Tuesday, October 18, 2011
Conference paper deadline: ICDM 2012
The deadline for papers submitted to the 12th Industrial Conference on Data Mining (ICDM) 2012 is 18 December 2011. This conference will be held in Berlin, Germany, 16-20 July, 2012.
Monday, October 17, 2011
Paper submission deadline: ICML 2012
The deadline for submitting papers to the International Conference on Machine Learning (ICML) 2012 is 24 February 2012. This conference will be held in Edinburgh, Scotland, June 26 - July 1, 2012.
Friday, October 14, 2011
On Presentations
Some presenters are applauded because the audience enjoyed their presentation. Other presenters are applauded because they ended their presentation.
Know which one you are.
Know which one you are.
Call for papers: WIVACE 2012
The deadline for submitting papers to the Italian Workshop on Artificial Life, Evolution and Complexity (WIVACE) 2012 is 6 January 2012. This workshop will be held in Parma, Italy, 20-21 February, 2012.
Thursday, October 13, 2011
Paper submission deadline: EvoStar 2012
The deadline for submitting papers to the European Conference on Evolutionary Computation (EvoStar) 2012 is 30 November 2012. This conference will be held in Malaga, Spain, 11-13 April, 2012.
Wednesday, October 12, 2011
Call for papers: ICCCI 2012
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.
Tuesday, October 11, 2011
Open research problems with Evolving Connectionist Systems
I described Evolving Connectionist Systems (ECoS) in an earlier post. A couple of years ago, I published a review article (PDF preprint) where I described the state of the art of ECoS, and identified several open research problems. There hasn't been much progress made in solving these problems, so I'm going to briefly describe them here, and hopefully stimulate a bit more work in this area. Of course, I'm doing a bit of work in some of these, but as I have a real job to do, I don't get as much time to spend on these problems as I'd like.
1) Input significance. With other ANN, especially the venerable MLP, it is possible to get an indication of how important each input variable is to the model. These methods are based on an analysis of the magnitude of the connection weights attached to each input neuron. This method won't work with ECoS networks, however, because the connection weights represent points in space. That is, the magnitude of the weight for an input neuron connection has nothing to do with how important that input is.
2) Optimisation of ECoS networks. While ECoS algorithms are fast learning, they can grow to be quite large, which makes them expensive in terms of memory and computational load. Ideally, it would be possible to reduce their size without sacrificing their accuracy. That is, it would be ideal if we could somehow eliminate redundant information in the ECoS and only retain that which is necessary for maintaining accuracy. I investigated a couple of methods of doing this in my PhD, and a few other people have looked at it as well, but no one has yet cracked the problem in terms of coming up with an optimisation algorithm that will significantly reduce the size of a trained ECoS network without significantly reducing its accuracy. Also, the most effective optimisation methods in the published work use evolutionary algorithms like genetic algorithms or evolution strategies. These are so computationally intensive that the speed advantages of ECoS are lost. An ECoS optimisation algorithm would ideally be as fast, or nearly as fast, as the ECoS training algorithm. It may be that this is inherently impossible.
3) Non-triangular fuzzy membership functions in EFuNN. The Evolving Fuzzy Neural Network EFuNN has triangular fuzzy membership functions (MF) embedded in its structure. These are fast and efficient, but other MF types (such as Gaussian) may be more useful for other applications.
4) Learning in the MF of EFuNN. The fuzzy MF in EFuNN are fixed, that is, they are set once and do not change during the life of the EFuNN. This is in contrast to the open, adaptive nature of EFuNN itself. An extension of the EFuNN learning algorithm that would allow the MF to adapt as the rest of the network adapts, would be extremely useful for data mining applications. This algorithm would have to be as fast as the rest of the EFuNN learning algorithm, which may rule out backpropagation training of the MF, as is used in other fuzzy system optimisation.
Although ECoS networks are very useful algorithms, they could be made even more useful if the problems above were solved. I'm working on some of them, but I would love to see others working on them as well. Contact me if you are interested in collaborating.
1) Input significance. With other ANN, especially the venerable MLP, it is possible to get an indication of how important each input variable is to the model. These methods are based on an analysis of the magnitude of the connection weights attached to each input neuron. This method won't work with ECoS networks, however, because the connection weights represent points in space. That is, the magnitude of the weight for an input neuron connection has nothing to do with how important that input is.
2) Optimisation of ECoS networks. While ECoS algorithms are fast learning, they can grow to be quite large, which makes them expensive in terms of memory and computational load. Ideally, it would be possible to reduce their size without sacrificing their accuracy. That is, it would be ideal if we could somehow eliminate redundant information in the ECoS and only retain that which is necessary for maintaining accuracy. I investigated a couple of methods of doing this in my PhD, and a few other people have looked at it as well, but no one has yet cracked the problem in terms of coming up with an optimisation algorithm that will significantly reduce the size of a trained ECoS network without significantly reducing its accuracy. Also, the most effective optimisation methods in the published work use evolutionary algorithms like genetic algorithms or evolution strategies. These are so computationally intensive that the speed advantages of ECoS are lost. An ECoS optimisation algorithm would ideally be as fast, or nearly as fast, as the ECoS training algorithm. It may be that this is inherently impossible.
3) Non-triangular fuzzy membership functions in EFuNN. The Evolving Fuzzy Neural Network EFuNN has triangular fuzzy membership functions (MF) embedded in its structure. These are fast and efficient, but other MF types (such as Gaussian) may be more useful for other applications.
4) Learning in the MF of EFuNN. The fuzzy MF in EFuNN are fixed, that is, they are set once and do not change during the life of the EFuNN. This is in contrast to the open, adaptive nature of EFuNN itself. An extension of the EFuNN learning algorithm that would allow the MF to adapt as the rest of the network adapts, would be extremely useful for data mining applications. This algorithm would have to be as fast as the rest of the EFuNN learning algorithm, which may rule out backpropagation training of the MF, as is used in other fuzzy system optimisation.
Although ECoS networks are very useful algorithms, they could be made even more useful if the problems above were solved. I'm working on some of them, but I would love to see others working on them as well. Contact me if you are interested in collaborating.
Monday, October 10, 2011
Call for papers: IEEE SSCI 2013
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.
Thursday, October 6, 2011
The problem with academic journals 2
In this earlier post I linked to an article by George Monbiot that discussed the biggest problem with academic journals, which is the enormous expense of accessing journals or journal articles, given that the content and quality control are all provided for free.
Monbiot's original article touched off something of a storm in the academic web, with some echoing his sentiments, and others (some of whom, at least, were connected to the publishers) attacking him. The publishers themselves tried to justify their charges by writing a lot without saying very much (I find as I lurch remorselessly towards 40 that I have less and less tolerance for corporate weasel-wording). Still others discussed whether open access journals are feasible alternatives.
I serve on the editorial board of an open access journal, so I know that they can have a problem with legitimacy, or at least with being taken seriously. But open access journals are still fairly new, and it takes time for a journal to build up its article base, which in turn allows it to build up its citation rate, which in turn builds its impact factor, which is the most important, or at least the most widely known, measure of a journal's success, despite the problems with it.
From my own point of view, I review papers for open access journals with the same care as I review any other paper. As a working scientist and active author, if I can't find a copy of an article online, I won't cite it. It's the authors of the paper who are missing out then, it makes no difference to me whatsoever if they get cited or not.
It's interesting that an august institution like Princeton university, in an effort to promote open-access journals, has recently enacted a policy that forbids its staff from assigning copyright of articles to journal publishers. Let's hope that we see more top institutions doing the same thing: the sooner we break the stranglehold on top science held by the old publishers, the better for everyone.
Monbiot's original article touched off something of a storm in the academic web, with some echoing his sentiments, and others (some of whom, at least, were connected to the publishers) attacking him. The publishers themselves tried to justify their charges by writing a lot without saying very much (I find as I lurch remorselessly towards 40 that I have less and less tolerance for corporate weasel-wording). Still others discussed whether open access journals are feasible alternatives.
I serve on the editorial board of an open access journal, so I know that they can have a problem with legitimacy, or at least with being taken seriously. But open access journals are still fairly new, and it takes time for a journal to build up its article base, which in turn allows it to build up its citation rate, which in turn builds its impact factor, which is the most important, or at least the most widely known, measure of a journal's success, despite the problems with it.
From my own point of view, I review papers for open access journals with the same care as I review any other paper. As a working scientist and active author, if I can't find a copy of an article online, I won't cite it. It's the authors of the paper who are missing out then, it makes no difference to me whatsoever if they get cited or not.
It's interesting that an august institution like Princeton university, in an effort to promote open-access journals, has recently enacted a policy that forbids its staff from assigning copyright of articles to journal publishers. Let's hope that we see more top institutions doing the same thing: the sooner we break the stranglehold on top science held by the old publishers, the better for everyone.
Wednesday, October 5, 2011
Conference paper deadline: GECCO 2012
The deadline for submitting papers to the 2012 Genetic and Evolutionary Computation Conference (GECCO) is 13 January 2012. This conference will be held in Philadelphia 7-11 July, 2012.
Tuesday, October 4, 2011
Reminder: paper deadline for Collective Intelligence 2012
A reminder that the deadline for papers submitted to the 2012 conference on Collective Intelligence is 4 November, 2011. This conference will be held in Cambridge, Massachusetts, April 18-20, 2012.
Monday, October 3, 2011
Reminder: paper submission deadline for EAIS 2012
A reminder that the deadline for submitting papers to the IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) 2012 is 1 November 2011. This conference will be held in Madrid, Spain, 17-18 May, 2012.
Friday, September 30, 2011
Reminder: paper submission deadline ICIST 2012
The deadline for submitting papers to the International Conference on Intelligent Systems and Technologies (ICIST) 2012 is December 30, 2011. This conference will be held in Tokyo, Japan, 29-31 May 2012.
Thursday, September 29, 2011
Reminder: Paper submission deadline SAMI 2012
A reminder that the paper submission deadline to the 10th IEEE International Symposium and Applied Machine Intelligence and Informatics (SAMI) 2012 is 31 October 2011. This symposium will be held January 26-28 2011 in Herl'any, Slovakia.
Wednesday, September 28, 2011
Paper submission deadline: ISICA 2012
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.
Tuesday, September 27, 2011
Paper submission deadline: IEEE CISDA 2012
The deadline for papers submitted to the IEEE Workshop on Computational Intelligence for Security and Defence Applications (IEEE CISDA) 2012 is 19 March 2012. This workshop will be held in Ottawa, Canada, 11-13 July 2012.
Monday, September 26, 2011
Reminder: paper submission deadline for IEEE-IS
A reminder that the deadline for submitting papers to the IEEE International Conference on Intelligent Systems (IEEE-IS) is 20 December 2011. This conference will be held in Sofia, Bulgaria, September 6-8, 2012.
Friday, September 23, 2011
Reminder: Paper deadline for IEEE CIFEr 2012
A reminder that the deadline for papers submitted to the IEEE Computational Intelligence in Financial Engineering and Economics Conference, 2012 (CIFEr 2012) is 21 October, 2011. This conference will be held in New York City, 30 March 2012.
Thursday, September 22, 2011
Reminder: Paper submission deadline for ICAISC 2012
A reminder that the paper submission deadline for the 11th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2012 is 20 October, 2011. This conference will be held in Zakopane, Poland, April 29 - May 3, 2012.
Wednesday, September 21, 2011
Reminder: Paper deadline FUZZ-IEEE 2012
A reminder that the deadline for papers submitted to the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) is December 19, 2011. This conference is part of the 2012 IEEE World Congress on Computational Intelligence (WCCI 2012) and is held concurrently with the 2012 Congress on Evolutionary Computation (CEC) and the International Joint Conference on Neural Networks (IJCNN). WCCI 2012 will be held in Brisbane, Australia, June 10-15, 2012.
Reminder: Paper deadline CEC 2012
A reminder that the deadline for papers submitted to the 2012 Congress on Evolutionary Computation is December 19, 2011. This conference is part of the 2012 IEEE World Congress on Computational Intelligence (WCCI 2012) and is held concurrently with the 2012 International Joint Conference on Neural Networks (IJCNN) and IEEE Conference on Fuzzy Logic (Fuzz-IEEE). WCCI 2012 will be held in Brisbane, Australia, June 10-15, 2012.
Tuesday, September 20, 2011
Rules for giving technical presentations
This is an update of an old post, from nearly two years ago. Technical presentations have not improved in that time.
I have attended a lot of conference, scientific and technical presentations, and a significant proportion of those were pretty bad. Another large proportion were mediocre at best, and only a few were pretty good. From what I have observed, and from talking with other presenters, I have formulated the following general rules for giving technical or scientific presentations. While none of these rules are inviolable, please do at least give them some thought the next time you give a presentation.
General Rules
There are two general rules – most of the specific rules come from these two.
1. Don’t waste time, either yours or the audience’s.
2. Don’t insult the intelligence of your audience.
Specific Rules
1. If you have just been introduced with your name and the title of your presentation, don’t repeat this information. You may have them on the first slide, in fact this is probably a good idea, especially if that slide has your email address prominently displayed.
2. If you are presenting to a specialised audience, leave out the background material. For example, if you are presenting to a conference on evolutionary computation, spending even one or two slides explaining what evolutionary computation is violates both general rules.
3. If you have long sentences on your slides, don’t read them aloud. This violates both general rules. It is better to not have long sentences.
4. Outline slides are not necessary. They waste time and assume that the audience isn’t smart enough to notice what you are currently talking about. An exception to this is for long presentations, like hour-long seminars: in this case, it can be useful to repeat the outline slide at strategic points in your presentation. This is to show the audience what part of the talk you are up to, and what they can expect next. Often, different people will be interested in different parts of your talk, so doing this lets them know when they should pay attention.
5. Don’t place equations on your slides unless they are absolutely, positively and irrefutably necessary. If the math is complex enough that it needs to be explained, then it is unlikely that the audience will be able to parse it fast enough to be useful to the presentation. If it is simple, then it can be left out.
6. Know the length of your presentation. A good rule of thumb is an absolute maximum of one slide per minute of presentation, including title, summary and conclusions. Thus, for a fifteen minute presentation, fifteen slides is a good count, ten is better, less than ten is best.
7. Keep to the point of the presentation. If your talk is on bioinformatics, I don’t want to hear about your university’s teaching computer lab.
8. Proof-read your presentation. Use a spell checker. Have someone else check your presentation. If English is not your first language, have it proof-read by someone who is a native speaker. Try to avoid common grammatical errors (infer/imply, affect/effect, explicit/implicit, and so on). Know what words like "literally" actually mean (Jamie Oliver, I'm looking at you!).
9. Know your presentation material. If you have to stop talking to work out what something on a slide actually means, you are wasting everyone’s time. It also makes you look like an idiot.
10. If you are presenting a group of numbers, use a plot of the values, rather than a table, especially if the intention is to compare and contrast the groups. Be careful with the use of colours! A non-trivial proportion of the population can't distinguish between red and green. Be aware that pale colours, such as yellow, can't be seen easily when projected.
11. Moving about is good. Moving energetically is even better. A presenter with physical vigour commands more attention from, and inspires more energy in, an audience than one who stands still, or worse, sits while speaking. That said, moving around like your feet are on fire is distracting. Use your best judgement.
12. Make eye contact with your audience. You should try to make eye-contact with each member of the audience at least once during your presentation. They are here to listen to you speak, so you should acknowledge their existence by actually looking at them. That said, constantly looking at one particular member of the audience is likely to make that person feel uncomfortable.
13. If you cite published work, you must include enough information for audience members to find it! A citation in your presentation like Watts et al (2011) is absolutely useless on its own. At a bare minimum, you would need Watts et al (2011), Ecological Modelling. At least then someone has a chance of finding the paper.
14. Change media regularly. Write on the whiteboard. Show a diagram. Play a video. Hand around a piece of equipment. Anything that is a change from words on a slide.
I have attended a lot of conference, scientific and technical presentations, and a significant proportion of those were pretty bad. Another large proportion were mediocre at best, and only a few were pretty good. From what I have observed, and from talking with other presenters, I have formulated the following general rules for giving technical or scientific presentations. While none of these rules are inviolable, please do at least give them some thought the next time you give a presentation.
General Rules
There are two general rules – most of the specific rules come from these two.
1. Don’t waste time, either yours or the audience’s.
2. Don’t insult the intelligence of your audience.
Specific Rules
1. If you have just been introduced with your name and the title of your presentation, don’t repeat this information. You may have them on the first slide, in fact this is probably a good idea, especially if that slide has your email address prominently displayed.
2. If you are presenting to a specialised audience, leave out the background material. For example, if you are presenting to a conference on evolutionary computation, spending even one or two slides explaining what evolutionary computation is violates both general rules.
3. If you have long sentences on your slides, don’t read them aloud. This violates both general rules. It is better to not have long sentences.
4. Outline slides are not necessary. They waste time and assume that the audience isn’t smart enough to notice what you are currently talking about. An exception to this is for long presentations, like hour-long seminars: in this case, it can be useful to repeat the outline slide at strategic points in your presentation. This is to show the audience what part of the talk you are up to, and what they can expect next. Often, different people will be interested in different parts of your talk, so doing this lets them know when they should pay attention.
5. Don’t place equations on your slides unless they are absolutely, positively and irrefutably necessary. If the math is complex enough that it needs to be explained, then it is unlikely that the audience will be able to parse it fast enough to be useful to the presentation. If it is simple, then it can be left out.
6. Know the length of your presentation. A good rule of thumb is an absolute maximum of one slide per minute of presentation, including title, summary and conclusions. Thus, for a fifteen minute presentation, fifteen slides is a good count, ten is better, less than ten is best.
7. Keep to the point of the presentation. If your talk is on bioinformatics, I don’t want to hear about your university’s teaching computer lab.
8. Proof-read your presentation. Use a spell checker. Have someone else check your presentation. If English is not your first language, have it proof-read by someone who is a native speaker. Try to avoid common grammatical errors (infer/imply, affect/effect, explicit/implicit, and so on). Know what words like "literally" actually mean (Jamie Oliver, I'm looking at you!).
9. Know your presentation material. If you have to stop talking to work out what something on a slide actually means, you are wasting everyone’s time. It also makes you look like an idiot.
10. If you are presenting a group of numbers, use a plot of the values, rather than a table, especially if the intention is to compare and contrast the groups. Be careful with the use of colours! A non-trivial proportion of the population can't distinguish between red and green. Be aware that pale colours, such as yellow, can't be seen easily when projected.
11. Moving about is good. Moving energetically is even better. A presenter with physical vigour commands more attention from, and inspires more energy in, an audience than one who stands still, or worse, sits while speaking. That said, moving around like your feet are on fire is distracting. Use your best judgement.
12. Make eye contact with your audience. You should try to make eye-contact with each member of the audience at least once during your presentation. They are here to listen to you speak, so you should acknowledge their existence by actually looking at them. That said, constantly looking at one particular member of the audience is likely to make that person feel uncomfortable.
13. If you cite published work, you must include enough information for audience members to find it! A citation in your presentation like Watts et al (2011) is absolutely useless on its own. At a bare minimum, you would need Watts et al (2011), Ecological Modelling. At least then someone has a chance of finding the paper.
14. Change media regularly. Write on the whiteboard. Show a diagram. Play a video. Hand around a piece of equipment. Anything that is a change from words on a slide.
Monday, September 19, 2011
Reminder: Paper deadline IJCNN 2012
The deadline for papers submitted to the International Joint Conference on Neural Networks is December 19, 2011. This conference is part of the 2012 IEEE World Congress on Computational Intelligence (WCCI 2012) and is held concurrently with the 2012 Congress on Evolutionary Computation (CEC) and IEEE Conference on Fuzzy Logic (Fuzz-IEEE). WCCI 2012 will be held in Brisbane, Australia, June 10-15, 2012.
Friday, September 16, 2011
Paper submission deadline for ICIC 2012
The deadline for papers submitted to the International Conference on Intelligent Computing (ICIC) 2012 is January 1, 2012. This conference will be held in Huangshan, China, July 25-29, 2012.
Thursday, September 15, 2011
Reminder: paper submission deadline for PPSN 2012
A reminder that the deadline for submitting papers to the 12th International Conference on Parallel Problem Solving from Nature (PPSN) 2012 is March 15 2012. This conference will be held in Taormina, Italy, September 1-5, 2012.
Wednesday, September 14, 2011
Reminder: paper deadline for SIAM SDM 12
A reminder that the deadline for submitting papers to the SIAM International Conference on Data Mining (SDM) 2012 is 14 October 2011. This conference will be held in Anaheim, California, April 26-28, 2012.
Tuesday, September 13, 2011
Multi-lingual social internetworking
As I promised in this post, the social media sites of the IEEE Computational Intelligence Society (CIS) are now available in several new languages: in the last week, we have set up sites in Korean, Greek, German and Spanish.
I have also posted a document describing how it works: in short, a post is published on the CIS blog, which is then automatically translated by Yahoo! Pipes, then sent to the social media sites. So, it is now possible to follow IEEE CIS news in Chinese, Portuguese, French, Korean, Greek, German and Spanish. These are machine translations, so they can be a bit off sometimes, but I'm told that for the most part they're OK.
A complete listing of the English language social media sites is in this post. The addresses for the Chinese, French and Portuguese sites are listed in this post. Finally, the Korean, Greek, German and Spanish sites are as follows:
Korean
http://twitter.com/#!/ieeeciskr
Greek
http://twitter.com/#!/ieeecisgr
http://ieeecisgr.jaiku.com
http://www.plerb.com/ieeecisgr
http://ieeecisgr.tumblr.com
http://shoutitout.shoutem.com/ieeecisgr
German
http://twitter.com/#!/ieeecisde
http://ieeecisde.jaiku.com
http://www.plerb.com/ieeecisde
http://ieeecisde.tumblr.com
http://shoutitout.shoutem.com/ieeecisde
Spanish
http://twitter.com/#!/ieeecises
http://ieeecises.jaiku.com
http://www.plerb.com/ieeecises
http://ieeecises.tumblr.com
http://shoutitout.shoutem.com/ieeecises
I have also posted a document describing how it works: in short, a post is published on the CIS blog, which is then automatically translated by Yahoo! Pipes, then sent to the social media sites. So, it is now possible to follow IEEE CIS news in Chinese, Portuguese, French, Korean, Greek, German and Spanish. These are machine translations, so they can be a bit off sometimes, but I'm told that for the most part they're OK.
A complete listing of the English language social media sites is in this post. The addresses for the Chinese, French and Portuguese sites are listed in this post. Finally, the Korean, Greek, German and Spanish sites are as follows:
Korean
http://twitter.com/#!/ieeeciskr
Greek
http://twitter.com/#!/ieeecisgr
http://ieeecisgr.jaiku.com
http://www.plerb.com/ieeecisgr
http://ieeecisgr.tumblr.com
http://shoutitout.shoutem.com/ieeecisgr
German
http://twitter.com/#!/ieeecisde
http://ieeecisde.jaiku.com
http://www.plerb.com/ieeecisde
http://ieeecisde.tumblr.com
http://shoutitout.shoutem.com/ieeecisde
Spanish
http://twitter.com/#!/ieeecises
http://ieeecises.jaiku.com
http://www.plerb.com/ieeecises
http://ieeecises.tumblr.com
http://shoutitout.shoutem.com/ieeecises
Monday, September 12, 2011
On plagiarism
Plagiarism is one of the most unpleasant things to deal with when teaching. Panos Ipeirotis wrote a blog post that stimulated some discussion, and was then removed because of legal threats. In short, he detected a fairly large amount of plagiarism in a class, but calling the students out on it created a lot of antipathy towards him, leading to a lower student evaluation, which adversely effected his own financial propects.
The later discussions suggested setting assignments that are impossible for the students to plagiarise. During my tenure teaching at the University of Otago, I saw my fair share (or more than my fair share) of plagiarism, and some of it was pretty bad.
The worst I saw was while teaching my second-year data processing course. It's not like it was difficult to detect, either: the copied portions stood out because the writing style was completely different. A few seconds with Google was usually enough to find the exact source. The easiest-detected case of plagiarism I dealt with was when a student copied from the laboratory manual - which I had written. There were so many cases of plagiarism in that course that the higher-ups changed the way in which plagiarism was dealt with: originally, all cases of plagiarism were sent to the dean of School. After a few weeks of me sending students to them, the regulation was changed to sending them to the head of department. The only penalty the students received, though, was a zero for the work that they had plagiarsied in. By the end of the year, I'd detected more plagiarism than the rest of the department put together, which raises the question: did more students plagiarise in my course, or was I just better at detecting them? If the former, was it because my course was harder? Because it was a required course that the students weren't really interested in? Or were the students really not smart enough to do the course without cheating? If the latter, why did I detect more than the other teaching staff? Was I the only one who read the assignments carefully? Did the other teaching staff not care? Or was it that the assessments in other courses were such that plagiarism was harder to commit in the first place, that is, more practically oriented?
While most of the plagiarism I dealt with was from undergrads, I have come across it reviewing papers, as well. Again, it was easy to detect: most of the paper was written very badly, apart from two or three paragraphs. Again, a few seconds work on Google was enough to find the original source. Needless to say, the paper was rejected. Since it was only a conference paper, I doubt that there were any repercussions on the authors.
As far as student plagiarism is concerned, I agree with the notion that it is better to spend time setting assessments that can't be plagiarised. The one course I taught that never had a problem with plagiarism was my fourth-year computational intelligence course. Now, that is partly likely to be because the students were highly-motivated, honours-level students, but also because of the nature of the lectures and assessment. Rather than me giving lectures twice a week, students took turns researching and presenting on a topic. There was a list of permissible topics for each week, so that the presentations followed the curriculum I had set out for the course, the students got support in researching their talk, and I went over each presentation before it was given. The practical work was entirely project-oriented, where again the students selected a project that interested them. This actually worked very well: it taught the students valuable skills and left no scope for plagiarism. I wonder, though, how well it would work for third or even second year students?
Perhaps a more important question is, why do students plagiarise? If we could answer that question, could plagiarism be eradicated? Or would there always be some students who are simply so desperate (or so unable / unwilling to do the work) that they will always plagiarise?
The later discussions suggested setting assignments that are impossible for the students to plagiarise. During my tenure teaching at the University of Otago, I saw my fair share (or more than my fair share) of plagiarism, and some of it was pretty bad.
The worst I saw was while teaching my second-year data processing course. It's not like it was difficult to detect, either: the copied portions stood out because the writing style was completely different. A few seconds with Google was usually enough to find the exact source. The easiest-detected case of plagiarism I dealt with was when a student copied from the laboratory manual - which I had written. There were so many cases of plagiarism in that course that the higher-ups changed the way in which plagiarism was dealt with: originally, all cases of plagiarism were sent to the dean of School. After a few weeks of me sending students to them, the regulation was changed to sending them to the head of department. The only penalty the students received, though, was a zero for the work that they had plagiarsied in. By the end of the year, I'd detected more plagiarism than the rest of the department put together, which raises the question: did more students plagiarise in my course, or was I just better at detecting them? If the former, was it because my course was harder? Because it was a required course that the students weren't really interested in? Or were the students really not smart enough to do the course without cheating? If the latter, why did I detect more than the other teaching staff? Was I the only one who read the assignments carefully? Did the other teaching staff not care? Or was it that the assessments in other courses were such that plagiarism was harder to commit in the first place, that is, more practically oriented?
While most of the plagiarism I dealt with was from undergrads, I have come across it reviewing papers, as well. Again, it was easy to detect: most of the paper was written very badly, apart from two or three paragraphs. Again, a few seconds work on Google was enough to find the original source. Needless to say, the paper was rejected. Since it was only a conference paper, I doubt that there were any repercussions on the authors.
As far as student plagiarism is concerned, I agree with the notion that it is better to spend time setting assessments that can't be plagiarised. The one course I taught that never had a problem with plagiarism was my fourth-year computational intelligence course. Now, that is partly likely to be because the students were highly-motivated, honours-level students, but also because of the nature of the lectures and assessment. Rather than me giving lectures twice a week, students took turns researching and presenting on a topic. There was a list of permissible topics for each week, so that the presentations followed the curriculum I had set out for the course, the students got support in researching their talk, and I went over each presentation before it was given. The practical work was entirely project-oriented, where again the students selected a project that interested them. This actually worked very well: it taught the students valuable skills and left no scope for plagiarism. I wonder, though, how well it would work for third or even second year students?
Perhaps a more important question is, why do students plagiarise? If we could answer that question, could plagiarism be eradicated? Or would there always be some students who are simply so desperate (or so unable / unwilling to do the work) that they will always plagiarise?
Friday, September 9, 2011
Conference paper deadline: ICSI 2012
The deadline for papers submitted to the Third International Conference on Swarm Intelligence (ICSI) 2012 is December 31, 2011. This conference will be held in Shenzhen, China, June 17-20, 2012.
Thursday, September 8, 2011
Call for papers: PRICAI 2012
The deadline for papers submitted to the 12th Pacific Rim International Conference on Artificial Intelligence (PRICAI) 2012 is March 1, 2012. This conference will be held in Kuching, Malaysia, September 3-7, 2012.
Wednesday, September 7, 2011
Reminder: paper deadline for AAMAS 2012
A reminder that the deadline for submission of abstracts to the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2012 is 7 October 2011, with full papers due 12 October 2011. This conference will be held in Valencia, Spain, 4-8 June 2012.
Tuesday, September 6, 2011
Conference paper deadline: ICNC-FSKD 2012
The deadline for submitting papers to the 8th International Conference on Natural Computation and 9th International Conference on Knowledge Discovery is 15 November 2011. These conferences will be jointly held in Chongqing, China, 29-31 May, 2011.
Monday, September 5, 2011
Reminder: paper deadline for KES-IIMSS 2012
A reminder that the deadline for submitting papers to the 5th International Conference on Intelligent Interactive Multimedia Systems and Services (KES IIMSS 2012) is 1st December 2011. This conference will be held in Gifu, Japan, 23-25 May 2012, simultaneously with the 4th International Conference on Intelligent Decision Technologies.
Friday, September 2, 2011
Reminder: paper submission deadline for KES-IDT 2012
A reminder that the deadline for submitting papers to the 4th International Conference on Intelligent Decision Technologies (KES-IDT 2012) is 1 December 2011. This conference will be held in Gifu, Japan, 23-25 May, 2012.
Reminder: paper submission deadline for ICARIS 2012
A reminder that the deadline for submitting papers to the 11th International Conference on Artificial Immune Systems (ICARIS) 2012 is 1 March 2012. This conference will be held in Taormina, Italy, 28-21 July, 2012
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