85 citations · 88 across the 6 of their papers we have counts for
7 papers
Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction
Irsyaad Rijwan, Liam Brydon, Aidan Jared +1
The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that generates and curates synthetic…
Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis
Yan Cathy Hua, Paul Denny, Jörg Wicker +1
Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their catego…
EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks
Yan Cathy Hua, Paul Denny, Jörg Wicker +1
Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw fee…
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection
Jonathan J. Y. Kim, Martin Urschler, Patricia J. Riddle +1
With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate clo…
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends
Yan Cathy Hua, Paul Denny, Katerina Taskova +1
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digit…
Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
Xinglong Chang, Katharina Dost, Gillian Dobbie +1
The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors ar…
Fast Adversarial Label-Flipping Attack on Tabular Data
Xinglong Chang, Gillian Dobbie, Jörg Wicker
Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which…