Saturday, May 7, 2011
Submission deadline: EPIA 2011
The deadline for submission of abstracts to the XV Portuguese Conference on Artificial Intelligence is May 10 2011. This conference will be held in Lisbon, Portugal, 10-13 October, 2011.
Deadline extension: EA 2011
The deadline for the 2011 Artificial Evolution conference (EA2011) has been extended to 15 May 2011. This conference will be held in Angers, France, 24-26 October, 2011.
Friday, May 6, 2011
Peer review
Maggie Koerth-Baker writes about peer review at one of my favourite places on the web, Boing Boing. This article does a good job of describing peer review for the general public, especially in regards to why it's a good thing and what problems exist with it. As a working scientist, I have a slightly different point of view on peer review.
Firstly, most reviewers have the good intention of trying to make the paper better. This is something that many new authors don't get: they may even see the reviewers' comments as an attack on their competence. But that's seldom the case, at least not in my experience. Publishing bad papers, or papers with unreliable results, benefits no one. Whenever I review a paper, the first thing I ask myself is, "what can I suggest that could improve this paper?".
Secondly, reviewers have a lot of power. It seems to be the norm now that editors won't challenge anything a reviewer writes, even if it is egregiously wrong. If a reviewer criticizes an author on completely spurious grounds (for example, the reviewer is out of date in the field the paper is in, or if the reviewer simply doesn't know as much as they think they do) most editors will just allow the review through. This places the onus of disproving the reviewer on the author, which means authors have to spend time arguing with a reviewer (via the editor) instead of actually improving their paper.
Thirdly, reviewers do not have a lot of time. I've reviewed a lot of papers, more than a hundred, at last count, and to be honest, most of them sat on my desk for a few weeks before I managed to read them. Recently, the time frame to perform a review has been getting smaller, so there's even less time to do a quality review.
Computational intelligence papers have certain aspects that make them a challenge to review. Firstly, most have a fair amount of mathematics in them, but the number of authors who still don't define the variables in each equation is disappointingly large. Secondly, most papers have empirical work that test the proposed algorithms. But, a large proportion of those still don't produce statistically valid results - repeated trials, varied parameters, independent test sets, and statistical tests of significant differences over the results. I have noticed some progress on this in the last few years, but nowhere near as much as I would like.
I think I'll have to write this up in a future post...
Firstly, most reviewers have the good intention of trying to make the paper better. This is something that many new authors don't get: they may even see the reviewers' comments as an attack on their competence. But that's seldom the case, at least not in my experience. Publishing bad papers, or papers with unreliable results, benefits no one. Whenever I review a paper, the first thing I ask myself is, "what can I suggest that could improve this paper?".
Secondly, reviewers have a lot of power. It seems to be the norm now that editors won't challenge anything a reviewer writes, even if it is egregiously wrong. If a reviewer criticizes an author on completely spurious grounds (for example, the reviewer is out of date in the field the paper is in, or if the reviewer simply doesn't know as much as they think they do) most editors will just allow the review through. This places the onus of disproving the reviewer on the author, which means authors have to spend time arguing with a reviewer (via the editor) instead of actually improving their paper.
Thirdly, reviewers do not have a lot of time. I've reviewed a lot of papers, more than a hundred, at last count, and to be honest, most of them sat on my desk for a few weeks before I managed to read them. Recently, the time frame to perform a review has been getting smaller, so there's even less time to do a quality review.
Computational intelligence papers have certain aspects that make them a challenge to review. Firstly, most have a fair amount of mathematics in them, but the number of authors who still don't define the variables in each equation is disappointingly large. Secondly, most papers have empirical work that test the proposed algorithms. But, a large proportion of those still don't produce statistically valid results - repeated trials, varied parameters, independent test sets, and statistical tests of significant differences over the results. I have noticed some progress on this in the last few years, but nowhere near as much as I would like.
I think I'll have to write this up in a future post...
Thursday, May 5, 2011
Ten rules for good writing
Henry Gee writes about The ten rules for excellent writing. Not that there's actually ten rules in the article, but it's a nice title. The rules he does discuss are:
Rule 1: Write Every Day
Rule 2: Write as you would speak
Rule 3: Stick to the point
Rule 4: Take A Break
Rule 5: Finish it
Rule 6: Avoid cliché
I am trying to follow Rule 1 more often: this blog helps, and I also have a backlog of papers to deal with.
Rule 2 ties back to the idea of having a narrative in scientific papers, as discussed in this post. Scientific articles do expect a certain level of formality and a certain writing style: I have even heard of an author having to fight to get a journal to accept an article written in the active voice.
Sticking to the point (Rule 3) isn't all that hard for me, but I know I can improve - I have been told that my writing is a bit "information light".
Take a break (Rule 4) - always a good idea, and the best thing about having multiple projects on the go is that you can take a break from one project by working on another. That way, you are getting being productive while taking a break.
I also need to follow Rule 5 with more care: my backlog of papers isn't getting any smaller. One pitfall with this in science is that most papers have multiple authors, and they tend to be just as busy, if not more so, than you are. So, papers get delayed, because your coauthors don't have time to deal with them. One tactic I have found useful to is set a deadline: if they don't get back to you before the deadline, you assume that the paper is OK and can be submitted as is.
Rule 6 - are there cliches in scientific writing? Submit your favourites in the comments.
Rule 1: Write Every Day
Rule 2: Write as you would speak
Rule 3: Stick to the point
Rule 4: Take A Break
Rule 5: Finish it
Rule 6: Avoid cliché
I am trying to follow Rule 1 more often: this blog helps, and I also have a backlog of papers to deal with.
Rule 2 ties back to the idea of having a narrative in scientific papers, as discussed in this post. Scientific articles do expect a certain level of formality and a certain writing style: I have even heard of an author having to fight to get a journal to accept an article written in the active voice.
Sticking to the point (Rule 3) isn't all that hard for me, but I know I can improve - I have been told that my writing is a bit "information light".
Take a break (Rule 4) - always a good idea, and the best thing about having multiple projects on the go is that you can take a break from one project by working on another. That way, you are getting being productive while taking a break.
I also need to follow Rule 5 with more care: my backlog of papers isn't getting any smaller. One pitfall with this in science is that most papers have multiple authors, and they tend to be just as busy, if not more so, than you are. So, papers get delayed, because your coauthors don't have time to deal with them. One tactic I have found useful to is set a deadline: if they don't get back to you before the deadline, you assume that the paper is OK and can be submitted as is.
Rule 6 - are there cliches in scientific writing? Submit your favourites in the comments.
Wednesday, May 4, 2011
Conference paper deadline: iCAST 2011
The deadline for submitting papers to the 3rd International Conference on Awareness Science and Technology (iCAST 2011) is June 1, 2011. This conference is co-sponsored by the IEEE Computational Intelligence Society and will be held in Dalian, China, September 27-30, 2011.
Tuesday, May 3, 2011
IEEE Computational Intelligence Society Social Media Presences
The Computational Intelligence Society of the IEEE now has presences on social media sites:
Twitter
Facebook
LinkedIn
These presences result from the work of the Computational Intelligence Society's Social Media Subcommittee, which I have the privilege of serving on.
These presences result from the work of the Computational Intelligence Society's Social Media Subcommittee, which I have the privilege of serving on.
Conference paper deadline: ICAIS 2011
The deadline for submitting papers to the International Conference on Adaptive and Intelligent Systems 2011 (ICAIS 2011) is June 10, 2011. This conference is co-sponsored by the IEEE Computational Intelligence Society and will be held in Klagenfurt, Austria, September 6-8, 2011.
Monday, May 2, 2011
Competition: describe fuzzy logic in a video
Via the IEEE Computational Intelligence Society's Twitter feed comes word of the following competition: Create a video that explains fuzzy logic and its applications to an audience of high school students or the general public in a format suitable for posting on YouTube.
First prize is $3000, second prize is $2000 and third prize is $1000. Interest must be registered by the 10th of June and the deadline for submitting videos is the 10th of September.
First prize is $3000, second prize is $2000 and third prize is $1000. Interest must be registered by the 10th of June and the deadline for submitting videos is the 10th of September.
Sunday, May 1, 2011
Call for second-round submissions: ICNS'11-FSKD'11
The deadline for second-round submissions to the The 7th International Conference on Natural Computation and The 8th International Conference on Fuzzy Systems and Knowledge Discovery (ICNS'11-FSKD'11) is 16 May 2011. These conferences will be held jointly in Shanghai, China, 26-28 July, 2011.
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