Clashes Involving National Popular Vote, Hare (“RCV”), Maine, Alaska

ABSTRACT Apparently unnoticed by its advocates, a prominent effort to improve the troubled US presidential-election system—the National Popular Vote Interstate Compact (NPVIC)—is on a collision course with another effort at electoral change—“ranked-choice voting” (RCV, known previously by less ambiguous names). The NPVIC is a clever device intended, without constitutional amendment, to elect as president the nationwide popular-vote winner (i.e., the plurality-vote winner) rather than the electoral-vote winner. Election results in 2000, 2016, and 2020 enhanced its support. However, the (constitutional) ability of even one state to replace its plurality voting with another voting system causes the popular-vote total posited for the NPVIC to be undefined, thereby rendering the NPVIC unusable. Maine and Alaska recently switched from plurality voting to RCV for presidential elections. Consequently, tangled results and turmoil could occur with the NPVIC. To improve presidential elections, replacing plurality voting with other systems appears to be more sensible than pursuing the NPVIC.

MCDM Application of the Third Vote

When choosing among alternatives, group members may have various preferences regarding the properties of a solution being sought. Since the properties partially do and partially do not meet their collective wishes, the alternatives are in fact better or worse representatives of the collective will. This idea is implemented in the so-called Third Vote election method aimed at enhancing policy representation, and we show how to use it for collective multi-criteria decision making. To be specific, we consider an example of a committee charged with naming a campus library when neither plurality vote nor Condorcet method nor Borda count gives a unique solution. The committee members have differing opinions, such as whether the library should reflect the national affiliation, be named after a great man, relate to sciences, and so forth. Balancing opinion on these issues, the proposed library names are evaluated and the optimal compromise is found.

Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features

The detection of skin diseases is becoming one of the priority tasks worldwide due to the increasing amount of skin cancer. Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses. This work proposes a computer-aided diagnosis based on 1D fractal signatures of texture-based features combining with deep-learning features using transferred learning based in Densenet-201. This proposal works with three 1D fractal signatures built per color-image. The energy, variance, and entropy of the fractal signatures are used combined with 100 features extracted from Densenet-201 to construct the features vector. Because commonly, the classes in the dataset of skin lesion images are imbalanced, we use the technique of ensemble of classifiers: K-nearest neighbors and two types of support vector machines. The computer-aided diagnosis output was determined based on the linear plurality vote. In this work, we obtained an average accuracy of 97.35%, an average precision of 91.61%, an average sensitivity of 66.45%, and an average specificity of 97.85% in the eight classes’ classification in the International Skin Imaging Collaboration (ISIC) archive-2019.