.
Finally, they incorporated the classifiers into software called iBatsID that is freely available for anyone to use.
The authors reported a range of classification accuracies across the species, from a high of 100% to a low of 56.5%. They say that "This is almost certainly the results of our eANN [ensemble ANN] dealing with many more species". I think they're wrong when they say that, because the point of using ensembles is that the individual members of the ensemble can be highly specialised for a particular class. I suspect that the problem may be that the features they selected were not as useful for classifying the poorly-recognised species: rather than using the same twenty-four parameters for all thirty-four species, they might have gotten better results by selecting acoustic parameters for each species. Also, from the diagram (Figure 3 in the paper) it looks like they used the outputs of the regional and genus networks only to decide which groups of species MLP to use. An alternative would have been to use the output of the regional and genus MLP as input features for the following levels (similar to my approach in this paper), which would have added some more information into the classification process and probably boosted accuracy.
A final problem with this paper is that they have excluded a lot of the technical details about constructing and training the ANN, and about exactly how the different levels in the hierarchy interacted. This is probably because it is an ecology paper, not an ANN paper.
Overall, it's an interesting application, and I'm looking forward to seeing more work done on this problem in the future.
Firstly, they used a hierarchy of MLP ensembles to identify the species. First a level of MLP identified the geographic region (out of six) that the bat came from. Then a second level was used to identify the genus (out of seven) of the bat. Finally, an ensemble of species-specific MLP identified the species itself.
Secondly, they used a large data set to train the MLP, and performed a thorough data analysis to identify the significant features. Rather than just cramming every acoustic feature through the MLP and hoping for the best, they only used the most significant twenty-four.
Finally, they incorporated the classifiers into software called iBatsID that is freely available for anyone to use.
The authors reported a range of classification accuracies across the species, from a high of 100% to a low of 56.5%. They say that "This is almost certainly the results of our eANN [ensemble ANN] dealing with many more species". I think they're wrong when they say that, because the point of using ensembles is that the individual members of the ensemble can be highly specialised for a particular class. I suspect that the problem may be that the features they selected were not as useful for classifying the poorly-recognised species: rather than using the same twenty-four parameters for all thirty-four species, they might have gotten better results by selecting acoustic parameters for each species. Also, from the diagram (Figure 3 in the paper) it looks like they used the outputs of the regional and genus networks only to decide which groups of species MLP to use. An alternative would have been to use the output of the regional and genus MLP as input features for the following levels (similar to my approach in this paper), which would have added some more information into the classification process and probably boosted accuracy.
A final problem with this paper is that they have excluded a lot of the technical details about constructing and training the ANN, and about exactly how the different levels in the hierarchy interacted. This is probably because it is an ecology paper, not an ANN paper.
Overall, it's an interesting application, and I'm looking forward to seeing more work done on this problem in the future.
Monday, August 13, 2012
The problem with academic journals 6
In my previous posts on academic journals (see here, here, here, here, here, and here) I've discussed the major problem with academic journals in the context of the huge cost of accessing the content that the journals receive for free, as well as the importance of open-access journals. This post is concerned with another problem that is becoming apparent with journals: the declining acceptance rate for papers submitted to journals, in attempts to foster an image of exclusivity and quality.
A recent editorial by David Wardle describes a quantitative analysis he performed that compared the acceptance rates of four top-ranked ecological journals with the large open-access journal PLoS One, along with the citation rate of papers published in each. What he found was that the four traditional journals accepted less than 20% of the paper submitted to them, while PLoS One accepted around 69%. However, papers that are published in PLoS One are cited more than papers published in one of the traditional journals. His argument was that the traditional journals rejected papers that were of good scientific quality (that is, they described good work) but were not "worthy" of publication in such "august" journals, with the editors using the excuse that limited page space meant that there wasn't room to print the papers, even though they were quite good. He then goes on to explain that this exclusivity was motivated by a desire to increase the perception of quality of the journals. That is, the editors are trying to foster the impression that the journals must be really good, because they're really picky about which papers they publish.
But, the ultimate measure of the quality of a paper is how often it is cited, as that reflects how useful it is to other scientists, and papers published in the less-exclusive open-access journals are cited more. Thus, the concept that journals with low acceptance rates publish better papers is fatally flawed: these journals are rejecting papers that are scientifically sound and are useful to other scientists.
This leads me to think that the only reason the top journals are the top journals are because people think they are. If someone wants an authoritative citation to back up a statement they make in a paper, they will cite a paper in Nature or Science if they can, because these are the top journals (this doesn't happen much in computational intelligence, because very few papers in this field are published in Nature or Science). But the conclusion of Wardle's study is that acceptance rate is not a reliable metric of the quality of a journal. If anything, it is a measure of the snobbery of a journal.
The purpose of peer review (and of reviewers) is as a crap-filter for papers, to keep work that is incorrectly done or poorly presented from entering the literature. But with exclusive journals, the peer reviewers seem to be spending more time deciding which papers are significant enough to be published in the journal, rather than trying to identify flaws in the work. The whole thing reminds me of the reason the great physicist Richard Feynman quit the US National Academy of Science: because they spent most of their time deciding who was "worthy" of joining the Academy.
Not so long ago, we had to consider the quality of journals because it wasn't feasible to track the impact of a single paper. Now, with tools like Google Scholar, we can track the citation histories of individual papers. In short, the journal in which a paper is published is no longer that important: the usefulness, the contribution of the paper is what is important. By the same token, the quality of an academic is not measured by which institution they work for, but by their contributions. Unfortunately, the bean-counters who make the hiring and promotion decisions, and who make decisions on who gets competitive research funding, haven't grasped this concept yet.
Exclusive journals do not make a good contribution to science, as they keep too much useful material out of the public eye for too long: peer-reviewed open-access journals, with their more liberal acceptance rates, are more important then ever in this situation.
A recent editorial by David Wardle describes a quantitative analysis he performed that compared the acceptance rates of four top-ranked ecological journals with the large open-access journal PLoS One, along with the citation rate of papers published in each. What he found was that the four traditional journals accepted less than 20% of the paper submitted to them, while PLoS One accepted around 69%. However, papers that are published in PLoS One are cited more than papers published in one of the traditional journals. His argument was that the traditional journals rejected papers that were of good scientific quality (that is, they described good work) but were not "worthy" of publication in such "august" journals, with the editors using the excuse that limited page space meant that there wasn't room to print the papers, even though they were quite good. He then goes on to explain that this exclusivity was motivated by a desire to increase the perception of quality of the journals. That is, the editors are trying to foster the impression that the journals must be really good, because they're really picky about which papers they publish.
But, the ultimate measure of the quality of a paper is how often it is cited, as that reflects how useful it is to other scientists, and papers published in the less-exclusive open-access journals are cited more. Thus, the concept that journals with low acceptance rates publish better papers is fatally flawed: these journals are rejecting papers that are scientifically sound and are useful to other scientists.
This leads me to think that the only reason the top journals are the top journals are because people think they are. If someone wants an authoritative citation to back up a statement they make in a paper, they will cite a paper in Nature or Science if they can, because these are the top journals (this doesn't happen much in computational intelligence, because very few papers in this field are published in Nature or Science). But the conclusion of Wardle's study is that acceptance rate is not a reliable metric of the quality of a journal. If anything, it is a measure of the snobbery of a journal.
The purpose of peer review (and of reviewers) is as a crap-filter for papers, to keep work that is incorrectly done or poorly presented from entering the literature. But with exclusive journals, the peer reviewers seem to be spending more time deciding which papers are significant enough to be published in the journal, rather than trying to identify flaws in the work. The whole thing reminds me of the reason the great physicist Richard Feynman quit the US National Academy of Science: because they spent most of their time deciding who was "worthy" of joining the Academy.
Not so long ago, we had to consider the quality of journals because it wasn't feasible to track the impact of a single paper. Now, with tools like Google Scholar, we can track the citation histories of individual papers. In short, the journal in which a paper is published is no longer that important: the usefulness, the contribution of the paper is what is important. By the same token, the quality of an academic is not measured by which institution they work for, but by their contributions. Unfortunately, the bean-counters who make the hiring and promotion decisions, and who make decisions on who gets competitive research funding, haven't grasped this concept yet.
Exclusive journals do not make a good contribution to science, as they keep too much useful material out of the public eye for too long: peer-reviewed open-access journals, with their more liberal acceptance rates, are more important then ever in this situation.
Friday, August 10, 2012
Final reminder: IEEE CIS Facebook Photo Competition
The IEEE Computational Intelligence Society are running a photo
competition on Facebook. Go to http://www.watts.net.nz/CIS/contests/photo/2012/ or see the flyer below for further details.
The deadline is three weeks away!
The deadline is three weeks away!
Thursday, August 9, 2012
Reminder: paper submission deadline for IJCNN 2013
A reminder that the deadline for submitting papers to the IEEE International Joint Conference on Neural Networks (IJCNN) 2013 is February 1, 2013. This conference will be held in Dallas, Texas, August 4-9, 2013.
Wednesday, August 8, 2012
Reminder: paper submission deadline for EvoStar 2013
Tuesday, August 7, 2012
IEEE Transactions on Autonomous Mental Development: Volume 4, Issue 2, 2012
1. The “Interaction Engine”: A Common Pragmatic Competence Across Linguistic and Nonlinguistic Interactions
Pezzulo, G.
Page(s): 105 - 123
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006515
2. Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning
Firouzi, H.; Ahmadabadi, M.N.; Araabi, B.N.; Amizadeh, S.; Mirian, M.S.; Siegwart, R.
Page(s): 124 - 138
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6032073
3. Tool–Body Assimilation of Humanoid Robot Using a Neurodynamical System
Nishide, S.; Tani, J.; Takahashi, T.; Okuno, H.G.; Ogata, T.
Page(s): 139 - 149
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095595
4. Are Robots Appropriate for Troublesome and Communicative Tasks in a City Environment?
Hayashi, K.; Shiomi, M.; Kanda, T.; Hagita, N.
Page(s): 150 - 160
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111246
5. Brain-Like Emergent Spatial Processing
Juyang Weng; Luciw, M.
Page(s): 161 - 185
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095596
Pezzulo, G.
Page(s): 105 - 123
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006515
2. Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning
Firouzi, H.; Ahmadabadi, M.N.; Araabi, B.N.; Amizadeh, S.; Mirian, M.S.; Siegwart, R.
Page(s): 124 - 138
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6032073
3. Tool–Body Assimilation of Humanoid Robot Using a Neurodynamical System
Nishide, S.; Tani, J.; Takahashi, T.; Okuno, H.G.; Ogata, T.
Page(s): 139 - 149
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095595
4. Are Robots Appropriate for Troublesome and Communicative Tasks in a City Environment?
Hayashi, K.; Shiomi, M.; Kanda, T.; Hagita, N.
Page(s): 150 - 160
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111246
5. Brain-Like Emergent Spatial Processing
Juyang Weng; Luciw, M.
Page(s): 161 - 185
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095596
Monday, August 6, 2012
IEEE Transactions on Computational Intelligence and AI in Games: Volume 4, Issue 2, 2012
1. N-Grams and the Last-Good-Reply Policy Applied in General Game Playing
Tak, M.J.W.; Winands, M.H.M.; Bjornsson, Y.
Page(s): 73 - 83
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203383
2. A Discrete Evolutionary Model for Chess Players' Ratings
Fenner, T.; Levene, M.; Loizou, G.
Page(s): 84 - 93
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6168229
3. Evolving Multimodal Networks for Multitask Games
Schrum, J.; Miikkulainen, R.
Page(s): 94 - 111
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6179519
4. Bitwise-Parallel Reduction for Connection Tests
Browne, C.; Tavener, S.
Page(s): 112 - 119
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6185647
5. Information Set Monte Carlo Tree Search
Cowling, P.I.; Powley, E.J.; Whitehouse, D.
Page(s): 120 - 143
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203567
6. Benchmarks for Grid-Based Pathfinding
Sturtevant, N.R.
Page(s): 144 - 148
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6194296
Tak, M.J.W.; Winands, M.H.M.; Bjornsson, Y.
Page(s): 73 - 83
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203383
2. A Discrete Evolutionary Model for Chess Players' Ratings
Fenner, T.; Levene, M.; Loizou, G.
Page(s): 84 - 93
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6168229
3. Evolving Multimodal Networks for Multitask Games
Schrum, J.; Miikkulainen, R.
Page(s): 94 - 111
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6179519
4. Bitwise-Parallel Reduction for Connection Tests
Browne, C.; Tavener, S.
Page(s): 112 - 119
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6185647
5. Information Set Monte Carlo Tree Search
Cowling, P.I.; Powley, E.J.; Whitehouse, D.
Page(s): 120 - 143
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203567
6. Benchmarks for Grid-Based Pathfinding
Sturtevant, N.R.
Page(s): 144 - 148
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6194296
Friday, August 3, 2012
IEEE Transactions on Evolutionary Computation: Volume 16, Issue 4, 2012
1. Solving Multicommodity Capacitated Network Design Problems Using Multiobjective Evolutionary Algorithms
Kleeman, M. P.; Seibert, B. A.; Lamont, G. B.; Hopkinson, K. M.; Graham, S. R.
Page(s): 449 - 471
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151105
2. An Integrated Neuroevolutionary Approach to Reactive Control and High-Level Strategy
Kohl, N.; Miikkulainen, R.
Page(s): 472 - 488
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151106
3. A Process Algebra Genetic Algorithm
Karaman, S.; Shima, T.; Frazzoli, E.
Page(s): 489 - 503
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6045330
4. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization
Schutze, O.; Esquivel, X.; Lara, A.; Coello, C. A. C.
Page(s): 504 - 522
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151115
5. Promoting Creative Design in Interactive Evolutionary Computation
Kowaliw, T.; Dorin, A.; McCormack, J.
Page(s): 523 - 536
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151108
6. Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games
Chiong, R.; Kirley, M.
Page(s): 537 - 555
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151098
7. A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments
Li, C.; Yang, S.
Page(s): 556 - 577
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151109
8. On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint
Beyer, H.-G.; Finck, S.
Page(s): 578 - 596
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151095
Kleeman, M. P.; Seibert, B. A.; Lamont, G. B.; Hopkinson, K. M.; Graham, S. R.
Page(s): 449 - 471
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151105
2. An Integrated Neuroevolutionary Approach to Reactive Control and High-Level Strategy
Kohl, N.; Miikkulainen, R.
Page(s): 472 - 488
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151106
3. A Process Algebra Genetic Algorithm
Karaman, S.; Shima, T.; Frazzoli, E.
Page(s): 489 - 503
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6045330
4. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization
Schutze, O.; Esquivel, X.; Lara, A.; Coello, C. A. C.
Page(s): 504 - 522
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151115
5. Promoting Creative Design in Interactive Evolutionary Computation
Kowaliw, T.; Dorin, A.; McCormack, J.
Page(s): 523 - 536
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151108
6. Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games
Chiong, R.; Kirley, M.
Page(s): 537 - 555
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151098
7. A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments
Li, C.; Yang, S.
Page(s): 556 - 577
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151109
8. On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint
Beyer, H.-G.; Finck, S.
Page(s): 578 - 596
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151095
Thursday, August 2, 2012
IEEE Transactions on Fuzzy Systems: Volume 20, Issue 4, 2012
1. Finite-Time $H_{infty}$ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback
He, S.; Liu, F.
Page(s): 605 - 614
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6094200
2. A Practical Approach to R&D Portfolio Selection Using the Fuzzy Pay-Off Method
Hassanzadeh, F.; Collan, M.; Modarres, M.
Page(s): 615 - 622
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6109284
3. Fuzzy Hardware: A Retrospective and Analysis
Zavala, A. H.; Nieto, O. C.
Page(s): 623 - 635
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111466
4. On Robust Fuzzy Rough Set Models
Hu, Q.; Zhang, L.; An, S.; Zhang, D.; Yu, D.
Page(s): 636 - 651
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111464
5. Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults
Shen, Q.; Jiang, B.; Cocquempot, V.
Page(s): 652 - 665
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111465
6. Constrained Fuzzy Hierarchical Analysis for Portfolio Selection Under Higher Moments
Nguyen, T. T.; Gordon-Brown, L.
Page(s): 666 - 682
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112209
7. An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification
Chen, Y-.C.; Pal, N. R.; Chung, I-.F.
Page(s): 683 - 698
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112676
8. Generalizing the Decentralized Control of Fuzzy Discrete Event Systems
Jayasiri, A.; Mann, G. K. I.; Gosine, R. G.
Page(s): 699 - 714
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112712
9. Participatory Learning of Propositional Knowledge
Yager, R. R.
Page(s): 715 - 727
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6119214
10. The $K$-Means-Type Algorithms Versus Imbalanced Data Distributions
Liang, J.; Bai, L.; Dang, C.; Cao, F.
Page(s): 728 - 745
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6121900
11. Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals
Kumar, M.; Neubert, S.; Behrendt, S.; Rieger, A.; Weippert, M.; Stoll, N.; Thurow, K.; Stoll, R.
Page(s): 746 - 759
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6127913
12. Fuzzy Preferences in the Graph Model for Conflict Resolution
Bashar, M. A.; Kilgour, D. M.; Hipel, K. W.
Page(s): 760 - 770
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6127912
13. Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems
Tong, S. C.; Li, Y. M.; Shi, P.
Page(s): 771 - 785
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6126023
14. On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images
Medina, J. M.; Jaime-Castillo, S.; Barranco, C. D.; Campana, J. R.
Page(s): 786 - 803
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6208854
He, S.; Liu, F.
Page(s): 605 - 614
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6094200
2. A Practical Approach to R&D Portfolio Selection Using the Fuzzy Pay-Off Method
Hassanzadeh, F.; Collan, M.; Modarres, M.
Page(s): 615 - 622
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6109284
3. Fuzzy Hardware: A Retrospective and Analysis
Zavala, A. H.; Nieto, O. C.
Page(s): 623 - 635
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111466
4. On Robust Fuzzy Rough Set Models
Hu, Q.; Zhang, L.; An, S.; Zhang, D.; Yu, D.
Page(s): 636 - 651
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111464
5. Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults
Shen, Q.; Jiang, B.; Cocquempot, V.
Page(s): 652 - 665
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111465
6. Constrained Fuzzy Hierarchical Analysis for Portfolio Selection Under Higher Moments
Nguyen, T. T.; Gordon-Brown, L.
Page(s): 666 - 682
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112209
7. An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification
Chen, Y-.C.; Pal, N. R.; Chung, I-.F.
Page(s): 683 - 698
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112676
8. Generalizing the Decentralized Control of Fuzzy Discrete Event Systems
Jayasiri, A.; Mann, G. K. I.; Gosine, R. G.
Page(s): 699 - 714
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6112712
9. Participatory Learning of Propositional Knowledge
Yager, R. R.
Page(s): 715 - 727
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6119214
10. The $K$-Means-Type Algorithms Versus Imbalanced Data Distributions
Liang, J.; Bai, L.; Dang, C.; Cao, F.
Page(s): 728 - 745
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6121900
11. Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals
Kumar, M.; Neubert, S.; Behrendt, S.; Rieger, A.; Weippert, M.; Stoll, N.; Thurow, K.; Stoll, R.
Page(s): 746 - 759
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6127913
12. Fuzzy Preferences in the Graph Model for Conflict Resolution
Bashar, M. A.; Kilgour, D. M.; Hipel, K. W.
Page(s): 760 - 770
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6127912
13. Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems
Tong, S. C.; Li, Y. M.; Shi, P.
Page(s): 771 - 785
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6126023
14. On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images
Medina, J. M.; Jaime-Castillo, S.; Barranco, C. D.; Campana, J. R.
Page(s): 786 - 803
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6208854
Wednesday, August 1, 2012
Reminder: paper submission deadline for AROB 2013
A reminder that 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.
Monday, July 30, 2012
IEEE Transactions on Neural Networks and Learning Systems; Volume 23, Issue 8, August 2012
1. Title: Twenty Years of Mixture of Experts
Authors: Seniha Esen Yuksel; Joseph N. Wilson; Paul D. Gader
Page(s): 1177 - 1193
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6215056
2. Title: Constrained Empirical Risk Minimization Framework for Distance Metric Learning
Authors: Wei Bian; Dacheng Tao
Page(s): 1194 - 1205
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203595
3. Title: Scale-Invariant Amplitude Spectrum Modulation for Visual Saliency Detection
Authors: Dongyue Chen; Hao Chu
Page(s): 1206 - 1214
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212362
4. Title: Relaxed Fault-Tolerant Hardware Implementation of Neural Networks in the Presence of Multiple Transient Errors
Authors: Hamid Reza Mahdiani; Sied Mehdi Fakhraie; Caro Lucas
Page(s): 1215 - 1228
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213557
5. Title: Mapping Dynamic Bayesian Networks to $alpha$-Shapes: Application to Human Faces Identification Across Ages
Authors: Djamel Bouchaffra
Page(s): 1229 - 1241
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6215055
6. Title: Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion
Authors: Sabri Boutemedjet; Djemel Ziou
Page(s): 1242 - 1253
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218198
7. Title: SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps
Authors: Yuan Cao; Haibo He; Hong Man
Page(s): 1254 - 1268
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218200
8. Title: Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks
Authors: Li-Chiu Chang; Pin-An Chen; Fi-John Chang
Page(s): 1269 - 1278
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218199
9. Title: Spatial Gaussian Process Regression With Mobile Sensor Networks
Authors: Dongbing Gu; Huosheng Hu
Page(s): 1279 - 1290
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218781
10. Title: Adaptive Data Embedding Framework for Multiclass Classification
Authors: Tingting Mu; Jianmin Jiang; Yan Wang; John Y. Goulermas
Page(s): 1291 - 1303
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6220302
11. Title: Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation
Authors: Jose García Moreno-Torres; José A. Sáez; Francisco Herrera
Page(s): 1304 - 1312
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6226477
12. Title: Kernel Recursive Least-Squares Tracker for Time-Varying Regression
Authors: Steven Van Vaerenbergh; Miguel Lázaro-Gredilla; Ignacio Santamaría
Page(s): 1313 - 1326
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227361
13. Title: Discrete-Time Neural Inverse Optimal Control for Nonlinear Systems via Passivation
Authors: Fernando Ornelas-Tellez; Edgar N. Sanchez; Alexander G. Loukianov
Page(s): 1327 - 1339
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238379
14. Title: Equilibria of Perceptrons for Simple Contingency Problems
Authors: Michael R. W. Dawson; Brian Dupuis
Page(s): 1340- 1344
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213123
Authors: Seniha Esen Yuksel; Joseph N. Wilson; Paul D. Gader
Page(s): 1177 - 1193
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6215056
2. Title: Constrained Empirical Risk Minimization Framework for Distance Metric Learning
Authors: Wei Bian; Dacheng Tao
Page(s): 1194 - 1205
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203595
3. Title: Scale-Invariant Amplitude Spectrum Modulation for Visual Saliency Detection
Authors: Dongyue Chen; Hao Chu
Page(s): 1206 - 1214
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212362
4. Title: Relaxed Fault-Tolerant Hardware Implementation of Neural Networks in the Presence of Multiple Transient Errors
Authors: Hamid Reza Mahdiani; Sied Mehdi Fakhraie; Caro Lucas
Page(s): 1215 - 1228
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213557
5. Title: Mapping Dynamic Bayesian Networks to $alpha$-Shapes: Application to Human Faces Identification Across Ages
Authors: Djamel Bouchaffra
Page(s): 1229 - 1241
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6215055
6. Title: Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion
Authors: Sabri Boutemedjet; Djemel Ziou
Page(s): 1242 - 1253
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218198
7. Title: SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps
Authors: Yuan Cao; Haibo He; Hong Man
Page(s): 1254 - 1268
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218200
8. Title: Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks
Authors: Li-Chiu Chang; Pin-An Chen; Fi-John Chang
Page(s): 1269 - 1278
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218199
9. Title: Spatial Gaussian Process Regression With Mobile Sensor Networks
Authors: Dongbing Gu; Huosheng Hu
Page(s): 1279 - 1290
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6218781
10. Title: Adaptive Data Embedding Framework for Multiclass Classification
Authors: Tingting Mu; Jianmin Jiang; Yan Wang; John Y. Goulermas
Page(s): 1291 - 1303
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6220302
11. Title: Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation
Authors: Jose García Moreno-Torres; José A. Sáez; Francisco Herrera
Page(s): 1304 - 1312
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6226477
12. Title: Kernel Recursive Least-Squares Tracker for Time-Varying Regression
Authors: Steven Van Vaerenbergh; Miguel Lázaro-Gredilla; Ignacio Santamaría
Page(s): 1313 - 1326
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227361
13. Title: Discrete-Time Neural Inverse Optimal Control for Nonlinear Systems via Passivation
Authors: Fernando Ornelas-Tellez; Edgar N. Sanchez; Alexander G. Loukianov
Page(s): 1327 - 1339
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238379
14. Title: Equilibria of Perceptrons for Simple Contingency Problems
Authors: Michael R. W. Dawson; Brian Dupuis
Page(s): 1340- 1344
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213123
Friday, July 27, 2012
Fraud in science
Ars Technica has a slightly tongue-in-cheek article on how to commit scientific fraud and get away with it. The article discusses eight points:
A long list of co-authors is not as common in CI (point 2) as it is in other fields, but I have seen many, many papers that are going over the same topic as has been covered many times before (point 3). Also, many, many papers cover minimal, slightly incremental "improvements" to existing algorithms that are of little true interest to most other researchers (point 4).
While one of the great joys of working in computational intelligence lies in the broad range of applications the field can be applied to, it does provide more opportunity to publish in journals that specialise is other fields (point 5).
The remaining three points (6-8) are more concerned with how not to get caught, or rather, how not to draw attention to yourself while committing fraud.
Fraud is always a problem, and I don't think that it is any less common in CI than in any other field. A greater emphasis on the use of statistics in CI papers would help guard against fraud (see my posts here and here about increasing the statistical basis of CI papers). But apart from that, we still depend on the honesty and integrity of the authors.
- Fake data nobody ever expects to see
- Work with many collaborators
- Tell people what they already know
- Don't do research anyone cares about
- Don't publish in journals focused on your field
- Distribute responsibility
- Don't plagiarize
- Don't duplicate images
A long list of co-authors is not as common in CI (point 2) as it is in other fields, but I have seen many, many papers that are going over the same topic as has been covered many times before (point 3). Also, many, many papers cover minimal, slightly incremental "improvements" to existing algorithms that are of little true interest to most other researchers (point 4).
While one of the great joys of working in computational intelligence lies in the broad range of applications the field can be applied to, it does provide more opportunity to publish in journals that specialise is other fields (point 5).
The remaining three points (6-8) are more concerned with how not to get caught, or rather, how not to draw attention to yourself while committing fraud.
Fraud is always a problem, and I don't think that it is any less common in CI than in any other field. A greater emphasis on the use of statistics in CI papers would help guard against fraud (see my posts here and here about increasing the statistical basis of CI papers). But apart from that, we still depend on the honesty and integrity of the authors.
Thursday, July 26, 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.
Wednesday, July 25, 2012
More on open access journals
Continuing my series of posts on open access journals (see here and here), this article by Simon Owens in U.S. News is an excellent and detailed review of the debate. The article compares open access journals to e-books: while e-books have existed for a long time, it is only in the last five years that they have really taken off, after reaching a tipping point. Owens argues that open access journals have reached that tipping point, and the academic journal publishing business (known for the huge profits they extract from university libraries) is on the verge of serious disruption.
I tend to agree with his assessment, open access journals have been flying largely under the radar for a long time, but I get the sense that they are becoming more accepted among the top researchers: when more top researchers publish in open-access journals, they will gain credibility.
The old publishing model is being destroyed by greed: journals are just too expensive, and suck too much money out of universities that should be spent funding research and paying people's salaries. Open access is the future of scientific publishing.
I tend to agree with his assessment, open access journals have been flying largely under the radar for a long time, but I get the sense that they are becoming more accepted among the top researchers: when more top researchers publish in open-access journals, they will gain credibility.
The old publishing model is being destroyed by greed: journals are just too expensive, and suck too much money out of universities that should be spent funding research and paying people's salaries. Open access is the future of scientific publishing.
Tuesday, July 24, 2012
A small victory for open access 2
Following up from my earlier post, this article in The Economist gives a pretty good overview of the recent UK and EU move towards requiring the outputs of publicly-funded research being published as open access. The article also gives a lot of context about the different open access publishing models - the "gold" model practiced by PLoS, where authors pay a fee to publish; and the "green" model that the USA's NIH demands, whereby papers are published in traditional journals, but the journals must allow authors to publish their papers in an open repository like PubMed after one year.
So, when are we going to start seeing one of these models applied to computational intelligence journals? I'd be especially pleased if the IEEE were to adopt one of these models, as they lock every single paper they publish up behind a paywall, seemingly for all of time.
So, when are we going to start seeing one of these models applied to computational intelligence journals? I'd be especially pleased if the IEEE were to adopt one of these models, as they lock every single paper they publish up behind a paywall, seemingly for all of time.
Monday, July 23, 2012
Conference paper deadline: KES-IDT 2013
The deadline for submitting papers to the 5th International Conference on Intelligent Decision Technologies (KES-IDT) is 6 January 2013. This conference will be held in Sesimbra, Portugal, 26-28 June 2013.
Friday, July 20, 2012
A small victory for open access
All taxpayer-funded research in the UK must now be published as open access papers, according to this article in the BBC. The British government will be providing £50m in subsidies for researchers to pay the fees necessary to have their work available as open access.
This is a victory for open access. But, the victory is not complete. Firstly, the £50m is coming out of general research funding, it's not new money. In other words, there will be less research done because of this, as there will be less money available to fund it. Secondly, the money is going to the established academic publishers, who are just going to use it to further pad their profits. Finally, as the article states, many journals will still not accept articles that have the relevant data available from open data repositories.
I still think that eventually, open access journals will over-whelm the old publishers. But they can only do this if the top researchers contribute quality research articles to them. Meanwhile, I personally think that the next step is for reviewers (and editors) to start demanding payment for the labour they provide to the publishers. It is we reviewers and editors who provide the quality control for the journals, it's time we got paid for it.
Would anyone be willing to sign up for a boycott of all publishers, until reviewers and editors are paid?
This is a victory for open access. But, the victory is not complete. Firstly, the £50m is coming out of general research funding, it's not new money. In other words, there will be less research done because of this, as there will be less money available to fund it. Secondly, the money is going to the established academic publishers, who are just going to use it to further pad their profits. Finally, as the article states, many journals will still not accept articles that have the relevant data available from open data repositories.
I still think that eventually, open access journals will over-whelm the old publishers. But they can only do this if the top researchers contribute quality research articles to them. Meanwhile, I personally think that the next step is for reviewers (and editors) to start demanding payment for the labour they provide to the publishers. It is we reviewers and editors who provide the quality control for the journals, it's time we got paid for it.
Would anyone be willing to sign up for a boycott of all publishers, until reviewers and editors are paid?
Subscribe to:
Posts (Atom)
Posts
