Recently, an innovative voting method named quadratic voting (QV ) has been proposed which allows people to vote as much as they want, according to their preferences intensity. Little research has been done on the safe implementation of this method. In this paper, we first present a voting method based on QV . This method combines the voting and planning, and gives the ability to voters to express their opinions about the candidates programs in addition to voting. Then, a secure electronic voting protocol is proposed for implementing our method. This protocol gives the voters to check the verifiability of ballots and the safety of payment so that they would be sure that their votes are counted correctly.

Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. Ensemble clustering combines results of existing clusterings to achieve better performance and higher accuracy. Instead of combining all of existing clusterings, recent decade researchers show, if only a set of clusterings is selected based on quality and diversity, the result of ensemble clustering would be more accurate. This paper proposes a new method for ensemble clustering based on quality and diversity. For this purpose, firstly first we need a lot of different base clusterings to combine them. Different base clusterings are generated by k-means algorithm with random k in each execution. After the generation of base clusterings, they are put into different groups according to their similarities using a new grouping method. So that clusterings which are similar to each other are put together in one group. In this step, we use normalized mutual information (NMI) or adjusted rand index (ARI) for computing similarities and dissimilarities between the base clustering. Then from each group, a best qualified clustering is selected via a voting based method. In this method, Cluster-validity-indices were used to measure the quality of clustering. So that all members of the group are evaluated by the Cluster-validity-indices. In each group, clustering that optimizes the most number of Cluster-validity-indices is selected. Finally, consensus functions combine all selected clustering. Consensus function is an algorithm for combining existing clusterings to produce final clusters. In this paper, three consensus functions including CSPA, MCLA, and HGPA have used for combining clustering. To evaluate proposed method, real datasets from UCI repository have used. In experiment section, the proposed method is compared with the well-known and powerful existing methods. Experimental results demonstrate that proposed algorithm has better performance and higher accuracy than previous works.

The initial issue that must be addressed in teamwork is the manner in which decisions are made by the group and its members. voting is a procedure to aggregate individual votes to achieve a collective decision. Since individuals have varied opinions and preferences, preferential voting assists in conveying the priorities of the voters to the society or community. In many circumstances, such as voting-based managerial decisions, voters are of a voting preference of unequal voting power. This paper presents a method for the ranking of preferential voting with voters of unequal voting power, which, in addition to the utilization of preference voting models, employs the DEA and assurance region techniques. The deployment of DEA technique causes an increment in the competence of discriminating the ranking of candidates, and in the finale the proposed method is expressed for an empirical example to rank the petrochemical companies in the Tehran Stock Exchange.