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Open AccessArticle

The Effect of Practice and Musical Structure on Pianists’ Eye-Hand Span and Visual Monitoring

by Michel A. Cara

J. Eye Mov. Res. 2023, 16(2), 1-18; https://doi.org/10.16910/jemr.16.2.5 - 29 May 2023

Cited by 6 | Viewed by 1619

This study examines short-term improvement of music performances and oculomotor behaviour during four successive executions of a brief musical piece composed by Bartók, “Slovak Boys’ Dance”. Pianists (n = 22) were allowed to practice for two minutes between each trial. Eye-tracking data were collected as well as MIDI information from pianists’ performances. Cognitive skills were assessed by a spatial memory test and a reading span test. Principal component analysis (PCA) enabled us to distinguish two axes, one associated with anticipation and the other with dependence/independence on written code. The effect of musical structure, determined by the emergence of different sections in the score, was observed in all the dependent variables selected from the PCA; we also observed the effect of practice on the number of fixations, the number of glances at the keyboard (GAK) and the awareness span. Pianist expertise was associated with fewer fixations and GAK, better anticipation capacities and more effective strategies for visual monitoring of motor movements. The significant correlations observed between the reading span test and GAK duration highlight the challenge of working memory involvement during music reading. Full article

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21 pages, 5450 KB

Open AccessArticle

DPDRC, a Novel Machine Learning Method about the Decision Process for Dimensionality Reduction before Clustering

by Jean-Sébastien Dessureault and Daniel Massicotte

AI 2022, 3(1), 1-21; https://doi.org/10.3390/ai3010001 - 29 Dec 2021

Cited by 6 | Viewed by 3969

Abstract

This paper examines the critical decision process of reducing the dimensionality of a dataset before applying a clustering algorithm. It is always a challenge to choose between extracting or selecting features. It is not obvious to evaluate the importance of the features since the most popular methods to do it are usually intended for a supervised learning technique process. This paper proposes a novel method called “Decision Process for Dimensionality Reduction before Clustering” (DPDRC). It chooses the best dimensionality reduction method (selection or extraction) according to the data scientist’s parameters and the profile of the data, aiming to apply a clustering process at the end. It uses a Feature Ranking Process Based on Silhouette Decomposition (FRSD) algorithm, a Principal Component Analysis (PCA) algorithm, and a K-means algorithm along with its metric, the Silhouette Index (SI). This paper presents five scenarios based on different parameters. This research also aims to discuss the impacts, advantages, and disadvantages of each choice that can be made in this unsupervised learning process. Full article

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12 pages, 5418 KB

Open AccessArticle

Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran

by Vahid Khosravi, Aref Shirazi, Adel Shirazy, Ardeshir Hezarkhani and Amin Beiranvand Pour

Mining 2022, 2(1), 1-12; https://doi.org/10.3390/mining2010001 - 21 Dec 2021

Cited by 18 | Viewed by 5096

Abstract

The eastern Lut block of Iran has a high potential for porphyry copper mineralization due to the subduction tectonic regime. It is located in an inaccessible region and has harsh arid conditions for traditional mineral exploration campaigns. The objective of this study is to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for porphyry copper exploration in Simorgh Area, eastern Lut block of Iran. Hydrothermal alteration zones such as argillic, phyllic and propylitic zones associated with porphyry copper systems in the study were identified using false color composition (FCC), band ratio (BR), principal component analysis (PCA) and minimal noise fraction (MNF). The thematic alteration layers extracted from FCC, BR, PCA and MNF were integrated using hybrid Fuzzy-AHP model to generate a porphyry copper potential map for the study area. Four high potential zones were identified in the central, western, eastern and northeastern of the study area. Fieldwork was used to validate the approach used in this study. This investigation exhibits that the use of hybrid Fuzzy-AHP model for the identification of hydrothermal alteration zones associated with porphyry copper systems that is typically applicable to ASTER data and can be used for porphyry copper potential mapping in many analogous metallogenic provinces. Full article

(This article belongs to the Special Issue Mapping the Expressions of Ore-Forming Processes Using Remote Spectral Imagery and Geochemical Techniques)

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