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An example of top-down processing: Even though the second letter in each word is ambiguous, top-down processing allows for easy disambiguation based on the context. These terms are also employed in cognitive sciences including neuroscience, cognitive neuroscience and cognitive psychology to discuss the flow of information in processing. [6]
Kivy is the main framework developed by the Kivy organization, [3] alongside Python for Android, [4] Kivy for iOS, [5] and several other libraries meant to be used on all platforms. In 2012, Kivy got a $5000 grant from the Python Software Foundation for porting it to Python 3.3. [6] Kivy also supports the Raspberry Pi which was funded through ...
Longest-processing-time-first (LPT) is a greedy algorithm for job scheduling. The input to the algorithm is a set of jobs, each of which has a specific processing-time. There is also a number m specifying the number of machines that can process the jobs. The LPT algorithm works as follows:
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
In computer science, an operator-precedence parser is a bottom-up parser that interprets an operator-precedence grammar.For example, most calculators use operator-precedence parsers to convert from the human-readable infix notation relying on order of operations to a format that is optimized for evaluation such as Reverse Polish notation (RPN).
McClelland and Rumelhart's parallel processing model describes perception as the meeting of top-down (conceptual) and bottom-up (sensory) elements. In the late 1990s, the idea of top-down and bottom-up processing was translated into a computational model of vision by Rao and Ballard . [ 3 ]
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Stream processing hardware can use scoreboarding, for example, to initiate a direct memory access (DMA) when dependencies become known. The elimination of manual DMA management reduces software complexity, and an associated elimination for hardware cached I/O, reduces the data area expanse that has to be involved with service by specialized ...