An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sens
Since the binary quantization in hashing behaves like the clustering algorithms [4, 6, 15], minimizing such quantization loss faithfully preserves the similarity structure of the data in each view: 1n Equan = n hi − Rhu˜i 2+ 1 m m gj −Rguˆj 2. (1) i=1 j=1 Here Uh =...
2. Related Work A variety of learning based hashing approaches have been proposed in recent years, which can be broadly cat- egorized into unsupervised approaches and supervised ap- proaches [43, 44]. Unsupervised hashing approaches utilize the data distri- bution of training samples to learn ...
RIPEMD-160: Stands for RACE Integrity Primitives Evaluation Message Digest[15], and was developed by Hans Dobbertin, Antoon Bosselaers, and Bart Preneel in 1992. RIPEMD, represented with 40-digithexadecimal numbers, is based on another weak hash function MD4 derived to work with 32-bit processo...
Keywords: buckized cuckoo hashing · lock-free · data structure · multicore · parallel computing 1 Introduction With the rapid growth of data volume in the Big Data era, the massive amount of data puts increasing pressure on cloud computing systems [1,18]. As a key component of these ...
The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to k-mer of the length of the run. Conclusions: We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time ...
Python’s Built-In Hashing Function Python’s built-in hashing function,hash(), returns an integer value representing the input object. The code then uses the resulting hash value to determine the object’s location in the hash table. This hash table is a data structure that implements diction...
Yet in order to create a completely collision-resistant hash function, every single message (x) would have to have a hashed output of the same length as the input. Without hashes of a fixed length, we lose our ability to use them as a convenient data structure, yet by assigning a ...
This dynamic data structure enables the efficient insertion and deletion of an interval in O (log n). Because intervals in the tree cannot overlap, the query time is also O (log n). When a new data segment is received, the stream fuzzy hash algorithm finds the segment context by querying...
Lynch, Nancy; Malkhi, Dahlia; Ratajczak, David, Atomic Data Access in Distributed Hash Tables, 2002, pp. 295-305, LNCS 2429, Springer-Verlag Berlin Heidelberg. Attorney, Agent or Firm: Concert Technology Corporation Parent Case Data: