Imam Nur Bani Yusuf
Singapore Management University | smu · School of Information Systems

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Publications

316

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Citations

6 Research Items

26 Citations

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Publications

RecipeGen++: an automated trigger action programs generator

Conference Paper

Nov 2022

Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning

Conference Paper

Oct 2022

On the effectiveness of pretrained models for API learning

Conference Paper

Oct 2022

Figure 3: Modified Tokenization Approach 1 (TA1)

PTM Description Architecture Used Model Parameters Layers Hidden Heads

PTM's performance Name of the Model Checkpoints BLEU Score Improvement

PTM's performance: Tokenization Approach 1 (TA1)

On the Effectiveness of Pretrained Models for API Learning

Preprint

Full-text available

Apr 2022

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize inform...

BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems

Article

Dec 2021

Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predict...

Fig. 2. Example of POS-tagging, NER, and coreference resolution. There...

Fig. 6. An Illustrative Example for Case 1 and 2 of GenderBiasFinder

Fig. 8. An Illustrative Example for OccupationBiasFinder

BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems

Preprint

Full-text available

Feb 2021

Artificial Intelligence (AI) software systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human biases. Consequently, the machine learning model in such software systems may exhibit unintended demographic bias based on specific characteristics (e.g., gender, occupation, country-of-origin, et...