Web病毒式营销核心群体挖掘与推荐策略

Web病毒式营销已经成为电子商务领域中的重要营销策略, 核心群体在其中发挥着重要的作用。为了挖掘核心群体并对其进行商品推荐, 在Web客户信任网络(customer trust network, CTN)的基础上考虑了信任度、评价分数以及推荐次数等因素定义了影响度的概念, 提出了以影响度为基础的节点网络影响集的构建方法以及基于网络影响集的核心群体挖掘算法MCGNIS（mining core group based on network-influence set）, 并以挖掘出的核心群体为对象建立了基于网络影响集的推荐模型RCGNIS(recommending model for core group based on network-influence set）, 设计了相应的推荐算法来计算商品对核心群体的可推荐度。实验证明, 以节点网络影响集为基础挖掘出的核心群体在Web客户信任网络中具有较高的网络覆盖率（network-coverage, NC）, 推荐模型RCGNIS具有很好的推荐准确性, 同时又保持了推荐的多样性。     

基于运行时模型的混合云管理方法

随着云计算技术的普及,涌现出众多不同用途、不同类型的云计算平台.为了满足遗产系统整合和动态资源扩展等需求,常常需要构造混合云来统一管理不同云平台中的计算和存储资源.然而,不同云平台的管理接口和管理机制存在差异,使得开发混合云管理系统难度大、复杂度高.本文提出一种基于运行时模型的混合云管理方法：首先,在云平台管理接口基础上,构造单一云平台的运行时模型;其次,根据云平台领域知识,提出一种云平台软件体系结构的统一模型;最后,通过模型转换,实现云平台统一模型到运行时模型的映射.于是,管理程序可以建立在云平台统一模型的基础上,降低了混合云管理系统开发的难度和复杂度.本文还实现了基于运行时模型的CloudStack和亚马逊EC2混合云管理系统,并对方法的可行性和有效性进行了验证.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

Induced continuous Choquet integral operators and their application to group decision making

With respect to multi-attribute group decision making, in this study two induced continuous Choquet integral operators named as the induced continuous Choquet weighted averaging (ICCWA) operator and the induced continuous Choquet geometric mean (ICCGM) operator are defined, which reflect the interactive characteristics between elements. Meantime, some associated desirable properties are studied to provide assurance in applications. In order to globally reflect the interactions between elements, we further define the probabilistic generalized semivalue ICCWA (PGS-ICCWA) operator and the probabilistic generalized semivalue ICCGM (PGS-ICCGM) operator. If the information about the weights of experts and attributes is incompletely known, the models for the optimal fuzzy measures on experts set and on attribute set based on consistency principle and TOPSIS method are respectively established. Moreover, an approach to uncertain multi-attribute group decision making with incomplete weight information and interactive conditions is developed. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed procedure.     

Simultaneous design and control of processes under uncertainty: A robust modelling approach

This paper presents a new methodology to integrate process design and control. The key idea in this method is to represent the system’s closed-loop nonlinear behaviour as a linear state space model complemented with uncertain model parameters. Then, robust control tools are applied to calculate bounds on the process stability, the process feasibility and the worst-case scenario. The new methodology was applied to the simultaneous design and control of a mixing tank process. The resulting design avoids the solution of computationally intensive dynamic optimizations since the integration of design and control problem is reduced to a nonlinear constrained optimization problem.     

Where to go from here? Mobility prediction from instantaneous information

We present the work that allowed us to win the Next-Place Prediction task of the Nokia Mobile Data Challenge. Using data collected from the smartphones of 80 users, we explore the characteristics of their mobility traces. We then develop three families of predictors, including tailored models and generic algorithms, to predict, based on instantaneous information only, the next place a user will visit. These predictors are enhanced with aging techniques that allow them to adapt quickly to the users’ changes of habit. Finally, we devise various strategies to blend predictors together and take advantage of their diversity, leading to relative improvements of up to 4%.     

圆柱度误差评定中规范不确定度计算方法的研究

针对目前误差评定结果往往只提供测量不确定度的情况,根据新一代产品几何规范(GPS)不确定度体系,研究了圆柱度误差评定时规范不确定度的计算方法。基于最小区域法提出了圆柱度误差评定的数学模型,用改进粒子群算法得到圆柱度误差值,并通过研究影响规范不确定度每一个元素的传播系数及相关系数推导出了规范不确定度的详细计算公式,且以此为基础开发了圆柱度误差评定的图形界面。经实例验证,该方法可以在新一代GPS体系下准确、直观、方便地评定圆柱度误差。     

Learning against uncertainty in control engineering

In this paper, some data-based control design options that can be used to accommodate for the presence of uncertainties in continuous-state engineering systems are recalled and discussed. Focus is made on reinforcement learning, stochastic model predictive control and certification via randomized optimization. Some thoughts are also shared regarding the positioning of the control community in a data and AI-dominated period for which some suggestions and risks are highlighted.     

Dealing with uncertainty in ecosystem model scenarios: Application of the single-model ensemble approach

Complex ecosystem models are often used as a tool for resource managers in the application of ecosystem based management. The uncertainty associated with these models is a major stumbling block in their acceptance as a management tool. Yet, conducting a rigorous uncertainty analysis of complex models is often not feasible. We present an alternative approach to assessing the impact of parameter uncertainty on the outcome of management scenarios on a lake ecosystem. We applied the single-model ensemble approach to the ecosystem model DYRESM–CAEDYM and Lake Kinneret, Israel. We introduced uncertainty to parameters and conducted an ensemble of simulations for three scenarios. Despite the large degree of uncertainty in parameter values the trends in ecosystem response were consistent with those observed based on calibrated parameter values. The variation in results allowed us to estimate the consequences of parameter uncertainty on lake resource management without the need for a comprehensive uncertainty analysis.     

Multi-criteria group decision making under uncertainty with application to air traffic safety

There are many methods for solving problems of multi-criteria group decision making under uncertainty conditions. It is quite often that decision makers cannot formulate unequivocally their individual preference relations between variants. Analysing the causes of a serious aircraft incident is an example where a group of experts is required to have a very detailed yet interdisciplinary knowledge. Obviously, each expert has only a fraction of such knowledge. Hence, experts can make fuzzy evaluations when they are not sure about them or it is not possible to gain full knowledge. There is a need for a method that in such a case takes into account the strength of preference expressed in the significance of each criterion. Both the significance of criteria and the scores assigned to variants can be represented using fuzzy expressions.The proposed method reflects the problems of decision making when both objective (represented using non-fuzzy expressions) and subjective (represented using linguistic expressions) criteria, are involved. The proposed method enables to obtain a solution without having to conduct negotiations between decision makers. This is of advantage when there is a risk that some experts will be dominated by others. The method not only helps define a single preferred solution but also create the preference relation within a group. By applying this method, it is possible to reproduce the actual preference relations of individual decision makers. Presenting them to decision makers may induce them to change their evaluation of the weights of criteria or how they score variants.     

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decision-maker's attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.     

Managing privacy boundaries together: Exploring individual and group privacy management strategies in Facebook

Most research on privacy management within the context of social network sites (SNSs) treats users as individual owners of private information. Privacy, however, is beyond individual control and is also managed on a group level. This study applies the Communication Privacy Management theory (CPM) to explore the individual and group privacy management strategies in Facebook. We present a survey completed by 900 members of a youth organization regarding their online behaviors and membership. We found that women are more likely to employ individual privacy management strategies, while men are more likely to employ group privacy management strategies. For group privacy management, we found common bond and the role an individual is attributed within the youth organization to be the strongest predictors. The results generated from this study are a first but important step to illustrate the differences and similarities between individual and group privacy management. We argue that it is necessary to further study and understand group privacy to better approach users’ privacy needs.     

Managing transitivity and consistency of preferences in AHP group decision making based on minimum modifications

Preference transitivity characterized by ordinal consistency is a fundamental principle for decision making models based on pairwise comparison matrices (PCMs). However, little previous research has addressed ordinal consistency in an optimal way. Further, because ordinal consistency is not considered in the consensus reaching process, non-transitive preferences may still exist in the revised PCMs. In this paper, optimization models are proposed to obtain transitive preferences for solving individual consistency and group consensus problems. First, the conditions satisfying the ordinal consistency of PCMs are analysed and a system of constraints is derived to allow for the ordinal consistency to be explicitly controlled in the optimization model. A mixed integer linear optimization model is then proposed to assist decision makers satisfy both the ordinal and cardinal consistencies. A second mixed integer linear optimization model is then designed to ensure that the consensus level in group decision making problems can be achieved when both the group as a whole and all individuals have acceptably cardinal and ordinal consistencies. Optimization models considering ordinal consistency and classical cardinal consistency indices are open problems needing to be managed in future. Compared with existing methods, the proposed models provide an optimal way to minimize modifications in deriving transitive preferences. Finally, the feasibility and validity of the models are verified through comparisons with classic models.     

On uncertainty problems in decision-making

The paper continues studying the uncertainty problem based on [2]. A decision-maker is assumed to have a prevalence relation on the consequences of his actions. The necessary and sufficient existence conditions for uncertainty in a decision-making problem for a nonparametric situation are given. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 116–119, March–April 2008.     

Model reduction in the presence of uncertainty in model parameters

This short note describes how to extend a certain class of existing model reduction techniques to take into account uncertainty in model parameters. The key idea of this extension is that the reduced-order model should not only contain the model parameters, but that the reduction procedure itself has to be geared for dealing with parametric uncertainty. This goal is achieved by augmenting the vector of inputs to the system with the uncertain parameters and by performing model reduction on the augmented system. It is shown that error bounds for the reduced-order model can be computed if the underlying system is linear with respect to the states, parameters, and inputs. A comparison between the presented technique and a conventional approach is made via two examples.     

热电偶测温系统的不确定度评定与分析

按照ISO标准"Guide to the Expression of Uncertainty in Measurement"和国家计量技术规范JJF1059-1999"测量不确定度评定与表示"的规定,推导了热电偶测温系统不确定度评定的一般方法.以一个典型热电偶测温系统为实例,给出不确定度计算的具体步骤,分析了各主要不确定度分量的影响,并提出了减少不确定度的相关措施,为优化热电偶测温系统的测量提供了指导.     

Integrating model management with data management in decision support systems

Because of the increasing complexity of the problems faced by decision makers and the nature of decision support systems, decision models are becoming more and more crucial to their success. In this paper, a general framework for model management, which can integrate model management and data management and handle issues in model management such as model creation, model modification and model use is proposed.