A general stability analysis on regional and national voting schemes against noise—why is an electoral college more stable than a direct popular election?

By discarding the previous restrictive weak average distribution assumption on region sizes, we have developed a new general probabilistic model on the regional voting (known as “direct popular voting” in political science) and the national voting (typically, the electoral college), where we regard the percentage of a candidate's supporters in the nation as the probability of a voter voting for the candidate. Our analysis demonstrates that the regional voting is always more stable than the national voting, and that the stability margin of the regional voting always increases as the size of such partitioned regions decreases down to a certain critical value of region size, beyond which the stability margin starts to decrease, asymptoting to a national voting level where the size of the partitioned regions approaches the unit of voting cell so that the improved stability of the regional voting by localizing the effects of noise into a restricted number of smaller effective areas will not be effective. Our stability analysis remains valid over the entire range in size of the partitioned regions for regional voting. We show that the regional voting asymptotes to the national voting in two extreme limiting cases, when the region size decreases to a voting cell size and when the region size increases to the size of the nation.     

区域极点配置在机器人手臂运动模型中的分析及比较

针对一类线性系统的区域极点配置的稳定问题,以机器人手臂为研究对象,对被控对象进行相关描述并且建立运动模型。给出线性系统区域极点配置在机器人手臂的运动模型稳定中的重要结论。利用LMI求解出控制器,并例举一个具体实例通过计算机仿真证实结论的正确有效性,通过分析得出区域极点配置的控制方式较精确极点配置的控制方式在稳定性特征上能够更好的控制系统,使得系统满足静态与动态特性。     

ROBUSTNESS INSTEAD OF ACCURACY SHOULD BE THE PRIMARY OBJECTIVE FOR SUBJECTIVE PATTERN RECOGNITION RESEARCH: STABILITY ANALYSIS ON MULTICANDIDATE ELECTORAL COLLEGE VERSUS DIRECT POPULAR VOTE

Subjective pattern recognition is a class of pattern recognition problems, where we not only merely know a few, if any, the strategies our brains employ in making decisions in daily life but also have only limited ideas on the standards our brains use in determining the equality/inequality among the objects. Face recognition is a typical example of such problems. For solving a subjective pattern recognition problem by machinery, application accuracy is the standard performance metric for evaluating algorithms. However, we indeed do not know the connection between algorithm design and application accuracy in subjective pattern recognition. Consequently, the research in this area follows a “trial and error” process in a general sense: try different parameters of an algorithm, try different algorithms, and try different algorithms with different parameters. This phenomenon can be observed clearly in the nearly 30 years research of the face recognition: although huge advances have been made, no algorithm has ever been shown a potential to be consistently better than most of the algorithms developed earlier; it was even shown that a naïve algorithm can work, in the sense of accuracy, at least no worse than many newly developed ones in a few benchmarks. We argue that, the primary objective of subjective pattern recognition research should be moved to theoretical robustness from application accuracy so that we can evaluate and compare algorithms without or with only few “trial and error” steps. We in this paper introduce an analytical model for studying the theoretical stabilities of multicandidate Electoral College and Direct Popular Vote schemes (aka regional voting scheme and national voting scheme, respectively), which can be expressed as the a posteriori probability that a winning candidate will continue to be chosen after the system is subjected to noise. This model shows that, in the context of multicandidate elections, generally, Electoral College is more stable than Direct Popular Vote, that the stability of Electoral College increases from that of Direct Popular Vote as the size of the subdivided regions decreases from the original nation size, up to a certain level, and then the stability starts to decrease approaching the stability of Direct Popular Vote as the region size approaches the original unit cell size; and that the stability of Electoral College approaches that of Direct Popular Vote in the two extremities as the region size increases to the original national size or decreases to the unit cell size. It also shows a special situation of white noise dominance with negligibly small concentrated noise, where Direct Popular Vote is surprisingly more stable than Electoral College, although the existence of such a special situation is questionable. We observe that “high stability” in theory indeed always reveals itself in “high accuracy” in applications. Extensive experiments on two human face benchmark databases applying an Electoral College framework embedded with standard baseline and newly developed holistic algorithms have been conducted. The impressive improvement by Electoral College over regular holistic algorithms verifies the stability theory on the voting systems. It also shows an evidential support for adopting theoretical stability instead of application accuracy as the primary objective for subjective pattern recognition research.     

A Parallel Voting Scheme for Aspect Recovery

Recently,a qualitative approach was proposed for 3-D shape recovery based on a hybrid object representation^[1].In this approach,aspect recovery is the most important stage which binds regions in the image into meaningful aspects to support 3-D primitive recovery.There is no known polynomial time algorithm to solve this problem.the previous approach dealt with this problem by using a heuristic method based on the conditional probability.Unlike the previous method,this paper presents a novel parallel voting scheme to conquer the problem for efficiency.For this purpose,the previous global aspect representation is replaced with a distributed representation of aspects.Based on this representation,a three-layer parallel voting network for aspect recovery is proposed.For evaluating likelihood,a continuous Hopfield net is employed so that all aspect coverings in decreasing order of likelihood can be enumerated.The paper describes this method in detail and demonstrates its usefulness with simulation.     

Computer usage by small and medium sized European firms: An empirical study

The central concern of this study is: Why aren't more small and medium sized firms utilizing computers? A secondary point considered is: For firms that do have computer systems, what were the chief indicators and reasons for selecting a computer system? These questions were explored, primarily via questionnaires in France, West Germany and the Netherlands. A total of 267 valid responses were analyzed. A major conclusion of the study is that many small and medium sized firms do not utilize computers due to ignorance of their capabilities and a misunderstanding of the cost/ benefit factors involved.     

The spatial organization of accessibility and functional hierarchy: The case of Israel

Accessibility is a well-known basic term in spatial science and planning and is inherently related to functional aspects of places and regions. Although previous studies have examined functional systems and spatial accessibility few have attended to the association among them across geographical scales. Our study attempts to fill this gap. Using the space syntax methodology, spatial accessibility was analyzed for the entire national road network of Israel across different geographic scales – from local culminating in the national scale. The analysis was based on angular segment analyses of the road center-line network. Following this, the correlation between spatial accessibility across scales and functional performance of employment and commuting flows was examined.The study findings show a significant correspondence and exposes transitions between local, regional and national spatio-functional systems. First, a significant correlation between local (2 km radius) accessibility levels of settlements with the number of employees and commuters. Second, the regional/metropolitan system (10–15 km radius) accessibility is highly correlated to the emergence of the main employment centers. Third, the main metropolitan areas are integrated at higher scale (from 30 km radius) and form together a core region characterized by high accessibility as well as well connectivity through commuting flows. In contrast, no substantial commuting flows were found within the periphery, as well as between periphery-core. The findings show clearly that this functional structure corresponds to the multi-scale accessibility levels of settlements. We conclude that the core region functions at multiple scales (local-regional-national) while the periphery functions mostly at a local scale.     

A framework for regional association rule mining and scoping in spatial datasets

The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists.     

Impact of the New Global Environment on US National Security Planning — Challenges to the OR Community

Recent events have changed the world political/military environment from a relatively stable bi-polar one to one with much less regional stability. This paper will describe the impacts of this change on US national security planning. It summarizes the global and national security environment changes, corresponding changes in the analysis activity to support national security planning, some approaches used to conduct analyses in the new environment, and some emerging results.     

基于等价分解中立型系统的时滞相关稳定性分析

利用微分方程理论,将具有不确定性的中立型系统的稳定性问题转换为二个迟后系统的稳定性问题,这一转换可以大大减少稳定性讨论的复杂程度。通过引用迟后系统的相关研究结果,得到中立型系统时滞相关和时滞无关两类稳定性判据,每一类结果包括工程上实用的代数法和理论上具较小保守性的线性矩阵不等式（LMI）表示的判据。给出二个数值例子,并将研究结果与同类研究结果进行比较,算例表明,提出的转换方法是有效的。     

Voting from the Home or Office? Don't Hold Your Breath

This article describes the challenges of Internet voting defined here as casting an electronic national or state direct election ballot from a location other than a polling station. The widely used terms "electronic voting" or "e-voting" represent the highly publicized (and controversial) use of special electronic devices at polling stations. Our interest here is the next step in governance - remote voting via the Internet, without having to physically go to a polling place.     

View matching with blob features

This article introduces a new region based feature for object recognition and image matching. In contrast to many other region based features, this one makes use of colour in the feature extraction stage. We perform experiments on the repeatability rate of the features across scale and inclination angle changes, and show that avoiding to merge regions connected by only a few pixels improves the repeatability. We introduce two voting schemes that allow us to find correspondences automatically, and compare them with respect to the number of valid correspondences they give, and their inlier ratios. We also demonstrate how the matching procedure can be applied to colour correction.     

影响最大化问题中基于K-truss的投票改进算法

在社交网络的影响最大化（IM）问题中,近似算法通过大量的Monte-Carlo模拟计算节点集的影响范围,导致时间复杂度提高,而多数启发式算法在具有不同拓扑结构的图上存在稳定性较差的问题。提出基于K-truss的改进投票算法TrussVote。在投票阶段,通过引入K-truss的相关理论及算法定义节点的有效投票能力,用于表示节点对其不同邻居的投票倾向,同时在计算得票分数时考虑边的传播概率,提高解决IM问题的效率。在每轮投票结束后,将得票分数最高的节点选为种子节点。在更新阶段,结合节点间的相似性指标定义衰减因子,以有效区分邻居节点投票能力的弱化程度。此外,基于IC模型下的原始传播结果,提出传播差异作为传播范围的等价分析指标。在不同规模真实网络数据集上的实验结果表明,相比RNR、VoteRank++等算法,该算法不仅能有效降低时间复杂度,而且可在最短的时间内感染更多的节点,具有广泛的影响范围。     

基于加权投票K—近邻法的生物医学缩略语消歧

生物医学文献信息抽取对充分挖掘利用生物医学领域取得的重要成果,促进生物医学的进一步发展具有重要意义。本文针对生物医学缩略语的分析理解问题,提出了基于加权投票K—近邻法的生物医学缩略语消歧算法。该算法基于“One Sense Per Discourse”假设自动生成带类标实例数据,消歧特征选用能表达文本主题的全局特征词,分类算法采用加权投票K—近邻法。在包含177 762篇Medline摘要的真实语料上进行的实验表明,本文所提出的算法明显优于相关工作中的算法。此外,实验还表明,对于缩略语消歧,加权投票K—近邻法与经典K—近邻法相比,不但具有高的预测准确率,而且性能更加稳定。     

Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.     

Outlier detection for scanned point clouds using majority voting

When scanning an object using a 3D laser scanner, the collected scanned point cloud is usually contaminated by numerous measurement outliers. These outliers can be sparse outliers, isolated or non-isolated outlier clusters. The non-isolated outlier clusters pose a great challenge to the development of an automatic outlier detection method since such outliers are attached to the scanned data points from the object surface and difficult to be distinguished from these valid surface measurement points. This paper presents an effective outlier detection method based on the principle of majority voting. The method is able to detect non-isolated outlier clusters as well as the other types of outliers in a scanned point cloud. The key component is a majority voting scheme that can cut the connection between non-isolated outlier clusters and the scanned surface so that non-isolated outliers become isolated. An expandable boundary criterion is also proposed to remove isolated outliers and preserve valid point clusters more reliably than a simple cluster size threshold. The effectiveness of the proposed method has been validated by comparing with several existing methods using a variety of scanned point clouds.     

Exponential periodicity and stability of neural networks with reaction-diffusion terms and both variable and unbounded delays

In this paper, the exponential periodicity and stability of neural networks with Lipschitz continuous activation functions are investigated, without assuming the boundedness of the activation functions and the differentiability of time-varying delays, as needed in most other papers. The neural networks contain reaction-diffusion terms and both variable and unbounded delays. Some sufficient conditions ensuring the existence and uniqueness of periodic solution and stability of neural networks with reaction-diffusion terms and both variable and unbounded delays are obtained by analytic methods and inequality technique. Furthermore, the exponential converging index is also estimated. The methods, which does not make use of Lyapunov functional, is simple and valid for the periodicity and stability analysis of neural networks with variable and/or unbounded delays. The results extend some previous results. Two examples are given to show the effectiveness of the obtained results.     

Estimating stability regions for mismatched uncertain variable structure systems with bounded controllers

We address the problem of the estimation of stability regions of a class of mismatched uncertain variable structure systems with bounded controllers. The estimation of stability regions such as the practical stability region, the asymptotic stability region, and the exponential stability region are discussed. Moreover, the state transformation used in previous paper is no longer needed so that a new estimation result is directly related to the original system states without state transformation.     

Measuring regional competitiveness through Data Envelopment Analysis: A Peruvian case

It is well known that competitiveness has a positive effect on long-term economic growth. Concerned, thus, with creating and maintaining an environment that sustains more value creation for its enterprises and more prosperity for its people, the goal of this research paper is to assist the Peruvian national and regional policy makers, business, and academic community in their endeavor to improve regional and national competitiveness by means of developing a methodology based on Data Envelopment Analysis (DEA) to measure and rank the competitiveness of all the regions of Peru. It is important to highlight that DEA is a method that has never been used before in the calculation of regional competitiveness and this research paper is the first of its kind in Peru to adapt this method to develop a regional competitiveness index. Results revealed that coastal regions are highly competitive when compared to the mountains and jungle regions. Because of the large differences in the competitiveness of the regions of Peru, the research results point out to the need for a unified approach in creating a development strategy and improving the competitiveness of all the regions of Peru.     

基于两阶段投票的小样本目标检测方法

为了在只有几个样本的情况下在待检测图像中采用模板匹配的方式定位目标,提出了一种两阶段投票的目标检测方法。首先采用概率模型通过几个样本离线构造投票空间,然后采用两阶段投票的方式在待测图像中检测目标：第一阶段在待测图像中通过投票检测目标的图像块,并记录下这些部件块在样本中所处的位置信息;第二阶段基于第一阶段所得到的图像块来投票计算样本整体的相似度,从而定位目标。理论推导和实验结果验证了所提出方法比前人的工作具有更低的时间复杂度和更高的目标检测准确率。     

基于投票机制的拜占庭容错共识算法

针对现有的区块链中实用拜占庭容错（PBFT）共识算法、基于动态授权的拜占庭容错（DDBFT）共识算法、联盟拜占庭容错（CBFT）共识算法普遍存在能耗高、效率低、扩展性差等问题，通过引入投票机制，提出了基于投票机制的拜占庭容错（VPBFT）共识算法。首先，以PBFT算法为基础，将网络中的节点划分为四类具有不同职责的节点。其次，算法中的投票节点具有投票和评分权，监督生产节点诚实可靠地生产数据块；生产有效的数据块的生产节点优先进入下一轮，候选节点能够被选为生产节点，而普通节点则能够成为投票节点或候选节点。最后，不同类型的节点之间具有一定的数量关系，能够在不同类型节点的数目或网络中的节点总数发生变化时动态调整参数，从而使得算法适应动态网络。通过性能仿真分析可知，VPBFT算法相较于PBFT、DDBFT、CBFT等共识算法，具有低能耗、低时延、高容错性和高动态性。