Explainable Artificial Intelligence

In the era of advanced artificial intelligence, the integration of emotional intelligence into AI systems has become crucial for developing Responsible Software Systems that are not only functional but also emotionally perceptive. The Microe dataset, a pioneering compilation focusing on micro-expressions, aims to revolutionize AI systems by enhancing their capability to recognize and interpret subtle emotional cues. This dataset encompasses over eight classes of common emotions, meticulously captured and categorized to aid in the synthesis and recognition of micro-expressions.

Last Updated On: 
Tue, 07/16/2024 - 11:30

This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.

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The increasing complexity of intelligent systems in the Internet of Things (IoT) domain makes it essential to explain their behavior and decision-making processes to users. However, selecting an appropriate explanation method for a particular intelligent system in this domain can be challenging, given the diverse range of available XAI (eXplainable Artificial Intelligence) methods and the heterogeneity of IoT applications. This dataset is a case base elicited from an exhaustive literature review on existing explanation solutions of AIoT (Artificial Intelligence of the Things) systems.

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