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.
Thursday, January 19, 2012
The problem with academic journals 3
Labels:
research craft
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
Labels:
social networking
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.
Labels:
call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences
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
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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
Labels:
call for papers,
conferences
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
Labels:
rants,
research craft
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
Labels:
research craft,
software
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.
Labels:
call for papers,
conferences
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.
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call for papers,
conferences,
reminder
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:
Labels:
call for papers,
journals
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.
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call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences
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!
Labels:
research craft
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.
Labels:
research craft
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.
Labels:
call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
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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.
Labels:
call for papers,
conferences
Thursday, October 27, 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.
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call for papers,
conferences,
reminder
Wednesday, October 26, 2011
Reminder: Paper submission deadline for ICNC-FSKD 2012
A reminder that 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.
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call for papers,
conferences,
reminder
Tuesday, October 25, 2011
Reminder: Paper deadline for IEEE CIBCB 2012
A reminder that the deadline for papers submitted to the 2012 conference on Computational Intelligence in Bioinformatics and Computational Biology is November 20, 2011. This conference will be held in San Diego, California, May 9-12, 2012.
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call for papers,
conferences,
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Monday, October 24, 2011
Paper submission deadline: CBR-MD 2012
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.
Labels:
call for papers,
conferences
Friday, October 21, 2011
Call for papers: UCNC 2012
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.
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call for papers,
conferences
Thursday, October 20, 2011
Call for papers: ESANN 2012
The deadline for submitting papers to the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2012 is 30 November 2011. This symposium will be held in Bruges, Belgium, 25-27 April, 2012.
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call for papers,
conferences
Wednesday, October 19, 2011
Paper submission deadline: MLDM 2012
The deadline for submitting papers to the 8th Industrial Conference on Machine Learning and Data Mining (MLDM) 2012 is 18 December 2011. This conference will be held in Berlin, Germany, 16-20 July, 2012. MLDM will be held jointly with ICDM 2012.
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call for papers,
conferences
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.
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call for papers,
conferences
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.
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call for papers,
conferences
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.
Labels:
research craft
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.
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call for papers,
conferences
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.
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call for papers,
conferences
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.
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call for papers,
conferences
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.
Labels:
neural networks
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.
Labels:
call for papers,
conferences
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.
Labels:
research craft
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.
Labels:
call for papers,
conferences
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
reminder
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.
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call for papers,
conferences,
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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.
Labels:
call for papers,
conferences
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.
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call for papers,
conferences
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.
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conferences,
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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.
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call for papers,
conferences,
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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.
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call for papers,
conferences,
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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.
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conferences,
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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.
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call for papers,
conferences,
reminder
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.
Labels:
research craft
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.
Labels:
call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences
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.
Labels:
call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences,
reminder
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
Labels:
social networking,
societies
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?
Labels:
research craft,
teaching
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.
Labels:
call for papers,
conferences
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.
Labels:
call for papers,
conferences
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.
Labels:
call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences
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.
Labels:
call for papers,
conferences,
reminder
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.
Labels:
call for papers,
conferences,
reminder
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
Labels:
call for papers,
conferences,
reminder
Thursday, September 1, 2011
Reminder: 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.
Labels:
call for papers,
conferences,
reminder
Wednesday, August 31, 2011
The problem with academic journals
George Monbiot nicely summarises the problems with academic journals as they currently stand.
Monbiot argues that this has the effect of shutting cutting-edge science behind extremely high paywalls, which has the effect of making science inaccessible to most of the population. When this happens, is it any surprise that hokum like the anti-vaccination movement takes hold in the population? Or that creationist baloney circulates so widely?
I think it's time for scientists, and leading scientists at that, to start submitting more to open-access journals. More importantly, it's time for managers and funding bodies to ditch the overly simplistic measures of performance that are derived from impact factors. Otherwise, things are not going to end well.
- Journals get their content for free (papers submitted by authors).
- Journals get their quality control for free (reviewers volunteering their time).
- Journals get their editors for free (more volunteers).
- Journals charge thousands of dollars per year for subscriptions.
Monbiot argues that this has the effect of shutting cutting-edge science behind extremely high paywalls, which has the effect of making science inaccessible to most of the population. When this happens, is it any surprise that hokum like the anti-vaccination movement takes hold in the population? Or that creationist baloney circulates so widely?
I think it's time for scientists, and leading scientists at that, to start submitting more to open-access journals. More importantly, it's time for managers and funding bodies to ditch the overly simplistic measures of performance that are derived from impact factors. Otherwise, things are not going to end well.
Labels:
research craft
Tuesday, August 30, 2011
Reminder: paper deadline for CINTI 2011
A reminder that the deadline to submit papers to the 12th IEEE International Symposium on Computational Intelligence and Informatics (CINTI) 2011 is September 30 2011. This conference will be held in Budapest, Hungary, November 21-22 2011.
Labels:
call for papers,
conferences,
reminder
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