13969434
Genre based navigation on the Web
We report on our ongoing study of using the genre of Web pages to facilitate information exploration. By genre, we mean socially recognized regularities of form and purpose in documents (e.g., a letter, a memo, a research paper). Our study had three phases. First, through a user study, we identified genres which most/least frequently meet searchers' information needs. We found that certain genres are better suited for certain types of needs. We identified five major groups of document genres that might be used in an interactive search tool that would allow genre based navigation. We tried to balance the following dual objectives: i) each group should be recognizable by a computer algorithm as easily as possible; ii) each group has a better chance of satisfying particular types of information needs. Finally, we developed a novel user interface for Web searching that allows genre based navigation through three major functionalities: 1) limiting search to specified genres, 2) visualizing the hierarchy of genres discovered in the search results and 3) accepting user feedback on the relevancy of the specified genres.
7988049
Word Sense Disambiguation for Cross-Language Information Retrieval
We have developed a word sense disambiguation algorithm, following Cheng and Wilensky (1997), to disambiguate among WordNet synsets. This algorithm is to be used in a cross-language information retrieval system, CINDOR, which indexes queries and documents in a language-neutral concept representation based on WordNet synsets. Our goal is to improve retrieval precision through word sense disambiguation. An evaluation against human disambiguation judgements suggests promise for our approach.
204983610
Pyramidic Clustering of Large-Scale Microarray Images
With ongoing research and development of imaging techniques such as those involved in brain MRIs, cDNA microarrays and satellite reconnaissance, the need for tools that can intelligently parse larger images is ever increasing. One group of such techniques often used is that of segmentation, an example of which is that of clustering algorithms. In order to deal with large data sets, current approaches require the data to be sampled or summarized before true analysis can take place. In this paper we propose a novel image analysis technique using pyramidic type grouping, namely copasetic clustering, which focuses on the problem of applying traditional clustering techniques to these large-scale image data sets with limited resources. A further benefit of the technique is the transparency of its intermediate clustering steps; when applied to spatial data sets this allows the capture and incorporation of contextual information to improve result accuracy. The algorithm achieves an ∼1--3 dB w-to-noise ratio when compared with the conventional techniques described.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm
848425
Location awareness in unstructured peer-to-peer systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.
14452513
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of these search methods to temporal Bayesian networks [Tucker01]. A characteristic of many datasets from medical to geographical data is the spatial arrangement of variables. In this paper we investigate a set of operators that have been designed to exploit the spatial nature of such data in order to learn dynamic Bayesian networks more efficiently. We test these operators on synthetic data generated from a Gaussian network where the architecture is based upon a Cartesian coordinate system, and real-world medical data taken from visual field tests of patients suffering from ocular hypertension.
426496
Freeze: engineering a fast repeater insertion solver for power minimization using the ellipsoid method
This paper presents a novel repeater insertion algorithm for the power minimization of realistic interconnect trees under given timing budgets. Our algorithm judiciously combines a local optimizer based on the dynamic programming technique and a global search engine using the ellipsoid method. As a result, our approach is capable of producing high-quality solutions at a very fast speed. Furthermore, our scheme is robust and does not need any manual tuning of the iteration-control parameters. We have developed a repeater insertion tool, called Freeze, using the proposed algorithm and applied it to various interconnect trees with different timing targets. Experimental results demonstrate the high effectiveness of our approach. In comparison with the state-of-the-art low-power repeater insertion schemes, Freeze requires 5.8 times fewer iterations on the average, achieving up to 27 times speedup with even better power savings. When compared with a dynamic programming based scheme, which guarantees the optimal solution, our tool delivers up to 50 times speedup with 0.9% power increase on the average.