A taxonomy for resource discovery

Resource discovery systems become more and more important as distributed systems grow and as their pool of resources becomes more variable. As such, an increasing amount of networked systems provide a discovery service. This paper provides a taxonomy for resource discovery systems by defining their design aspects. This allows comparison of the designs of the deployed discovery services and is intended as an aid to system designers when selecting an appropriate mechanism. The surveyed systems are divided into four classes that are separately described. Finally, we identify a hiatus in the design space and point out genuinely distributed resource discovery systems that support dynamic and mobile resources and use attribute-based naming as a main direction for future research in this area.

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Supporting ranked Boolean similarity queries in MARS

To address the emerging needs of applications that require access to and retrieval of multimedia objects, we are developing the Multimedia Analysis and Retrieval System (MARS). In this paper, we concentrate on the retrieval subsystem of MARS and its support for content-based queries over image databases. Content-based retrieval techniques have been extensively studied for textual documents in the area of automatic information retrieval. This paper describes how these techniques can be adapted for ranked retrieval over image databases. Specifically, we discuss the ranking and retrieval algorithms developed in MARS based on the Boolean retrieval model and describe the results of our experiments that demonstrate the effectiveness of the developed model for image retrieval     

Shape similarity measures for open curves

The shape of an open curve is defined which does not depend on its position, size and orientation. Two distance measures between shapes are constructed on the basis of which two shape similarity measures are defined.     

Anytime similarity measures for faster alignment

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.     

Internet resource discovery services

An overview of resource discovery services currently available on the Internet is presented. The authors concentrate on the following discovery tools: the Wide Area Information Servers (WAIS) project, Archie, Prospero, Gopher. The World-Wide Web (WWW), Netfind, the X.500 directory, Indie, the Knowbot Information Service (KIS), Alex, Semantic File Systems, and Nomenclator. The authors summarize the surveyed tools by presenting a taxonomy of their characteristics and design decisions. They also describe where to find and how to access several of the surveyed discovery services. They conclude with a discussion of future directions in the area of resource discovery and retrieval     

VEGA Infrastructure for Resource Discovery in Grids

Grids enable users to share and access large collections and various types of re-sources in wide areas, and how to locate resources in such dynamic, heterogeneous and autonomousdistributed environments is a key and challenging issue. In this paper, a three-level decentralizedand dynamic VEGA Infrastructure for Resource Discovery (VIRD) is proposed. In this architec-ture, every Border Grid Resource Name Server (BGRNS) or Grid Resource Name Server (GRNS)has its own local policies, governing information organization, management and searching. Changesin resource information are propagated dynamically among GRNS servers according to a link-state-like algorithm. A client can query its designated GRNS either recursively or iteratively. Optimizing techniques, such as shortcut, are adopted to make the dynamic framework more flexible and effi-cient. A simulator called SimVIRD is developed to verify the proposed architecture and algorithm.q.Experiment results indicate that this architecture could deliver good scalability and performance for grid resource discovery.     

Vague集相似度量方法

提出一种新的Vague集相似度量方法,并证明了它满足若干准则。指出了现有方法的不足,并通过实例说明了该方法的有效性和优越性。     

Evaluation of similarity measures for video retrieval

Multimedia Tools and Applications - Similarity measures are very crucial especially in the field of information retrieval. Thus, various distance/similarity measures were proposed throughout the...     

Distance and similarity measures for fuzzy operators

We introduce and study a new family of normalized distance measures between binary fuzzy operators, along with its dual family of similarity measures. Both are based on matrix norms and arise from the study of the aggregate plausibility of set-operations. We also suggest a new family of normalized distance measures between fuzzy sets, based on binary operators and matrix norms, and discuss its qualitative and quantitative features. All measures proposed are intended for applications and may be customized according to the needs and intuition of the user.     

基于资源区域的网格资源发现方法

资源发现是整个网格系统的热点研究问题。结合P2P技术提出了一种基于资源区域的网格资源发现的新方法,按照资源的类型将网格资源划分为多个区域,具有相同类型资源的网格信息节点组织在同一区域中,各区域由一个域中心节点进行管理,各域中心节点形成P2P层。把网格中的资源分为域间－域内两类,从而大大提高了查询效率和通信效率,减少了网络流量。     

User utility discovery for priority-based network resource pricing

While equilibrium analysis has been commonly used for network pricing under the assumption that user utility functions are precisely known, many researchers have criticized the validity of the assumption. In this paper, we propose a solution for bridging the gap between the existing theoretical work on optimal pricing and the unavailability of precise user utility information in real networks. In the proposed method, the service provider obtains increasingly more accurate estimates of user utility functions by iteratively changing the prices of service levels and observing the users’ service-level choices under various prices. Our study’s contribution is twofold. First, we have developed a general principle for estimating user utility functions. Especially, we present the utility estimation for dynamic user population. Second, we have developed a method for setting prices that can optimize the extraction of information about user utility functions. The extensive simulation results demonstrate the effectiveness of our method.     

一种基于最小代价的网格资源发现模型

引入域和资源路由节点的概念,设置最小代价函数对网格资源进行分层按域划分,形成一种基于最小代价的分层次网格资源发现模型;并为模型设计了相应的资源路由器节点选择算法、资源注册和查询算法。性能分析和模拟实验结果表明,模型具有良好的可扩展性和容错性,且系统代价低;模型能够在屏蔽网格资源异构性的同时很好地满足其动态性、分布性和扩展性的要求,具有较高的资源搜索性能。     

Vague集相似度量的新方法

根据区间相似性的原理,指出影响Vague集相似性度量的4个因素,指出了现有方法的不足,提出了一种新的Vague集相似性度量方法,证明了它满足若干准则。通过例子证明了方法的有效性和优越性。     

Distance and similarity measures for hesitant fuzzy sets

In this paper, we propose a variety of distance measures for hesitant fuzzy sets, based on which the corresponding similarity measures can be obtained. We investigate the connections of the aforementioned distance measures and further develop a number of hesitant ordered weighted distance measures and hesitant ordered weighted similarity measures. They can alleviate the influence of unduly large (or small) deviations on the aggregation results by assigning them low (or high) weights. Several numerical examples are provided to illustrate these distance and similarity measures.     

Vague集相似度量的新方法

指出了现有方法的不足,提出一种新的Vague集相似度量方法,并证明了它满足若干准则。通过实例说明了方法的有效性和优越性。     

面向选择类题型求解的相似问题发现研究

在人工智能火热的今天,智能解题逐渐成为一大研究热点。研究基于知识关联和推理的选择类问题求解,尝试解决问题理解和相似问题发现两方面内容。针对问题理解,使用TextRank和词性标注两种方法完成关键信息提取,并使用word2vec词聚类的结果完成关键信息扩展;针对相似问题发现,首先根据问题理解生成的关键信息,从已有题库中抽取候选问题集,然后结合word2vec生成的词向量分别使用基于BM25变体、词项向量加权、改进的编辑距离三种方法计算句子相似度,并根据相似度大小,确定答案选择,最终完成问题求解。在地理选择题解答的相关实验中,获得了最高75.88%的平均准确率,也验证了问题求解的可行性。     

Cross level semantic similarity: an evaluation framework for universal measures of similarity

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.     

基于兴趣热点的移动社交网络资源发现机制研究

摘 要: 针对移动社交网络中的资源发现问题,提出了一种基于兴趣热点的资源发现机制（IHRD）,IHRD考虑了人类对感兴趣地点的访问偏好,利用移动节点对热点的访问轨迹计算节点之间的社会关系,根据兴趣热点与社会关系之间的关联,设计了基于兴趣热点的资源搜索办法,解决了网络中存在未共享兴趣节点的资源搜索问题。引入马尔科夫预测模型,对兴趣热点的变化进行有效预测,进一步提高了资源搜索效率,降低了系统开销。仿真实验表明,IHRD与同类发现机制相比,具有较高的资源发现效率,较低的平均时延与通信开销。