Blink Rate Variability during resting and reading sessions

It has been shown that blinks occur not only to moisturize eyes and as a defensive response to the environment, but are also caused by mental processes. In this paper, we investigate statistical characteristics of blinks and blink rate variability of 11 subjects. The subjects are presented with a reading/memorization session preceded and followed by a resting session. EEG signals were recorded during these sessions. The signals from the two front electrodes were then analyzed, and times of the blinks were detected. We discovered that compared to the resting sessions, reading session is characterized by a lower number of blinks. However, there was no significant difference in standard deviation in the blink rate variability. We also noticed that in terms of complexity measures, the blink rate variability is located somewhere in between white and pink noises, being closer to the white noise during reading. We also found that the average of inter-blink intervals increases during reading/memorization, thus longer inter-blink intervals could be associated with a mental workload

Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow

As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.Comment: Third International Workshop on Modelling Uncertainty in the Financial World (MUFin'23

A Movie Genre Prediction Based on Multi-Variate Bernoulli Model and Genre Correlations

In this paper, a movie category based on Bayesian model and categories correlations is proposed. Although several methods have been reported on improving the user satisfaction based on unexpectedness metric, to the best of our knowledge, predicting items’ categories rather than predicting items’ rating is a new attempt. This in turn completes the items’ categories given by experts and improves user satisfaction by providing a surprise effect in the recommendations given to users. We employ Bernoulli multivariate model to estimate a likelihood of a movie given category and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach