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Value in science is currently measured by the index of citations received via your journal articles. More specifically, the Journal Citation Reports (JCR) is the organism that designates the "value" (in academic terms) of the scientific journals scientists publish on. To this purpose, this organism gives a ranking in terms of what is called impact factor (IF)---to be honest they give distinct rankings but for this post we will stick to the IF. In short and greatly simplifying, this metric is measured as the number of citations the articles of a particular journal has divided by the total number of articles published in the journal. In this regard, the more papers a scientist has in high IF journals the better CV the researcher has. So, as a scientist and wannabe researcher your interest is to publish articles in journals that appear in the JCR list.  In the particular case of Computer Science, publishing in journals is, generally, for free. The journal gets the income ...

Doing a PhD involves building walls and then demolish them and build them back learning something during the process.

Since the current world-wide crisis began scientists have had very difficult times. Cuts in budgets and grants are a common difficulty that researchers have to handle in order to progress with their projects. Nevertheless, curiosity and perseverance  are major forces that can drive a scientist towards their goal even in such a harsh condition. However, there is a critical boundary that can kill the bravest researcher and this happens when the manager in charge of science in your own country; the one who has to protect you as a researcher, tells in a prestigious media that " we must now stop talking about the importance of science, and instead commit ourselves to the need for excellence in science " and " we need to change the number of researchers by maintaining and improving the quality of the contracts while reducing the quantity. "  You can find  the full article in this link . Of course the response of the scientific community has been quick, and with ...

The discipline of machine learning (ML) can be understood as a particular study of data. Data is everything , and it contains patterns and a certain degree of uncertainty. The aim of the field is to identify those and exploit them to solve complex problems. Often algorithms for obtaining such patterns are not easy to implement, involving abstract mathematical concepts and vague descriptions of the internal processes that guide the learning mechanisms. In this particularly discouraging environment there are several useful tools to help with the understanding of the ML techniques. Octave is such a tool. Octave allows the extensive use of vectorization to exploit parallelism and modeling complex algorithms in a very fourth-generation programming language fashion. I started with Octave due to Standord's ml-class course and I have to say that the experience has been very positive. In one of the exercises we had to implement a handwritten character recognizer, that is, a type of percep...

After nearly a two years' trip into the deep mysteries of LCSs one expects to comprehend the intrinsics of these family of techniques. Dozens of papers and unimaginable hours behind and I am, still, hooked with the very basics. These are the thoughts of a wannabe researcher that makes baby steps into the research world. But first let me introduce myself briefly: I have spent a reasonable amount of time and effort with XCS-alike algorithms. I implemented myself, from the very scratch, the most relevant ones, and tested them uncountable times. But I am still learning about the basics. Every single time. Recently I re-implemented the good old XCSR, and I found a lot of trouble with it. Devil is in every detail, and I get him face to face. What is one supposed to do when stuck with a technique he is supposed to be a master of (or, at least, with a certain amount of experience behind with)? I confess I was very upset with myself. Depressed, with thoughts of failure flooding i...