Most journals require that the authors sign an agreement in which the publisher retains the copyright of the paper (e.g. [1]).
Although a few publishers allow the authors to provide a pdf of the paper in their personal home-page, they do allow it to be uploaded to an open access platform.
"However, yet another recent theory proposes that grasses competing for water and nutrients - limited resources in the Namib desert - create the circles, explaining why they never overlap."
Maybe this model could be used to analyze this hypothesis.
I have worked with image feature extraction in the past. Although using DCT coefficients has been used as a way to analyze texture features, the idea idea of generating the hash (step 5) seems to be new.
I am curious however on why you are discarding color information. Usually for reverse image search this kind of information can be quite useful.
Log-binning can be useful. However it has some disadvantages.
I think that in your case your data (server response time?) looks good because you probably have a log-logistic or log-normal distribution.
Suppose you were working with values that are exponentially distributed which is also a reasonable hypothesis for your data.
In that case the log-binned histogram would like a plateau with the exception of the beginning and ending bins. In this scenario a linear-binning approach would probably be better.
Unfortunately, I think that there is no approach for bucketing that is good for all situations. Usually the best approach will depend on your data and also on what you are trying to analyze.
That is a very relevant question, since Mendeley is free.
Reading the Web site the only advantage that I found is that it allows you to use your Dropbox account to sync the files, while for Mendeley, if you want more space than a free account offers, you have to pay.
https://stackoverflow.com/questions/891643/twitter-image-enc...
Some of the solutions took a similar approach that used geometric primitives.