On the non-emptiness of the one-core and the bargaining set of committee games

We study the committee decision making process using game theory. Shenoy  [15] introduced two solution concepts: the one-core and the bargaining set, and showed that the one-core of a simple committee game is nonempty if there are at most four players. We extend this result by proving that whether the committee is simple or not, as far as there are less than five players, the one-core is nonempty. This result also holds for the bargaining set.     

PageRank model of opinion formation on social networks

We propose the PageRank model of opinion formation and investigate its rich properties on real directed networks of the Universities of Cambridge and Oxford, LiveJournal, and Twitter. In this model, the opinion formation of linked electors is weighted with their PageRank probability. Such a probability is used by the Google search engine for ranking of web pages. We find that the society elite, corresponding to the top PageRank nodes, can impose its opinion on a significant fraction of the society. However, for a homogeneous distribution of two opinions, there exists a bistability range of opinions which depends on a conformist parameter characterizing the opinion formation. We find that the LiveJournal and Twitter networks have a stronger tendency to a totalitarian opinion formation than the university networks. We also analyze the Sznajd model generalized for scale-free networks with the weighted PageRank vote of electors.     

Quantum anonymous voting with anonymity check

We propose a new protocol for quantum anonymous voting having serious advantages over the existing protocols: it protects both the voters from a curious tallyman and all the participants from a dishonest voter in unconditional way. The central idea of the protocol is that the ballots are given back to the voters after the voting process, which gives a possibility for two voters to check the anonymity of the vote counting process by preparing a special entangled state of two ballots. Any attempt of cheating from the side of the tallyman results in destroying the entanglement, which can be detected by the voters.     

New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis–linear discriminant analysis (PCA–LDA), partial least squares–discriminant analysis (PLS–DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA–LDA and PLS–DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.     

应用Voting Machine构建研究型、互动型的双语物理课堂的研究与实践

高等教育"质量工程"的实施为高等学校本科教学提出了更新、更高的要求和挑战.本文报道了应用Voting Machine这一具有强大的互动和统计功能的教学设备在双语物理课堂上开展研究型、互动型教学的实践和研究成果.     

A general model for voting systems with multiple alternatives

In this paper, the extension of simple games to the vector case is proposed. Games with multiple qualitative criteria and multi-criteria simple games are introduced as a natural tool for modelling voting systems and related social-choice situations. After formally defining these games, the special class of monotonic multi-criteria simple games is characterized. We show that these games enable the formulation and analysis of several collective decision models proposed in the literature. Furthermore, our model can be applied to group-decision problems which cannot be analyzed in the existing frameworks.     

The Shapley–Shubik index for multi-criteria simple games

In this paper we address multi-criteria simple games which constitute an extension of the basic framework of voting systems and related social-choice situations. For these games, we propose the extended Shapley–Shubik index as the natural generalization of the Shapley–Shubik index in conventional simple games, and establish an axiomatic characterization of this power index.     

Achievable hierarchies in voting games with abstention

It is well known that he influence relation orders the voters the same way as the classical Banzhaf and Shapley–Shubik indices do when they are extended to the voting games with abstention (VGA) in the class of complete games. Moreover, all hierarchies for the influence relation are achievable in the class of complete VGA. The aim of this paper is twofold. Firstly, we show that all hierarchies are achievable in a subclass of weighted VGA, the class of weighted games for which a single weight is assigned to voters. Secondly, we conduct a partial study of achievable hierarchies within the subclass of H-complete games, that is, complete games under stronger versions of influence relation.     

基于卷积神经网络与投票机制的蒲黄炮制品近红外判别方法

蒲黄炭是由香蒲花粉炮制而成,具有止血、化瘀、通淋等多种功效,被广泛应用于临床抗血栓,创面和出血。然而蒲黄炭在炒炭过程中,常常会出现炭化过轻或者炭化过重的现象,从而出现不同炭化程度的蒲黄炭药品,主要为轻度炭化、标准炭化与重度炭化三种不同的蒲黄炭药品。由于炭化程度不同,蒲黄炭的凝血效果优劣不等,其中标准炭化的蒲黄炭药品药效最优。目前,鉴别蒲黄炭药品的方法多为人工凭借肉眼与经验进行判别。基于人工的蒲黄炭药品判别方法判别效率低,受主观因素影响大,判别结果不稳定,难以区分出标准炭化的蒲黄炭。为有效地对不同炭化程度的蒲黄炭进行识别,提出一种基于卷积神经网络与投票机制的蒲黄炮制品近红外判别方法。该方法创新性地结合深度学习与机器学习算法,有效利用卷积神经网络强大表征提取能力的同时通过投票决策提升算法模型的泛化能力与鲁棒性。首先通过近红外光谱技术获取蒲黄炭的近红外光谱,并通过卷积神经网络分别提取样本经过四种预处理方法所得到光谱图的高阶特征,并计算预测结果。按照样本准确率与损失值为四种预处理方法分配相应权重得到蒲黄炮制品预测模型。该模型将所得到的四种预测结果结合权重共同投票出样本的最终结果,从而鉴别出蒲黄炭的炭化程度。实验结果表明所提方法可以有效判别蒲黄炮制品的炭化程度。当训练集所占样本比例为80%时,预测准确率达到95.4%。所提方法与传统卷积神经网络方法、线性判别分析方法以及标准正太变量变换-线性判别分析方法相比预测准确率分别提高8.6%,4.3%和2.6%。同时,所提方法具有一定的稳定性,当训练集所占样本比例大于70%时,测试准确率高于90%;当训练集比例仅占10%时,预测准确性仍然能够达到约80%。