Data-dependent hashing methods aim to learn hash functions from the pairwise or triplet
relationships among the data, which often lead to low efficiency and low collision rate by …

Unsupervised hashing has attracted much attention for binary representation learning due to
the requirement of economical storage and efficiency of binary codes. It aims to encode high …

The article developed a new algorithm for electronic digital signature in the composition of
existing difficulties: discrete logarithm in a finite field, decomposition of a sufficiently large …

As multimedia data grows exponentially, searching for and retrieving a relevant image is
becoming a challenge for researchers. Hashing is a widely adopted method because of its …

В статье разработан новый алгоритм электронной цифровой подписи в композиции
существующих сложностей: дискретного логарифмирования в конечном поле …

In article the new algorithm of a digital signature in composition of the existing difficulties is
developed: discrete logarithming in a final field, decomposition of rather large natural …

Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not
accurately preserve the similarity between data. This leads to low down the performance of …

In recent times, there has been a growing interest in online hashing for cross-modal retrieval
tasks with streaming data. This technique involves encoding streaming data into compact …

Online hashing methods usually learn the hash functions online, aiming to efficiently adapt
to the data variations in the streaming environment. However, when the hash functions are …

Weighted Hamming distance, as a similarity measure between binary codes and binary
queries, provides superior accuracy in search tasks than Hamming distance. However, how …