We propose a two-stage process called minority voting to allocate public projects in a polity. In the first period, a society decides by a simple majority decision whether to provide the public project. If the proposal in the first period is rejected, the process ends. Otherwise the process continues, but only the members of the minority keep agenda and voting rights for the second stage, in wh...

In a symmetric information voting model, an individual (information controller) can influence voters’ choices by designing the information content of a public signal. We characterize the controller’s optimal signal. With a non-unanimous voting rule, she exploits voters’ heterogeneity by designing a signal with realizations targeting di↵erent winning-coalitions. Consequently, under simple-majori...

Electronic voting, or e-voting, is a relatively closed process that contains inherent risks associated with the potential for voting irregularities, translation errors, and inappropriate manipulation (Oravec, 2005). To develop a greater understanding of trust issues surrounding the use of e-voting, an investigation into the public trust and the relationship between trust and electronic voting t...

In this paper, we propose majority voting neural networks for sparse signal recovery in binary compressed sensing. The majority voting neural network is composed of several independently trained feedforward neural networks employing the sigmoid function as an activation function. Our empirical study shows that a choice of a loss function used in training processes for the network is of prime im...

Using Machine Learning and Deep to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast the fields of computer vision natural language processing, data amount these trials still rather tiny. Developing PC-based machine learning technique increase participation non-expert end-users could help solve this col...

Plurality voting is widely used in pattern recognition practice. However, there is little theoretical analysis of plurality voting. In this paper, we attempt to explore the rationales behind plurality voting. The recognition/error/rejection rates of plurality voting are compared with those of majority voting under different conditions. It is demonstrated that plurality voting is more efficient ...

<span lang="EN-US">Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, processing large is time-consuming memory intensive. As a result, the researchers adopted partitioning strategy to improve controllability performance reduce time required handle datasets. Unfortunately, numerous clustering techniques available literatur...