Online scheduling with rejection and withdrawal

We study an online scheduling problem with rejection, in which some rearrangement of the solution is allowed. This problem is called scheduling with rejection and withdrawal. Each arriving job has a processing time and a rejection cost associated with it, and it needs to be either assigned to a machine or rejected upon arrival. At termination, it is possible to choose at most a fixed number of scheduled jobs and withdraw them (i.e., decide to reject them). We study the minimization version, where the goal is to minimize the sum of the makespan and the total rejection cost (which corresponds to the penalty), and the maximization problem, where the goal is to maximize the sum of the minimum load and the total rejection cost (which corresponds to profit). We study environments of machines, which are the case of m identical machines and the case of two uniformly related machines, and show a strong relation between these problems and the related classic online scheduling problems which they generalize, in contrast to standard scheduling with rejection, which typically makes the scheduling problems harder.     

基于在线社会网络的网络协议增强机制研究

在线社会网络已得到广泛应用,综合利用在线社会网络的社会关系信息和拓扑特征可以明显增强各层网络协议功能、性能和安全特性而使其成为新的研究热点。主要研究基于在线社会网络的网络协议增强机制,在分析现有增强机制特点的基础上,对它们进行了分类。然后,深入分析了各类增强机制的关键技术,总结相关研究存在的不足,最后提出一种集成社会感知平面的网络协议增强模型。     

消费者在线评论有用性影响因素模型研究

消费者在线评论的价值已经得到消费者和在线零售商的公认,对评论有用性的研究已经成为新的研究热点。从减少消费者决策风险出发,在感知诊断性概念基础上定义了评论有用性概念,构建了一个评论有用性影响因素模型。从传播说服理论的维度考察,评论等级、评论长度、好评率和使用互联网经验是影响评论有用性的重要因素。商品类型对评论有用性具有调节作用。     

Adaptive soft k-nearest-neighbour classifiers

A novel classifier is introduced to overcome the limitations of the k-NN classification systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data points to store. Experimental results in two hand-written classification problems demonstrate the potential of the proposed classification system.     

Online handwriting recognition: the NPen++ recognizer

This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on a multi-state time delay neural network and yields recognition rates from 96% for a 5,000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without losing too much recognition performance compared to an exhaustive search. This enables the NPen++ recognizer to be run in real-time with large dictionaries. Initial recognition rates for whole sentences are promising and show that the MS-TDNN architecture is suited to recognizing handwritten data ranging from single characters to whole sentences. Received September 3, 2000 / Revised October 9, 2000     

基于ASP.NET的在线考试与试题管理系统实现

随着互联网时代的到来,无纸化办公、教学和考试成为趋势,在线考试被越来越多地采用;B/S结构具有简单,容易实现等优点;基于B/S结构,采用ASP.NET技术,设计和实现了在线考试与试题管理系统。     

A dynamic bibliometric model for identifying online communities

Predictive modelling of online dynamic user-interaction recordings and community identification from such data becomes more and more important with the widespread use of online communication technologies. Despite of the time-dependent nature of the problem, existing approaches of community identification are based on static or fully observed network connections. Here we present a new, dynamic generative model for the inference of communities from a sequence of temporal events produced through online computer- mediated interactions. The distinctive feature of our approach is that it tries to model the process in a more realistic manner, including an account for possible random temporal delays between the intended connections. The inference of these delays from the data then forms an integral part of our state-clustering methodology, so that the most likely communities are found on the basis of the likely intended connections rather than just the observed ones. We derive a maximum likelihood estimation algorithm for the identification of our model, which turns out to be computationally efficient for the analysis of historical data and it scales linearly with the number of non-zero observed (L + 1)-grams, where L is the Markov memory length. In addition, we also derive an incremental version of the algorithm, which could be used for real-time analysis. Results obtained on both synthetic and real-world data sets demonstrate the approach is flexible and able to reveal novel and insightful structural aspects of online interactions. In particular, the analysis of a full day worth synchronous Internet relay chat participation sequence, reveals the formation of an extremely clear community structure.     

Leveraging online communities in fostering adaptive schools

There has long been a call for schools to prepare students for the twenty-first century where skills and dispositions differ significantly from much of what has historically characterized formal education. The knowledge based economy calls for policy and pedagogical efforts that would transform schools. Schools are to foster communities of learners. This paper suggests that para-communities may be points of leverage in the fostering of adaptive schools. A critical analysis is done on the differences between para-communities (such as online communities) and schools; and an argument is made that they each serve differing goals and should be left distinct because they achieve different societal and economic demands.     

改进的选择神经网络结构的方法

有关人工神经网络作为在线状态估计器组成基于神经网络的传感器故障检测的研究已有报导。但是这些文章中没有提到如何选择神经网络的结构。神经网络的输入延迟数和隐基单元数影响其对系统的拟合精度,从而影响故障检测的灵敏度。研究了一些现有的选择神经网络结构的方法,以系统化的交叉证实法为基础,经过改进,提出了针对作为在线状态估计器的神经网络选择输入延迟数和隐层单元数的系统化的方法,并用某船在试验场中航行时平台罗经输出的一段数据作了仿真,结果证明该法可行,具有工程实用意义。     

Using OGRO and CertiVeR to improve OCSP validation for Grids

Authentication and authorization in many distributed systems rely on the use of cryptographic credentials that in most of the cases have a defined lifetime. This feature mandates the use of mechanisms able to determine whether a particular credential can be trusted at a given moment. This process is commonly named validation. Among available validation mechanisms, the Online Certificate Status Protocol (OCSP) stands out due to its ability to carry near real time certificate status information. Despite its importance for security, OCSP faces considerable challenges in the computational Grid (i.e. Proxy Certificate’s validation) that are being studied at the Global Grid Forum’s CA Operations Work Group (CAOPS-WG). As members of this group, we have implemented an OCSP validation infrastructure for the Globus Toolkit 4, composed of the CertiVeR Validation Service and our Open GRid Ocsp (OGRO) client library, which introduced the Grid Validation Policy. This paper summarizes our experiences on that work and the results obtained up to now. Furthermore we introduce the prevalidation concept, a mechanism analogous to the Authorization Push-Model, capable of improving OCSP validation performance in Grids. This paper also reports the results obtained with OGRO’s prevalidation rules for Grid Services as a proof of concept.