改进的Michael Merrit投票协议

互联网的快速发展使得网上投票成为可能。Michael Merrit协议是一种无须中央制表机构的投票系统，它有两个缺陷：选票能被复制，某个人将比其他人更早得知选票结果。该文提出了对MichaelMerrit协议的改进，克服了这两个缺陷。改进后的协议将适用于小型的无须中央制表机构的投票系统。     

张量投票在MR脑图像边界提取中的应用

当前社会由于各种原因．致使脑部疾病高发．而精确勾画脑部各区域轮廓是手术和治疗成功的重要前提。提出一种基于张量投票的脑区域边界提取算法,利用医学图像中元素的邻近性、连续性等特点,对临床MR（核磁共振）图像进行处理,实现符合人类感知的边界轮廓勾画。实验表明。该算法具有良好的实际效果。     

DAMN: a distributed architecture for mobile navigation

An architecture is presented in which distributed task-achieving modules, or behaviours, cooperatively determine a mobile robot's path by voting for each of various possible actions. An arbiter then performs command fusion and selects that action which best satisfies the prioritized goals of the system, as expressed by these votes, without the need to average commands. Command fusion allows multiple goals and constraints to be considered simultaneously. Examples of implemented systems are given, and future research directions in command fusion are discussed.     

Containing smartphone worm propagation with an influence maximization algorithm

In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g., the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.     

有冗余通道的测发控系统可靠性评估研究

本文对在弹道式飞行器准备与发射过程中使用的各种多通道测试与发射控制系统运行可靠性进行了比较评估,给出了选择测试与发射控制系统合理结构和确定硬件与软件冗余度极限的建议,所得到的结果能够在复杂信息控制系统设计阶段使用。     

Privacy and verifiability in voting systems: Methods,developments and trends

One of the most challenging aspects in computer-supported voting is to combine the apparently conflicting requirements of privacy and verifiability. On the one hand, privacy requires that a vote cannot be traced back from the result to a voter, while on the other hand, verifiability states that a voter can trace the effect of her vote on the result. This can be addressed using various privacy-enabling cryptographic primitives which also offer verifiability.As more and more refined voting systems were proposed, understanding of first privacy and later verifiability in voting increased, and notions of privacy as well as notions of verifiability in voting became increasingly more refined. This has culminated in a variety of verifiable systems that use cryptographic primitives to ensure specific kinds of privacy. However, the corresponding privacy and verifiability claims are not often verified independently. When they are investigated, claims have been invalidated sufficiently often to warrant a cautious approach to them.The multitude of notions, primitives and proposed solutions that claim to achieve both privacy and verifiability form an interesting but complex landscape. The purpose of this paper is to survey this landscape by providing an overview of the methods, developments and current trends regarding privacy and verifiability in voting systems.     

Analysis of a Plurality Voting-based Combination of Classifiers

In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition result. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), m (number of classes), and p (probability that a single classifier is correct). Experimental results on the human face recognition problem show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates.     

多赢家投票理论的研究进展

随着智能时代的到来,集体决策的方式也在发生着改变,人们不再满足于单一的决策结果,需要多个赢家共同组成的委员会成为获胜集合,并将此集合应用于推荐系统、搜索引擎、政策表决以及企业决策等领域.多赢家投票理论最大的优点是决策成本低并且决策效率高,是非常优秀的集体决策方法.多赢家投票理论的研究核心在于找到适合不同应用场景的多赢家...     

一种字符组合词包模型的少约束车牌定位算法

针对如何在复杂背景、拍摄角度和车牌尺度发生变化等少约束条件下实现基于字符组合词包模型的车牌定位算法.本文首先构造包含车牌数字字符和英文字符的数据库;然后利用本算法识别提取车牌字符的SIFT特征,并精准计算识别特征点在字符识别区域的相对位置、物理方向等信息组成视觉识别词汇;最后把本车牌字符的视觉词汇聚合后搭建车牌字符视觉...     

Incremental construction of classifier and discriminant ensembles

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ensembles, we test subset selection and trees. Fusion is by voting or by a linear model. Using 14 classifiers on 38 data sets, incremental search finds small, accurate ensembles in polynomial time. The discriminant ensemble uses a subset of discriminants and is simpler, interpretable, and accurate. We see that an incremental ensemble has higher accuracy than bagging and random subspace method; and it has a comparable accuracy to AdaBoost, but fewer classifiers.