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Relevant bibliographies by topics / Information filtering

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Academic literature on the topic 'Information filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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Journal articles on the topic "Information filtering"

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Peyravi, Farzad, and Alimohammad Latif. "Secure Multi Agent Information Filtering." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 240–46. http://dx.doi.org/10.7763/ijcte.2014.v6.869.

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Loeb, Shoshana, and Douglas Terry. "Information filtering." Communications of the ACM 35, no. 12 (December 1992): 26–28. http://dx.doi.org/10.1145/138859.138860.

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Rowe, Timothy. "Filtering Information." Journal of Obstetrics and Gynaecology Canada 32, no. 4 (April 2010): 303–4. http://dx.doi.org/10.1016/s1701-2163(16)34471-1.

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FITZPATRICK, TAMARA, and LYNDA FITZPATRICK. "Filtering consistent information." Nursing 26, no. 9 (September 1996): 9. http://dx.doi.org/10.1097/00152193-199609000-00003.

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Ciaccia, Paolo, and Marco Patella. "Metric information filtering." Information Systems 36, no. 4 (June 2011): 708–20. http://dx.doi.org/10.1016/j.is.2010.09.007.

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Silva, Josep. "Information Filtering and Information Retrieval with the Web Filtering Toolbar." Electronic Notes in Theoretical Computer Science 235 (April 2009): 125–36. http://dx.doi.org/10.1016/j.entcs.2009.03.008.

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Belkin, Nicholas J., and W. Bruce Croft. "Information filtering and information retrieval." Communications of the ACM 35, no. 12 (December 1992): 29–38. http://dx.doi.org/10.1145/138859.138861.

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Zhu, Li, and Lan Bai. "Web Information Filtering Technology Based on Mutual Information." Applied Mechanics and Materials 687-691 (November 2014): 2224–28. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2224.

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The popularity of Internet application makes web page illegal information filtering an important work. Based on mutual information model study of illegal information filtering in web page, this article first elaborated the theoretical basis of mutual information filtering algorithm, then designed a mutual information filtering algorithm based on improved formula, and designed and implemented illegal web page filtering system by using the algorithm and related technology and did a series of experiments, which effectively reduced the impact of negative factors on healthy development of network culture industry, and it is advantageous to the purification of social morality and social stability.

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Newton, Nigel J. "Information geometric nonlinear filtering." Infinite Dimensional Analysis, Quantum Probability and Related Topics 18, no. 02 (June 2015): 1550014. http://dx.doi.org/10.1142/s0219025715500149.

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This paper develops information geometric representations for nonlinear filters in continuous time. The posterior distribution associated with an abstract nonlinear filtering problem is shown to satisfy a stochastic differential equation on a Hilbert information manifold. This supports the Fisher metric as a pseudo-Riemannian metric. Flows of Shannon information are shown to be connected with the quadratic variation of the process of posterior distributions in this metric. Apart from providing a suitable setting in which to study such information-theoretic properties, the Hilbert manifold has an appropriate topology from the point of view of multi-objective filter approximations. A general class of finite-dimensional exponential filters is shown to fit within this framework, and an intrinsic evolution equation, involving Amari's -1-covariant derivative, is developed for such filters. Three example systems, one of infinite dimension, are developed in detail.

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Quintana, Yuri. "Intelligent medical information filtering." International Journal of Medical Informatics 51, no. 2-3 (August 1998): 197–204. http://dx.doi.org/10.1016/s1386-5056(98)00115-4.

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Dissertations / Theses on the topic "Information filtering"

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Chambers, Brian D. "Adaptive Bayesian information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ45945.pdf.

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Webster, David Edward. "Realising context-oriented information filtering." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:2724.

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The notion of information overload is an increasing factor in modern information service environments where information is ‘pushed’ to the user. As increasing volumes of information are presented to computing users in the form of email, web sites, instant messaging and news feeds, there is a growing need to filter and prioritise the importance of this information. ‘Information management’ needs to be undertaken in a manner that not only prioritises what information we do need, but to also dispose of information that is sent, which is of no (or little) use to us.The development of a model to aid information filtering in a context-aware way is developed as an objective for this thesis. A key concern in the conceptualisation of a single concept is understanding the context under which that concept exists (or can exist). An example of a concept is a concrete object, for instance a book. This contextual understanding should provide us with clear conceptual identification of a concept including implicit situational information and detail of surrounding concepts.Existing solutions to filtering information suffer from their own unique flaws: textbased filtering suffers from problems of inaccuracy; ontology-based solutions suffer from scalability challenges; taxonomies suffer from problems with collaboration. A major objective of this thesis is to explore the use of an evolving community maintained knowledge-base (that of Wikipedia) in order to populate the context model from prioritise concepts that are semantically relevant to the user’s interest space. Wikipedia can be classified as a weak knowledge-base due to its simple TBox schema and implicit predicates, therefore, part of this objective is to validate the claim that a weak knowledge-base is fit for this purpose. The proposed and developed solution, therefore, provides the benefits of high recall filtering with low fallout and a dependancy on a scalable and collaborative knowledge-base.A simple web feed aggregator has been built using the Java programming language that we call DAVe’s Rss Organisation System (DAVROS-2) as a testbed environment to demonstrate specific tests used within this investigation. The motivation behind the experiments is to demonstrate that the combination of the concept framework instantiated through Wikipedia can provide a framework to aid in concept comparison, and therefore be used in news filtering scenario as an example of information overload. In order to evaluate the effectiveness of the method well understood measures of information retrieval are used. This thesis demonstrates that the utilisation of the developed contextual concept expansion framework (instantiated using Wikipedia) improved the quality of concept filtering over a baseline based on string matching. This has been demonstrated through the analysis of recall and fallout measures.

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Yu, Kai. "Statistical Learning Approaches to Information Filtering." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-25120.

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Dolbear, Catherine. "Personalised information filtering using event causality." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:31e94de4-5dda-4312-968b-d0ef34dea8e2.

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Previous research on multimedia information filtering has mainly concentrated on key frame identification and video skim generation for browsing purposes, however applications requiring the generation of summaries as the final product for user con- sumption are of equal scientific and commercial interest. Recent advances in computer vision have enabled the extraction of semantic events from an audio-visual signal, so it can be assumed for our purposes that such semantic labels are already available for use. We concentrate instead on developing methods to prioritise these semantic elements for inclusion in a summary which can be personalised to meet a particular user's needs. Our work differentiates itself from that in the literature as it is driven by the results of a knowledge elicitation study with expert summarisers. The experts in our study believe that summaries structured as a narrative are better able to convey the content of the original data to a user. Motivated by the information filtering problem, the primary contribution of this thesis is the design and implementation of a system to summarise sequences of events by automatic modelling of the causal relationships between them. We show, by com- parison against summaries generated by experts and with the introduction of a new coherence metric, that modelling the causal relationships between events increases the coherence and accuracy of summaries. We suggest that this claim is valid, not only in the domain of soccer highlights generation, in which we carry out the bulk of our experiments, but also in any other domain in which causal relationships can be iden- tified between events. This proposal is tested by applying our summarisation system to another, significantly different domain, that of business meeting summarisation, using the soccer training set and a manually generated ontology mapping. We introduce the concept of a context-group of causally related events as a first step towards modelling narrative episodes and present a comparison between a case based reasoning and a two-stage Markov model approach to summarisation. For both methods we show that by including entire context-groups in the summary, rather than single events in isolation, more accurate summaries can be generated. Our approach to personalisation biases a summary according to particular narrative plotlines using different subsets of the training data. Results show that the number of instances of certain event classes can be increased by biasing the training set appropriately. This method gives very similar results to a standard weighting method, while avoiding the need to tailor the weights to a particular application domain.

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Shardanand, Upendra. "Social information filtering for music recommendation." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11667.

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Olsson, Tomas. "Information Filtering with Collaborative Interface Agents." Thesis, SICS, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22235.

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This report describes a distributed approach to social filtering based on the agent metaphor. Firstly, previous approaches are described, such as cognitive filtering and social filtering. Then a couple of previously implemented systems are presented and then a new system design is proposed. The main goal is to give the requirements and design of an agent-based system that recommends web-documents. The presented approach combines cognitive and social filtering to get the advantages from both techniques. Finally, a prototype implementation called WebCondor is described and results of testing the system are reported and discussed.

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Lanquillon, Carsten. "Enhancing text classification to improve information filtering." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963801805.

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Khan, Imran. "Personal adaptive web agent for information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq23361.pdf.

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Sheth, Beerud Dilip. "A learning approach to personalized information filtering." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37998.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (leaves 96-100).
by Beerud Dilip Sheth.
M.S.

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Akkapeddi, Raghu C. "Grouping annotating and filtering history information in VKB." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/227.

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History mechanisms available in hypertext systems allow users access to past interactions with the system and help users incorporate those interactions into the current context. The history information can be useful to both the system and the user. The Visual Knowledge Builder (VKB) creates spatial hypertexts - visual workspaces for collecting, organizing, and sharing. It is based on prior work on VIKI. VKB records all edit events and presents them in the form of a "navigable history" as end-users work within an information workspace. My thesis explores attaching user interpretations of history via the grouping and annotation of edit events. Annotations can take the form of a plain text statement or one or more attribute/value pairs attached to individual events or group of events in the list. Moreover, I explore the value of history event filtering, limiting the edits and groups presented to those that match user descriptions. My contribution in this thesis is the addition of mechanisms whereby users can cope with larger history records in VKB via the process of grouping, annotating and filtering history information.

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Books on the topic "Information filtering"

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Information Filtering and Retrieval. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46135-9.

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Chambers, Brian D. Adaptive bayesian information filtering. Toronto: Univsity of Toronto, Dept. of Computer Science, 1999.

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Ma, Hongbin, Liping Yan, Yuanqing Xia, and Mengyin Fu. Kalman Filtering and Information Fusion. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0806-6.

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Chen, Badong, Lujuan Dang, Nanning Zheng, and Jose C. Principe. Kalman Filtering Under Information Theoretic Criteria. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33764-2.

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Herlocker, Jonathan Lee. Collaborative filtering for digital libraries. [Corvallis, OR: Oregon State University, Dept. of Computer Science, 2003.

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Emerging Ideas on Information Filtering and Retrieval. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68392-8.

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Lai, Cristian, Giovanni Semeraro, and Eloisa Vargiu, eds. New Challenges in Distributed Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31546-6.

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Stinson, Catherine Elizabeth. Adaptive information filtering with labelled and unlabelled data. Ottawa: National Library of Canada, 2002.

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name, No. Knowledge-based information retrieval and filtering from the Web. Boston, MA: Kluwer Academic Publishers, 2003.

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Distributed Systems and Applications of Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40621-8.

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Book chapters on the topic "Information filtering"

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Fluhr, Christian. "Information Filtering." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_951-2.

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Fluhr, Christian. "Information Filtering." In Encyclopedia of Database Systems, 1481–84. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_951.

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Fluhr, Christian. "Information Filtering." In Encyclopedia of Database Systems, 1926–30. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_951.

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Chen, Badong, Lujuan Dang, Nanning Zheng, and Jose C. Principe. "Kalman Filtering." In Kalman Filtering Under Information Theoretic Criteria, 11–51. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33764-2_2.

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Blanchet, Gérard, and Maurice Charbit. "Additional Information About Filtering." In Digital Signal and Image Processing Using Matlab®, 15–50. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118999592.ch2.

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Chien, Sheng-Fen, and Shen-Guan Shih. "Design through Information Filtering." In Computer Aided Architectural Design Futures 2001, 103–10. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0868-6_8.

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Tauritz, Daniel R., and Ida G. Sprinkhuizen-Kuyper. "Adaptive Information Filtering Algorithms." In Advances in Intelligent Data Analysis, 513–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_43.

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Ouyang, Yuanxin, Wenqi Liu, Wenge Rong, and Zhang Xiong. "Autoencoder-Based Collaborative Filtering." In Neural Information Processing, 284–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12643-2_35.

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Basile, Pierpaolo, Annalina Caputo, Giovanni Semeraro, and Lucia Siciliani. "Time Event Extraction to Boost an Information Retrieval System." In Information Filtering and Retrieval, 1–12. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46135-9_1.

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Di Nunzio, Giorgio Maria. "Interactive Text Categorisation: The Geometry of Likelihood Spaces." In Information Filtering and Retrieval, 13–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46135-9_2.

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Conference papers on the topic "Information filtering"

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Schweighofer, Erich, Werner Winiwarter, and Dieter Merkl. "Information filtering." In the fifth international conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/222092.222205.

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Sta, Jean-David. "Information filtering." In INTERACT '93 and CHI '93 conference companion. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/259964.260190.

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Lanquillon, Carsten, and Ingrid Renz. "Adaptive information filtering." In the eighth international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319950.320061.

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Shardanand, Upendra, and Pattie Maes. "Social information filtering." In the SIGCHI conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/223904.223931.

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BARCHIELLI, ALBERTO, and GIANCARLO LUPIERI. "INFORMATION GAIN IN QUANTUM CONTINUAL MEASUREMENTS." In Quantum Stochastics and Information - Statistics, Filtering and Control. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812832962_0015.

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WATANABE, NOBORU. "NOTE ON INFORMATION TRANSMISSION IN QUANTUM SYSTEMS." In Quantum Stochastics and Information - Statistics, Filtering and Control. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812832962_0019.

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Nanas, Nikolaos, Stefanos Kodovas, and Manolis Vavalis. "Revisiting Evolutionary Information Filtering." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586070.

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Jianying, Zhang, Wang Jiamei, Tang Xue, and Zhao Fei. "Sensitive information filtering system." In 3rd International Conference on Green Communications and Networks. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/gcn130521.

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Vatani, Nasim, Amir Masoud Rahmani, and Mohammad Ebrahim Shiri. "An Information Filtering Approach." In 2009 Third International Conference on Network and System Security. IEEE, 2009. http://dx.doi.org/10.1109/nss.2009.45.

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Zhang, Yan. "Session details: Information filtering." In CIKM '11: International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2011. http://dx.doi.org/10.1145/3244888.

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Reports on the topic "Information filtering"

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Zhang, Yi. Adaptive Information Filtering. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada563638.

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Kuperman, Gilbert G. Bandpass Spatial Filtering and Information Content. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada303025.

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Mu, Ruihui. Integrating Rating Information and Social Information for Collaborative Filtering Recommendation. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2019. http://dx.doi.org/10.7546/crabs.2019.05.03.

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Knoblock, Craig A. Intelligent Agents for Retrieving, Filtering, and Managing Information. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada387501.

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Yan, Tak W., and Hector Garcia-Molina. Index Structures for Information Filtering Under the Vector Space Model,. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada326033.

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George, R., B. Leiba, and A. Melnikov. Sieve Email Filtering: Use of Presence Information with Auto-Responder Functionality. RFC Editor, July 2011. http://dx.doi.org/10.17487/rfc6133.

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Davis, Alan D. Filtering and Trust as Tools for the Operational Commander in the Information Age. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada463567.

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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of these sampling techniques is to employ filtering techniques to remove outliers associated with trip chaining, but not remove important features in the data associated with incidents or traffic congestion. This paper describes a curated data set that was developed from high-fidelity GPS trajectory data. The curated data contained 31,621 vehicle observations spanning 42 days; 2550 observations had travel times greater than 3 minutes more than normal. From this baseline data set, outliers were determined using GPS waypoints to determine if the vehicle left the route. Two performance measures were identified for evaluating three outlier-filtering algorithms by the proportion of true samples rejected and proportion of outliers correctly identified. The effectiveness of the three methods over 10-minute sampling windows was also evaluated. The curated data set has been archived in a digital repository and is available online for others to test outlier-filtering algorithms.

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Yilmaz, Ihsan, and Kenes Bulent. Digital Authoritarianism in Turkish Cyberspace: A Study of Deception and Disinformation by the AKP Regime’s AKtrolls and AKbots. Populism & Politics (P&P), November 2023. http://dx.doi.org/10.55271/pp0026.

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This article explores the evolving landscape of digital authoritarianism in Turkish cyberspace, focusing on the deceptive strategies employed by the AKP regime through AKtrolls, AKbots and hackers. Initially employing censorship and content filtering, the government has progressively embraced sophisticated methods, including the weaponization of legislation and regulatory bodies to curtail online freedoms. In the third generation of information controls, a sovereign national cyber-zone marked by extensive surveillance practices has emerged. Targeted persecution of critical netizens, coupled with (dis)information campaigns, shapes the digital narrative. Central to this is the extensive use of internet bots, orchestrated campaigns, and AKtrolls for political manipulation, amplifying government propaganda and suppressing dissenting voices. As Turkey navigates a complex online landscape, the study contributes insights into the multifaceted tactics of Erdogan regime’s digital authoritarianism.

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George. PR-015-13603-R01 Meter Station Design Procedures to Minimize Pipe Flow-Induced Pulsation Error. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 2013. http://dx.doi.org/10.55274/r0010099.

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This project explored the sources of flow-induced pulsations in natural gas meter stations and the detrimental effects the pulsations have on the measurement capabilities of three types of gas custody transfer meters: orifice meters, turbine meters, and ultrasonic meters. To supplement the PRCI-funded research on the effect of compressor pulsations on ultrasonic flow measurement, Southwest Research Institute (SwRI) has performed a study to identify best practices for meter station piping design that avoid generating or amplifying flow-induced pulsations. This study involved a literature review to identify features of meter station piping that (1) generate pulsations independently of compressors or (2) amplify pulsating flows entering station piping. Where possible, the study also worked to quantify the effect of flow-induced pulsations on meter accuracy. This information has been used to create best practices and methodologies for minimizing metering errors caused by pulsation. As a result of this research, various design techniques able to reduce or eliminate pulsations in meter station piping systems have been compiled. Mitigation practices specific to obstructions, flow restrictions, piping geometries, gaps, acoustically-induced pulsation, and flow-induced turbulence are discussed. Additionally, common methods of filtering high-frequency pulsations are discussed in detail.

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