Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

虚拟计算环境中的覆盖网技术

互联网资源的成长、自治和多样等特性给资源的有效聚合带来了巨大挑战.通过覆盖网动态组织互联网资源并支持资源的高效搜索,是虚拟计算环境中资源按需聚合的重要途径之一.文中概述了虚拟计算环境中覆盖网技术的研究进展.针对互联网资源的成长性和自治性等特点,阐述了基于Kautz图的高效覆盖网拓扑构造方法,进而给出了适用于任意正则图的通用覆盖网拓扑构造方法;针对互联网资源的多样性等特点,提出了支持分组的覆盖网拓扑构造方法;在此基础上,阐述了基于覆盖网的高效区间搜索技术,并对覆盖网拓扑的优化方法及其它复杂搜索技术进行探讨.     

Motif patterns in 2D

Motif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of such motifs. The remainder of the paper presents approaches to the discovery of irredundant motifs both by offline and incremental algorithms.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

一种交换式以太网拓扑结构的发现算法

首先介绍了目前局域网拓扑发现的主要方法及存在的问题,然后提出一个基于生成树协议 地址转发表的交换式以太网物理拓扑结构的自动发现算法.算法首先利用生成树协议建立起交换机之间的连接关系,在此基础上进一步利用地址转发表建立起交换机与主机的连接关系.该算法 1)能给发现被生成树协议阻塞的连接;2)通过处理连接冲突,能够准确无误的发现网络物理拓扑结构;3)算法开销小,时间复杂度低.文中算法已成功地应用于社区宽带综合业务网络管理系统(CBISNMS)中.     

面向业务应用的服务发现研究

现有的服务发现研究大多基于功能,以服务为粒度考察服务.而服务是以操作为粒度与用户进行交互的.服务发现与使用粒度上的差异,使服务不能发挥最大效能.为此提出了一种从业务层面,以操作为粒度,考察服务对用户需求业务适用程度的服务发现方法.通过定义面向业务应用的服务与用户需求描述模型,建立领域业务应用本体为服务提供者和使用者的业务级交互奠定基础.通过服务与用户业务需求的深层匹配,在发现可用服务及其操作的同时,对服务的业务规范性和业务完备性进行评估.最终给出了一种面向业务应用的服务发现方法.     

基于扩展VSM的Web服务发现

在扩展空间向量模型(VSM)的基础上设计并实现了一种Web服务发现机制。该机制结合Web服务语法和结构信息,利用相似度计算实现Web服务的发现。系统通过分析Web服务描述文档结构特点,改进现有的VSM模型,并加入WordNet扩展同义词相似概念,使得语义上等价的词被映射到相似的特征向量上,消除存在歧义的上下文,定义Web服务语法相似度函数,实现Web服务潜在语义信息的进一步挖掘。实验评测和分析表明,基于扩展VSM的方式使Web服务发现的查准率提高了9.7%,错误率降低了8.5%。     

基于Agent与分布式缓存的P2P资源发现策略

提出一种基于multi-agent与分布式缓存技术的资源发现模型,静态agent可维护本地资源信息,移动agents可通过在网络中的迁移来完成资源的发现,分布式缓存可以提供一种高效的搜索方式。在P2P网络中,使用该模型可快速找到资源,减少网络阻塞,改善扩展性与健壮性。     

基于Fuzzing的蓝牙OBEX漏洞挖掘技术

Fuzzing是一种自动化的漏洞挖掘技术。该文在分析OBEX 协议的基础上,利用Fuzzing技术,设计并实现了蓝牙OBEX协议的Fuzzer工具——OBEX-Fuzzer,并且利用该工具对OBEX协议在Nokia N73和SMH-BT555蓝牙适配器上的实现进行了漏洞测试,发现存在多个安全漏洞,实践结果表明了研究思路的正确性以及利用OBEX-Fuzzer工具进行安全漏洞测试的高效性。     

基于WSRF的药物网格设计与实现

分析了原有的药物网格系统在可扩展性和互操作性方面存在的问题,借助于Web服务和网格的主流标准技术,设计了基于WSRF(Web Services Resource Framework)的药物网格分层结构.讨论了基于该结构Web服务的实现,最后通过实验对新的药物网格平台进行了测试和分析.