Ahoud Alhazmi

Orcid: 0000-0002-3471-8753

Affiliations:
  • Macquarie University, Department of Computing, NSW, Australia
  • Umm Al-Qura University, Mecca, Saudi Arabia


According to our database1, Ahoud Alhazmi authored at least 11 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Distractor Generation for Multiple-Choice Questions: A Survey of Methods, Datasets, and Evaluation.
CoRR, 2024

Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2021
Towards a Deep Learning-Driven Service Discovery Framework for the Social Internet of Things: A Context-Aware Approach.
Proceedings of the Web Information Systems Engineering - WISE 2021, 2021

A Unified Framework for Improving Misclassifications in Modern Deep Neural Networks for Sentiment Analysis.
Proceedings of the International Joint Conference on Neural Networks, 2021

A Fast and Accurate Approach for Inferencing Social Relationships Among IoT Objects.
Proceedings of the Advanced Data Mining and Applications - 17th International Conference, 2021

2020
Adversarial Attacks on Deep-learning Models in Natural Language Processing: A Survey.
ACM Trans. Intell. Syst. Technol., 2020

Are Modern Deep Learning Models for Sentiment Analysis Brittleƒ An Examination on Part-of-Speech.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

The 10 Research Topics in the Internet of Things.
Proceedings of the 6th IEEE International Conference on Collaboration and Internet Computing, 2020

SIoTPredict: A Framework for Predicting Relationships in the Social Internet of Things.
Proceedings of the Advanced Information Systems Engineering, 2020

2019
Generating Textual Adversarial Examples for Deep Learning Models: A Survey.
CoRR, 2019


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