A Differential Evolution Algorithm Assisted by ANFIS for Music Fingering

Music fingering is a cognitive process whose goal is to map each note of a music score to a fingering on some instrument. A fingering specifies the fingers of the hands that the player should use to play the notes. This problem arises for many instruments and it can be quite different from instrument to instrument; guitar fingering, for example, is different from piano fingering. Previous work focuses on specific instruments, in particular the guitar, and evolutionary algorithms have been used. In this paper, we propose a differential evolution (DE) algorithm designed for general music fingering (any kind of music instruments). The algorithm uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that learns the fingering from music already fingered. The algorithm follows the basic DE strategy but exploits also some customizations specific to the fingering problem. We have implemented the DE algorithm in Java and we have used the ANFIS network in Matlab. The two systems communicate by using the MatlabControl library. Several tests have been performed to evaluate its efficacy

Evaluation of heavy metals, cytotoxicity, and antioxidant activity of tomatoes grown in toxic muddy soils

This research studies tomatoes grown in polluted soils to ascertain their phytochemical and nutritive features. Pulp and seeds from tomatoes grown in muddy soils were analyzed for their antioxidant power and their toxicity because of the possibility that heavy metals were present in the soils. An antioxidant assay on methanol extracts was made by using DDPH, while an ABTS [2,2'-Azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid)] assay was used to evaluate the antioxidant activity of lipophilic fractions. Results of the antioxidant assay showed that the tomatoes maintained a high level of antioxidant activity especially in the lipophilic fractions which contain the most representative compounds. Cytotoxic activity was performed on HeLa, PDAC, and A375 cell lines by [3-(4,5-dimethylthiazol-2-yl)-2,5-phenyl-2H-tetrazolium bromide] (MTT) assay. Results showed that neither the seeds, nor the pulp, of the extracts was cytotoxic. The presence of heavy metals was evaluated by using spectroscopy of atomic absorption with a graphite oven. Test results show the absence of heavy metals and these results have an interesting scientific role because they provide useful information for promoting food safety

The Conundrum of Success in Music: Playing it or Talking About it?

Nowadays social media are the main means for conducting discussions and sharing opinions. The huge amount of information generated by social media users is helpful for predicting outcomes of real-world events in different fields, including business, politics and the entertainment industry. In this paper, we studied the possibility of forecasting the success of music albums by analyzing heterogeneous data sources spanning from social media (Twitter, Instagram and Facebook) to mainstream American newspapers (e.g., New York Times, Rolling Stones). The idea is to exploit music albums' pre-release hype and post-release approval to predict the album's rank with reference to the well-known Billboard 200 album chart, which tabulates the weekly popularity of music albums in the USA. To predict the success of a music album, that is its rank in the chart, we identified metrics based on the messages' posting trend, the variation of the sentiment associated to such messages, the number of followers of the album's author, and the importance of the people who talk about it. To evaluate the effectiveness of the proposed metrics we have compared the prediction performances of several models based on supervised learning approaches among those most used in literature. As a result, we obtained that the Random Forest approach is able to predict the music album rank in the Billboard 200 Chart with an expected accuracy of 97%. As a further validation, using this specific model, we also conducted an additional real usage test obtaining an almost matching result (accuracy of 94%)

An improved privacy attack on smartphones exploiting the accelerometer

We define and implement a novel side-channel attack that exploits a smartphone’s accelerometer to eavesdrop entire words that the device itself is reproducing through its loudspeakers. The proposed approach consists of two modules: (i) a deep learning-based system that, using a Convolutional Neural Network (CNN), learns to recognize a set of significant speech units, using the spectrogram representation of the corresponding acceleration signals; (ii) an evolutionary-based segmentation method that, given the accelerometer measurements corresponding to an input speech, finds the best way to split it so that the proposed CNN maintains a high classification performance on each of the segments obtained, guarantying the recognition of a significant percentage of words from the original speech. Results of experiments performed to assess the effectiveness of the proposed attack, show its ability to recognize a percentage of words which is higher for short speeches and diminishes as the speeches get longer. We experimented with speeches of lengths ranging from 5 to 60 s, obtaining a recognition percentage going from about 80% for the shortest speeches, down to about 54% for the longest ones