基于语义的QoS感知Web服务发现机制

随着提供相同功能的Web服务数量的日益增多,服务质量(Quality of Service,QoS)成为用户选择Web服务的重要考虑因素.目前,通过对服务QoS属性在语法层匹配来提供Web服务选取的机制不能很好地满足复杂QoS属性匹配的要求.研究了基于用户QoS需求偏好,将用户需求的QoS与候选服务的QoS进行语义比较,结合约束规划(Constraint Programming)方法,在语义层匹配Web服务的QoS属性,选取满足匹配要求的服务,最后对满足QoS属性值约束的候选服务进行优化选择处理,获取最终匹配的候选服务.     

分布式系统中的资源发现机制综述

资源发现问题亦即由给定的资源描述找到满足该描述的资源地址。如何快速并且准确地从分布存储的资源中找到所需的资源,是一个挑战性的问题。传统的网格资源发现系统采用注册中心和索引的方式,但这些方式不能满足网格系统规模不断扩大的需求。而对等网络是一种扩展性好的分布式系统,采用已有的P2P技术解决网格环境下的资源发现问题是一种有效的途径。介绍了网格系统中的资源发现、P2P系统的资源发现以及基于P2P的网格资源发现系统,并对它们进行了比较。     

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.     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

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.