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pro vyhledávání: '"Asyrofi, Muhammad Hilmi"'
Autor:
Yang, Zhou, Shi, Jieke, Asyrofi, Muhammad Hilmi, Xu, Bowen, Zhou, Xin, Han, DongGyun, Lo, David
With the wide adoption of automated speech recognition (ASR) systems, it is increasingly important to test and improve ASR systems. However, collecting and executing speech test cases is usually expensive and time-consuming, motivating us to strategi
Externí odkaz:
http://arxiv.org/abs/2302.00330
Deep neural networks (DNN) have been widely applied in modern life, including critical domains like autonomous driving, making it essential to ensure the reliability and robustness of DNN-powered systems. As an analogy to code coverage metrics for te
Externí odkaz:
http://arxiv.org/abs/2201.00191
Developers need to perform adequate testing to ensure the quality of Automatic Speech Recognition (ASR) systems. However, manually collecting required test cases is tedious and time-consuming. Our recent work proposes CrossASR, a differential testing
Externí odkaz:
http://arxiv.org/abs/2105.14881
Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the community
Externí odkaz:
http://arxiv.org/abs/2105.14874
Autor:
Asyrofi, Muhammad Hilmi, Yang, Zhou, Yusuf, Imam Nur Bani, Kang, Hong Jin, Thung, Ferdian, Lo, David
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
Externí odkaz:
http://arxiv.org/abs/2102.01859
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