The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

Anomaly detection is an important problem with multiple applications, and thus has been
studied for decades in various research domains. In the past decade there has been a …

We propose a novel scene graph generation model called Graph R-CNN, that is both
effective and efficient at detecting objects and their relations in images. Our model contains …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

Sequential recommendation models the dynamics of a user's previous behaviors in order to
forecast the next item, and has drawn a lot of attention. Transformer-based approaches …

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f
which is only accessible via point evaluations. It is typically used in settings where f is …

Advanced Persistent Threats (APTs) are difficult to detect due to their" low-and-slow" attack
patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Background Some applications, especially those clinical applications requiring high
accuracy of sequencing data, usually have to face the troubles caused by unavoidable …

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-
to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics …