The Impact of Serial Solutions’ Summon™ on Information Literacy Instruction: Librarian Perceptions

Web-scale discovery systems, such as Serial Solutions’ Summon? Service, are replacing older federated search technologies as the tool for users to access library resources quickly and easily. The impact of Web-scale discovery systems on instruction is not known. This study of instruction librarian perceptions of Summon's impact on instruction and student information literacy skills is based on a survey. The survey reveals librarians’ ambivalence toward Summon. While some librarians agree that Summon has the potential to change the way librarians teach information literacy skills, it has not been fully integrated into the classroom.     

一种基于服务覆盖网络的业务运行平台

针对分布式业务的动态性和可扩展性需求,设计并实现了业务运行平台BEPSON（Business Execution Platform Based On Service Overlay Network）。该平台基于服务覆盖网络,采用分层架构,提出并实现了具有QoS保证的动态服务发现算法以及分布式的执行策略。最后,通过实例验证了平台的有效性和灵活性。     

MANET手机通信的资源互发现算法及其实现

针对现有的移动Ad hoc网络(MANET)资源互发现算法存在问题进行研究,提出用于MANET手机通信的移动资源互发现算法(MRDA).该算法中每个节点都是等价的,使用组播和单播相结合来发送信息,可以更合理利用带宽,解决了已有MA-NET资源互发现算法采用广播方式带来的信息泛滥,占用带宽高,节点计算能力下降等问题.MRDA算法使用分布式哈希表(DHTs)存储路由表项信息,减小了网络总开销,增强了设备计算能力.同时,针对手机内存、电力受限等特点,对报文的格式进行了设计,简化了报文结构,采用动态广告报文发送间隔,有效减少了内存占用和网络传输代价.最后,建立手机终端节点之间通信的MANET环境,利用MRDA算法实现手机终端即时通信原型.     

网络拓扑自动发现系统设计与实现

摘要：设计基于SNMP和其它协议的网络拓扑发现算法,实现三层交换机和路由器为网关的网络拓扑的快速发现,尽可能地发现完整的网络,且对网络带来的额外负栽和开销尽可能小。     

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.     

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.     

基于本体的文档知识图形化分析技术研究

网络信息的激增和多样化给有用信息的识别和获取带来了很多困难。本文通过对本体、Web挖掘、知识发现、概念提取、图形化等技术的集成,建立了基于本体的文档知识图形化分析模型,并以仪表领域为例,开发了相应的试验分析工具,使用户能够快速准确地识别所需要的资源,并对所拥有资源的有用性进行准确直观地判断。