This paper attempts to answer a central question in unsupervised learning: what does it
mean to “make sense” of a sensory sequence? In our formalization, making sense involves …

Composite event recognition (CER) systems process streams of sensor data and infer
composite events of interest by means of pattern matching. Data uncertainty is frequent in …

Surveillance camera usage has increased significantly for visual surveillance. Manual
analysis of large video data recorded by cameras may not be feasible on a larger scale. In …

Activity recognition refers to the detection of temporal combinations of 'low-level'or 'short-
term'activities on sensor data. Various types of uncertainty exist in activity recognition …

Imagine a machine, equipped with sensors, receiving a stream of sensory information. It
must, somehow, make sense of this stream of sensory data. But what, exactly, does this …

The Apperception Engine is an unsupervised learning system. Given a sequence of sensory
inputs, it constructs a symbolic causal theory that both explains the sensory sequence and …

Composite maritime event recognition systems support maritime situational awareness as
they allow for the real-time detection of dangerous, suspicious and illegal vessel activities …

Symbolic event recognition systems detect event occurrences using first-order logic rules.
Although existing online structure learning approaches ease the discovery of such rules in …

Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm
based on labeled samples. However, there is still a huge amount of unlabeled samples in …

We present a system for online composite event recognition over streaming positions of
commercial vehicles. Our system employs a data enrichment module, augmenting the …