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Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see “Dynamical complexity of human responses: A multivariate data-adaptive framework,” in Bulletin of Polish Academy of Science and Technology, vol...
We study statistical issues related to the use of words in messages on SNs, as related to specific potentially catastrophic events, such as fires and earthquakes. The analysis investigates the frequency of the words in the messages of interest, moreover their temporal correlation, and aims to determine a reasonable number of keywords for the searches of the events, especially in relation with analytics...
We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data. While metric learning enables a problem-adapted representation of data, the majority of methods has been proposed for vectorial data only. In this contribution, we extend the popular principle offered by the large margin...
In this work we develop a study about the high-frequency financial forecasting problem. It presents some evidences suggesting that the generator phenomenon of this kind of time series cannot be a random walk process. In this context, we present an empirical analysis to investigate the forecasting performance of multilayer perceptron (MLP) model for three problems of high-frequency financial time series...
We consider symbol rate estimation of an unknown signal linearly modulated by a sequence of symbols. We rely on the received signal is cyclostationarity, and consider an existing estimator obtained by maximizing in the cyclic domain a (possibly weighted) sum of modulus squares of cyclic correlation estimates. Although widely used, this estimate seems not to have been studied rigorously when the number...
Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper...
Speech dialogue systems, such as Apple's “Siri,” have gradually become more widespread, and in the near future, a greater number of general users will have the opportunity to communicate with such systems. To facilitate this, it is necessary for the system to communicate more naturally with users, and to realize that both verbal and nonverbal information must be taken into consideration. Therefore,...
Hermann Weyl's concept of a discrepancy measure is discussed in the context of time series analysis. A concept for autocorrelation based on this discrepancy notion is introduced. It is shown that in particular for high frequent signals as they, for example, are typically encountered in a financial context, the introduced autocorrelation concept stands out by a better discriminative power than its...
In home-based care, reliable contextual information of remotely monitored patients should be generated to recognize activities and to identify hazardous situations of the patient. This is difficult for several reasons. First, low level data obtained from multisensor have different degrees of uncertainty. Second, generated contexts can be conflicting even if they are acquired by simultaneous operations...
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