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User Queries (user + query)
Selected AbstractsReal-time navigating crowds: scalable simulation and renderingCOMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 3-4 2006Julien Pettré Abstract This paper introduces a framework for real-time simulation and rendering of crowds navigating in a virtual environment. The solution first consists in a specific environment preprocessing technique giving rise to navigation graphs, which are then used by the navigation and simulation tasks. Second, navigation planning interactively provides various solutions to the user queries, allowing to spread a crowd by individualizing trajectories. A scalable simulation model enables the management of large crowds, while saving computation time for rendering tasks. Pedestrian graphical models are divided into three rendering fidelities ranging from billboards to dynamic meshes, allowing close-up views of detailed digital actors with a large variety of locomotion animations. Examples illustrate our method in several environments with crowds of up to 35,000 pedestrians with real-time performance. Copyright © 2006 John Wiley & Sons, Ltd. [source] Effect of redundancy on the mean time to failure of wireless sensor networksCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 8 2007Anh Phan Speer Abstract In data-driven wireless sensor networks (WSNs), the system must perform data sensing and retrieval and possibly aggregate data as a response at runtime. As a WSN is often deployed unattended in areas where replacements of failed sensors are difficult, energy conservation is of primary concern. While the use of redundancy is desirable in terms of satisfying user queries to cope with sensor and transmission faults, it may adversely shorten the lifetime of the WSN, as more sensor nodes will have to be used to answer queries, causing the energy of the system to drain quickly. In this paper, we analyze the effect of redundancy on the mean time to failure (MTTF) of a WSN in terms of the number of queries the system is able to answer correctly before it fails due to either sensor/transmission faults or energy depletion. In particular, we analyze the effect of redundancy on the MTTF of cluster-structured WSNs for energy conservations. We show that a tradeoff exists between redundancy and MTTF. Furthermore, an optimal redundancy level exists such that the MTTF of the system is maximized. Copyright © 2007 John Wiley & Sons, Ltd. [source] Using clustering methods to improve ontology-based query term disambiguationINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 7 2006Ernesto William De Luca In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or "Sense Folders" (prototype categories) derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user-specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 693,709, 2006. [source] A view of the data on P2P file-sharing systemsJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 10 2009Wai Gen Yee Peer-to-peer (P2P) file sharing is a leading Internet application. Millions of users use P2P file-sharing systems daily to search for and download files, accounting for a large portion of Internet traffic. Due to their scale, it is important to fully understand how these systems work. We analyze user queries and shared files collected on the Gnutella system, draw some conclusions on the nature of the application, and propose some research problems. [source] Passage detection using text classificationJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 4 2009Saket Mengle Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy. [source] Question-driven segmentation of lecture speech text: Towards intelligent e-learning systemsJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 2 2008Ming Lin Recently, lecture videos have been widely used in e-learning systems. Envisioning intelligent e-learning systems, this article addresses the challenge of information seeking in lecture videos by retrieving relevant video segments based on user queries, through dynamic segmentation of lecture speech text. In the proposed approach, shallow parsing such as part of-speech tagging and noun phrase chunking are used to parse both questions and Automated Speech Recognition (ASR) transcripts. A sliding-window algorithm is proposed to identify the start and ending boundaries of returned segments. Phonetic and partial matching is utilized to correct the errors from automated speech recognition and noun phrase chunking. Furthermore, extra knowledge such as lecture slides is used to facilitate the ASR transcript error correction. The approach also makes use of proximity to approximate the deep parsing and structure match between question and sentences in ASR transcripts. The experimental results showed that both phonetic and partial matching improved the segmentation performance, slides-based ASR transcript correction improves information coverage, and proximity is also effective in improving the overall performance. [source] Markovian analysis for automatic new topic identification in search engine transaction logsAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 6 2009Huseyin C. Ozmutlu Abstract Topic analysis of search engine user queries is an important task, since successful exploitation of the topic of queries can result in the design of new information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in analysis of search engine user queries. This study presents an application of Markov chains in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals, query reformulation patterns and the continuation/shift status of the previous query. The findings show that Markov chains provide fairly successful results for automatic new topic identification with a high level of estimation for topic continuations and shifts. Copyright © 2009 John Wiley & Sons, Ltd. [source] DynJAQ: An adaptive and flexible dynamic FAQ systemINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2007David Camacho This article presents a new type of Frequently Asked Questions (FAQ) System, called DynJAQ (Dynamic Java Asked Questions) that has been designed with the purpose of making learning more appealing to beginner students of engineering disciplines and overcome the inconvenience of these systems. DynJAQ is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java), although it can be easily extended to any other topic. DynJAQ integrates case-based knowledge into a graph-based representation that can be easily learned and managed. The combination of both case-based knowledge and graphs allows it to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The management of these cases allows enriching the knowledge base. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 303,318, 2007. [source] Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval systemINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 9 2005E. Herrera-Viedma Information retrieval is an activity that attempts to produce documents that better fulfill user information needs. To achieve this activity an information retrieval system uses matching functions that specify the degree of relevance of a document with respect to a user query. Assuming linguistic-weighted queries we present a new linguistic matching function for a threshold weighting semantics that is defined using a 2-tuple fuzzy linguistic approach (Herrera F, Martínez L. IEEE Trans Fuzzy Syst 2000;8:746,752). This new 2-tuple linguistic matching function can be interpreted as a tuning of that defined in "Modelling the Retrieval Process for an Information Retrieval System Using an Ordinal Fuzzy Linguistic Approach" (Herrera-Viedma E. J Am Soc Inform Sci Technol 2001;52:460,475). We show that it simplifies the processes of computing in the retrieval activity, avoids the loss of precision in final results, and, consequently, can help to improve the users' satisfaction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 921,937, 2005. [source] Passage detection using text classificationJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 4 2009Saket Mengle Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy. [source] Probabilistic question answering on the WebJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 6 2005Dragomir Radev Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 on the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR. [source] | ||||||||||