We describe the FAUST entry to the BioNLP 2011 shared task on biomolecular event extraction. The FAUST system explores several stacking models for combination using as base models the UMass dual decomposition (Riedel and McCallum, 2011) and Stanford event parsing (McClosky et al., 2011b) approaches. We show that using stacking is a straightforward way to improving performance for event extracti...

A Hybrid Wireless Sensor network (HWSN) with static and mobile nodes is considered. Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. Mobile sensor will collect the information about the event and in turn will send it to the base station. As WSN is vulnerab...

In order to avoid transmitting raw data to a base station, sensor nodes are trained to cooperatively recognize deployment-specific events based on the data sampled by their sensors. As both training and event detection are performed without the need for central coordination or processing, only information about the detected event needs to be reported.

Usage of ontological knowledge about events and their relations to other concepts in the application domain can improve the quality of complex event processing (CEP). In this paper, we present a solution for knowledge-based event entity extraction using background knowledge bases. For our experiments we use the DeRiVE-2011 workshop dataset. We enrich the incoming event data stream with backgrou...

In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people's accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DeltaP), they tend to misperceive base-rate information (e.g., the cause densit...

Mental representations of everyday experience are rich, structured, and multimodal. In this article we consider the adaptive pressures that led to human construction of such representations, arguing that structured event representations enable cognitive systems to more effectively predict the trajectory of naturalistic everyday activity. We propose an account of how cortical systems and the hip...

Complex event processing is currently done primarily with proprietary definition languages. Future smart environments will require collaboration of multi-platform sensors operated by multiple parties. The goal of my research is to verify the applicability of standard-compliant SPARQL for complex event processing tasks. If successful, semantic web standards RDF, SPARQL and OWL with their establi...

The thesis explores the use of a knowledge-based AI system to assist people in executing procedures and detecting events in an observed world. We extend the Scone knowledge-base system with open-ended facilities for representing time and events. Then we use this episodic knowledge representation as the foundation for our event monitoring and event detection algorithms. This approach lets us rep...