Or, big trouble in little USyd.
In a previous post, I discussed the principle of "Publish or perish". This phrase is a succinct way of saying that the most important metric by which an academic is judged, is their publication record: those who do not publish enough will not be valued as highly as those who publish more. Recent developments in Australian academia have made this literally true.
The University of Sydney is dismissing 100 academics who have not published at least four times in the last three years. Early career researchers are apparently exempt from this threshold, so it is only senior academics (presumably permanent staff members) who are at risk. In other words, they are in senior positions, yet they have not published, so they are perishing.
To put that number (four papers is three years) in perspective, I'm not a permanent staff member, yet in the last three years I've published fifteen times, and have more than a dozen publications in the pipeline (I expect to publish them this year). While I am currently full-time research, I have taught full-time in the past, yet I still managed to publish research. During my final year teaching, I published papers, was finishing my PhD, and taught / administered two undergrad courses. I got married that year, too.
The only thing that garners any sympathy from me is that claims are being made that late last year academic staff were told that an average publication rate of 0.8 per year (four papers in five years) would be satisfactory. If that is true, then management have moved the goal-posts, which strikes me as rather unfair. However, even when I was working as a post-doc at the University of Sydney, I was expected to produce at least two papers per year. In my current position, my target output is at least six per year (I got ten last year, and I'm on track to get twelve this year - the best way to meet a target, is to aim to exceed it). Did senior lecturers really think they could get away with less? "Publish or perish" is a very old saying.
Now, the University of Sydney is taking this action to make up for a massive short-fall in income (the exact reasons for this sudden short-fall are rather murky) but what would the effect be if this hard form of publish or perish were enforced more often at universities? It might have the effect of sweeping out those academics who have become complacent in their positions, or who are approaching the end of their academic careers (that is, they are no longer capable of performing research at the level required). This would in turn free up positions for younger staff, who are capable of producing publications, yet can't find permanent positions because they're being held by unproductive senior staff. Are there any other professions where those who do not perform, get to keep their jobs? Of course not. A major part of being an academic is publishing: if you're not publishing, you're not doing your job. If you're not doing your job, do you deserve to keep it?
On a humane level, I'm sorry for the people who are losing their jobs. But honestly, I wish this standard were applied at more universities.
Tuesday, February 28, 2012
Monday, February 27, 2012
Conference paper deadline: FCTA 2012
The deadline for submitting papers to the International Conference on Fuzzy Computation Theory and Applications (FCTA) 2012 is 19 April 2012. This conference will be held in Barcelona, Spain, 5-7 October, 2012.
Friday, February 24, 2012
Reminder: paper submission deadline for UCNC 2012
A reminder that the deadline for submitting papers to the 11th Conference on Unconventional Computation and Natural Computation (UCNC) 2012 is 26 March 2012. This conference will be held in Orleans, France, 3-6 September, 2012.
Thursday, February 23, 2012
Reminder: paper submission deadline for CIDM 2012
A reminder that the deadline for submitting papers to the Third International Workshop on Computational Intelligence for Disaster Management (CIDM) 2012 is March 25 2012. This workshop will be held in Bucharest, Romania, 19-21 September 2012.
Wednesday, February 22, 2012
Using MLP to model the distribution of bacterial crop diseases
A new paper I co-authored with Sue Worner at Lincoln University is now available and describes how we used MLP to model the global distribution of bacterial crop diseases.
We had data on the presence or absence of certain species of bacterial crop disease (that is, bacteria that infect and cause diseases in plants we use as crops) in 459 geo-political regions throughout the world. We also had data on the climate in these regions and the presence of host plant species. We created MLP that predicted the presence or absence of the bacteria species from climate (abiotic factors). We also created MLP that predicted the presence or absence of the bacteria species from the host plant species assemblages (biotic factors). While both of these approaches worked, we got much better accuracies by combining the outputs into ensembles, and by using a cascaded or tandem ANN approach.
Ensembles are a way of combining the outputs of several ANN. An input vector is propagated through each of the ANN, and the output values combined either statistically (the final output value is the max, mean or median of the uncombined outputs) or algorithmically (output is determined as a majority vote of the uncombined outputs - that is, if the majority of the values is above a threshold, the output of the ensemble is a presence, otherwise, absence). We looked at three different kinds of ensemble: firstly, ensembles of the best ten MLP trained on abiotic inputs; secondly, ensembles of the best ten MLP trained on biotic inputs; and finally, ensembles that combined the best ten MLP trained on abiotic input as well as the best ten MLP trained on biotic inputs. These last ensembles were particularly interesting, as it allowed us to make predictions of species distributions using both biotic and abiotic factors simultaneously. The rationale behind ensembles is that different MLP learn different parts of the problem space: by combining the outputs of several MLP, it is possible to cover a larger part of the problem space, and therefore to boost prediction accuracy. Combining abiotic and biotic factors is the same idea. We know that an organism is affected by both of these factors, so combining both of them allows us to make more accurate predictions.
While the ensemble approach boosted the prediction accuracies, we thought we could do better, so we created MLP that took as inputs the outputs of the very best MLP trained on abiotic and biotic factors. In other words, the outputs of the climate and host networks were used as the input values for a second-level of MLP, which were then trained on the presence and absence of the bacteria species. The idea behind tandem ANN is that, if a first-level network makes a mistake - that is, if a climate or host MLP makes an incorrect prediction - then the tandem network can learn to correct it. Again, we were combining abiotic and biotic factors to make predictions.
The results of all these techniques were that while the single-level MLP were able to predict the distributions of the crop diseases fairly well, combining abiotic and biotic factors gave much better accuracy, whether the combination was achieved by a simple ensemble approach, or by using a tandem MLP approach.
This paper is published in Computational Ecology and Software, an open-access journal. Given my previous posts extolling the virtues of open access journals (see here, here, here and here) I'm putting my academic money where my mouth is, and submitting to open-access journals.
We had data on the presence or absence of certain species of bacterial crop disease (that is, bacteria that infect and cause diseases in plants we use as crops) in 459 geo-political regions throughout the world. We also had data on the climate in these regions and the presence of host plant species. We created MLP that predicted the presence or absence of the bacteria species from climate (abiotic factors). We also created MLP that predicted the presence or absence of the bacteria species from the host plant species assemblages (biotic factors). While both of these approaches worked, we got much better accuracies by combining the outputs into ensembles, and by using a cascaded or tandem ANN approach.
Ensembles are a way of combining the outputs of several ANN. An input vector is propagated through each of the ANN, and the output values combined either statistically (the final output value is the max, mean or median of the uncombined outputs) or algorithmically (output is determined as a majority vote of the uncombined outputs - that is, if the majority of the values is above a threshold, the output of the ensemble is a presence, otherwise, absence). We looked at three different kinds of ensemble: firstly, ensembles of the best ten MLP trained on abiotic inputs; secondly, ensembles of the best ten MLP trained on biotic inputs; and finally, ensembles that combined the best ten MLP trained on abiotic input as well as the best ten MLP trained on biotic inputs. These last ensembles were particularly interesting, as it allowed us to make predictions of species distributions using both biotic and abiotic factors simultaneously. The rationale behind ensembles is that different MLP learn different parts of the problem space: by combining the outputs of several MLP, it is possible to cover a larger part of the problem space, and therefore to boost prediction accuracy. Combining abiotic and biotic factors is the same idea. We know that an organism is affected by both of these factors, so combining both of them allows us to make more accurate predictions.
While the ensemble approach boosted the prediction accuracies, we thought we could do better, so we created MLP that took as inputs the outputs of the very best MLP trained on abiotic and biotic factors. In other words, the outputs of the climate and host networks were used as the input values for a second-level of MLP, which were then trained on the presence and absence of the bacteria species. The idea behind tandem ANN is that, if a first-level network makes a mistake - that is, if a climate or host MLP makes an incorrect prediction - then the tandem network can learn to correct it. Again, we were combining abiotic and biotic factors to make predictions.
The results of all these techniques were that while the single-level MLP were able to predict the distributions of the crop diseases fairly well, combining abiotic and biotic factors gave much better accuracy, whether the combination was achieved by a simple ensemble approach, or by using a tandem MLP approach.
This paper is published in Computational Ecology and Software, an open-access journal. Given my previous posts extolling the virtues of open access journals (see here, here, here and here) I'm putting my academic money where my mouth is, and submitting to open-access journals.
Tuesday, February 21, 2012
Conference paper deadline: NIPS 2012
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.
Monday, February 20, 2012
Call for papers: Applications of ECoS
Special issue of Evolving Systems on
Applications of Evolving Connectionist Systems
Guest Editor
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:
Please submit all papers via the online submission system:
Friday, February 17, 2012
Reminder: paper submission deadline for IEEE CISDA 2012
A reminder that 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.
Thursday, February 16, 2012
Call for papers: CIDU 2012
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.
Wednesday, February 15, 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 26-29, 2012.
Tuesday, February 14, 2012
Reminder: paper submission deadline for IEEE CIMSA 2012
A reminder that the deadline for submitting papers to the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA) 2012 is 15 March 2012. This conference will be held in Tianjin, China, 2-4 July 2012.
Monday, February 13, 2012
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 the 12th of March, 2012. This conference will be held in Taormina, Italy, 1-5 September 2012.
Friday, February 10, 2012
Paper deadline extension: ICIC 2012
The deadline for papers submitted to the International Conference on Intelligent Computing (ICIC) 2012 has been extended to March 31, 2012. This conference will be held in Huangshan, China, July 25-29, 2012.
Conference paper submission deadline for UKCI 2012
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.
Thursday, February 9, 2012
New article: IEEE CIS social media
An article I co-wrote with Albert Lam, Dongrui Wu and Pablo Estevez, describing the social media presence of the IEEE Computational Intelligence Society:
Via IEEE Xplore.
Alternative link.
Via IEEE Xplore.
Alternative link.
Conference paper deadline: iCAST 2012
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.
Wednesday, February 8, 2012
Call for papers: AI'12
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.
Tuesday, February 7, 2012
Reminder: Paper submission deadline for ECAI 2012
A reminder that the deadline for papers submitted to the 20th European Conference on Artificial Intelligence (ECAI) 2012 is 6 March 2012. This conference will be held in Montpellier, France, 27-21 August, 2012.
Monday, February 6, 2012
Conference paper deadline: NAFIPS 2012
The paper submission deadline for the 31st annual meeting of the North American Fuzzy Information Processing Society (NAFIPS) 2012 is February 26, 2012. This conference will be held in Berkeley, California, USA,
Friday, February 3, 2012
The problem with academic journals 4
Or, why I'm not boycotting Elsevier.
"You never change something by fighting the existing reality. To change something build a new model that makes the existing model obsolete." - R. Buckminster Fuller
This is the fourth post in a series about the problems with the big academic journal publishers. In summary: the journals get their content for free; get their quality control (peer review / refereeing) for free; and get their editors for free, yet they charge many thousands of dollars for subscriptions. This bad situation has been compounded by publishers like Elsevier basically trying to buy a law that directly attacks open-access journals.
In response to this, and to other bad behaviour, calls for a boycott of Elsevier have culminated in this website being launched, where scientists can pledge to not submit, referee or edit for Elsevier journals. At the time of writing, around 3000 scientists have already done so.
I am not one of them, and I will not be one of them. I have several reasons for taking this position, some of them idealistic, and others pragmatic.
Firstly, I agree with the sentiment from Fuller above: it is better to create something new than to tear down the old. In this case, it means that it is better to support open access journals than to boycott traditional journals. In other words, let the old journals wither away into history because they have been made obsolete, not because one particular publisher has been throttled by a boycott.
Secondly, boycotting one publisher (Elsevier, in this case) won't achieve much. Other publishers, who are guilty of much the same behaviour as Elsevier, will simply pick up the slack, that is, they will see an increase in submissions that would have otherwise have gone to Elsevier journals. The boycott might hurt Elsevier, but it won't fix the problem with academic journals.
Thirdly, there is a lack of alternative venues for some fields. For my ecological research, the most appropriate journals are Ecological Informatics and Ecological Modelling, both of which are published by Elsevier. There is an open-access alternative to them, Computational Ecology and Software, but it is less than a year old and has yet to become anywhere near as established as the incumbents.
Fourthly, students and junior researchers do not have much of a choice where they submit their papers. If your supervisor says to submit your paper to an Elsevier journal, do you really have a choice about it?
Finally, any early-career researcher who signs on to this boycott is setting themselves up for some trouble down the line. As an early-career researcher myself, I ask myself, do I want to mark myself as someone who will not try to publish in the top journals in my field, just because they are published by Elsevier? I need to publish in the most appropriate and highly-ranked journals in my field, and for me, most of those are published by Elsevier. Put another way, I don't have a permanent job yet, so I need to publish in the most appropriate and highly-ranked journals for the sake of my career. I need to develop my career because I have a family to support. If that makes me a coward, at least I'll be a coward who can feed his family.
We will not solve the problems with academic journals by boycotting any one publisher. We will solve the problems with academic journals by supporting open access journals. That means submitting papers to them, refereeing papers for them, and volunteering on their editorial boards. I am supporting CES, by publishing papers with them, and by working on their editorial board. I might even start my own open-access journal one day. But we need more top researchers to get involved with open access journals. We need the big names to support them, so they get the citations they need to increase their impact factors, so the journals become more desirable venues in which to publish.
If there were a site where scientists could pledge to submit, referee and do editorial work for open-access journals, I would sign up there. I might even set it up myself - it's not like I need to sleep, is it?
"You never change something by fighting the existing reality. To change something build a new model that makes the existing model obsolete." - R. Buckminster Fuller
This is the fourth post in a series about the problems with the big academic journal publishers. In summary: the journals get their content for free; get their quality control (peer review / refereeing) for free; and get their editors for free, yet they charge many thousands of dollars for subscriptions. This bad situation has been compounded by publishers like Elsevier basically trying to buy a law that directly attacks open-access journals.
In response to this, and to other bad behaviour, calls for a boycott of Elsevier have culminated in this website being launched, where scientists can pledge to not submit, referee or edit for Elsevier journals. At the time of writing, around 3000 scientists have already done so.
I am not one of them, and I will not be one of them. I have several reasons for taking this position, some of them idealistic, and others pragmatic.
Firstly, I agree with the sentiment from Fuller above: it is better to create something new than to tear down the old. In this case, it means that it is better to support open access journals than to boycott traditional journals. In other words, let the old journals wither away into history because they have been made obsolete, not because one particular publisher has been throttled by a boycott.
Secondly, boycotting one publisher (Elsevier, in this case) won't achieve much. Other publishers, who are guilty of much the same behaviour as Elsevier, will simply pick up the slack, that is, they will see an increase in submissions that would have otherwise have gone to Elsevier journals. The boycott might hurt Elsevier, but it won't fix the problem with academic journals.
Thirdly, there is a lack of alternative venues for some fields. For my ecological research, the most appropriate journals are Ecological Informatics and Ecological Modelling, both of which are published by Elsevier. There is an open-access alternative to them, Computational Ecology and Software, but it is less than a year old and has yet to become anywhere near as established as the incumbents.
Fourthly, students and junior researchers do not have much of a choice where they submit their papers. If your supervisor says to submit your paper to an Elsevier journal, do you really have a choice about it?
Finally, any early-career researcher who signs on to this boycott is setting themselves up for some trouble down the line. As an early-career researcher myself, I ask myself, do I want to mark myself as someone who will not try to publish in the top journals in my field, just because they are published by Elsevier? I need to publish in the most appropriate and highly-ranked journals in my field, and for me, most of those are published by Elsevier. Put another way, I don't have a permanent job yet, so I need to publish in the most appropriate and highly-ranked journals for the sake of my career. I need to develop my career because I have a family to support. If that makes me a coward, at least I'll be a coward who can feed his family.
We will not solve the problems with academic journals by boycotting any one publisher. We will solve the problems with academic journals by supporting open access journals. That means submitting papers to them, refereeing papers for them, and volunteering on their editorial boards. I am supporting CES, by publishing papers with them, and by working on their editorial board. I might even start my own open-access journal one day. But we need more top researchers to get involved with open access journals. We need the big names to support them, so they get the citations they need to increase their impact factors, so the journals become more desirable venues in which to publish.
If there were a site where scientists could pledge to submit, referee and do editorial work for open-access journals, I would sign up there. I might even set it up myself - it's not like I need to sleep, is it?
Thursday, February 2, 2012
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.
Wednesday, February 1, 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.
Tuesday, January 31, 2012
Reminder: Paper submission deadline for PRICAI 2012
A reminder that 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.
Monday, January 30, 2012
Publishing or perishing
The single most important metric by which an academic is judged is their peer-reviewed publication record. Promotion, grant applications, and finding new jobs all depends on having a strong publication record. This has long been described as "Publish or perish", because if you don't publish, you perish - either you don't advance in your job, you can't find a job, or you don't keep your job. Now, I've got a reasonably long publication record, but I'm always looking for ways of boosting my research output (see my previous posts on publishing in computational intelligence).
Several years ago, biologist Phil Clapham published an excellent essay on the need for academics to publish their research. One of his rules, that I am applying to my own work, is to have at least one paper under review at all times. Now, this can be pretty hard work - there is always variation in the amount of time that papers spend in review, and reviewer comments can take a long time to address. But, it does lead to building up your publication record quite quickly.
One outcome of this rule, though, is that one should also be writing at least one paper at any given time, while also generating sufficient publishable results for at least one paper at any given time. That is, while at least one paper is in review, you need to be writing at least one paper, and also writing code / designing experiments / performing analyses to go into at least one other paper. So, publishing is like being on a treadmill: as soon as you submit one paper, you need to get to work on getting the next submitted, while lining up the material for the one after that.
While this does encourage the practice of breaking research projects into small, easily published (and easily understood) chunks, I suspect it may also encourage further proliferation of single publon papers. Whether or not this is a bad thing, I've leave to you to decide.
Another way to boost your publication count is to collaborate. A lot. Computational intelligence is a particularly useful field to come from for collaborating, as the algorithms we study can be applied to so many problems. But that's all a topic for another post.
Several years ago, biologist Phil Clapham published an excellent essay on the need for academics to publish their research. One of his rules, that I am applying to my own work, is to have at least one paper under review at all times. Now, this can be pretty hard work - there is always variation in the amount of time that papers spend in review, and reviewer comments can take a long time to address. But, it does lead to building up your publication record quite quickly.
One outcome of this rule, though, is that one should also be writing at least one paper at any given time, while also generating sufficient publishable results for at least one paper at any given time. That is, while at least one paper is in review, you need to be writing at least one paper, and also writing code / designing experiments / performing analyses to go into at least one other paper. So, publishing is like being on a treadmill: as soon as you submit one paper, you need to get to work on getting the next submitted, while lining up the material for the one after that.
While this does encourage the practice of breaking research projects into small, easily published (and easily understood) chunks, I suspect it may also encourage further proliferation of single publon papers. Whether or not this is a bad thing, I've leave to you to decide.
Another way to boost your publication count is to collaborate. A lot. Computational intelligence is a particularly useful field to come from for collaborating, as the algorithms we study can be applied to so many problems. But that's all a topic for another post.
Friday, January 27, 2012
Call for papers: Applications of ECoS
Special issue of Evolving Systems on
Applications of Evolving Connectionist Systems
Guest Editor
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:
Please submit all papers via the online submission system:
Conference paper deadline: IJCCI 2012
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.
Thursday, January 26, 2012
Paper submission deadline: IEEE CIG 2012
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.
Wednesday, January 25, 2012
Call for papers: IEEE CIMSA 2012
The deadline for submitting papers to the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (IEEE CIMSA) 2012 is 15 March 2012. This conference will be held in Tianjin, China, 2-4 July 2012.
Tuesday, January 24, 2012
Reminder: paper submission deadline for ICML 2012
A reminder that 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.
Monday, January 23, 2012
Call for papers: CIDM 2012
The deadline for submitting papers to the Third International Workshop on Computational Intelligence for Disaster Management (CIDM) 2012 is March 25 2012. This workshop will be held in Bucharest, Romania, 19-21 September 2012.
Saturday, January 21, 2012
Conference paper deadline: AAIA 2012
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.
Thursday, January 19, 2012
The problem with academic journals 3
I've tried to stay away from politics on this blog, as I want to keep it as professional as I can, and politics is one of the two things (along with religion) guaranteed to make otherwise pleasant and rational people lose all self control. But sometimes, politics intrudes into the academy: as Pericles put it "Just because you do not take an interest in politics does not mean politics won't take an interest in you".
I've discussed the problems with academic journals twice before. What has prompted me to revisit the topic, and break my taboo on politics on this blog, is the Research Works Act, which is nothing less than a direct assault by the US government on open access journals. Not that the US government per se has anything against open access journals: journal publishers like Elsevier do, though, so what they've done is whack down a sack of cash in front of their tame congress members and bought a law. This law, if passed, would ban US government funding agencies from requiring that papers resulting from their funding be made available as open access publications. This has caused a firestorm in the academic blogosphere, with calls for scholarly societies to withdraw from the American Association of Publishers (one of the groups lobbying in favour of the law), and with some even going so far as to call academic publishers enemies of science.
I have published in open-access journals. I'm even on the editorial board of one, and I find this law extremely disturbing. It is in direct contrast to other countries such as the UK, where a new policy from the science minister requires the results of publicly funded research to be open-access. It is simply a desperate attempt by the established journal publishers to protect a business model that is going the way of horse-drawn carriages and rotary-dial telephones.
Although one could argue that, as a New Zealand citizen resident in Australia, this will not really effect me, the USA has a habit of forcing it's laws on other countries. How long before other countries, like Australia or New Zealand, get similar laws? Even if this proposed law is defeated in the USA, does anyone seriously think that the publishers will give up trying to kill open access journals?
Open access is the future of academic publishing. Publishers are trying to protect a failing business model by exploiting a political system that seems, to someone raised in a Westminster-style democracy, quite corrupt. Since we have no chance of changing that system, the only response open to scientists is to move away from publishing in the journals published by companies like Elsevier, and publish instead in open-access journals. In other words, cut the journal publishers out completely and starve them of the quality papers they need to be successful.
Over the next several weeks, I will be collating a list of open-access computational intelligence journals, and reviewing a selection of papers from each. It's time to take open-access seriously. It's time to embrace the future, and to leave the old publishers in the dustbin of history.
I've discussed the problems with academic journals twice before. What has prompted me to revisit the topic, and break my taboo on politics on this blog, is the Research Works Act, which is nothing less than a direct assault by the US government on open access journals. Not that the US government per se has anything against open access journals: journal publishers like Elsevier do, though, so what they've done is whack down a sack of cash in front of their tame congress members and bought a law. This law, if passed, would ban US government funding agencies from requiring that papers resulting from their funding be made available as open access publications. This has caused a firestorm in the academic blogosphere, with calls for scholarly societies to withdraw from the American Association of Publishers (one of the groups lobbying in favour of the law), and with some even going so far as to call academic publishers enemies of science.
I have published in open-access journals. I'm even on the editorial board of one, and I find this law extremely disturbing. It is in direct contrast to other countries such as the UK, where a new policy from the science minister requires the results of publicly funded research to be open-access. It is simply a desperate attempt by the established journal publishers to protect a business model that is going the way of horse-drawn carriages and rotary-dial telephones.
Although one could argue that, as a New Zealand citizen resident in Australia, this will not really effect me, the USA has a habit of forcing it's laws on other countries. How long before other countries, like Australia or New Zealand, get similar laws? Even if this proposed law is defeated in the USA, does anyone seriously think that the publishers will give up trying to kill open access journals?
Open access is the future of academic publishing. Publishers are trying to protect a failing business model by exploiting a political system that seems, to someone raised in a Westminster-style democracy, quite corrupt. Since we have no chance of changing that system, the only response open to scientists is to move away from publishing in the journals published by companies like Elsevier, and publish instead in open-access journals. In other words, cut the journal publishers out completely and starve them of the quality papers they need to be successful.
Over the next several weeks, I will be collating a list of open-access computational intelligence journals, and reviewing a selection of papers from each. It's time to take open-access seriously. It's time to embrace the future, and to leave the old publishers in the dustbin of history.
Wednesday, January 18, 2012
IEEE Computational Intelligence Society Call for Social Media Subcommittee members
Dear CIS members,
This is a call for active participation in the CIS Social Media Subcommittee.
The Social Media Subcommittee, established in 2011, is a subcommittee under the Member Activities committee of CIS. Our objectives are to promote CIS membership and activities, to leverage our online presence, and build our leadership in CIS-related research and industrial communities.
Social Media have become very popular in recent years. Examples include Facebook, Twitter, LinkedIn, and Second Life. They have become part of our lives and some of the major channels to get updates about our friends and the rest of the world. Our mission is to keep you up-to-date about CIS and computational intelligence-related information in the most direct and timely manner. You will no longer miss deadlines for submitting papers to our conferences and you will be able to get involved in discussions of the hottest topics in computational intelligence with our professionals.
The Subcommittee is continuously seeking to develop new and innovative initiatives for promoting CIS with Social Media. We are looking for enthusiastic members who are keen to get involved in the activities of the Subcommittee. The main tasks are to set up and manage the accounts in some of the major Social Media (i.e., Facebook, Twitter, LinkedIn, Second Life, etc.) and to pursue suggestions for using Social Media to other parties of CIS. Experience in programming in Facebook and Second Life is preferred.
We believe you may have experience in and use one or more Social Media on a regular basis. To be a Subcommittee member, you will just need to spare some of your time working with Social Media for CIS. You will provide valuable experience in committee membership and society involvement that could be useful on your CV, or as a stepping-stone towards further CIS technical committee involvement.
Being a member will require enthusiasm, dedication and the investment of some of your time for meetings, preparing documents for initiatives, and managing the CIS Social Media activities. As the Chairs of the IEEE CIS Social Media Subcommittee, we would like to invite any interested IEEE CIS members worldwide to join us.
If you are interested in joining this Social Media Subcommittee and further promoting your professional careers, please send your CV to us (cis.socialmedia@gmail.com) with email subject title “[SMS] Recruitment 2012” before January 28, 2012.
If you are not interested in joining the Subcommittee but are keen to provide input and feedback on CIS initiatives for Social Media, please also contact us.
We would appreciate it if you could forward this call to any IEEE CIS members who may be interested.
We look forward to hearing from you. Thank you very much for your attention.
Best Regards,
Albert Y.S. Lam
Chair, IEEE CIS Social Media Subcommittee
Monday, January 16, 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.
Friday, January 13, 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.
Thursday, January 12, 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.
Friday, December 30, 2011
Reminder: conference paper deadline for EANN
A reminder that the deadline for submitting papers to the 13th International Conference on Engineering Applications of Neural Networks (EANN 2012) is 31 March 2012. This conference will be held in London, UK, 20-23 September, 2012.
Friday, December 23, 2011
Reminder: paper submission deadline for UCNC 2012
A reminder that the deadline for submitting papers to the 11th Conference on Unconventional Computation and Natural Computation (UCNC) 2012 is 26 March 2012. This conference will be held in Orleans, France, 3-6 September, 2012.
Monday, December 19, 2011
Reminder: paper submission deadline for IEEE CISDA 2012
A reminder that 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.
Friday, December 16, 2011
Reminder: paper submission deadline for BICS 2012
A reminder that the deadline for submitting papers to the International Conference on Brain Inspired Cognitive Systems (BICS) 2012 is 15 January 2012. This conference will be held in Shenyang, China, 11-14 July, 2012.
Thursday, December 15, 2011
Reminder: paper submission deadline for ISNN 2012
A reminder that the deadline for submitting papers to the 2012 International Symposium on Neural Networks (ISNN 2012) is 15 January 2012. This symposium will be held in Shenyang, China, July 11-14, 2012.
Wednesday, December 14, 2011
WCCI 2012 Deadline Extended
The deadline for the World Conference on Computational Intelligence (WCCI) 2012 has been extended to 18 January, 2012. There will be no further extensions. WCCI 2012 combines IJCNN 2012, FUZZ-IEEE 2012 and CEC 2012.
Tuesday, December 13, 2011
Reminder: paper deadline for GECCO 2012
A reminder that 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.
Monday, December 12, 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 the 12th of March, 2012. This conference will be held in Taormina, Italy, 1-5 September 2012.
Friday, December 9, 2011
Conference paper deadline: MAICS 2012
The deadline for the 23rd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS) 2012 is February 10, 2012. This conference will be held in Cincinnati, Ohio, April 21-22, 2012. Note that the deadline for authors who need visas to enter the USA is January 13, 2012.
Thursday, December 8, 2011
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
Wednesday, December 7, 2011
Call for papers: CISE 2012
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.
Tuesday, December 6, 2011
Reminder: Paper submission deadline for ECAI 2012
A reminder that the deadline for papers submitted to the 20th European Conference on Artificial Intelligence (ECAI) 2012 is 6 March 2012. This conference will be held in Montpellier, France, 27-21 August, 2012.
Monday, December 5, 2011
Reminder: Paper submission deadline for PRICAI 2012
A reminder that 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.
Saturday, December 3, 2011
Call for papers: IEEE WCCI 2012 Special Session on Fuzzy Ontologies and FML Applications
This call for papers comes from Dr Giovanni Acampora of the University of Salerno. I have previously discussed on this blog some of Giovanni's work on Fuzzy Markup Language. Note that the deadline for submitting papers for this session is already very close: December 19, 2011.
CfP: IEEE WCCI 2012 Special Session on Fuzzy Ontologies and FML Applications
We would like to invite you to submit a paper for a special session "Fuzzy Ontologies and Fuzzy Markup Language Applications" at the Fuzz-IEEE 2012 conference which will be held as part of the 2012 IEEE WCCI in Brisbane, Australia at June 10-15, 2012.
It is widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real world applications like personal diabetic diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. Recently, the research on the ontology has been spread widely to be critical components in the knowledge management, Semantic Web, business-to-business applications, and several other application areas. Ontologies are a suitable way for representing complex knowledge and facilitating knowledge share and reuse. The concept of ontology has been widely embraced by the fuzzy research community by playing an important role in the development of distributed systems and the composition reveals a vital strategy for enterprise collaboration. In this context, the Fuzzy Markup Language (FML) is one of the most important results because it allows fuzzy scientists to express their ideas in abstract and interoperable way by improving their productivity and, at the same time, increasing the average quality of their works. This special session invites high-quality conceptual, analytical and empirical articles representing intelligent agent and knowledge mining information systems and their integration. The objective of the proposed special issue is to highlight an ongoing research on fuzzy and FML approaches for ontology applications as well as their applications on various domains.
Topics of interest (not limited to):
Further information about the special session can be found at: http://www.dmi.unisa.it/people/acampora/www/WCCI2012/Home.html
The session deadlines are as follows:
- Paper submission: December 19, 2011
- Notification of acceptance/rejection: February 20, 2012
- Camera-ready papers: April 2, 2012
- Early registration: April 2, 2012
- Conference dates: June 10-15, 2012
CfP: IEEE WCCI 2012 Special Session on Fuzzy Ontologies and FML Applications
We would like to invite you to submit a paper for a special session "Fuzzy Ontologies and Fuzzy Markup Language Applications" at the Fuzz-IEEE 2012 conference which will be held as part of the 2012 IEEE WCCI in Brisbane, Australia at June 10-15, 2012.
Topics of interest (not limited to):
• Ontologies and Ontological Agents
• FML Agents for Software Engineering
• FML Agents for Knowledge Discovery
• FML Agents in a Neuro Fuzzy Approach
• Environment-aware Agents
• Agents for Intelligent Manufacturing Systems
• Intelligent Agent Applications
• Knowledge Representation with Ontology
• FML in Evolvable Hardware
• Knowledge-Based Systems
• Semantic Interoperability
• Semantic Web Agents
• Healthcare Ontological Agents
• Agents for Knowledge Management
• Embedded Agents
• FML Agents for E-Commerce
• FML Agents for Smart Environments
• FML Agents for Ambient Intelligence
• Knowledge Sharing with FML Agents
• Distributed Mining with FML Agents
• Web Services Applications based on FML
• FML Applications
Further information about the special session can be found at: http://www.dmi.unisa.it/people/acampora/www/WCCI2012/Home.html
The session deadlines are as follows:
- Paper submission: December 19, 2011
- Notification of acceptance/rejection: February 20, 2012
- Camera-ready papers: April 2, 2012
- Early registration: April 2, 2012
- Conference dates: June 10-15, 2012
Friday, December 2, 2011
Reminder: paper 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.
Thursday, December 1, 2011
Reminder: paper submission deadline for ICIC 2012
A reminder that 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.
Wednesday, November 30, 2011
Reminder: paper submission deadline for 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.
Tuesday, November 29, 2011
Common grammatical errors
English is the result of Norman soldiers attempting to pick up Anglo-Saxon barmaids, and is no more legitimate than any of the other results. - H. Beam Piper
Despite the murky origins of the English language, it is the language of international commerce and science: if you attend an international conference in, for example, China, the presentations and proceedings will be in English. The same applies to the international scholarly journals. Being able to communicate effectively in English is therefore essential for anyone who wants a good career in academia.
Now, if you ever heard me speaking, you'd have probably have trouble understanding me: my New Zealand accent means that I flatten my vowels, my voice is nasal, and I speak too quickly. But, I do write fairly well, and I do avoid the grammatical errors below that really annoy me. Don't feel too badly if you aren't a native English speaker and make these mistakes: I noticed most of these mistakes while teaching New Zealand undergrads. That is, these are the kinds of mistakes that native speakers make in some parts of the world. They're still mistakes, though, and should be avoided.
1) Reversing imply and infer. For example "What are you inferring by saying that?". An inference is made from an implication. That is, the implication is made, then the inference is drawn from it. For example, someone once said to me: "Are you inferring I'd lie?", to which I replied "No, I'm implying you'd lie - you are inferring it". I know, I'm a bad, bad man.
2) Following from that is the distinction between implicit and explicit. If something is implicit, you know about it through inference. If something is explicit, it is stated. Saying that something was "explicitly implied" (and I have seen that written, but a university student no less) is not just bad grammar, it is complete nonsense. If something is explicit, it cannot be implied.
3) Literally means what you said is as it actually is. If you were to say "it is literally raining cats and dogs", then cats and dogs should be falling from the sky. Jamie Oliver is particularly bad for abusing this word: "and you literally put the mint in the mixture..."!
4) Your and you're. "Your" means it belongs to you. "You're" means you are. Can you see what is wrong with this picture?
Dear Tick Tax: this is your sign, and you're using incorrect grammar.
5) Using the term "begs the question". The original meaning of this is to assume as true something that cannot be taken for granted. The more correct way to saying this is "raises the question". This is probably a lost cause now, as the incorrect meaning of begging the question is widely established.
6) Its and it's. It's is short for it is. Its means something belongs to it. For example: "I saw a dog and said "it's wagging its tail"".
7) There, their and they're. There is a place. Their means something belongs to them. They're means they are. Example: "They're at their house, which is over there".
These are the errors that annoy me the most. For two excellent articles on common errors in academic writing, see "Don't torture your reader" parts I and II, by Professor Corey Bradshaw at the University of Adelaide.
Despite the murky origins of the English language, it is the language of international commerce and science: if you attend an international conference in, for example, China, the presentations and proceedings will be in English. The same applies to the international scholarly journals. Being able to communicate effectively in English is therefore essential for anyone who wants a good career in academia.
Now, if you ever heard me speaking, you'd have probably have trouble understanding me: my New Zealand accent means that I flatten my vowels, my voice is nasal, and I speak too quickly. But, I do write fairly well, and I do avoid the grammatical errors below that really annoy me. Don't feel too badly if you aren't a native English speaker and make these mistakes: I noticed most of these mistakes while teaching New Zealand undergrads. That is, these are the kinds of mistakes that native speakers make in some parts of the world. They're still mistakes, though, and should be avoided.
1) Reversing imply and infer. For example "What are you inferring by saying that?". An inference is made from an implication. That is, the implication is made, then the inference is drawn from it. For example, someone once said to me: "Are you inferring I'd lie?", to which I replied "No, I'm implying you'd lie - you are inferring it". I know, I'm a bad, bad man.
2) Following from that is the distinction between implicit and explicit. If something is implicit, you know about it through inference. If something is explicit, it is stated. Saying that something was "explicitly implied" (and I have seen that written, but a university student no less) is not just bad grammar, it is complete nonsense. If something is explicit, it cannot be implied.
3) Literally means what you said is as it actually is. If you were to say "it is literally raining cats and dogs", then cats and dogs should be falling from the sky. Jamie Oliver is particularly bad for abusing this word: "and you literally put the mint in the mixture..."!
4) Your and you're. "Your" means it belongs to you. "You're" means you are. Can you see what is wrong with this picture?
Dear Tick Tax: this is your sign, and you're using incorrect grammar.
5) Using the term "begs the question". The original meaning of this is to assume as true something that cannot be taken for granted. The more correct way to saying this is "raises the question". This is probably a lost cause now, as the incorrect meaning of begging the question is widely established.
6) Its and it's. It's is short for it is. Its means something belongs to it. For example: "I saw a dog and said "it's wagging its tail"".
7) There, their and they're. There is a place. Their means something belongs to them. They're means they are. Example: "They're at their house, which is over there".
These are the errors that annoy me the most. For two excellent articles on common errors in academic writing, see "Don't torture your reader" parts I and II, by Professor Corey Bradshaw at the University of Adelaide.
Monday, November 28, 2011
Reminder: Paper submission deadline for ICSI 2012
A reminder that 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.
Friday, November 25, 2011
Reminder: paper submission deadline for ICML 2012
A reminder that 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.
Thursday, November 24, 2011
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.
Wednesday, November 23, 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.
Tuesday, November 22, 2011
Reminder: Paper deadline IJCNN 2012
A reminder that 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.
Monday, November 21, 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, November 18, 2011
Google Scholar Citations
Google has just launched a useful tool for academics: Google Scholar Citations. This is a service on top of Google Scholar that allows you to track the number of citations each of your publications has received. One of the metrics by which academics are judged is the number of citations their publications have received, the theory being that good and useful papers will be cited more than papers that are not useful, or good. This has been encapsulated by measures such as the h-index: to have an h-index of n, you must have at least n-papers that have been cited at least n-times each. It is useful for things like grant applications to be able to quote the number of citations you have received and your current h-index, insofar as convincing the grants committees that you can do the work you propose.
It was possible to track citations with Google Scholar in the past, and to calculate your h-index manually, but this could be a bit laborious and error-prone: Scholar Citations makes it a lot easier. I was impressed to see that even with a common name like mine (there are a lot of Michael Watts in the world, and some of them are also academics) the software found almost all of my publications - there are a few that aren't available online yet - and to find the citations to them. I was quite pleased to find that I had a few more citations than I thought.
It was possible to track citations with Google Scholar in the past, and to calculate your h-index manually, but this could be a bit laborious and error-prone: Scholar Citations makes it a lot easier. I was impressed to see that even with a common name like mine (there are a lot of Michael Watts in the world, and some of them are also academics) the software found almost all of my publications - there are a few that aren't available online yet - and to find the citations to them. I was quite pleased to find that I had a few more citations than I thought.
Wednesday, November 16, 2011
Conference paper deadline: EANN
The deadline for submitting papers to the 13th International Conference on Engineering Applications of Neural Networks (EANN 2012) is 31 March 2012. This conference will be held in London, UK, 20-23 September, 2012.
Tuesday, November 15, 2011
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.
Monday, November 14, 2011
Call for papers: Applications of ECoS
Special issue of Evolving Systems on
Applications of Evolving Connectionist Systems
Guest Editor
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:
Please submit all papers via the online submission system:
Friday, November 11, 2011
Reminder: paper submission deadline for IEA AIE 2012
A reminder that the paper submission deadline for the 25th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE) 2012 is 11 November, 2011. This conference will be held in Dalian, China, June 9-12, 2012.
Wednesday, November 9, 2011
Conference paper deadline: KES-AMSTA 2012
The deadline for submitting papers to the 6th KES International Conference on Agents and Multi-agent Systems - Technologies and Applications (KES-AMSTA-2012) is 20 December 2011. This conference will be held in Dubrovnik, Croatia, 25-27 June, 2012.
Tuesday, November 8, 2011
Hang in there
A wise person once told me that doing a PhD is as much a test of endurance as it is a test of intelligence. You face years of late nights, mountains of literature, numerous false-starts and dead-ends, and the nagging fear in the back of your mind that it might not be all that worth it.
Whether or not it is worth it is a question that only you can answer. Financially, it probably isn't. People who do PhDs tend to earn less over their lives than those who do not. They take longer to settle down and tend to delay parenthood and home-ownership until later in life. On the other hand, a PhD is your ticket into academia: the chances of getting a good, stable academic position without a PhD are, now, practically nil. A PhD can also gain you respect from the community: although I seldom use my title, it is useful when I do. Finally, there is the satisfaction of knowing that you achieved something most people never will, or never could. Personally, I did a PhD because I wanted to see if I could. It was the challenge of doing it that appealed to me. With my undergrad degree (first class honours in Information Science) I could have gone into the corporate world and made a very good living. I'd probably be in a high management position now, making a lot more money than I am making as a researcher, but I'd probably be miserable at the same time, because I'd never know how far I could have gone in research. And at the end of my life, I'd be asking myself, how much of a difference did I really make?
There are several factors that contribute to a successful PhD. Firstly, you must have a good supervisor. In fact, I'd go as far to say that you need two supervisors, one senior and one junior. By that I mean that you need one supervisor who is an established academic who is well-respected in their field, and another supervisor who has recently completed their PhD. This is because the junior supervisor still remembers what it is like to do a PhD in the current time, while someone who did their PhD twenty years ago has probably forgotten. You must actively engage with your supervisors, to make sure that they are up-to-date with what you are doing and what you plan to do. A supervisor who is ignorant of what you are doing is a useless supervisor. Don't keep them in the dark!
You must have a clear idea of what your PhD is about. In other words, you must have a hypothesis, and research questions, and research goals. I even went so far as to make these explicit in the introduction to my thesis. It might take you a while to be clear about these, but you'll save a lot more time in the long run.
You must not underestimate the requirements for a PhD. Most universities award a PhD for "a significant original contribution to knowledge" (although most of them do not define "significant" "original" or "contribution"). So, a new algorithm for determining the contributions of the input variables of a neural network probably wouldn't be enough for a PhD, while the algorithm in the context of a rigorous theoretical analysis of the neural network itself, along with an analysis of the algorithm, probably would.
You must not over-estimate the requirement for a PhD. In other words, you're not going to find a cancer cure, or discover the Higgs boson, or bring peace to the Middle East during the course of your PhD. Your PhD research problem needs to be enough for a PhD, and no more. Feature-creep kills PhDs as easily as it kills software projects. From chatting with more senior academics, I've come to believe that this is a more common problem than underestimating a PhD. A good supervisor will help you define the scope of your PhD project, while a bad supervisor will not. Get rid of a bad supervisor and find a better one. Or, at least seek help elsewhere.
You must stick with it. Everyone has a period during their PhD when it all looks hopeless, when you don't want to go on and just want to pack it all in. Hang in there. If you've decided that it's worth it before starting your PhD, it probably is still worth it, even if you don't feel like it. The enormous high you will get when you pass your examination is something you'll not feel often in your life (I found out I had passed my PhD examination two weeks before becoming a father, so I had all of my enormous highs in a short period of time).
It is likely that your examiners will want you to make some revisions to your thesis. Don't take this personally! The best thing to do is to just shut the hell up, make the changes as quickly as you can, and get the degree confirmed. Don't waste too much time arguing with the examiners, unless they are egregiously wrong (one of my examiners was egregiously wrong, in several places, and making the changes he wanted would have made my thesis worse, not better. In the end, I had to show my examination convener a pile of literature that showed that the examiner was wrong, and educate him on how innumerate the examiner was).
When you have passed your PhD exam, the next step is to get a job. If you want to be an academic, that means getting a post-doc. If you're organised, or lucky, then you might even have a post-doc organised before you finish your PhD. Don't restrict your search to just the field you did your PhD in. My PhD was in computational intelligence, but my two post-docs were in ecological informatics, and my current position is in ecological modelling. I'm not an ecologist, by any stretch of the imagination (although I do know a lot about ecology now) but because I am a flexible and fairly clever person I was able to work in these fields, and work effectively. Know what skills you have, and know how to advertise them to potential post-doc supervisors.
Once you're in a post-doc position, the only goal you should have is to publish as many papers as you can, as widely as you can and as quickly as you can. It can also be good to co-supervise some PhD students of your own, to attend conferences, edit journal special issues, and generally show the world that you are a good, hard-working and professional researcher.
But, above all, you must hang in there!
Whether or not it is worth it is a question that only you can answer. Financially, it probably isn't. People who do PhDs tend to earn less over their lives than those who do not. They take longer to settle down and tend to delay parenthood and home-ownership until later in life. On the other hand, a PhD is your ticket into academia: the chances of getting a good, stable academic position without a PhD are, now, practically nil. A PhD can also gain you respect from the community: although I seldom use my title, it is useful when I do. Finally, there is the satisfaction of knowing that you achieved something most people never will, or never could. Personally, I did a PhD because I wanted to see if I could. It was the challenge of doing it that appealed to me. With my undergrad degree (first class honours in Information Science) I could have gone into the corporate world and made a very good living. I'd probably be in a high management position now, making a lot more money than I am making as a researcher, but I'd probably be miserable at the same time, because I'd never know how far I could have gone in research. And at the end of my life, I'd be asking myself, how much of a difference did I really make?
There are several factors that contribute to a successful PhD. Firstly, you must have a good supervisor. In fact, I'd go as far to say that you need two supervisors, one senior and one junior. By that I mean that you need one supervisor who is an established academic who is well-respected in their field, and another supervisor who has recently completed their PhD. This is because the junior supervisor still remembers what it is like to do a PhD in the current time, while someone who did their PhD twenty years ago has probably forgotten. You must actively engage with your supervisors, to make sure that they are up-to-date with what you are doing and what you plan to do. A supervisor who is ignorant of what you are doing is a useless supervisor. Don't keep them in the dark!
You must have a clear idea of what your PhD is about. In other words, you must have a hypothesis, and research questions, and research goals. I even went so far as to make these explicit in the introduction to my thesis. It might take you a while to be clear about these, but you'll save a lot more time in the long run.
You must not underestimate the requirements for a PhD. Most universities award a PhD for "a significant original contribution to knowledge" (although most of them do not define "significant" "original" or "contribution"). So, a new algorithm for determining the contributions of the input variables of a neural network probably wouldn't be enough for a PhD, while the algorithm in the context of a rigorous theoretical analysis of the neural network itself, along with an analysis of the algorithm, probably would.
You must not over-estimate the requirement for a PhD. In other words, you're not going to find a cancer cure, or discover the Higgs boson, or bring peace to the Middle East during the course of your PhD. Your PhD research problem needs to be enough for a PhD, and no more. Feature-creep kills PhDs as easily as it kills software projects. From chatting with more senior academics, I've come to believe that this is a more common problem than underestimating a PhD. A good supervisor will help you define the scope of your PhD project, while a bad supervisor will not. Get rid of a bad supervisor and find a better one. Or, at least seek help elsewhere.
You must stick with it. Everyone has a period during their PhD when it all looks hopeless, when you don't want to go on and just want to pack it all in. Hang in there. If you've decided that it's worth it before starting your PhD, it probably is still worth it, even if you don't feel like it. The enormous high you will get when you pass your examination is something you'll not feel often in your life (I found out I had passed my PhD examination two weeks before becoming a father, so I had all of my enormous highs in a short period of time).
It is likely that your examiners will want you to make some revisions to your thesis. Don't take this personally! The best thing to do is to just shut the hell up, make the changes as quickly as you can, and get the degree confirmed. Don't waste too much time arguing with the examiners, unless they are egregiously wrong (one of my examiners was egregiously wrong, in several places, and making the changes he wanted would have made my thesis worse, not better. In the end, I had to show my examination convener a pile of literature that showed that the examiner was wrong, and educate him on how innumerate the examiner was).
When you have passed your PhD exam, the next step is to get a job. If you want to be an academic, that means getting a post-doc. If you're organised, or lucky, then you might even have a post-doc organised before you finish your PhD. Don't restrict your search to just the field you did your PhD in. My PhD was in computational intelligence, but my two post-docs were in ecological informatics, and my current position is in ecological modelling. I'm not an ecologist, by any stretch of the imagination (although I do know a lot about ecology now) but because I am a flexible and fairly clever person I was able to work in these fields, and work effectively. Know what skills you have, and know how to advertise them to potential post-doc supervisors.
Once you're in a post-doc position, the only goal you should have is to publish as many papers as you can, as widely as you can and as quickly as you can. It can also be good to co-supervise some PhD students of your own, to attend conferences, edit journal special issues, and generally show the world that you are a good, hard-working and professional researcher.
But, above all, you must hang in there!
Thursday, November 3, 2011
Cargo Cult Statistics
One of the nice things about working in a world-class ecology group is the statistical rigor with which ecologists analyse their results. Unfortunately, this rigor is often missing in computational intelligence. Although I touched on some of these issues in a previous post on Minimum requirements for computational intelligence papers, I recently read an article (that shall remain anonymous) that actually made me groan. While I am starting to notice more papers with repeated trials, and even investigating several parameters, the analysis of these results leave a lot to be desired.
Sometimes it is enough to simply list the mean and standard deviation of your accuracy measures. By itself, the mean is useful as a statistic that represents the population of accuracies that the algorithm yielded. The standard deviation is also good as a measure of spread of the values. But if your standard deviation is large, that needs some comment in the paper on why the algorithm is so variable? This is even more important when comparing different algorithms. An author might for example like to say that a neural network trained with evolutionary programming is better than logistic regression for their application, but if they are seeing a coefficient of variation of more than 60% then that implies that the algorithm is giving highly variable or even inconsistent results. To say that these results show that ANN are better than regression, without any statistical tests for significant differences is simply nonsense.
Even if you do do such tests, you need to make sure that you are using the correct tests. What is the distribution of your results? Are they normally distributed? If they are not normally distributed, then you can't use simple parametric tests of significant differences like t-tests. If you are comparing several groups of numbers then a n-way ANOVA is more appropriate than performing n t-tests. These kinds of comparisons, of several groups of numbers, are very common in computational intelligence (the authors are comparing different algorithms over several data sets, or with different parameterisations) but I can't remember ever seeing a paper that mentioned ANOVA (if you can prove me wrong, please do so in the comments).
I call this kind of shallow statistical analysis Cargo Cult Statistics.The term is inspired by Richard Feynman's famous speech about Cargo Cult Science. In this case, it means that while it looks like the authors are doing a statistical analysis of their results (they are calculating the means and standard deviations) it isn't really so, because they are missing out a huge amount of analysis that might actually tell them something useful about their results.
Now, I'm still learning about statistics (but, I'm still learning about everything, and will be until the day I die). But at least I know to ask someone with a better knowledge of statistics than me for advice on how to analyse my results, and I think it makes my papers much better.
Sometimes it is enough to simply list the mean and standard deviation of your accuracy measures. By itself, the mean is useful as a statistic that represents the population of accuracies that the algorithm yielded. The standard deviation is also good as a measure of spread of the values. But if your standard deviation is large, that needs some comment in the paper on why the algorithm is so variable? This is even more important when comparing different algorithms. An author might for example like to say that a neural network trained with evolutionary programming is better than logistic regression for their application, but if they are seeing a coefficient of variation of more than 60% then that implies that the algorithm is giving highly variable or even inconsistent results. To say that these results show that ANN are better than regression, without any statistical tests for significant differences is simply nonsense.
Even if you do do such tests, you need to make sure that you are using the correct tests. What is the distribution of your results? Are they normally distributed? If they are not normally distributed, then you can't use simple parametric tests of significant differences like t-tests. If you are comparing several groups of numbers then a n-way ANOVA is more appropriate than performing n t-tests. These kinds of comparisons, of several groups of numbers, are very common in computational intelligence (the authors are comparing different algorithms over several data sets, or with different parameterisations) but I can't remember ever seeing a paper that mentioned ANOVA (if you can prove me wrong, please do so in the comments).
I call this kind of shallow statistical analysis Cargo Cult Statistics.The term is inspired by Richard Feynman's famous speech about Cargo Cult Science. In this case, it means that while it looks like the authors are doing a statistical analysis of their results (they are calculating the means and standard deviations) it isn't really so, because they are missing out a huge amount of analysis that might actually tell them something useful about their results.
Now, I'm still learning about statistics (but, I'm still learning about everything, and will be until the day I die). But at least I know to ask someone with a better knowledge of statistics than me for advice on how to analyse my results, and I think it makes my papers much better.
Wednesday, November 2, 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.
Tuesday, November 1, 2011
Reminder: paper submission deadline 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.
Monday, October 31, 2011
Reminder: conference paper deadline ICFSNC 2012
A reminder that the deadline for papers submitted to the International Conference on Fuzzy Systems and Neural Computing (ICFSNC) 2012 is 30 November 2011. This conference will be held in Barcelona, Spain, April 11-13, 2012.
Friday, October 28, 2011
Conference paper submission deadline: BICS 2012
The deadline for submitting papers to the International Conference on Brain Inspired Cognitive Systems (BICS) 2012 is 15 January 2012. This conference will be held in Shenyang, China, 11-14 July, 2012.
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