Learning to sense from events via semantic variational autoencoder

• Published in: PloS one Vol. 16; no. 12; p. e0260701
• Main Authors: Gôlo, Marcos Paulo Silva, Rossi, Rafael Geraldeli, Marcacini, Ricardo Marcondes
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.12.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Classification; Computer and Information Sciences; Decision making; Earth Sciences; Emergency communications systems; Event history analysis; Geography; Human performance; Humans; Influence; Influenza; Learning; Learning - physiology; Life Change Events; Machine learning; Methods; Perception - physiology; Semantics; Sensors; Social networks; Social Sciences; Stress, Psychological; Temporal variations; Text categorization; Trends; Brazil
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260701

In this paper, we introduce the concept of learning to sense, which aims to emulate a complex characteristic of human reasoning: the ability to monitor and understand a set of interdependent events for decision-making processes. Event datasets are composed of textual data and spatio-temporal features that determine where and when a given phenomenon occurred. In learning to sense, related events are mapped closely to each other in a semantic vector space, thereby identifying that they contain similar contextual meaning. However, learning a semantic vector space that satisfies both textual similarities and spatio-temporal constraints is a crucial challenge for event analysis and sensing. This paper investigates a Semantic Variational Autoencoder (SVAE) to fine-tune pre-trained embeddings according to both textual and spatio-temporal events of the class of interest. Experiments involving more than one hundred sensors show that our SVAE outperforms a competitive one-class classification baseline. Moreover, our proposal provides desirable learning requirements to sense scenarios, such as visualization of the sensor decision function and heat maps with the sensor's geographic impact.