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Understanding the limitations of self-supervised learning for tabular anomaly detection
While self-supervised learning has improved anomaly detection in computer vision and natural language processing, it is unclear whether tabular data can benefit from it.
Large Language Models in Cryptocurrency Securities Cases: Can a GPT Model Meaningfully Assist Lawyers?
Our research is the first to systematically study an LLM's legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.
Textwash -- automated open-source text anonymisation
The increased use of text data in social science research has benefited from easy-to-access data (e. g., Twitter).
Self-Supervised Losses for One-Class Textual Anomaly Detection
The separability of anomalies and inliers signals that a representation is more effective for detecting semantic anomalies, whilst the presence of narrow feature directions signals a representation that is effective for detecting syntactic anomalies.
Detecting DeFi Securities Violations from Token Smart Contract Code
Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains.
Brittle Features May Help Anomaly Detection
In addition, separability between anomalies and normal data is important but not the sole factor for a good representation, as anomaly detection performance is also correlated with more adversarially brittle features in the representation space.
Probabilistic map-matching using particle filters
Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services.
