Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental
bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential …

Text augmentation is a popular technique in natural language processing (NLP) that has
been shown to improve the performance of various downstream tasks. The goal of text …

Abstract Foundation and Large Language Models (FLLMs) are models that are trained using
a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs …

Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …

With the increasing popularity of open-source software, embedded vulnerabilities have been
widely propagating to downstream software. Due to different maintenance policies, software …

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing
(CC) technology to overcome limitations such as power, storage, scalability, management …

What dataset features affect machine learning (ML) performance has primarily been
unknown in the current literature. This study examines the impact of tabular datasets' …

Social media has an impact on many aspects of human life ranging from sharing personal
information to revolutionizing political systems of entire countries. One not so well studied …

We introduce SubFlow-a dynamic adaptation and execution strategy for a deep neural
network (DNN), which enables real-time DNN inference and training. The goal of SubFlow is …