Software project effort estimation with voting rules

Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is again placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present this method and extensions using weightings derived from genetic algorithms. We detail a validation based on several well-known data sets and show that estimation accuracy similar to classic methods can be achieved with considerably lower demands on input data.     

The learnability of voting rules

Scoring rules and voting trees are two broad and concisely-representable classes of voting rules; scoring rules award points to alternatives according to their position in the preferences of the voters, while voting trees are iterative procedures that select an alternative based on pairwise comparisons. In this paper, we investigate the PAC-learnability of these classes of rules. We demonstrate that the class of scoring rules, as functions from preferences into alternatives, is efficiently learnable in the PAC model. With respect to voting trees, while in general a learning algorithm would require an exponential number of samples, we show that if the number of leaves is polynomial in the size of the set of alternatives, then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.     

Practical Internet voting system

Recently, Internet voting systems have gained popularity and have been used for government elections and referendums in the United Kingdom, Estonia and Switzerland as well as municipal elections in Canada and party primary elections in the United States and France. Current Internet voting systems assume either the voter's personal computer is trusted or the voter is not physically coerced. In this paper, we present an Internet voting system, in which the voter's choice remains secret even if the voter's personal computer is infected by malware or the voter is physically controlled by the adversary. In order to analyze security of our system, we give a formal definition of coercion-resistance, and provide security proof that our system is coercion-resistant. In particular, our system can achieve absolute verifiability even if all election authorities are corrupt. Based on homomorphic encryption, the overhead for tallying in our system is linear in the number of voters. Thus, our system is practical for elections at a large scale, such as general elections.     

Decision criteria with partial information

In this paper we use an extreme point approach to analyze some usual decision criteria for multiple attribute decision-making problems when partial information about the importance of the attributes is available. The obtained results show that the decision criteria to be chosen depend not only on the rationality principles, but also on the structure of the information set. We apply the obtained criteria to problems where the set of actions to be evaluated are either in qualitative and/or quantitative scales.     

基于Harn部分盲签名的安全电子投票方案

本文设计了一种新的Harn部分盲签名方案,并将此方案用于电子投票的设计中,形成了一个安全可靠的电子投票方案。这种电子投票方案不仅保证了投票者身份的匿名性和选票的安全、公平、保密性,还能实现投票的不可抵赖性。此方案是传统的盲签名电子投票方案的一个改进,是一个很实用的方案。     

The partial sequenced route query with traveling rules in road networks

In modern geographic information systems, route search represents an important class of queries. In route search related applications, users may want to define a number of traveling rules (traveling preferences) when they plan their trips. However, these traveling rules are not considered in most existing techniques. In this paper, we propose a novel spatial query type, the multi-rule partial sequenced route (MRPSR) query, which enables efficient trip planning with user defined traveling rules. The MRPSR query provides a unified framework that subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. The difficulty in answering MRPSR queries lies in how to integrate multiple choices of points-of-interest (POI) with traveling rules when searching for satisfying routes. We prove that MRPSR query is NP-hard and then provide three algorithms by mapping traveling rules to an activity on vertex network. Afterwards, we extend all the proposed algorithms to road networks. By utilizing both real and synthetic POI datasets, we investigate the performance of our algorithms. The results of extensive simulations show that our algorithms are able to answer MRPSR queries effectively and efficiently with underlying road networks. Compared to the Light Optimal Route Discoverer (LORD) based brute-force solution, the response time of our algorithms is significantly reduced while the distances of the computed routes are only slightly longer than the shortest route.     

Bayesian analysis of Birnbaum-Saunders distribution with partial information

In Bayesian analysis with objective priors, it should be justified that the posterior distribution is proper. In this paper, we show that the reference prior (or independent Jeffreys prior) of a two-parameter Birnbaum-Saunders distribution will result in an improper posterior distribution. However, the posterior distributions are proper based on the reference priors with partial information (RPPI). Based on censored samples, slice sampling is utilized to obtain the Bayesian estimators based on RPPI. Monte Carlo simulations are used to compare the efficiencies of different RPPIs, to assess the sensitivity of the choice of the priors, and to compare the Bayesian estimators with the maximum likelihood estimators, for various scales of sample size and degree of censoring. A real data set is analyzed for illustrative purpose.     

Slotted Aloha as a game with partial information

This paper studies distributed choice of retransmission probabilities in slotted ALOHA. Both the cooperative team problem as well as the noncooperative game problem are considered. Unlike some previous work, we assume that mobiles do not know the number of backlogged packets at other nodes. A Markov chain analysis is used to obtain optimal and equilibrium retransmission probabilities and throughput. We then investigate the impact of adding retransmission costs (which may represent the disutility for power consumption) on the equilibrium and show how this pricing can be used to make the equilibrium throughput coincide with the optimal team throughput.     

复杂网络环境下群体投票规则的评价模型-----基于民主的视角

基于复杂网络投票框架,构建以全局最优候选人的评价函数作为Condorcet规则与多数赞成规则决策结果的评价标准.通过引入投票者观点支持域,以及全局最优候选人的有效性判定条件,构造“妥协度-支持度”置换度指标.设置支持度与妥协度的不同目标优先级,采用一种基于Delaunay三角剖分的全局最优化算法,求出全局最优候选人搜索解,并建立评价函数衡量投票规则的优劣.数值仿真表明,在大多数偏好网络下,所提出的评价标准能够更准确地衡量Condorcet规则与多数赞成规则所选获胜者偏离投票群体真实意愿的程度.     

基于偏序粒的动态决策规则挖掘

针对决策表，引入了偏序粒的概念，提出了一种基于偏序粒的动态决策规则提取算法。该算法利用了偏序粒在粒度表达上的优势，区别于经典粗糙集理论中采用的单一等价关系，对同一决策表从不同的角度和多个层次来研究。实例表明该算法是有效的。     

A distributed feature selection scheme with partial information sharing

Machine Learning - This paper introduces a novel feature selection and classification method, based on vertical data partitioning and a distributed searching architecture. The features are divided...     

On stochastic incentive control problems with partial dynamic information

In this paper we consider a stochastic incentive decision problem with N > 1 followers and decentralized static information, where the leader's dynamic information comprises only a linear combination of the followers' actions. We obtain an incentive policy, affine in this dynamic information, which yields the same overall performance as the one the leader would obtain if he had observed the followers' actions separately. The existence conditions involved have been obtained explicitly for the case of finite probability spaces, and some challenging issues have been identified when the random variables are infinite valued. The results presented here have no counterparts in deterministic incentive problems.     

Protection and administration of information networks with partial orderings

In this paper, we combine and generalize integrity and security lattices to a simpler flow model, demonstrate some common pitfalls in administration under this model, and show that the most general structure required for representing information flow in a general purpose transitive information network is a partial ordering. We show means for calculating information and time effects under this model, and show the design of a provably correct automate administrative assistant for protection administration in networks under this model.     

Partitioning rules for orchestrating mobile information systems

New mobile technologies such as Bluetooth or Wi-Fi suffer from many limitations and problems, especially when they are used in combination, whereas they are quite stable in small networks. The lack of specialised mobile middleware requires new methods in the design and execution of mobile information systems. We propose a two-phase approach to manage a mobile business process by partitioning a given workflow into several workflows, with each one governed by a controller. In the first phase, we introduce synchronisation tasks between different controllers. In the second phase, we create for each controller a local process view. Thanks to added tasks, the overall execution of all local workflows achieve the same result as the original one. The mobile scenario and the necessity for more automation lead us to choose the Business Process Execution Language for Web Services (BPEL4WS) as the language for the process definition.     

Learning object identification rules for information integration

When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects’ shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone. In our approach. Active Atlas learns to tailor mapping rules, through limited user input, to a specific application domain. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.     

部分权重信息下对方案有偏好的多属性决策法

研究只有部分权重信息且对方案有偏好的多属性决策问题．首先对方案的偏好信息以互反判断矩阵和互补判断矩阵这两种形式给出的情形，分别建立一个目标规划模型，通过求解这两个模型可确定属性的权重；然后提出一种基于目标规划模型的多属性决策方法；最后通过实例说明了该方法的可行性和有效性。     

Optimal decentralized control in a multiaccess channel with partial information

We consider two transmission stations sharing a single communication channel. For different values of the input message ratesr_{i}, i = 1,2, a simple open-loop control policy is shown to be optimal for the long-run average throughput criterion.     

Asynchronous consensus of multiple second-order agents with partial state information

This article studies the asynchronous consensus problem of multiple second-order agents in a sampled-data setting, where asynchrony means that the sampling period of each agent is independent of the others. It is assumed that each agent can only obtain the information of its positions relative to its neighbours at sampling instants. First, a discrete-time protocol is provided based on velocity estimation, and a sufficient and necessary condition for consensus under this protocol is established in virtue of properties of periodic systems. Second, a continuous-time protocol is presented by the theory of dynamic output feedback control, and a sufficient condition for consensus under this protocol is obtained by applying an input delay approach. Simulations are performed to illustrate the effectiveness of the theoretical results.     

Robustness of classification rules that incorporate additional information

The discrimination problem for two normal populations with the same covariance matrix when additional information on the population is available is considered. A study of the robustness properties against training sample contamination of classification rules that incorporate this additional information is performed. These rules have received recently attention where their total misclassification probability (TMP) is proved to be lower than Fisher's linear discriminant rule. The results of a simulation study on the TMP which compares the behaviour of the new rules against Fisher's rule and some of its robustified versions under different types of contamination are presented. These results show that the rules that incorporate the additional information not only have lower TMP, but they also prevent against some types of contamination. In order to achieve prevention from all types of contamination a robustifed version of these rules is recommended.