Chaos-based cryptography: a new promise to online security

A team of researchers at IFSC at the University of S. Paulo is working on the development and improvement of a new encryption system based on chaos theory, more efficient than conventional methods which could offer better security for online financial transactions, for instances, with the advantage of operating at high speed even on devices with limited hardware and low processing capacity. (read more)

Biodiversity and Math

Computer Science, Physics, Mathematics and biodiversity, are there connections between these fields ? In this link you can find an article about how the SCG deals and connects these topics.

SCG Admission

Post-doctorate: There are two positions available for post-doctorate researchers at the SCG, if you are interested send a message on the contact page.

Doctorate: There are no positions avaiable for PhD candidates at the moment. Send a message to enter in the PhD candidates queue for the next selection contact page.

Master Degree: There are no positions avaiable for Master Degree at the moment. Send a message to enter in the Master candidates queue for the next selection contact page.

Undergraduate: There are available positions to undergraduate students.

Thesis CTDIA prize

The Academic Dalcimar Casanova won the second best dissertation in the field of Artificial Intelligence. The prize was awarded by the Special Committee on Artificial Intelligence of the Brazilian Computer Society (SBC-CEIA) during the 2010 Joint Conference.

To read more visit: VII Best MSc Dissertation/PhD Thesis Contest in Artificial Intelligence

knn / k-nearest neighbors

Knn stands for k-nearest neighbors, it's a pattern recognition algorithm that, as the name suggests, work with the k-nearest given samples to determine to which class an unowned sample belongs to (we call a set of predetermined samples a class and the goup of all classes a training set).

Here, we have an illustration of how a basic knn works.

This knn work's up to 6 classes and the number of samples to each class is left for the reader to decide (the standard is 15 samples per class) as is the number of neighbors (k) to be analyzed.

Now when the reader click's the mouse on the knn screen, lines will connect the k-nearest samples from the training set to where the click is made, now notice that there will be situations where the result, number of closest samples of the training set, will be a draw between 2 or more classes, in these cases the program automatically calculates the algorithm again but, with k-1 neighbors.

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

Number of Classes (1-6): . . . Number of Samples (1-90): K (1-10):

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