The University of the District of Columbia (UDC), established in 1851, is a public urban land-grant institution of higher education located in Washington, D.C. It offers affordable postsecondary education through various programs, preparing students for the workforce, further education, and lifelong learning, with a focus on high-quality learning, research, and public service. In collaboration with the University of the District of Columbia, NEC Labs America explores AI for social good, urban analytics, and the fusion of public safety data. Please read about our latest news and collaborative publications with the University of the District of Columbia.

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Multi-Label Temporal Evidential Neural Networks for Early Event Detection

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Early event detection aims to detect events even before the event is complete. However, most of the existing methods focus on an event with a single label but fail to be applied to cases with multiple labels. Another non-negligible issue for early event detection is a prediction with overconfidence due to the high vacuity uncertainty that exists in the early time series. It results in an over-confidence estimation and hence unreliable predictions. To this end, technically, we propose a novel framework, Multi-Label Temporal Evidential Neural Network (MTENN), for multi-label uncertainty estimation in temporal data. MTENN is able to quality predictive uncertainty due to the lack of evidence for multi-label classifications at each time stamp based on belief/evidence theory. In addition, we introduce a novel uncertainty estimation head (weighted binomial comultiplication (WBC)) to quantify the fused uncertainty of a sub-sequence for early event detection. We validate the performance of our approach with state-of-the-art techniques on real-world audio datasets.

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