Chaos based communications have drawn increasing attention over the past years. Chaotic signals are derived from non-linear dynamic systems. They are aperiodic, broadband and deterministic signals that appear random in the time domain. Because of these properties, chaotic signals have been proposed to generate spreading sequences for wide-band secure communication recently. Like conventional DS...

Understanding the listening habits of users is a valuable undertaking for musicology researchers, artists, consumers and online businesses alike. With the rise of Online Music Streaming Services (OMSSs), large amounts of user behavioral data can be exploited for this task. In this paper, we present SWIFT-FLOWS, an approach that models user listening habits in regards to how user attention trans...

We study ways of automatically inferring the level of attention a user is paying to auditory content, with applications for example in automatic podcast highlighting and auto-pause, as well as in a selection mechanism in auditory interfaces. In particular, we demonstrate how the level of attention can be inferred in an unsupervised fashion, without requiring any labeled training data. The appro...

Abstract Time preference reversal refers to systematic inconsistencies between preferences and bids for intertemporal options. From the two eye-tracking studies (N1 = 60, N2 110), we examined underlying mechanisms of time reversal. We replicated effect in which individuals facing a pair options choose smaller-sooner option but assign higher value larger-later one. Results revealed that mean fix...

Attention mechanism is crucial for sequential learning where a wide range of applications have been successfully developed. This basically trained to spotlight on the region interest in hidden states sequence data. Most attention methods compute score through relating between query and discrete-time state trajectory represented. Such could not directly attend continuous-time which represented v...