Search results
Results from the WOW.Com Content Network
Hidden Fields Equations (HFE), also known as HFE trapdoor function, is a public key cryptosystem which was introduced at Eurocrypt in 1996 and proposed by (in French) Jacques Patarin following the idea of the Matsumoto and Imai system.
Gregory the Great: " Otherwise; The treasure hidden in the field is the desire of heaven; the field in which the treasure is hidden is the discipline of heavenly learning; this, when a man finds, he hides, in order that he may preserve it; for zeal and affections heavenward it is not enough that we protect from evil spirits, if we do not ...
In statistics, a hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields have an underlying Markov random field. Suppose that we observe a random variable , where .
Clues for where the treasures were buried are provided in a puzzle book named The Secret produced by Byron Preiss and first published by Bantam in 1982. [1] The book was authored by Sean Kelly and Ted Mann and illustrated by John Jude Palencar, John Pierard, and Overton Loyd; JoEllen Trilling, Ben Asen, and Alex Jay also contributed to the book. [2]
Ancient Roman fort found hidden in ‘unassuming’ field near Scottish elementary school. ... The field is actually home to a nearly 2,000-year-old Roman fortlet that was “thought lost to time ...
A hidden Markov model is a Markov chain for which the state is only partially observable or noisily observable. In other words, observations are related to the state of the system, but they are typically insufficient to precisely determine the state. Several well-known algorithms for hidden Markov models exist.
3. Puzzle Boards. These are plastic or wooden boards that come with compartments or sliding parts that challenge dogs to use their noses, paws, or mouths to reveal hidden treats.
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account.