Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations

Underspecification and fairness in machine learning (ML) applications have recently become two prominent issues in the ML community. Acoustic scene classification (ASC) applications have so far remained unaffected by this discussion, but are now becoming increasingly used in real-world systems where fairness and reliability are critical aspects.

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Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning

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Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy.

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Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit

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Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of daylong recordings from preterm infants’ audio environments were collected from two hospitals in Finland and Estonia in the context of so-called APPLE study.

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Ethics Advisory Board in Horizon Europe: Functions and Role

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There are various approaches to ethical issues. While some of these issues invite simple solutions, other demand complex, multilayered solutions. Regardless of the favored approach, the actions of the Ethics Advisory Board (EAB) and its Ethics Advisors (EA) must be carried out with the best interests of the general public and civil society in perspective.

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