I was asked to review my own papers three times during 2018. Or more precisely, I was asked to review papers by other people that contain the same content as some of my most well-cited papers. The review requests didn’t come from IEEE journals but less reputed journals. However, the papers were still written in such a way that they would likely pass through the automatic plagiarism detection systems that IEEE, EDAS, and others are using. How is that possible? Here is an example of how it could look like.

Original:         

Plagiarized version:

As you can see, the authors are using the same equations and images, but the sentences are slightly paraphrased and the inline math is messed up. The meanings of the sentences are the same, but the different wording might be enough to pass through a plagiarism detection system that compares the words in different documents without being able of understanding the context. (I have better examples of this than the one shown above, but I didn’t want to reveal myself as a reviewer of those papers.)

This approach to plagiarism is known as rogeting and basically means that you replace words in the original text with synonyms from a thesaurus with the purpose of fooling plagiarism detection systems. There are already online tools that can do this, often resulting unnatural sentence structures, but the advances in deep learning and natural language processing will probably help to refine these tools in the near future.

Is this an increasing problem?

This is hard to tell, but there are definitely indications in that direction. The reason might be that digital technology has made it easier to plagiarize. If you want to plagiarize a scientific paper, you don’t need to retype every word by hand. You can simply download the LaTeX code of the paper from ArXiV.org (everything that an author uploads can be downloaded by others) and simply change the author names and then hide your misconduct by rogeting.

On the other hand, plagiarism detection systems are also becoming more sophisticated over time. My point is that we should never trust these systems as being reliable because people will always find ways to fool them. The three plagiarized papers that I detected in 2018 were all submitted to less reputed journals, but they apparently had a functioning peer-review system where researchers like me could spot the similarities despite the rogeting. Unfortunately, there are plenty of predatory journals and conferences that might not have any peer-review whatsoever and will publish anything if you just pay them to do so.

Does anyone benefit from plagiarism?

I am certainly annoyed by the fact that some people have the dishonesty to steal other people’s research and pretend that it is their research. At the same time, I’m wondering if anyone really benefits from doing that? The predatory journals make money from it, but what is in it for the authors? Whenever I review the CV of someone that applies for a position in my group, I have a close look at their list of publications. If it only contains papers published in unknown journals and conferences, I treat it as if the person has no real publications. I might even regard it as more negative to have such publications in the CV than to have no publications at all! I suppose that many other professors do the same thing, and I truly hope that recruiters at companies also have the skills of evaluating publication lists. Having published in a predatory journal must be viewed as a big red flag!

I found an interesting news article by Reily Gregson where he is interviewing Martin Cooper, who is considered the father of the handheld cell phone. Cooper is talking about spatial division multiple access, the early name of the multi-user MIMO technology, and how “computers weren’t powerful enough to operate it” at the time it was invented at his startup-company ArrayComm.

Cooper points out that “Today we spray energy in all directions. Why not aim it directly?” He then provides three examples of why multi-user MIMO solves important practical problems:

“First, deploying cellular and derivative technologies is costly. Second, the quality of wireless communicating must be comparable or better than that of wireline in order to compete with wireline, said Cooper. And as spectrum is finite, technology must work toward greater efficiency.”

Furthermore, he says that ArrayComm’s multi-user MIMO solution “requires fewer base stations, which cuts costs. The technology also is adaptive, which simplifies network design and reduces site acquisition and installation costs.”

By the way, did I forgot to say that this interview is from 1996…?

I explained in a previous blog post why the efforts to commercialize multi-user MIMO in the nineties were not as successful as Cooper and others might have hoped for. Now, more than 20 years later, we are about to witness a large-scale deployment of 5G technology, in which MIMO is a key component. The industry has hopefully learned from the negative past experiences when Massive MIMO is now being deployed in commercial networks. One thing we know for sure is that computational complexity is not a problem anymore.

“Open science is just science done right” is a quote from Prof. Jon Tennant in a recent podcast. He is referring to the movement away from the conventionally closed science community where you need to pay to gain access to research results and everyone treats data and simulation code as confidential. Since many funding agencies are requiring open access publishing and open data nowadays, we are definitely moving in the open science direction. But different research fields are at different positions on the scale between fully open and entirely closed science. The machine learning community has embraced open science to a large extent, maybe because the research requires common data sets. When the Nature Machine Intelligence journal was founded, more 3000 researchers signed a petition against its closed access and author fees and promised to not publish in that journal. However, research fields that for decades have been dominated by a few high-impact journals (such as Nature) have not reached as far.

IEEE is the main publisher of Massive MIMO research and has, fortunately, been quite liberal in terms of allowing for parallel publishing. At the time of writing this blog post, the IEEE policy is that an author is allowed to upload the accepted version of their paper on the personal website, the author’s employer’s website, and on arXiv.org. It is more questionable if it is allowed to upload papers in other popular repositories such as ResearchGate – can the ResearchGate profile pages count as personal websites?

It is we as researchers that need to take the steps towards open science. The publishers will only help us under the constraint that they can sustain their profits. For example, IEEE Access was created to have an open access alternative to the traditional IEEE journals, but its quality is no better than non-IEEE journals that have offered open access for a long time. I have published several papers in IEEE Access and although I’m sure that these papers are of good quality, I’ve been quite embarrassed by the poor review processes.

Personally, I try to make all my papers available on arXiv.org and also publish simulation code and data on my GitHub whenever I can, in an effort to support research reproducibility. My reasons for doing this are explained in the following video: