Abstract

Abstract
We propose a method for computing the similarity of symbolically-encoded Portuguese folk melodies. The main novelty of our method is the use of a preprocessing melodic reduction at multiple hierarchies to filter the surface of folk melodies according to 1) pitch stability, 2) interval salience, 3) beat strength, 4) durational accents, and 5) the linear combination of all former criteria. Based on the salience of each note event per criteria, we create three melodic reductions with three different levels of note retention. We assess the degree to which six folk music similarity measures at multiple reduction hierarchies comply with collected ground truth from experts in Portuguese folk music. The results show that SIAM combined with 75th quantile reduction using the combined or durational accents best models the similarity for a corpus of Portuguese folk melodies by capturing approximately 84-90% of the variance observed in ground truth annotations.

Abstract
In recent years growing volumes of data have made the task of applying various machine learning algorithms a challenge in a great number of cases. This challenge is posed in two main ways: training time and processing load. Normally, problems in these two categories may be attributed to irrelevant, redundant, or noisy features. So as to avoid this type of feature most pre-processing pipelines include a step dedicated so selecting the most relevant features or combining existing ones into a single better representation. These techniques are denominated dimensionality reduction techniques. In this work, we aim to present a short look at the current state of the art in this area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud-edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi-Sugeno-Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods.

Abstract
The MiFiRE (Microgravity Fine Regolith Experiment) experiment, which will be launched this year on a suborbital space flight, currently scheduled for August 2023, was designed with the aim of better understanding the initial stages of planetary formation. The fundamental and embryonic question is to contribute to the study of how the mineral and rock particles, which do not have enough mass for the gravitational force to be influential, can then aggregate through electrostatic forces. In order to recreate the environment of deep space, it is assumed that the composition of meteorites that collide with the Earth, are mainly of silicate mineralogical composition or rich in metallic alloys (eg Fe-Ni). Therefore, in the experiment some fine material, identical to the lunar regolith (JSC-1), is used, in other words, amphiboles, pyroxenes, olivines and volcanic glass, along with two larger elements, a basalt cube and a metalic (siderite) meteorite cube (Octahedrite from Campo del Cielo, Argentina). It is intended that the particles be subjected to the microgravity environment and thus contribute to a better understanding of the general behaviour and the processes of preference of aggregation between the various components. This, in turn, contributes the characterization of the progressive development of planetesimals. This experiment was selected amongst 5 competing proposals in a contest launched by Massachusetts Institute of Technology's national representation, MIT Portugal, in 2020. © 2023 International Multidisciplinary Scientific Geoconference. All rights reserved.

Abstract
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