Sequential pattern mining vs Sequence prediction ?

In this blog post, I will answer a question that I have received in my e-mail about what is the difference between sequential pattern mining and sequence prediction. I think that this is a good question and sharing the answer can help to clarify some concepts for some people.

Generally speaking, the goal of sequential pattern mining is to find some patterns that appear in many sequences of symbols.  For example, lets say that you have some sequences of purchases made by customers in a retail store. You can then apply a sequential pattern mining algorithm to find sequential patterns, that is to know what are some sequence of purchases that are common to many customers. For example, you may find that  <harrypotter1, spiderman, batman>  is a sequential pattern. This pattern means that many people have bought the movie Harry potter 1, and then Spiderman, and then Batman.  If you find such patterns, it can help you to understand the data. If you are the retail store manager, you may use such pattern to take some business decisions such as to offer some discount to customers on Batman if they previously buy harrypotter and spiderman.

But there are many other usages of sequential patterns. You can also use the sequential patterns to make some sequence prediction. For example, if someone buys Harry Potter 1 and Spiderman, you may predict that he will buy Batman based on the above sequential pattern. This can be used to perform recommendation

Another example about the applications of sequential pattern mining is to find patterns in text documents.  A text document is a set of sentences, and each sentences is a sequence of words. Thus, you can apply a sequential pattern mining algorithm to find the sequential patterns that tell you some frequent sequence of words appearing many times in a book. This can tell you about some writing patterns used by some authors, and you can even use these patterns to try to guess who is the author of some anonymous book (if you are curious, I actually did that in a paper: https://www.philippe-fournier-viger.com/FLAIRS2016__AUTHORSHIP_ATTRIBUTION.pdf).

On the other hand, the goal of sequence prediction is to predict what is the next symbol of a sequence of symbols.  For example, some people buy  the movies Harry Potter 1,  Hulk, Batman, and then Star Wars, and we want to know what is the next movie that this person will buy?  There are many ways to do sequence prediction. One way is to use the sequential patterns or a variation called sequential rules. For example, we did sequence prediction using sequential rules in apaper to predict the next webpage that someone will click: https://www.philippe-fournier-viger.com/sequential_rules_prediction_2012.pdf
But there are also many other models for sequence predictions that do not rely on sequential patterns like the CPT and CPT+ models (video presentation here: https://data-mining.philippe-fournier-viger.com/video-sequence-prediction-with-the-cpt-and-cpt-models/) , the all-k order markov model, the DG model, TDAG, and LZ78.

Thus, to summarize, the goal of sequential pattern mining is to find patterns.  You can find these patterns in data for multiple purpose. It can be just to understand the data and learn something about it. It can be to use these patterns to do sequence prediction, or other tasks like clustering, authorship attribution, etc.  Thus, sequential pattern mining has many applications and sequence prediction is one of them.  And the goal of sequence prediction is to predict the next symbol of a sequence. There are many methods to do sequence prediction and sequential pattern mining is one of them.

Hope that his short answer will be helpful. Some additional blog posts that I wrote on these topics:


Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 180 data mining algorithms.

Posted in Big data, Data Mining | Tagged , , , , | 2 Comments

Email invitation to be a “special” speaker, a scam?

Have you ever received an e-mail from some small conference telling you that they want to invite you to give a talk as a special speaker, honorary speaker or distinguished speaker? I received several e-mails like this and while something it is some valuable invitation, most of the time is just spam. In this blog post, I will discuss this.

Several small conference organizers send unsollicited e-mails to try to attract papers for their conferences and ultimately to collect money in the form of registration fees. As many people ignore these e-mails, some strategies that they use is to mention the title of one of your recent paper and to invite you as a special speaker. This is an example:

Dear Dr. XXXXXX

Please accept my apology if this email bothers you, as I have tried to send you this invitation in last months but without any response from you.

On behalf of the Organizing Committee, it is our delight to extend to NAME_OF_CONFERENCEwhich is going to be held during 2021 (next year) in CITY, Japan.It is our great pleasure and privilege to welcome you to join the NAME_OF_CONFERENCE act as the chair/speaker while presenting about TITLE OF_MY_PAPER.

Another example:

Dear Dr. XXXXX,

Hope you receive this letter in a wonderful mood. Please accept my apology if this email bothers you, as I have tried to send you this invitation to you but without any response. Would you please send a reply?

Thanks for your time to this email from our committee, the committee of XXXXXX-2022 cordially welcome you to share a presentation as a session speaker/chair regarding your research. Sincerely wish you can give us an opportunity to include your research in our program and proceeding.


If you read such e-mails for the first time, you may think that the sender has read your paper and is really interested in your research and wants to invite you as a special guest to their conference to give a talk. So you may be tempted to accept. However, most of the time, this is just SPAM and they only mention the title of one of your paper and that you are invited as a special speaker to catch your attention.

Before accepting such invitation, you should check: (1) is this a well-known conference in your field that has been held for several years? (2) do you know other people having attended it? (3) is it associated with a famous institutions or publisher (beware that the website may be fake and provide misleading information, though). But perhaps the most important is to check if the conference organizers will be asking you to pay a registration fee or give you some special benefits. The reason is that as an invited guest, you would expect some kind of preferential treatment over regular attendees. If they do not give you any special benefits as a special speaker, then you are just another speaker for their conference, and the goal is just to earn money. If you are not sure about whether a conference is legit or not, it is best also to ask your supervisor or senior researchers for their opinion.

There are also many other types of SPAM e-mails that target academic researchers such as e-mails asking you to publish your thesis as a book. I may talk about this in more details in a future blog post! That is all for today!


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.


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Two journal special issues with deadlines in 2021

This is a short blog post tto let you know that I am co-organizing two special issues in some journals related to data mining, data science and machine learning:

(1) Special issue on Spatiotemporal Big Data Analytics
Journal: Electronics
Publisher: MDPI
Deadline: 30th September 2021
Details: https://www.mdpi.com/journal/electronics/special_issues/Spatiotemporal-BD-Analytics

(2) Special issue on Generative Adversarial Networks for Multi-Modal Multimedia Computing
Journal: Wireless Communication and Mobile Computing
Publisher: Wiley / Hindawi
Deadline: 28th May 2021
Link: https://www.hindawi.com/journals/wcmc/si/710287/

Note that those are open-access journal. Thus, there is a publication fee. If you do not want to have a publication fee, you may consider submitting your paper to the Data Science and Pattern Recognition journal (DSPR), which is currently free to publish and has a fast review time. I am editor-in-chief of that journal.

If you have any questions or are not sure whether your research is relevant to these journals, you may send me a message, and we can discuss about it.


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

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Atomic Habits to Become a Better Researcher

In this blog post, I will talk about the concept of atomic habits and how it can help you achieve your goals in general, but especially to become a better researcher.

The concept of Atomic Habits, was popularized in a book called “Atomic Habits” by James Clear. The idea in this book is quite simple but yet, it is a powerful way of achieving your goals by applying some simple steps in your daily life.

Setting your goal

The first step is to set some clear goal about what you want to do. This is important as it is the target that you want to achieve. A goal could for example be to improve your paper writing skills.

The importance of the process or system

Having a goal is good but it is yet not enough because persons who succeed and fail still have the same goal (e.g. the winner of a race vs the losers, or the researchers who got a tenured positions vs those who don’t). Thus, what makes the difference between succeeding and failing is not the goal but the system or process that is used to achieve it.

The system of atomic habits

The main idea in the book of James Clear is that we can achieve big goals by changing our daily habits. This can be by adopting some good habits. For example, if your goal is to improve your English writing skills, working on it 20 minutes every day may not do much in the short term but in the long term may lead to major improvements. But it can also be useful to remove the bad habits. For example, one may want to stop wasting too much time browsing the Web every day.

However, as many people knows, it is often hard to start a good habit and keep it for a long time. Many people will for example start to do some physical exercises for a few weeks and then give up quickly. It is also hard to stop bad habits.

To help change habits, the key points proposed by James Clear are:

  • Make it easy: Do not try to make some changes that are too challenging early. For example, if you decide to study English 5 hours per day, it would be perhaps be difficult to sustain over the long term. It is perhaps better to start with 20-30 minutes per day, and later you may increase. But at first, consistency is what is important and will help you to not give up.
  • Make it obvious: To make sure that you continue your good habit every day, it is important that you do not forget about it. Thus, you may try to connect your new habit with your previous habits. For example, if you want to take some medicine every day, you may put it beside your toothbrush to not forget to take it. If you want to read a book every night, you may put the book on your pillow.
  • Make it attractive and satisfying: Because the long term goals may take a lot of time to achieve, it is important to make sure you associate some short term rewards to your good habits . Thus, you may think about some rewards such as: If I study English every day for one week, I will buy myself a hot chocolate cup.
  • Make it harder to keep the bad habits: You may think about strategies to make your bad habits harder to do. For example, if you want to drink less alchool, you may put the bottles out of sight.
  • Track your habits: You may use some book to keep a record of your habits over time.
  • Find the right environment and the right people. The environment and the people that we interact with also play a role in changing habits. Changing the environment or getting along with other people having the same goals may help.

These ideas are quite simple and can be applied to many aspects of life (loosing weight, etc.) but can be also used by researchers to become better researchers. Some good habits for researchers may be to waste less time on the Internet, to have a fixed schedule and sleep well every day, to eat well, to exercise, to improve writing skills, to write more papers, to write a book, to improve programming skills, to write a blog post every week, to improve presentation skills, etc.

Conclusion

In this blog post, I gave a short overview of the book Atomic Habits and discussed a little bit about how it can be used in the life of a researcher. If you do not have time to read the book, you may have got the main idea from this blog post. There is also some good video presentations by James Clear that can be watched online (it is shorter than reading the book, if you are busy like me!).

If you have any comments, please post in the comment section below.


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Research | Leave a comment

The Hard Road to Success in Academia

Today, I will talk about at topic that not many researchers talk about, which is the long and hard road to become a professor in academia, and why some people give up before reaching their goal at different stages of their career. I will talk about this topic because I was recently reading about researchers who decided to leave academia to do something else due to the difficulty of getting a tenured professor position or permanent researcher position.
A list of researchers that have posted about the reasons why they have left academia can be found below, and has inspired this blog post:
https://docs.google.com/spreadsheets/u/0/d/1OODoiZKeAtiGiI3IAONCspryCHWo5Yw9xkQzkRntuMU/edit

By reading these posts, some observations are:

  • Several people complained that there are not enough professor positions that are available. This is true as there are much more persons who obtain a PhD than persons who can become a professor.
  • Someone can become a post-doctoral researcher after the PhD to gain more experience, but there is typically a limit on the number of years that one can work as a post-doctoral researcher. So this is a temporary solution to get more time before finding a professor position. Some persons have done up to 6 years as a post-doctoral researcher after their Ph.D but could still not find a faculty position and thus decided to give up and do something else.
  • Some persons complained about the low salary of working as a post-doctoral researcher compared to what they would earn in the industry. Some also mentioned the unability to have a stable job, sometimes having to sign a 1 year contract, while some other have been more lucky to sign for 2 or 3 years.
  • Several people complained about not choosing where they would live (for example having to accept a post-doc position in another city far from their family)
  • Some people enjoyed working as a post-doctoral researcher but it is is a temporary position as there is typically a limit on the number of years that one can be a post-doctoral researcher.
  • Some persons complained that many entry-level faculty positions in universities are short-term contracts and are not permanent. This is a reality in many places. In fact, after my Ph.D and doing my post-doc, I even started with a 9 month contract as an adjunct professor position in Canada, before getting a 3 year contract, and now a 5 year contract. Also, several permanent professor who retired were replaced by temporary jobs to reduce costs.
  • Some people have given up on academia to work in the industry or start their own business among other things. Some have decided to do something completely different such as starting a knitting store! Some have decided to even go back to studying to obtain a degree in another field.
  • Some people have said that they actually gave up on many other things to try to succeed in academia such as spending less time with their daugther, working every evening and week-ends to work on papers and books, and also gave up on other things that they like. Thus, by leaving academia, some have said that they are now happy to pursue other dreams.
  • Several adjunct professors have tried hard to get a tenured position but gave up. Some reasons are the inability to get national funding, after applying multiple times and failing. In some countries like USA, the success rate appears to be very low in some fields.
  • Some researchers have complained about some toxic working environments such as other people trying to sabotage their research, etc.
  • Some researchers have talked about the negative psychological effect and depression due to various factors such as having to work hard, a toxic work environment, and to pressure to obtain grants and get tenured.
  • Some people claim that a good amount of luck is required to be successful in academia.

I have to say that it is true that it is not easy to succeed in academia. I personally had to go through many challenges to become a professor and eventually get a full professor position, and be successful in my field. I also had to give up on several other things that I like to succeed. And I had to work hard for more than a decade, almost every day from morning this late at night (which I still do, by the way). But now, I have a good position and I am quite happy of what I do. For me, all this hard work was worth it as I like doing research and I enjoy teaching. But I certainly gave up on some other things that I enjoy to focus on my research career. For example, I also enjoy other things like learning languages, drawing, playing guitar and running as hobbies, which I do not have too much time to do.

For the young researchers, my first advice is to learn to know yourself and what you really like. If your dream is to become a professor, it is possible but you need to work hard and work smart to be the most effective and reach your goal. You also need to make a realistic plan of how to attain your goal. I have written several blog posts to give advices about how to be successful in academia that you can read:

Hope that this blog post has been interesting! If you want to add something, please share your comments in the comment section below.


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Research | Tagged , , , , | 2 Comments

How to become a journal or conference reviewer?

Today, I will write a blog post aimed a young researchers, who want to know what is the work of a reviewer in academiahow to become a reviewer of international journals or conferences, and what are the benefits of being a reviewer.

What is the work of a reviewer?

The main work of a reviewer is to read articles and evaluate if the content is suitable to be published. The role of reviewers is very important for the publication process in academia as it helps to filter bad papers and provide advice for improving other papers.

Toreview an article, a reviewer may spend a few minutes (e.g. when a paper is clearly bad or contain plagiarism, and is directly rejected by the reviewer) to several hours (when the paper is complex and read carefully by the reviewer) . Typically, in good journals and conferences, a paper will be evaluated by several reviewers as they may have different opinions and backgrounds, which help to take a fair decisions on whether to publish papers or not.

In general, the review process of top journals and conferences is quite effective at eliminating bad papers as those can recruit excellent reviewers. Smaller or less famous journals sometimes have more difficulty to find good reviewers related to the topics of papers. And sometimes, some bad manuscripts will once in a while pass through the review process due to various reasons. And for some predatory journals, they often do not have reviewers and will publish anything just to earn money.

What are the benefits of being a reviewer?

The main benefits is to help the academic community by providing feedback to publishers and authors.

The reviewers typically work for free.  But sometimes publishers provides some gift to reviewers. For example, Elsevier had been offering a free one month subscription to one of its online service named Scopus to reviewers, while some top journals of Springer offer to download a free e-book from the Springer library after completing a review on time. Such offers may help to convince researchers to work as reviewers.

Some other benefits of reviewing are:

  • Read the latest research and learn about topics that one would maybe not take the time to read otherwise.
  • Obtain some visibility in the research community. Some conferences will for example publish the names of reviewers in  conference proceedings or on their websites.
  • Learn to think like a reviewer, and become more familiar with the review process of journals. This help to write better papers and to know what to expect when submitting papers to the same conferences and journals.
  • Put this on a CV. Especially for those aiming to work in academia, being a reviewer of a good journal or conference can be useful on a CV.

How to become a reviewer?

A graduate student may start to review papers for his supervisor. In that case, the supervisor will let the student write the review and then the supervisor will check it carefully before the review is submitted to ensure that it is a good and fair review. This will give the opportunity to students to learn how to be a reviewer.

Sometimes, a PhD student may also find some opportunities to review papers by himself. For example, when I was a PhD student I visited some journal websites that advertised that they needed reviewers. I then sent an email with my CV to ask to be a reviewer. The journal was not famous, but it gave me some experience to start review some papers.

But generally, most journals will contact potential reviewers rather than the other way. Generally, when a paper is submitted, the journal editor will search for reviewers that have papers on the same topic or work in a related area, to ask them to review the paper. Thus, reviewers of a journal paper are often expert in the field. Often, editors will prefer to ask researchers who have previously published in the same journal, to review papers. Typically a reviewer, may have a PhD or at least be a PhD student with good publications. In some cases, a master degree students may be asked to review papers. I have seen it once or twice. But this is quite rare and these students had very strong publications.

For a conference, there is typically a program committee that is established. It is a set of researchers that will review the submitted papers. Each reviewer may for example have to review 3 to 5 papers. To join the program committee of a conference, one may email the organizers to ask if they need additional reviewers. Then, the organizers may accept if the applicant has a good CV. But for famous conferences, joining the program committee typically require to be recommended by a conference organizer or some members of the program committee. It is generally not easy to join the program committee (to be a reviewer) of top conferences.

Drawback of being a reviewer

One of the drawback of being a reviewer is that it can require some considerable amount of time. For example, I receive numerous emails to ask me to review journal papers for various journals and on a wide range of topics. Although I do several reviews every month, I also decline several invitations because of time constraints, and that I just receive too many invitations. I will for instance often decline to review papers not in my research area or that are for unknown journals, or journals not related to my research area.

How to review a paper?

If you have been selected to review papers, you may be interested to read a blog post that I wrote about how to review papers.

Conclusion

 Today, I have discussed about the work of reviewers and how to become a reviewer. If you have comments, please share them in the comment section below. I will be happy to read them.


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

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If I would do a PhD again, what would I do differently?

Recently, I gave an invited talk at University of Pisa in Italy (online). A PhD student asked me: If you would do a PhD again, what would you do differently? In this blog post, I will answer this question, which I think can be interesting for graduate students.

First, I think that one of the key aspects to consider for a PhD is to choose a good research team, preferably in a good university, where you will have a good research environment and can work on some important research topic.. In my case, I did my PhD in a university that is maybe not so high in the world rankings but is still good, and more importantly my supervisor was great and gave me several opportunities through his social network. Thus, for this, I would not  change.

A second important aspect is about time management. If I would do a PhD again, I would try to manage my time in a better way to be more effective. As a student, I had a lot of time but sometimes spent time on things that were not so important. It is important to be able to assess what is the most important and to choose carefully how  to spend time. For example, if you have one day left to submit a paper, is it more important to spent it improving the colors of figures or proofreading? Generally, the latter is more useful.

A third important aspect is to collaborate more with other researchers. As a PhD researcher, it is easy to work by yourself on your thesis. But having the feedback of others can be highly valuable. Moreover, collaborating with others can help write   more papers and find other opportunities. On this aspect, I did well during my PhD as I had several collaborations but I could have perhaps discussed more about my project with   other researchers.

A fourth important aspect is to choose a research topic that you like. Personally, during my PhD, I first started doing something on e-learning before gradually moving towards data mining, which is my current research area. If I had made that decision earlier, it would have been better. But this is easy to say, afterwards. Although I also liked working on e-learning, the community was quite small to work on Intelligent Tutoring Systems and thus it was hard to have some impact in this field despite doing good research. Another reason why I stopped working on this is that conducting experiments was quite time-consuming and complex, while in data mining it can be as simple as running algorithms on a benchmark dataset that you download to test a new algorithm. Besides, I personally like research on algorithm design.

A fifth important aspect is to have clear goals for your career path after the PhD. It is never too early to search for jobs or opportunities such as postdoc positions. I think I did quite well on this part as I got a postdoc position in a good data mining team. But I could have started searching earlier.

A sixth important aspect is to focus on having quality papers in good journals and conferences, recognized worldwide if you intend to have an international career. In some countries like Canada, some conference papers are well regarded in computer science, and there is not much pressure to write journal papers for PhD students. Even, some PhD students may graduate without any papers at some universities. But internationally, several countries consider journal papers as highly important and have various ranking systems to evaluate journals and conferences. For researchers on intend to work internationally, it is thus something important to consider. Sometimes, it is better a few very good papers than have too many papers.

Conclusion

In this blog post, I gave some answers to the question of what would I do differently if I would do another PhD. Hope it was interesting. If you have some comments, please write below.

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, General, Research | Tagged , , | Leave a comment

Analyzing COVID-19 tweets to understand the public opinion

In this blog post, I will talk briefly about how tweets collected on Twitter can be analyzed to understand the public opinion about COVID-19. This is based on the below research paper, that I have recently participated to:


Noor, S., Guo, Y., Shah, S. H. H., Fournier-Viger, P., Nawaz, M. S. (2020). Analysis of Public Reaction to the Novel Coronavirus (COVID-19) Outbreak on Twitter. Kybernetes, Emerald Publishing, to appear.

I will give an overview of the above paper. For more details, you can click on the above link to see the whole research paper.

Why analyzing Tweets? There has been a lot of research about analyzing tweets in the past such as to detect the sentiment and feelings of people on different topics, or even to detect fake news and bots among other things. The interest of analyzing Twitter data is that Twitter is used by millions of people and that tweets are posted in real-time. Thus, tweets can be used to analyze what people are saying about a topic such as the coronavirus.

How can we understand public opinion about COVID-19 on Twitter? In the above research paper, we applied the following methodology. We have first collected thousands of tweets in English about COVID-19 during the first months of the pandemic. Then we applied some clustering algorithms to discover the main themes that were talked about on Twitter related to COVID-19. Moreover, we applied sequential pattern mining algorithms to find frequent words patterns in Tweets.

What have we discovered? We have found several interesting things. For the cluster analysis, we found seven main clusters of tweets that indicate some main themes discussed by Twitter users:

  • Cluster 1 (green): public sentiments about COVID-19 in the USA.
  • Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a
  • vaccine,
  • Cluster 3 (purple): public sentiments about doomsday and science credibility.
  • Cluster 4 (blue): public sentiments about COVID-19 in India.
  • Cluster 5 (yellow): public sentiments about COVID-19’s emergence.
  • Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines.
  • Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report.

For example, this is the cluster 1:

And this is the cluster 2:

Cluster 3:

Some part of cluster 4:

Some part of cluster 5:

Some part of cluster 6:

We also found several patterns related for example to “Coronavirus, testing, lockdown”. Here is for example, some of the most frequent words:

More results are presented in the paper.

The above results represent what the sampled tweets have been talking about on Twitter in English from January to March 2020, related to COVID-19.

Conclusion

In this blog post, I have just given a very brief overview of what can be learnt from Tweets related to public opinion. For more details, please check the above paper! There is also obviously some limitations to that study such that Tweets were not geolocalized and that only the English language was used. If you have any comments you may post in the comment section below. Hope this has been interesting.


Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in artificial intelligence, Data Mining, Data science | Tagged , , , , | Leave a comment

The Controversy around Extreme Learning Machines (ELM) and related models

Today, I will talk about an interesting topic in academia which is the controversy around ELM (Extreme Learning Machine) and its origins. This has been a hot topic of discussion in the field of machine learning for more than a decade, when some researchers started to question the high similarity of ELM to other models published before such as RBF (Radial Basis Function). There has also been recently some researchers arguing about the similarities between ELM and RVFL (Random Vector Functional Link) and other models.

In this blog post, I will give an overview of this controversy and impact but I will not take any sides. I will just look at it from an outsider’s persective. You can read the arguments from both sides and make your opinion and draw your own conclusions.

Some arguments against ELM

ELM was proposed in 2004. The controversy around the origins of ELM started around 2008 with a letter in IEEE transactions that claimed that it is unecessary to give a new name to a model that existed already with perhaps minor modifications:

  • L. P. Wang and C. R. Wan, “Comments on “The Extreme Learning Machine,” in IEEE L. P. Wang and C. R. Wan, “Comments on ‘The extreme learning machine’,” IEEE Trans. Neural Networks, Vol. 19, No. 8, 1494-1495, 2008.

Other researchers have raised this issue. And to understand this perspective, there is an anonymous website that provides a good summary of the issues raised by some researchers against ELM. It is called : ELM Origin (webs.com)

A problem with this website though is that it is anonymous, which means that we cannot be sure who wrote it. However, the website provides annotated ELM papers and claim that several ELM models are similar to papers published many years before. For example, it is said that ELM-Kernel is similar to LS-SVM with zero bias and kernel ridge regression.

I did not read the information in details asthis is outside my main research field so I am personally not sure whether all the claims are reasonable or not.

Some arguments for ELM

There has been researchers that have responsed to these claims to support that there are indeed differences between ELM and previous work. For example:

  • G.-B. Huang, “Reply to comments on ‘the extreme learning machine’,” IEEE Trans. Neural Networks, vol. 19, no. 8, pp. 1495-1496, Aug. 2008.
  • G.-B. Huang, “What are Extreme Learning Machines? Filling the Gap between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle,” Cognitive Computation, vol. 7, 2015.

However, some researchers argue that these differences are tiny. It was also argued in the defense of ELM that researchers may have simply missed some related work and thus not been aware of the prior work. This might be true… as it has happened in the past that some discoveries were made independently by several researchers.

Yann LeCun’s opinion

One of the fathers of deep learning has also given his opinion on this topic in a Facebook post:

He was clearly not impressed by ELM. However, this is just a Facebook post and it seems that LeCun perhaps did not read all the papers about ELM to have a clear idea about the topic (perhaps?).

Who is right?

As I said previously, I will not take position as this is not my main area. You may make your own mind or write your opinion in the comment section below if you have one.

What is the impact of this controversy?

This controversy has resulted in a kind of war between some researchers working in that area. I have observed that there are researchers against ELM and some that are for ELM that have been quite aggressive towards each other, and there are also many researchers that do not want to take sides but are caught between the two sides.

As I work as associate editor for various journals I have noticed for example, at some point that a reviewer wanted to directly reject a paper just for using the name of ELM. I also noticed some researchers that tried to push their citations against ELM or for ELM. In other cases, I have also seen some reviewer arguing that authors should change their paper because it had shown that ELM was better than some other models and the reviewer could not accept that conclusion, even arguing that this must have been due to experimental errors.

I personally dont really know what to think about this. But as an outsider, it seems to me that today, there is still a kind of war on this topic involving various people, and I think it is a pity for the people who are caught in the middle of that war but do not want to take side.

Conclusion

This is a short blog post to talk about the controversy around ELM. I just report about this topic, as I think it is interesting. As said above, you can read about it and make your own opinion. But personally, I think it is better to not take any side to avoid conflicts.

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Discovering Alarm Correlation Rules for Network Fault Management (video)

In this blog post, I will share the video of our new paper about analyzing alarms in telecomunication networks presented at the AIOPS 2020 workshop. This work is part of an industrial collaboration project. The motivation for this project is that there are typically thousands of alarms in a telecomunication network, and not all of them are important. To allows network operators to focus on fixing issues that are the most important, we propose a method to discover correlations between alarms.

For this purpose, we view a telecommunication network as an attributed graph where nodes represent devices, edges indicates connections between devices, and attributes of vertices represent alarms. Then, we apply a novel algorithm to find rules of the form A–>B indicating that if alarm A appears, Alarm B is likely to occur. Then, using these rules, we can reduce the number of alarms presented to network maintenance workers. Though, the approach is designed for analyzing alarms it could be applied to other data modelled as graphs.

Here is the link to watch the paper presentation:
http://philippe-fournier-viger.com/AIOPS.mp4

And here is the reference to the paper:


Fournier-Viger, P., Ganghuan, H., Zhou, M., Nouioua1, M., Liu, J. (2020). Discovering Alarm Correlation Rules for Network Fault Management. Proc. of the International Workshop on Artificial Intelligence for IT Operations (AIOPS), in conjunctions with the 18th International Conference on Service-Oriented Computing (ICSOC2020) conference,

That is all I wanted to write for today!

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

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