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Macarious Abadeer | IEEE Xplore Author Details. Macarious Abadeer. Affiliation. School of Computer Science, Carleton University, Ottawa, Canada. Publication ...
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Bidirectional Encoder Representations from Transformers (BERT) models achieve state-of-the-art performance on a number of Natural Language Processing tasks.
Dynamic Extraction of BERT-based Embeddings for the Detection of Malicious JavaScript · Author Picture Macarious Abadeer. Carleton University, Ottawa, Canada.
Abadeer, Macarious Philip Aziz. Abstract. We introduce FLightNER, a Federated Learning model that extends a state-of-the-art Named-Entity Recognition model ...
Bidirectional Encoder Representations from Transformers (BERT) models achieve state-of-the-art performance on a number of Natural Language Processing tasks.
Macarious Abadeer. 2020. Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical ...
The goal of this paper is to evaluate a state-of-the-art machine learning technique, known as Deep-SE [3], for estimating story points in a commercial project.
Macarious Abadeer. MA. Macarious Abadeer. 0 followers. Follow. Presentations 0 Events 0 Followers 0 About. No presentations found. No events found. No followers ...
Name: Macarious Abadeer. School of Computer Science Carleton University, Ottawa, Canada. Project Outline. Privacy-Preserving Data Publishing (PPDP) is an ...
Jul 11, 2024 · Request PDF | On Dec 1, 2022, Macarious Abadeer and others published FLightNER: A Federated Learning Approach to Lightweight Named-Entity ...