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Torrent File Editor cross-platform GUI editor for BEncode files; bencode-tools - a C library for manipulating bencoded data and a XML schema like validator for bencode messages in Python; Bento - Bencode library in Elixir. Beecoder - the file stream parser that de/encoding "B-encode" data format on Java using java.io.* stream Api. Bencode ...
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2]
The purpose is to reduce the file size of torrent files, which reduces the burden on those that serve torrent files. A torrent file using Merkle trees does not have a pieces key in the info list. Instead, such a torrent file has a root_hash key in the info list. This key's value is the root hash of the Merkle hash:
Suppose is a data set containing elements . is a ranking method applied to .Then the in can be represented as a binary matrix. If the rank of is higher than the rank of , i.e. < , the corresponding position of this matrix is set to value of "1".
In statistics, ranking is the data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted.. For example, if the numerical data 3.4, 5.1, 2.6, 7.3 are observed, the ranks of these data items would be 2, 3, 1 and 4 respectively.
This yields an average time complexity of O(n log n), with low overhead, and thus this is a popular algorithm. Efficient implementations of quicksort (with in-place partitioning) are typically unstable sorts and somewhat complex but are among the fastest sorting algorithms in practice.