The Internet has led to the creation of a digital society, where (almost) everything is
connected and is accessible from anywhere. However, despite their widespread adoption …

This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Studying data memorization in neural language models helps us understand the risks (eg, to
privacy or copyright) associated with models regurgitating training data and aids in the …

Federated learning (shorted as FL) recently proposed by Google is a privacy-preserving
method to integrate distributed data trainers. FL is extremely useful due to its ensuring …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

Bloom filter (BF) has been widely used to support membership query, ie, to judge whether a
given element x is a member of a given set S or not. Recent years have seen a flourish …

Internet was developed as a packet data network where users and data sources (server)
with specific IP addresses interacted over a pre-established communication channel. This …

In this paper, we show that key-value stores backed by an LSM-tree exhibit an intrinsic trade-
off between lookup cost, update cost, and main memory footprint, yet all existing designs …

In this paper, we show that all mainstream LSM-tree based key-value stores in the literature
and in industry are suboptimal with respect to how they trade off among the I/O costs of …

Over the last few years, we have witnessed an exponential increase in the computing and
storage capabilities of smart devices that has led to the popularity of an emerging …