Human Activity Recognition through Weighted Finite Automata

ABSTRACT: This work addresses the problem of human activity identification in an ubiquitous environment, where data is collected from a wide variety of sources. In our approach, after filtering noisy sensor entries, we learn user?s behavioral patterns and activities? sensor patterns through the construction of weighted finite automata and regular expressions respectively, and infer the inhabitant?s position for each activity through frequency distribution of floor sensor data. Finally, we analyze the prediction results of this strategy, which obtains 90.65% accuracy for the test data.+This research was funded by Ministerio de Ciencia e Innovación (MICINN), Spain grant number MTM2014-55262-P and by Sociedad para el Desarrollo Regional de Cantabria (SODERCAN) grant number TI16IN-007

Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page