Reminder: paper submission deadline for IEEE-SSCI 2013

A reminder that the deadline for the IEEE Symposium Series in Computational Intelligence 2013 is 10 October 2012. This series of symposia will be held in Singapore 16-19 April 2013.

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.

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.

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.

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.

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.

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.

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.

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.

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!

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?

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.

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

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

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

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.

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

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!

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.

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.

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!

Competition Call for IEEE CIS GOLDs and Students: Pitch your CI Research Idea and Win an iPad 2!!!

The following is cross-posted from the IEEE Computational Intelligence Society blog.

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The CIS GOLD subcommittee is hosting a “Novel CI Research Idea Pitch” competition during the Student and GOLD reception at WCCI 2012 in Brisbane Australia.

Your Challenge: Design a one-page research proposal of your Computational Intelligence idea and get a chance to pitch your idea to a panel of CI experts and your peers using an “elevator pitch” (3 minute time limit). An “elevator pitch” is a short summary of a research idea. The research area must be “computational intelligence” and the participants must “sell” their idea to the judges to qualify for prizes. A panel of three CI experts will select 3 best pitches and the audience (your peers) will rank 1st, 2nd and 3rdwinner through secret ballot. Prizes will include 1 iPad for 1st winner, certificates and free full year IEEE CIS memberships. Register now for a chance to be heard!

Submission Guidelines: Interested GOLDs and Students should consult the full Brief and Submission Guidelines by going to http://tinyurl.com/cp8kdw8. Registration and submission deadline is June 11th 2012, Midnight EST. (You can register for the competition without submitting the research summary).

Register Now (Space is Limited!): http://tinyurl.com/7tror22

Publishing and perishing under gameable metrics

My alma mater is in the New Zealand news again, and again it is to do with gaming the metrics by which the research performance of New Zealand tertiary institutions are measured. This time, the article describes how many staff with poor publishing records have been made redundant from the university (that is, they have lost their jobs) prior to the assessment later this year. While I have little sympathy for those in permanent lecturing positions who do not publish (see my previous comments here and here) in this case it seems like the staff who have lost their jobs are predominantly teaching staff, or staff who are still developing their research record (see this post from one who lost her job for the same reason some time ago). If that is the case, then I have to say that the university administration is making a mistake.

Teaching takes a lot of time and energy (my last semester teaching at Otago, I was in the office at least six days a week, and often worked from 7:30 in the morning to 9 or 10 at night). The purpose of having teaching-only staff is to take some of that load off of the lecturers so that they can do their research. Indeed, the major thrust of the article is that the redundancies are putting more stress on the remaining staff, as they are having to pick up extra teaching in addition to lifting their own research outputs. While the teaching load could in theory be reduced by hiring contract lecturers (who would not, as I understand it, be assessed) I have already posted on why this is a bad idea.

From my research with evolutionary algorithms, I know that optimising to one criteria or metric seldom results in optimal or robust systems. By optimising their staff to one (flawed and gameable) metric, the University of Otago is reducing the robustness of their institution. The long-term outcome of these redundancies is yet to be seen, but I do not think that it will be good for anyone concerned. Non-performers need to be removed, for sure, but early-career researchers need coaching and leadership to develop. They don't need the great big stick stick of the threat of redundancy waved at them (such threats are more often than not a sign of dysfunctional management, rather than a sign of competent leadership).

Ultimately, only those who set the metrics can resolve this situation. As long as a metric can be gamed, then institutions will game them. In the meantime, people will have their lives upended and their careers destroyed by narrow-minded administrators and cynical political operators who are trying to wring a few more points out of the system to make themselves look good.

The problem with academic journals: An update

 A brief update on the status of the Elsevier boycott (described here): to date, more than 11 000 academics have pledged to not review, submit or do editorial work for any Elsevier journals. My previous post has already described why I oppose such a boycott of a single publisher, and I expect that this boycott is going to cause some unanticipated consequences.

I suspect that this boycott explains why the papers I have under review in Ecological Modelling and Ecological Informatics are taking so long to go through the review process: it's hard enough finding reviewers as it is, and with people refusing to review for Elsevier, it's going to get even harder. That's not punishing Elsevier, that's punishing the researchers who are trying to get their work published and advance their careers.

As I said before, the way real change will come about is by the top researchers supporting open-access journals. At least one of the people who could do this has just done so: Winston Hide, an associate editor at the highly-ranked Elsevier journal Genomics has just resigned from the editorial board, with the avowed intention of focusing his energies on open-access alternatives. I can only hope that some of the top researchers in computational intelligence will do the same.

Call for papers: INNS-WC 2012

INNS-WC2012 – 2012 International Neural Network Society Winter Conference


Bangkok, Thailand, October 3-5, 2012

http://inns.sit.kmutt.ac.th/wc2012/

Important Dates

Paper submission deadline:                May 31, 2012
Notification of acceptance:                June 30, 2012
Camera-ready paper:                        July 31, 2012

The 3rd International Neural Network Society Winter Conference (INNS-WC2012) will be held in Bangkok, Thailand, on October 3-5, 2012. INNS-WC2012 aims to bring together scientists, practitioners, and students worldwide, to discuss the past, present, and future challenges and trends in the area of natural and machine intelligence. This event has been a bi-annual conference of the International Neural Network Society (INNS) to provide a forum for international researchers to exchange latest ideas and advances on neural networks and related discipline. INNS-WC2012 solicits contributions to the following tracks in natural and machine intelligence and related areas:

• INNS-WC general track: Trends in Natural and Machine Intelligence
• INNS Symposium on Nature Inspired Creativity (SoNIC2012)
• INNS Symposium on Vision and Image Processing (SoVIP2012)
• INNS Symposium on Data Analytics and Competitions (SoDAC2012)

Prospective authors are invited to submit original, high quality manuscripts of up to twelve (12) pages electronically. Short papers of 4-6 pages will also be considered. The submission must conform to the Elsevier Procedia Computer Science format. All accepted papers will be published in the proceedings of INNS-WC2012 as anElsevier Procedia Computer Science open access volume (indexed by EI, Scopus and Conference Proceedings Citation Index - formerly ISI Proceedings). Extended version of selected papers may be invited for publication in special issues of international journals after the conference. All submissions will be checked by VeriGuide for originality.

The range of topics for the general conference track on

"Trends in Natural and Machine Intelligence" includes but is not limited to

• Autonomous machine learning
• Neural network theory & models
• Computational neuroscience
• Cognitive models
• Brain-machine interfaces
• Embodied robotics
• Evolutionary neural systems
• Neurodynamics
• Neuroinformatics
• Neuroengineering
• Neural hardware
• Neural network applications
• Pattern recognition
• Machine vision
• Speech science and technology
• Collective intelligence
• Hybrid systems
• Self-aware systems
• Data mining
• Sensor networks
• Agent-based systems
• Computational biology
• Bioinformatics
• Artificial life

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SoNIC2012

The range of topics for the INNS Symposium on Nature Inspired Creativity (SoNIC2012) includes but is not limited to

• Application of Nature Inspired Computing in Creative Industries:
• Creative computing for digital media
• Computer aided design
• Computer generated special effects for film
• Cartoon animation
• Games
• Music
• Edutainment, etc.
• Art and Cognition:
  • Art and the Brain
  • Creative process
  • Emotion and meaning in paintings, music, sculptures, poetry, etc.
• Generative Art:
  • Systems that create drawings, images, animations, sculptures, poetry, text, graphic designs, musical pieces, sound-fonts, sound effects, film music, etc.
• Aesthetic evaluation:
• Aesthetic analysis of film, image, music, sound, sculpture, etc.
  

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SoVIP2012

The range of topics for the INNS Symposium on Vision and Image Processing (SoVIP2012) includes but is not limited to:

• Low-level image processing
• Feature extraction and image description
• Image classification and clustering
• 3D sensing and depth measuring systems
• 3D object modeling and reconstruction
• Tracking and surveillance
• Motion estimation
• Human gesture recognition
• Human motion analysis
• Human face detection and tracking
• Human-robot interactions
• Robot intelligence
• Humanoid and mobile robotics
• Video indexing and retrieval
• Intelligent compression of massive imaging data
• Bio-medical imaging applications
• Bio-robotics
• Biometrics

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SoDAC2012

The range of topics for the INNS Symposium on Data Analytics and Competitions (SoDAC2012) includes but is not limited to:

• Business intelligence
• Air quality and environmental issues
• Chemo-informatics
• Social networks and analytics
• Speech prosody
• Geo-informatics
• Neuro-informatics
• Internet and web analytics
• Data visualization techniques
• Data quality analysis
• Decision support and analytics
• Knowledge management and discovery
• Advanced database analytics
• Content and information retrieval
• Modeling and simulation of complex systems
• Optimization techniques
• Bio-data analysis
• Complex scheduling problems
• Scalability of data analysis 
• Data competitions

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Collocated Conferences

• The 3rd International Conference on Computational Systems-Biology and Bioinformatics (CSBio2012 - www.csbio.org)
• The 11th International Conference on Bioinformatics (InCoB2012 - www.incob2012.org)

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.

Reminder: paper submission deadline for CISE 2012

A reminder that the deadline for papers submitted to the 4th International Conference on Computational Intelligence and Software Engineering (CISE) 2012 is 11 June 2012. This conference will be held in Wuhan, China, December 14-16, 2012.

Call for papers: Fuzz-IEEE 2013

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.

Call for papers: IJCNN 2013

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.

Call for papers: CEC 2013

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.

Call for papers: EvoStar 2013

The paper submission deadline for EvoStar 2013 is 1 November, 2012. This conference will be held in Vienna, Austria, 3-5 April, 2013.

Deadline extension: UKCI 2012

The paper submission deadline for the 12th UK Annual Workshop on Computational Intelligence (UKCI) 2012  has been extended to May 31, 2012. This workshop will be held in Edinburgh, Scotland, UK, 5-7 September, 2012.

Reminder: conference paper deadline for NIPS 2012

A reminder that the deadline for submitting papers to Neural Information Processing Systems (NIPS) 2012 is 2 June 2012. This conference will be held at Lake Tahoe, Nevada, 3-6 December, 2012.

Reminder: submission deadline for ELM 2012

A reminder that the deadline for submitting papers to the International Symposium on Extreme Learning Machines (ELM) 2012 is 1 June, 2012. This symposium will be held in Singapore 11-13 December, 2012.

There's no un-gameable metric

I've been a bit quiet on the blog front lately, mostly because I've been working like a dog on several projects, including writing tools for ecological modelling, re-working some websites, and fulfilling my duties both as guest editor of my special issue of Evolving Systems on Applications of Evolving Connectionist Systems, and as vice-chair of the IEEE CIS Social Media Subcommittee. It was also school holidays the last two weeks here in South Australia, and I was able to spend some quality time with my little girl.

Never fear, I'm working on several new blog posts on a variety of topics, including: the relationship between computational intelligence and data mining; further thoughts on doing a PhD (a follow-up to this post); my thoughts on the value of a computational intelligence degree; and my thoughts on collaborating with other researchers. The topic of today's post, though, is assessing academics and universities.

My alma mater has been in the New Zealand news lately (see here and here) after the release of a report by accounting firm KPMG that suggests that Otago had gamed the New Zealand government research assessment process to give themselves a higher score than they were entitled to.

The Performance-Based Research Funding (PBRF) framework rates the research outputs of eligible staff and uses those ratings, along with metrics of institutional performance such as number of research degrees completed, to assign an overall score to the institution. Staff can be rated as R (research inactive - bad for this exercise), C (research active / good), B (very good) or A (world-class). The fewer R's and C's an institution has, and the more B's and A's, the better the institution's score. Something like 25-30% of an institution's income will be determined by this score. There is also the huge marketing advantage of an institution scoring highly in relation to the other universities: in the first PBRF round in 2004, Auckland University made a lot of the fact that their staff were, on average, ranked highest in the country, while Otago made a lot of the fact that they were ranked highest as an institution. This is despite the government of the day clearly saying that PBRF wasn't supposed to be used for such comparisons, or as a management tool.

Eagle-eyed readers may have noticed the term "eligible staff" earlier in the previous paragraph: it is this facet of the process that Otago is accused of gaming.

The accusation is that Otago inappropriately classified staff it knew would get low scores as ineligible for assessment, and thus artificially boosted its ranking compared to other New Zealand institutions. Otago is also accused of firing, or pushing into retirement, staff based on their anticipated PBRF score. The vice-chancellor denies these accusations, and the whole thing is turning into a "he said / she said" situation.

Did Otago really do this? I honestly don't know. I do know that when I was working at Otago in 2004 (the first PBRF assessment round), I was assessed fully, and fairly, even though it would have been pretty easy to classify me as ineligible for assessment. I don't think my score in PBRF at the time was particularly helpful to their overall ranking, but maybe it wasn't too harmful, either. My point is, this entire drama shows that there is no metric of academic performance, of an individual, an institution, or a publication, that can't be gamed. That is, there is no metric that can't be manipulated so that an individual, institution or publication gets a higher score than they otherwise would. Journals can boost their impact factor by asking authors to cite articles from within that publication (and I have had editors ask me to do this). Individuals can boost their h-index by auto-citations, or by organising a special issue and asking every author to cite a review article they have written. Institutions can raise their assessment by head-hunting the top-performers in their fields, or by hiding staff from assessment. Some might argue that it is only prudent to game metrics whenever possible: after all, the future employment prospects of an academic, or the future financial security (and, therefore, job security of staff of) an institution depends on getting a good score on whatever metric is being used. As long as no rules are being broken, and the questions are being answered honestly, what's the harm? If there is wiggle-room, or room for gaming of the metric, isn't it the assessor's fault for designing an inexact metric? Others might argue that adherence to the spirit of the assessment is more important, more fair, and that gaming should be avoided.

This all means that there is no one metric you can use to assess an academic. You have to look at the entire picture: you have to look at their publication count; where they have published; what fields they have published in; how much teaching they have done; their teaching assessments; the quality of their institution; and their service to their institution(s), to professional societies, and to the community. I hope that one day I will rate highly in all of those areas, but for now, don't judge me just by my h-index alone.

The future of universities

Or, why contract lecturers are probably a bad idea.

The last time I was job-hunting, I noticed a number of positions advertised as "sessional" or "contract" lecturers. These were positions where a person would present a few lectures a week for a certain course, for a fixed period of time, then leave the institution. In this article, the use of contract lecturers in American universities is described as a crisis, where quality of teaching is suffering and the highly-skilled educators end up severely under-paid. While administrators justify this as a way of cutting monetary costs, the educational costs are huge.

Firstly, contract lecturers are not available for struggling students. This is because they are seldom paid full-time, which makes it difficult to find time for out-of-class student consultation: people don't like to work for nothing.

Secondly, the fly-by-night nature of contract lecturers prevents them from forging bonds with cohorts of students: the students see them for one course, then never see them again. In other words, the contract lecturer has no motivation and little opportunity to see their students as anything other than faceless blobs that sit in the lectures absorbing information. This is not conducive to high-quality teaching.

This also makes it harder to recruit post-graduate students. I vividly remember the first time I was lectured by the man who would go on to be my PhD supervisor: I was a first-year undergraduate, sitting in a lecture theatre on a cold Dunedin evening, and he described a world of computational intelligence that I knew right then was a world I wanted to explore myself. I knew that if I worked hard in my first and second year courses, I would be able to do his third-year honours-track course, and if I did well in third-year, I could do his fourth-year honours course, and if I did well in that, I could do a PhD with him. If he had been a fly-by-night contract lecturer, would I have been as inspired? I probably would have skipped honours and gone into the workforce straight after third year. While that might have placed me in a slightly better financial position, my life would be much less rich than it is now.

While I don't have evidence for it, I suspect that contract lecturing does not overall attract the best teaching talent. Now, I'm not trying to denigrate contract lecturers, and I know several people who have worked as contract lecturers to support themselves while looking for post-docs, immediately after completing their PhDs. But as a highly-trained professional (which is what anyone with a PhD is) it is hard to justify taking a contract lecturer position if there are any other options available. I never even bothered applying for the contract lecturing positions I saw advertised, even though I was capable of doing them well, simply because it was not worth my while to shift myself and my family to do the job. If I were a single man, perhaps I could embrace the digital nomad lifestyle, and drift about doing contract lecturing here and there. But with a family to support, including a primary-school age daughter, it simply is not an option.

On the flip side, contract lecturing can provide a way for junior staff to get some experience lecturing. Also, technology is getting to the point where the lecturer no longer has to be in the same physical location as the class: the success of the Khan Academy and open courses (like the courses run by Sebastian Thrun) has shown that it is possible to have a class that is far away from the instructors. If the option to teach remotely were there, it might be easier to get top-talent as contract lecturers. I wouldn't mind being a contract lecturer if it meant I didn't have to relocate. That is, I wouldn't mind the job so much if I didn't have to move to do it. Of course, the alienation between lecturer and student that I discussed above could become even greater.

I think that the use of contract lecturers is probably going to increase, especially for first-year or general "service" courses, like for introductory programming or basic web development. But for more advanced under-graduate courses, or for post-graduate teaching, permanent staff are absolutely essential, due to the multi-year nature of post-graduate study. This also requires a level of specialisation that contract lecturers simply cannot develop: they are treated like interchangeable parts, which is no way to treat anyone, let alone someone who you expect to teach, and to inspire, students.

Reminder: paper submission deadline for CIDU 2012

A reminder that the deadline for submitting papers to the 2012 Conference on Intelligent Data Understanding (CIDU) 2012 is 18 May 2012. This conference will be held in Boulder, Colorado, 24-26 October, 2012.

Special session: Computational Intelligence and Social Media

The below is a call for papers for a special session of the 2012  IEEE Workshop on Computational Intelligence for Security and Defence Applications (IEEE CISDA) 2012. The deadline for submitting papers for this special session is 23 April, 2012. This conference will be held in Ottawa, Canada, 11-13 July 2012.

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Computational Intelligence and Social Media

Organizers: John Verdon, DRDC Ottawa, Canada
Contact: john.verdon -at- drdc-rddc.gc.ca

Military and security communities are hard-pressed to develop the capabilities required to exploit the huge volumes of data, the new forms of information, and rapidly changing content of Social Media (SM) such as blogs, wikis, videos, social graph based systems (such as Facebook, Twitter) and many other SM systems that are being deployed. SM is also in its infancy, so there is a huge potential for SM to evolve far beyond its current capabilities and types of information.

Computational Intelligence techniques (Neural network, Evolutionary computation, Fuzzy Systems, Particle Swarms, etc) have often been based on, and and have been related to, highly complex, structured, and dynamic natural systems in [biology, neuroscience, brain, psychology, sociology]. This may make them particularly well suited for the extraction of intelligence from existing forms of SM, for the [modeling, prediction, control] of SM activity, as well as for providing some capability of keeping up with rapidly evolving and new forms of SM. Papers that deal with massive datasets are of particular interest, and, naturally, papers should relate to defense and security needs, applications, and tool-sets. Security & Defense needs and Social Media [some examples from Forrester 2011, Verdon 2012] topics include, but are not limited to:

• Language translation - filtering a collection of documents down to those that should be translated by humans
• Knowledge extraction - validating facts from unstructured and questionable sources
• Document summarization - extracting the sense of a document or a group of topically-related documents, and establishing the main points of consensus and divergence
• Trend identification - as well as possible causal linkages among trends and supporting evidence
• Active learning - determining where information is lacking and which data would be most productive to acquire
• Security - at what threshold does secrecy become a liability rather than an asset for security?
• Reputation and/or Recommendation - ‘quick trust’ of the participants and possibly of the information

Deadline extended: IEEE CISDA 2012

The deadline for papers submitted to the IEEE Workshop on Computational Intelligence for Security and Defence Applications (IEEE CISDA) 2012 has been extended to 23 April 2012. This workshop will be held in Ottawa, Canada, 11-13 July 2012.

Call for papers: NCEI'12

The deadline for submitting abstracts to the Neuro-Computing and Evolving Intelligence2012 (NCEI'12) is 30 April 2012. This conference will be held in Auckland, New Zealand, 30 June 2012.

Reminder: paper submission deadline ICONIP 2012

A reminder that the paper submission deadline for the International Conference on Neural Information Processing (ICONIP) 2012 is May 15, 2012. This conference will be held in Doha, Qatar, November 12-15, 2012.

Reminder: paper submission deadline for IDA 2012

A reminder that the deadline for submitting papers to the Eleventh International Symposium on Intelligence Data Analysis (IDA) 2012 is 12 May 2012. This symposium will be held in Helsinki, Finland, 27-27 October, 2012.

Call for papers: iFuzzy 2012

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.

Reminder: paper submission deadline for IEEE-SSCI 2013

A reminder that the deadline for the IEEE Symposium Series in Computational Intelligence 2013 is 10 October 2012. This series of symposia will be held in Singapore 16-19 April 2013.

Building an online presence as an academic

There are several reasons an academic might want to establish an online presence. The first is good old-fashioned self-promotion: this is especially important for early-career academics. No-one else is going to promote your work, so you have to do it yourself. Carefully building an online presence that connects your name with your area of expertise is one way to build your profile and to get your name known.

I have quite a common name ("Michael" and "John" are something like the second or third most common given names for males in my generation, and "Watts" is the second or third most common surname for people of English ancestry) but if you Google for "Michael Watts computational intelligence" 45 of the first 50 hits are either my pages or pages that specifically mention me, such as committees I serve on. So, as far as Google is concerned, my name is linked pretty strongly with computational intelligence (certainly more strongly than it is with ecological modelling - 29/50 - which is what I get paid to do).

Secondly, communicating your work to other scientists and to the public is at the heart of what scientists do: idealistically, our work is done to benefit humanity, but it cannot do that if no-one knows about what you do. Of course, the primary means of communicating with other scientists is via papers and conferences, but papers are not very accessible to the general public: they are written for other scientists, that is, they can be quite abstract and hard to read, and papers can be hard to find, that is, locked behind pay-walls. An online presence, however, can be made much more accessible. It does not need to be written in the strict "scientific style", it can include links to supporting material to assist reader comprehension, and it is freely available.


Having a website is a good start, and is a good place to put things like software and teaching materials that you want to make available for others to use. If you have something to say though (and every scientist should have something to say) then a blog is an excellent way of saying it. I started this blog because I was inspired to do so by two of the people I work with, both of whom run popular blogs, on climate change and conservation biology respectively. After studying their blogs and realising that there was nothing equivalent for computational intelligence, I started this blog. It takes me an hour or two per week to produce new content for the blog, which I personally think is time well spent.

There are many social media and networking sites out there, and it is worth your time to establish profiles in as many of them as you can. The obvious ones are Facebook*, LinkedIn, Twitter and Google+ but there are also a number of networking sites specifically for academics. The big ones appear to be Academia.edu and ResearchGate, but others are network.nature.com, Epernicus, KES International, Research pages, the Research Cooperative, scholarz.net, biomedexperts.com, scispace.com, mynetresearch.com, labroots.com, researchiscool.com, iamresearcher.com, researchr.org and hypertope.com. There are also publication trackers like Google Scholar Citations and Researcher ID. Most networking and social media sites allow you to specify a research interest and a home page, so I always list computational intelligence and point them all to my own web site. This has the effect of creating a lot of points on the web that, firstly, associate my name with computational intelligence, and secondly, associate my name with my website. This has the effect of boosting my name in the search engine results. It can be a lot of work to set these profiles up, especially if you have a lot of publications, but maintenance after that is limited to updating sites when you publish new papers.

Having a website isn't that expensive (I pay about AU$110 per year for the website and domain name). Blogs are free (unless you want to associate it with a domain name, which is still pretty cheap), as are the social media and networking sites I use. The best thing is, many of these can be linked together so that an update on one site is propagated to others (see the report I wrote here about linking this blog to other sites).

There are several articles about scientists and social media that are well worth a read: here, here, here, here, and here. These cover things like using Twitter to communicate more effectively. So, why not invest some time and a bit of money, and start establishing your own online presence?

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*You may notice that I haven't linked to my Facebook profile: this is because I mostly use Facebook to keep in touch with old friends and family members, who aren't particularly interested in Computational Intelligence.

Edited 17 April to add link to researchr.org
Edited 11 April to add link to iamresearcher.com

Conference paper deadline: ICIIC 2012

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.

Website on Evolving Connectionist Systems updated

I've updated my website on Evolving Connectionist Systems at ecos.watts.net.nz. The major change is that the equations are all rendered using MathML, which makes them much neater and easier to read.

Call for papers: IEEE ICACI 2012

The deadline for submitting papers to the IEEE International Conference on Advanced Computational Intelligence 2012 is 15 May 2012. This conference will be held in Nanjing, China, 18-20 October, 2012.

Conference paper deadline: MLSP 2012

The deadline for submitting papers to the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2012 is 7 May 2012. This workshop will be held in Santanda, Spain, 23-26 September 2012.

Deadline extended: Special Issue: Applications of ECoS

The deadline for submitting papers to the special issue of Evolving Systems on Applications of Evolving Connectionist Systems has been extended to 14 May 2012.

Reminder: paper submission deadline for UKCI 2012

A reminder that the paper submission deadline for the 12th UK Annual Workshop on Computational Intelligence (UKCI) 2012 is May 1, 2012. This workshop will be held in Edinburgh, Scotland, UK, 5-7 September, 2012.

Open source textbooks

The principle of Open Source Software (OSS) has been established for a long time. The Linux OS (or GNU/Linux, for the purists out there) made the idea of freely giving away software and source code respectable. Concerns about the quality of the software, and whether or not companies could make money from open source, have all been washed away over the years. OSS tends to be more stable, has bugs fixed faster, and evolves faster than commercial software. Also, companies have been making money from OSS for years: Red Hat being just one example.

In this pod cast transcript Steven Cherry from IEEE Spectrum talks with Richard Baraniuk of Rice University about Open Source Textbooks (OST). Baraniuk has founded the Connexions platform, a platform for developing open source textbooks.

I can think of several objections to the idea of OST, but I believe that, in common with OSS, these objections are not insurmountable problems:

Firstly, there is the issue of quality control. When an author submits a book proposal to a publisher, the publisher will send the outline and a sample chapter to reviewers. But, the reviewers tend to be people the author knows, as unlike anonymous peer review of journal articles, a textbook author can often nominate the reviewers of their proposal.

Secondly, there is the issue of formatting the book. If you use an authoring system like LaTeX, formatting a book isn't really that hard (certainly easier than formatting a book in Word). Publishers tend to only provide an author with a template, anyway.

Thirdly, advertising the book. This seems to vary fairly widely between different publishers, with some putting a lot of effort into it, and others doing much less. With the reach that the Internet provides people now, I don't see advertising as a large issue. If you have a blog, website, or networking profile (and I think that a serious academic should have all of these), you can advertise your book there. If you can afford it, you can buy some ads through Google or one of the other advertising services. It takes a bit of work, but not as much as writing the book in the first place.

Fourthly, producing the book. If you are going entirely for a soft-copy, open-source approach, that's not a problem: just whack the book up on a website, and let people download it. If you want to sell hard copies, then you can go with a publishing-on-demand (POD) service like lulu.com. Using POD has the advantage that you don't need to pay for inventory before you can start selling copies. That is, while most traditional publishers like to produce the hard copies themselves, they also like to print several thousand copies, and then sell them. With POD, copies are printed as they are sold. No inventory, so no big pile of books (money!) sitting in a warehouse where they might get sold later on. If the publisher doesn't decide to kill the book, or sell the lot off at a loss, or just pulp them.

Finally, money! Traditional publishers take a big chunk of the sale price of a book for themselves: around 90%, or more. Combined with the relatively small number of copies that most textbooks sell, an author isn't going to make a lot of money from the exercise (there are exceptions, but it's a pretty long tail: most textbook authors will make very little money, and just a few will make a lot). If you publish open-source, then there are other ways of making money from the book - advertising on the website you host it on, soliciting donations, and selling hard copies via POD services, which tend to give larger shares to the authors. For an early-career author like myself, the biggest problem I face isn't missing out on a royalty cheque, it's obscurity.

I've come to realise that, in common with the problems with academic journal publishers, textbook publishers really don't add that much value. Sure, there is the cachet associated with publishing with certain publishers, just as there is with publishing with certain journals, but is that enough of a reason to put up with their disadvantages?

An OST system like Connexions also solve most of the objections I listed above: material that is submitted to Connexions is subject to peer review, it is becoming well-established as a place to go to for OST, and they sell hard copies. I really do think that, just as open access journals are the future for publishing papers, open source textbooks are the future of textbooks, and that within a generation (certainly within my working lifetime) we will see traditional text book publishers diminish in importance.

Is Connexions to OST as Source Forge is to OSS? Would you spend money to buy a hard-copy of an OST textbook? Would you contribute money in other ways to support the work of an OST author? Would you assign an OST as a class textbook?

Reminder: paper submission deadline SEAL 2012

A reminder that the deadline for submitting papers to the 9th International Conference on Evolution and Learning (SEAL) 2012 is 1 May 2012. This conference will be held in Hanoi, Vietnam, 16-19 December, 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.

Squirrel detection with SVM

Below is an entertaining video explaining how to automatically squirt squirrels with a water gun. What's interesting about this is that the presenter used a Support Vector Machine (SVM) to classify the images from the camera as either a squirrel or not a squirrel. I haven't talked about SVM on this blog much, but they are very powerful, learning algorithms that often outperform neural networks in classification applications.

He starts talking about the details of squirrel detection about the 7:30 mark - before that he describes the image processing toolkit he used to segment the images from the camera into blobs, where each blob needed to be classified as either a squirrel or not a squirrel. I was particularly interested in how he used three different kinds of features as inputs to the SVM: size of the blob segmented from the image; the colour histogram of the blob; and the entropy of the blob, where entropy is used as a measure of the "fuzziness" of the blob - squirrels have fuzzy, furry tails, while birds do not. This shows that careful thought is always required when selecting the inputs to a classifier or a learning algorithm. You can't just throw everything in and hope to get something useful out!

Scientific Writing

Adam Ruben has a written a rather tongue-in-cheek essay on How to Write Like a Scientist. He asks why can't we scientists write the way other people write? Why are scientific papers written the way they are?

Scientific papers are written the way the are because of their purpose. The whole point of a paper is to describe what the authors did and what they found, and to communicate this as widely as possible, to readers who may not have English as a first language, or who may be approaching the paper from a different field. If papers are going to do this effectively, they have to be unambiguous. The problem with being unambiguous in English is that there are so many words that have the same, or nearly the same, meaning - only, lone, sole, and so on.

Papers use the past tense because you are describing what you have done, not what you are doing or what you will do. Papers have used the passive voice for a long time, but I've noticed a change to active voice recently, and I'm trying to move to active voice in my own writing (my current supervisor once admonished me with "No-one in my lab uses the passive voice!"). The same thing applies to using "we" or "the author" instead of "I" - it's been the fashion to not use "I", but that's changing. If the work was done by more than one person, then it's entirely appropriate to use "we".

I especially liked his comments about the use of "obviously" and I'll admit I've used it a few times myself. Not to demonstrate my intellectual superiority, but to forestall comments from reviewers: the times when I've not inserted a phrase like "obviously, ovens can be hot"*, at least one reviewer has pointed it out.

The use of idioms should be avoided. Idioms can be highly specific to a certain culture. For example, Australians and New Zealanders both speak English. Also, the New Zealand accent is close enough to the Australian accent that most of the time, when I speak, I can pass for a local. The one thing that gives me away as a New Zealander in Australia is the idioms I use: I use New Zealand idioms that just aren't used in Australia. Now, imagine I used those idioms in a paper read by people all over the world: how many people would understand it?**

I've touched on this issues in a previous post, as well as common grammatical errors to avoid and ten rules for good writing. I think papers can be made more accessible without losing clarity, but it's going to take time, and a lot more work from authors.

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*This refers to a sign on the oven in the tea room shared by the IT staff at Lincoln University: "Warning: Oven may be hot". Other signs in the area read "Warning: fridge may be cold" and "Warning: floor".

**That said, I have recently used the term "munted" in a paper - look it up if you want to know what it means!

Reminder: paper submission deadline for AIAA 2012

A reminder that the deadline for submitting papers to the 7th International Symposium Advances in Artificial Intelligence and Applications (AIAA) 2012 is 22 April 2012. This conference will be held in Wroclaw, Poland, 9-12 September, 2012.

Reminder: paper submission deadline for iCAST 2012

A reminder that the deadline for submitting papers to the 4th International Conference on Awareness Science and Technology (iCAST) 2012 is 15 April 2012. The conference will be held in Seoul, Korea, 21-24 August, 2012.

Call for papers: Applications of ECoS

Special issue of Evolving Systems on

Applications of Evolving Connectionist Systems

Guest Editor

Michael J. Watts
University of Adelaide, Australia
mjwatts@ieee.org

Scope

The topic of this special issue is “Applications of Kasabov’s Evolving Connectionist Systems”.

In modern society, the volume and rate of data production are huge and set to increase. To process and utilise this avalanche of data, methods are needed that can rapidly and accurately model it as it becomes available. These models must be able to learn throughout their lifetimes, without forgetting what they have previously learned, and be able to explain themselves.

Kasabov’s Evolving Connectionist Systems (ECoS) are able to fulfil each of these requirements. They are a class of constructive neural networks that learn via structural growth and adaptation. They have a fast, one-pass learning algorithm, where all that can be learnt from the data is learned in the first training pass. Because of their open structure, they exhibit continuous, life-long learning whereby the structure expands as necessary to accommodate new data. Finally, they have a strong resistance to catastrophic forgetting following additional training on new data.

Examples of ECoS networks include the Evolving Fuzzy Neural Network (EFuNN), which was the first ECoS network published and is characterised by embedded fuzzy logic elements. There is also the Simple Evolving Connectionist System (SECoS), which is essentially an EFuNN with the fuzzy elements removed, and the Dynamic Evolving Fuzzy Inference System (DENFIS) for discovering Takagi-Sugeno style fuzzy rules. Many ECoS networks use fuzzy rule extraction algorithms that allow for the explanation of what the networks have learned, in a comprehensible manner.

ECoS networks are well suited to applications that are dealing with new data continuously and that have dynamic, time-critical aspects. Previous applications of ECoS include:

• Stock market prediction and macroeconomic modelling
• Speech recognition, especially multi-speaker speech recognition
• Bioinformatics and medical modelling
• Image and video parsing
• Robot control
• Information system security

The special issue is concerned with all aspects of the application of ECoS networks to real-life problems and data sets. Topics of interest include, but are not limited to:

• Applications of ECoS to real-world problems
• Data mining of complex data sets using ECoS
• Comparisons of ECoS with other algorithms over real-world data sets
• Modifications of ECoS algorithms to fit them to real-world problems

Proposed Schedule

• Submission due date: 16 April, 2012
• Preliminary notification of acceptance: 4 June, 2012
• Revised manuscripts due: 9 July, 2012
• Final acceptance notification: 6 August, 2012
• Final version due: 3 September, 2012
• Intended publication date: January, 2013

Submission

 The special issue invites original contributions within the specified scope. Manuscripts must not be under review elsewhere, nor can they have been previously published. Extended conference papers must contain at least 30% new material. Please format all manuscripts according to the Instructions for Authors:

 http://www.springer.com/physics/complexity/journal/12530

Please submit all papers via the online submission system:

 https://www.editorialmanager.com/evos/

Reminder: conference paper deadline for IJCCI 2012

A reminder that the deadline for submitting papers to the International Joint Conference on Computational Intelligence (IJCCI) 2012 is 19 April 2012. This conference will be held in Barcelona, Spain, 5-7 October, 2012.

Reminder: paper submission deadline for ISICA 2012

A reminder that the deadline for papers submitted to the International Symposium on Intelligence Computing and Applications (ISICA) 2012 is 15 April 2012. This conference will be held in Wuhan, China, 27-28 October, 2012.

Reminder: paper deadline for ICCCI 2012

A reminder that the deadline for papers submitted to the 4th International Conference on Computational Collective Intelligence (ICCCI) 2012 is 15 April 2012. This conference will be held in Ho Chi Minh City, Vietnam, 28-30 November, 2012.

Reminder: paper submission deadline: IEEE CIG 2012

A reminder that the deadline for submitting papers to the IEEE Conference on Computational Intelligence in Games (IEEE CIG) 2012 is 15 April 2012. This conference will be held in Granada, Spain, 12-15 September, 2012.

Reminder: paper submission deadline for CBR-MD 2012

A reminder that the deadline for submitting papers to the International Workshop Case-Based Reasoning (CBR-MD) 2012 is 13 April 2012. This workshop will be held in Berlin, Germany, 20 July 2012.

Publishing or perishing 3

Following this previous post, I've found a few more articles about the dismissal of staff from the University of Sydney, for not publishing at least four papers in three years. This article is scathing of the university management, while Alex Burns, in this post, is in a similar vein to what I wrote: that four papers in three years is not an excessive number and that most academics should be able to meet it.

The most interesting post I found was this one, from David McGloin at the University of Dundee, where he lists a number of reasons someone might not reach the target number of publications. This is useful, as it informs as to what strategies academics could follow to avoid finding themselves in this sort of situation. Below, I reproduce each of his points, along with my responses:

It could be that you’re on the brink of some big breakthrough and the focus of this has taken many years to complete.

It is very dangerous for any academic to focus on one project to the exclusion of all others. Also, any project can be broken up into small, publishable chunks. That is, rather than writing up a single magnum opus of a paper at the end of the project, one should publish several smaller papers during the course of the work. This is more likely to make funding agencies happy as well, as it shows them that you are being consistently productive.

It may be that you’ve had bad luck with papers getting accepted, or have been doing good work, but maybe aiming to high up the journal league table.

Reformatting a rejected paper for another journal doesn't take very long. Waiting for reviews takes a long time (six months or more for some of my papers) but again, no academic should be working on just one project at a time. Everyone gets a period of bad luck with getting papers accepted (I had a long string of rejections throughout 2010 - I made up for it in 2011). The important thing is to have more than one project on the go at any one time, and to have a fast turn-around on rejected papers. If you follow a journal article submission strategy similar to the one I suggest, you should already have a good idea of other journals you could submit to.

It could be that you have had a run of poor luck on grant funding, and have not had the support to do your work and write up results, in terms of PhD students, postdocs, or simply a lack of equipment.

I do tend to agree with this point, especially for fields that need a lot of equipment to get results. I will point out, though, that having a good publication record will help with getting research grants.

Maybe a series of experiments didn’t work, but the next one will (and it will win a Nobel Prize).

Again, it is very important to pursue more than one research project at a time. That way, if one set of experiments doesn't work out, you can still publish the results from another.

Maybe your research output is highly subjective (think works by artists) and the critics didn’t like your last show.

This is more of a problem for researchers in the humanities. As a scientist I can't really comment on this, other than to point again to my responses above.

Maybe your research rivals knew about your employment conditions and decided to reject your last paper (to make it to the magic four) to get you sacked.

This is the most frightening point. Not only is such behavior completely unethical, it undermines the entire peer-review process. Anyone engaging in such  behavior - rejecting papers just because the author is a rival - should be sacked immediately. If this does happen, it is also a failure on the part of the journal editors. Most journal submission systems allow the authors to list reviewers they are opposed to. Any editor who sends a paper to a reviewer who they have reasonable suspicion is a rival of the author, is failing to do their job.

Or maybe, just maybe you didn’t feel the need to publish every last little bit of work to avoid saturating the journals and keep the overall quality of published work high enough to make it bearable to read them.

This is a non sequitor. Submitting a lot of papers does not mean a lack of quality in the papers. As I mentioned above, you can break your project up into smaller, publishable chunks and submit a paper on each. Planning your research program is an essential skill for the professional researcher. Also, if the quality of the paper is so low that it's unreadable, how would it get published to begin with?

Maybe you published one Nature paper a year over the last three years and nothing else, and they each got 500 citations

This is one of those situations that could occur, but is really unlikely. The people I know who do publish in Nature, are the people who publish 20+ papers (journal articles) per year! I really can't see how anyone could be good enough to publish three papers in a row in Nature yet not be good enough to get one more paper published in another journal.

Publish or perish is the rule of academia, and has been for a long time. Sensible and positive ways you can publish without perishing are:

• pursuing more than one research project at a time 
• collaborating widely (this is pretty easy to do in computational intelligence, as our algorithms are so widely applicable 
• breaking your research into small, publishable chunks 
• rapid turn-around on rejected papers 

It is time that the non-publishing staff at universities were put on notice to start producing. I know of more than one university computing department where the combined journal article output for all staff in 2011 was less than my own output. Why do these people have safe, permanent jobs, when so many young, productive researchers don't?

Reminder: paper submission deadline for CISE 2012

A reminder that the deadline for papers submitted to the 4th International Conference on Computational Intelligence and Software Engineering (CISE) 2012 is 11 June 2012. This conference will be held in Wuhan, China, December 14-16, 2012.

Conference paper deadline: IDA 2012

The deadline for submitting papers to the Eleventh International Symposium on Intelligence Data Analysis (IDA) 2012 is 12 May 2012. This symposium will be held in Helsinki, Finland, 27-27 October, 2012.

Call for papers: ELM 2012

The deadline for submitting papers to the International Symposium on Extreme Learning Machines (ELM) 2012 is 1 June, 2012. This symposium will be held in Singapore 11-13 December, 2012.

Conference paper deadline: ICANN 2012

The deadline for submitting papers to the 22nd International Conference on Artificial Neural Networks (ICANN) 2012 is 19 March 2012. This conference will be held in Lausanne, Switzerland, 11-14 September, 2012.