Search results for "SIMILARITY"

showing 10 items of 474 documents

Time series clustering with different distance measures to tell Web bots and humans apart

2022

The paper deals with the problem of differentiating Web sessions of bots and human users by observing some characteristics of their traffic at the Web server input. We propose an approach to cluster bots’ and humans’ sessions represented as time series. First, sessions are expressed as sequences of HTTP requests coming to the server at specific timestamps; then, they are pre-preprocessed to form time series of limited length. Time series are clustered and the clustering performance is evaluated in terms of the ability to partition bots and humans into separate clusters. The proposed approach is applied to real server log data and validated with the use of different time series distance meas…

Web sessionTime seriesUnsupervised classificationWeb bot detectionInternet robotSimilarity measureWeb botClusteringDistance measureECMS 2022 Proceedings edited by Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat
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Distance measures for biological sequences: Some recent approaches

2008

AbstractSequence comparison has become a very essential tool in modern molecular biology. In fact, in biomolecular sequences high similarity usually implies significant functional or structural similarity. Traditional approaches use techniques that are based on sequence alignment able to measure character level differences. However, the recent developments of whole genome sequencing technology give rise to need of similarity measures able to capture the rearrangements involving large segments contained in the sequences. This paper is devoted to illustrate different methods recently introduced for the alignment-free comparison of biological sequences. Goal of the paper is both to highlight t…

Whole genome sequencingComputer sciencebusiness.industryApplied MathematicsSequence alignmentMachine learningcomputer.software_genreBioinformaticsMeasure (mathematics)GenomeDistance measuresSimilitudeTheoretical Computer ScienceArtificial IntelligenceSimilarity (psychology)Metric (mathematics)Artificial intelligencebusinesscomputerSoftwareInternational Journal of Approximate Reasoning
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Conceptual vs. perceptual wine spaces: Does expertise matter?

2008

Abstract This study explores the differences in wine categorization between wine experts and novice wine consumers using 10 Melon de Bourgogne (MB) and 10 Chardonnay (CH) wines. Participants performed a free sorting task based on odor similarity followed by a CH and a MB typicality rating task and a liking rating. All tasks were performed orthonasally. We observed a clear agreement between experts concerning typicality scores. Moreover, despite a slight overlap we found a clear differentiation between CH and MB for experts’ typicality scores. For novices, no such agreement on typicality scores was observed and we found a complete overlap between both types of wines. These results suggest th…

Wine0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsmedia_common.quotation_subject[SCCO.NEUR]Cognitive science/Neuroscience[SCCO.NEUR] Cognitive science/NeuroscienceCognition04 agricultural and veterinary sciences040401 food scienceCorrelation03 medical and health sciences0404 agricultural biotechnologyCategorizationPerception[ SCCO.NEUR ] Cognitive science/NeuroscienceSimilarity (psychology)Multidimensional scalingWine tastingPsychologySocial psychologyComputingMilieux_MISCELLANEOUSFood Sciencemedia_common
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Comparison of wine discrimination with orthonasal and retronasal profilings. Application to Burgundy Pinot Noir wines

1999

Two sensory spaces, corresponding to the same wine sample profiled by nose (BN) and profiled by mouth (BM), were compared. The similarity between the two maps of product differences were measured by multivariate analysis, showing a good agreement and comparable product discrimination by the panel in the two modes, slightly in favor of BN discrimination. The superiority of one particular mode was not established from the comparison of individual performances BN versus BM, but differences between panelists and between descriptor use were found. Two-way canonical variate analysis of BN minus BM scores was also performed: the results revealed that panelists had higher influence than products in…

Wine0303 health sciencesNutrition and DieteticsSense organ030309 nutrition & dietetics04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineeringOral cavity040401 food scienceSensory analysis03 medical and health sciences0404 agricultural biotechnologyCanonical variate analysisSimilarity (network science)[SDV.IDA]Life Sciences [q-bio]/Food engineeringStatisticsComputingMilieux_MISCELLANEOUSFood ScienceMathematicsFood Quality and Preference
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Algebraic Properties to Optimize kNN Queries

2011

International audience; New applications that are being required to employ Database Management Systems (DBMSs), such as storing and retrieving complex data (images, sound, temporal series, genetic data, etc.) and analytical data processing (data mining, social networks analysis, etc.), increasingly impose the need for new ways of expressing predicates. Among the new most studied predicates are the similarity-based ones, where the two commonest are the similarity range and the k-nearest neighbor predicates. The k-nearest neighbor predicate is surely the most interesting for several applications, including Content-Based Image Retrieval (CBIR) and Data Mining (DM) tasks, yet it is also the mos…

[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]similarity algebra[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]algebraic propertiesunary similarity queriesquery optimization
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User profile matching in social networks

2010

International audience; Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) w…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Matching (statistics)Computer science[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarity measurecomputer.software_genreElectronic mail[SCCO.COMP] Cognitive science/Computer science020204 information systemsFOAF0202 electrical engineering electronic engineering information engineeringPattern matchingUser profileSocial networkbusiness.industrycomputer.file_formatProfile MatchingSocial Networks[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businesscomputerData integration
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Toward Approximate GML Retrieval Based on Structural and Semantic Characteristics

2010

International audience; GML is emerging as the new standard for representing geographic information in GISs on the Web, allowing the encoding of structurally and semantically rich geographic data in self describing XML-based geographic entities. In this study, we address the problem of approximate querying and ranked results for GML data and provide a method for GML query evaluation. Our method consists of two main contributions. First, we propose a tree model for representing GML queries and data collections. Then, we introduce a GML retrieval method based on the concept of tree edit distance as an efficient means for comparing semi-structured data. Our approach allows the evaluation of bo…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Tree edit distanceSimilarity (geometry)[INFO.INFO-WB] Computer Science [cs]/WebComputer sciencecomputer.internet_protocol[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genre[SCCO.COMP] Cognitive science/Computer science020204 information systemsEncoding (memory)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]GML SearchStructural & Semantic Similarity[INFO.INFO-WB]Computer Science [cs]/WebProcess (computing)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]GISConstraint (information theory)[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Ranked retrieval020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXMLDecision tree model
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Integrating user preference to similarity queries over medical images datasets

2010

International audience; Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the user's intention. In this paper, we propose a technique to allow expressing the user's preference over attributes associated to the images, so simil…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceComputed tomography02 engineering and technologyContent-based image retrievalSemanticsImage (mathematics)Similarity (network science)[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineeringmedicine[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Image retrieval[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]medicine.diagnostic_test[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]020207 software engineeringPreferenceImportant research[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Relating RSS News/Items

2009

Merging related RSS news (coming from one or different sources) is beneficial for end-users with different backgrounds (journalists, economists, etc.), particularly those accessing similar information. In this paper, we provide a practical approach to both: measure the relatedness, and identify relationships between RSS elements. Our approach is based on the concepts of semantic neighborhood and vector space model, and considers the content and structure of RSS news items. © 2009 Springer Berlin Heidelberg.

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarityTheoretical Computer ScienceWorld Wide Web[SCCO.COMP] Cognitive science/Computer science020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Neighbourhood (mathematics)[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]Structure (mathematical logic)[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Measure (data warehouse)[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Information retrievalRelationship[INFO.INFO-WB]Computer Science [cs]/WebComputer Science (all)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_format[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]RSS Relatedne[ SCCO.COMP ] Cognitive science/Computer scienceVector space model020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]InformationSystems_MISCELLANEOUSNeighbourhoodcomputer
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Qualifying semantic graphs using model checking

2011

International audience; Semantic interoperability problems have found their solutions using languages and techniques from the Semantic Web. The proliferation of ontologies and meta-information has improved the understanding of information and the relevance of search engine responses. However, the construction of semantic graphs is a source of numerous errors of interpretation or modeling and scalability remains a major problem. The processing of large semantic graphs is a limit to the use of semantics in current information systems. The work presented in this paper is part of a new research at the border of two areas: the semantic web and the model checking. This line of research concerns t…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/Web0102 computer and information sciences02 engineering and technologycomputer.software_genre01 natural sciencesSocial Semantic Webtemporal logicSemantic similaritySemantic computing0202 electrical engineering electronic engineering information engineeringSemantic analyticsSemantic integrationSemantic Web StackInformation retrievalbusiness.industry[INFO.INFO-WB]Computer Science [cs]/WebSemantic search020207 software engineeringSemantic interoperability[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationModel-checking010201 computation theory & mathematicsSemantic graphTheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS[INFO.INFO-MO] Computer Science [cs]/Modeling and SimulationArtificial intelligencebusinesscomputerNatural language processing2011 International Conference on Innovations in Information Technology
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