With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …

Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

Automatic text summarization is an exertion of contriving the abridged form of a text
document covering salient knowledge. Numerous statistical, linguistic, rule-based, and …

State-of-the-art edge devices are capable of not only inferring machine learning (ML)
models but also training them on the device with local data. When this local data is sensitive …

A traditional classification approach based on keyword matching represents each text
document as a set of keywords, without considering the semantic information, thereby …

Embeddings have become a key paradigm to learn graph representations and facilitate
downstream graph analysis tasks. Existing graph embedding techniques either sample a …

Most of existing text automatic summarization algorithms are targeted for multi-documents of
relatively short length, thus difficult to be applied immediately to novel documents of …

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network embedding aims to learn a low-dimensional representation for each node of a …

Min-Hash, which is widely used for efficiently estimating similarities of bag-of-words
represented data, plays an increasingly important role in the era of big data. It has been …

Perceptual image hashing and digital watermarking are two of the extensively investigated
techniques for content authentication and copyright verification of digital images …