Effectively identifying the influential spreaders in large-scale social networks

With great theoretical and practical significance, the studies of information spreading on social media become one of the most exciting domains in many branches of sciences. How to control the spreading process is of particular interests, where the identification of the most influential nodes in larger-scale social networks is a crucial issue. Degree centrality is one of the simplest method which supposes that the node with more neighbours may be more influential. K-shell decomposition method partitions the networks into several shells based on the assumption that nodes in the same shell have similar influence and nodes in higher-level shells (e.g., central) are probably to infect more nodes. Degree centrality and k-shell decomposition are local methods which are efficient but less relevant. Global methods such as closeness and betweenness centralities are more exact but time-consuming. For effectively identifying the more influential spreaders in large-scale social networks, in this paper we proposed an algorithm framework to solve this dilemma by combining the local and global methods. All the nodes are graded by the local methods and then the periphery of the network is removed according to their central values. At last, the global methods are employed to find out which node is more influential. The experimental results show that our framework can be efficient and even more accurate than the global methods     

Remarks on composite integration methods for ordinary differential equations

Convergence theorems for cyclic methods have been proved in the case of globally Lipschitz-continuous right-hand-sidesg for the differential equationy′=g(t, y). In the present paper it is shown that this strong restriction can be omitted. Moreover, a new definition for consistency of cyclic methods is pressented. The consistency conditions are weaker than the classical ones in the case of real cyclic methods (m>1). This leadsbesides order-of-convergence-arguments — to an additional motivation for the construction of cyclic methods, since more coefficients will be available for fitting other purposes and more methods can be shown to be convergent. Examples are given.     

下采样和插值同一性的实现及其应用

针对常用的图像下采样方法无法满足不同应用需要的问题,提出下采样和插值在实现技术上具有同一性的特点,下采样可以采用插值的大量先进技术。将下采样与插值均看作是对邻域未知像素的预测,建立了统一的像素预测模型。实验结果验证了该同一性的思想,并表明与常用的下采样方法相比,在具有保持特征、保护边缘、维持平滑等特性的基础上,能够使下采样后的图像保持更多的信息,从而为下采样在不同应用中的实现提供了更多可选择的方法。     

The case for formal methods in standards

It is argued that applying formal methods to standards making would result in more accurate, more understandable, and more useful standards. The main benefit seen in using formal methods in a standard's development and expression is improving the standard's quality. In the early stages of standards development, formal methods can result in considerable clarification during the development and expression of the underlying conceptual model for a standard or family of related standards. They can also define precisely the relation among the components of both the standard being developed and other standards. Later in the development process, formal methods can improve a standard's quality during its use by letting it be expressed clearly, unambiguously, and concisely in a way that natural language does not allow. Finally, formal methods can aid standards development at the maintenance stage, for example, by allowing the adequacy of a proposed change to be proved. Guidelines for applying formal methods in the standardization process are provided     

综述人脸识别中的子空间方法

如何描述每个个体人脸的特征,使之区别于其他个体,是人脸识别研究中的关键问题 之一.近年来提出了大量的方法,其中随着主元分析在人脸识别中的成功应用之后,子空间分析 因其具有描述性强、计算代价小、易实现及可分性好的特点,受到了广泛的关注.文中结合近年 来已发表的文献,按照线性和非线性的划分,对子空间分析在人脸识别中的应用作一回顾、比较 和总结,以供其他人参考.     

A family of Numerov-type exponentially fitted methods for the numerical integration of the Schrödinger equation

A family of predictor-corrector exponential Numerov-type methods is developed for the numerical integration of the one-dimensional Schrödinger equation. The Numerov-type methods considered contain free parameters which allow it to be fitted to exponential functions. The new fourth algebraic order methods are very simple and integrate more exponential functions than both the well-known fourth order Numerov-type exponentially fitted methods and the sixth algebraic order Runge-Kutta-type methods. Numerical results also indicate that the new methods are much more accurate than the other exponentially fitted methods mentioned above.     

A new quality assessment criterion for nonlinear dimensionality reduction

A new quality assessment criterion for evaluating the performance of the nonlinear dimensionality reduction (NLDR) methods is proposed in this paper. Differing from the current quality assessment criteria focusing on the local-neighborhood-preserving performance of the NLDR methods, the proposed criterion capitalizes on a new aspect, the global-structure-holding performance, of the NLDR methods. By taking both properties into consideration, the intrinsic capability of the NLDR methods can be more faithfully reflected, and hence more rational measurement for the proper selection of NLDR methods in real-life applications can be offered. The theoretical argument is supported by experiment results implemented on a series of benchmark data sets.     

基于多Agent系统的软件开发方法研究

面向Agent的软件工程近年来获得很多人的关注,其中MAS是一个重要的研究方向.到目前为止,已经出现了20多种MAS开发方法.本文对现有的主要方法进行了分类和总结,并利用三层次模型对常用的三类方法进行了比较,希望对读者有一定的借鉴作用.     

步态表征和步态融合方法新进展

作为可远距离感知的生物特征识别技术之一,步态识别受到越来越多的关注.有效的步态表征方法是步态识别的关键,信息融合是提高步态识别性能的重要手段.从步态表征方法和信息融合方法两方面总结了步态识别技术的最新进展;对步态表征方法做了详细的总结;从多特征融合、多视角融合和多模态生物特征融合3个方面归纳了融合在步态识别方面的发展.在此基础上,分析了步态识别的发展趋势.     

不平衡数据分类数据层面方法：现状及研究进展

在不平衡数据的分类中,标准分类器为优化整体的分类误差会牺牲少数类的分类准确率,而实际应用中通常更重视对少数类的准确识别。数据层面方法因其有独立于分类器、泛化能力较强、实现简单等优势,成为解决不平衡数据分类问题的有效策略。围绕不平衡数据分类的数据层面方法开展综述研究,分析造成不平衡数据分类问题的影响因素,从样本空间优化、特征空间优化两个方向对重采样方法及特征选择方法的相关研究进行梳理和评述,并对两类方法进行横向比较。最后提出了需要重点关注的问题和可能的研究机会,以期为不平衡数据分类算法研究及应用提供借鉴和参考。     

On the disclosure risk of multivariate microaggregation

The aim of data protection methods is to protect a microdata file both minimizing the disclosure risk and preserving the data utility. Microaggregation is one of the most popular such methods among statistical agencies.Record linkage is the standard mechanism used to measure the disclosure risk of a microdata protection method. However, only standard, and quite generic, record linkage methods are usually considered, whereas more specific record linkage techniques can be more appropriate to evaluate the disclosure risk of some protection methods.In this paper we present a new record linkage technique, specific for microaggregation, which obtains more correct links than standard techniques. We have tested the new technique with MDAV microaggregation and two other microaggregation methods, based on projections, that we propose here for the first time. The direct consequence is that these microaggregation methods have a higher disclosure risk than believed up to now.     

Individual differences in attachment are associated with usage and perceived intimacy of different communication media

Effective communication is vital to the health and functioning of romantic relationships. Technology use is becoming more pervasive, making it more important than ever to understand which forms of media enhance communication in close relationships. People differ in which communication methods they prefer, and it is important to understand how people perceive and use various media. Our study uses an attachment theory framework to explore how people perceive the intimacy of different media and their preferred methods of communication with romantic partners. We collected online survey data from partnered individuals regarding their romantic attachment orientation, perceptions of the intimacy of various media (face-to-face, phone call, text message, email), and preferred use of those media for communicating with romantic partners. People with a more avoidant attachment orientation (i.e., who prefer self-reliance over interdependence) were less likely to prefer communication methods that are generally perceived as more close and immediate (e.g., face-to-face); however, our findings suggest that avoidant individuals prefer not to use these methods because they perceive them to be less intimate and less likely to resolve interpersonal conflicts. Our findings suggest that certain forms of communication may be more beneficial for avoidant individuals and their romantic relationships.     

Development and application of a framework for comparing early design methods for young children

When designing with young children, designers usually select user centred design methods based on the children’s required level of engagement and the inspiration expected to be created according to the designer. User centred design methods should be selected for their suitability for children and for the quality of the output of the design method. To understand the suitability of design methods, a framework was developed to describe design methods in terms of required design skills as identified by the Theory of Multiple Intelligences. The proposed framework could provide the basis for a tool to compare design methods and to generate hypotheses about what design method would work optimally with children in a specific school grade. The initial examination of the viability of the framework is a comparison of design methods by the number of skills involved; earlier work showed that the involvement of more skills (as with, e.g. low-fi prototyping) could result in more options for a design problem than the involvement of fewer skills (as with e.g. brainstorming). Options and Criteria were counted to understand the quality of the method in terms of the amount of design-information. The results of the current paper indicate that 8-to-10-year-old children generate significantly more options in prototyping sessions than when they are involved in sessions applying a Nominal Group Technique. The paper indicates that (a) with the framework we can generate hypotheses to compare design methods with children and (b) that the outcome of various design methods, which might lead to very different representations, can be compared in terms of Options and Criteria. Further usage of the framework is expected to result in empirical support for selecting a design method to be applied with young children.     

The Strangest Thing About Software

Although there are times when random search is dangerous and should be avoided, software analysis should start with random methods because they are so cheap, moving to the more complex methods only when random methods fail     

CR数字胸片图像的几种肋骨分割方法

在医学领域里，计算机X线摄影(computed radiography，CR)影像系统已经进入全新的发展阶段。图像分割在医学图像处理中占有很重要的位置，由于医学图像的一些特殊性，不同的分割方法会产生不同的效果。以CR数字胸片图像为研究对象，给出了概率松弛迭代法、K-均值聚类法和高斯曲面阈值法在胸片肋骨分割中的应用，并且对其结果给予了一定的评价。实验结果表明，几种分割方法中高斯曲面阈值法更为有效，它方便后继处理，可以得到比较完整的肋骨信息，为后期的计算机辅助诊断提供更为可靠的实验数据。     

大学计算机网络课程教学研究

随着计算机网络的广泛普及，网络技术人才的需求量在不断增加，高校中的计算机网络课程的教学越来越受到重视。计算机网络本身是一门实践性非常强的课程，因而在讲授计算机网络基础理论的同时要重视开展网络实验教学的作用。本文对计算机网络教学过程中的教学方法进行了初步探索，从教学内容的安排、教学方法和手段的采用、网络实验教学的开展以及课程的期终考核几个角度来探讨如何有效的开展计算机网络课程的教学。     

Stochastic projective methods for simulating stiff chemical reacting systems

In this paper, stochastic projective methods are proposed to improve the stability and efficiency in simulating stiff chemical reacting systems. The efficiency of existing explicit tau-leaping methods can often severely be limited by the stiffness in the system, forcing the use of small time steps to maintain stability. The methods presented in this paper, namely stochastic projective (SP) and telescopic stochastic projective (TSP) method, can be considered as more general stochastic versions of the recently developed stable projective numerical integration methods for deterministic ordinary differential equations. SP and TSP method are developed by fully re-interpreting and extending the key projective integration steps in the deterministic regime under a stochastic context. These new stochastic methods not only automatically reduce to the original deterministic stable methods when applied to simulating ordinary differential equations, but also carry the enhanced stability property over to the stochastic regime. In some sense, the proposed methods are stochastic generalizations to their deterministic counterparts. As such, SP and TSP method can adopt a much larger effective time step than is allowed for explicit tau-leaping, leading to noticeable runtime speedup. The explicit nature of the proposed stochastic simulation methods relaxes the need for solving any coupled nonlinear systems of equations at each leaping step, making them more efficient than the implicit tau-leaping method with similar stability characteristics. The efficiency benefits of SP and TSP method over the implicit tau-leaping is expected to grow even more significantly for large complex stiff chemical systems involving hundreds of active species and beyond.     

Decision-making in management information systems research: the utility of policy capturing methodology

Although decision-making represents a fundamental issue in management information systems (MIS), obtaining accurate assessments of the factors affecting employees' decisions may be difficult using traditional methods such as ratings and rankings. Policy capturing, a little-used method in MIS, represents a potentially important alternative to more traditional methods. After demonstrating that policy capturing has been underutilized in MIS, the paper illustrates the use of policy capturing in two decision-making contexts—computer training and software selection. These two studies contrast policy capturing results with more traditional methods, and draw implications for research.     

高分辨率卫星影像车辆检测研究进展

高分辨率卫星遥感技术具有在更小的空间尺度上探测地表目标的能力,利用其影像数据进行车辆检测已成为新的研究热点。在概述遥感影像车辆检测研究现状的基础上,对车辆目标影像特征及车辆检测过程进行了探讨;将车辆检测方法分为利用光谱/几何结构特征的基本检测方法和综合运用多种特征的智能化检测方法,并详细叙述了多种车辆检测方法的原理与适用性以及车辆提取中的关键技术。通过分析发现：结合多特征的机器学习和面向对象的车辆检测方法更适合较复杂环境下的车辆检测。     

On combining classifier mass functions for text categorization

Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the support vector machine, kNN (nearest neighbors), kNN model-based approach (kNNM), and Rocchio methods, but the analysis and methods apply to any methods. We review these learning methods briefly, and then we describe our method for combining the classifiers. In a previous study, we suggested that the combination could be done using evidential operations and that using only two focal points in the mass functions gives good results. However, there are conditions under which we should choose to use more focal points. We assess some aspects of this choice from an reasoning perspective and suggest a refinement of the approach.