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semantic clustering for region-based image retrieval

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2009年第2期20卷 157-166页

作者： Liu, Ying Chen, Xin Zhang, Chengcui Sprague, Alan Univ Alabama Birmingham Dept Comp & Informat Sci Birmingham AL 35294 USA

With the proliferation of applications that demand content-based image retrieval, two merits are becoming more desirable. The first is the reduced search space, and the second is the reduced "semantic gap." This paper proposes a semantic clustering scheme to achieve these two goals. By performing clustering before image retrieval, the search space can be significantly reduced. The proposed method is different from existing image clustering methods as follows: (1) it is region based, meaning that image subregions, instead of the whole image, are grouped into. The semantic similarities among image regions are collected over the user query and feedback history: (2) the clustering scheme is dynamic in the sense that it can evolve to include more new semantic categories. Ideally, one cluster approximates one semantic concept or a small set of closely related semantic concepts, based on which the "semantic gap" in the retrieval is reduced. (C) 2008 Elsevier Inc. All rights reserved.

关键词： semantic clustering Content-based image retrieval Outlier detection Network flow Region-based retrieval semantic gap Kd-tree Multiple instance learning (MIL)

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semantic clustering and Transfer Learning in Social Media Texts Authorship Attribution

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IEEE ACCESS 2024年 12卷 39783-39803页

作者： Fedotova, Anastasia Kurtukova, Anna Romanov, Aleksandr Shelupanov, Alexander Tomsk State Univ Control Syst & Radioelect Dept Secur Tomsk 634050 Russia

This paper is the fourth part of a research series that focuses on determining the authorship of Russian-language texts by analyzing short social media comments, including those from mass media and communities associated with destructive content. semantic text clustering was used to analyze content and employed a transfer learning technique based on a pre-trained model to identify sensitive topics. Authorship attribution is implemented as a classical classification task with a closed set of authors and a more challenging open-set task. In the latter case, multiple experiments were conducted, incorporating the identification of destructive content with known authors and artificially generated texts. For open attribution, a method combining One-Class SVM and fastText was proposed. Results demonstrate high accuracy (92% or higher) for cases with 2 and 5 authors, regardless of comment length and the additional task of identifying authors of destructive text. Mixed-data experiments involving 10 or more authors yielded results comparable to or more accurate (84% or higher) than previous studies.

关键词： semantics Transfer learning Task analysis Support vector machines Media Training Long short term memory Machine learning Natural language processing clustering methods Authoring systems Electronic messaging Text processing Social networking (online) Authorship attribution machine learning natural language processing semantic clustering transfer learning

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semantic clustering and sleep in patients with amnestic mild cognitive impairment or with vascular cognitive impairment-no dementia

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INTERNATIONAL PSYCHOGERIATRICS 2016年第9期28卷 1493-1502页

作者： Sun, Qingna Luo, Lanlan Ren, Honglei Wei, Changjuan Xing, Mengya Cheng, Yan Zhang, Nan Tianjin Med Univ Key Lab Posttraumat Neurorepair & Regenerat Cent Tianjin Key Lab Injuries Variat & Regenerat Nervo Dept NeurolMinist EducTianjin Neurol InstGen H Tianjin Peoples R China

Background: Cognition and sleep deficits occur in amnestic mild cognitive impairment (aMCI) and vascular cognitive impairment-no dementia (VCIND). However, how memory and sleep deficits differ between aMCI and VCIND remains unclear. Methods: Fifty aMCI and 50 VCIND patients and 38 sex- and age-matched healthy controls (HCs) were administered the Hopkins Verbal Learning Test-Revised (HVLT-R), Trail Making Test-A/B (TMT-A/B), Wisconsin Card Sorting Test (WCST), Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Benton Judgment of Line Orientation (JLO) test, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI) to quantify cognitive deficits and subjective sleep disturbance. Results: Compared with VCIND patients, aMCI patients had lower HVLT-R scores for total recall (p < 0.001), delayed recall (p < 0.001) and recognition (p = 0.001), and for total-recall (p = 0.002) and delayed-recall (p < 0.001) semantic clustering ratios (SCRs). However, VCIND patients exhibited more obvious executive dysfunction (TMT-A, p < 0.001;TMT-B, p < 0.001;WCST, p < 0.001), lower information processing speed (PASAT, p = 0.003;SDMT, p < 0.001), and more severe sleep disturbance (PSQI, p < 0.001;ESS, p < 0.001;ISI, p < 0.001). Additionally, sleep quality and efficiency were related to total and delayed recall (all r values from -0.31 to -0.60, p < 0.05) in aMCI and VCIND. Conclusions: aMCI and VCIND differ in cognitive function, memory strategy and sleep impairment;these characteristics are helpful to identify and distinguish patients with very early cognitive impairment. Our results also suggest that memory deficits are associated with sleep disturbance in aMCI and VCIND.

关键词： amnestic mild cognitive impairment vascular cognitive impairment-no dementia memory semantic clustering sleep

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semantic clustering fon common list-learning tasks: a systematic review of the state of the literature and recommendations for future directions

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JOURNAL OF CLINICAL AND EXPERIMENTAL NEUROPSYCHOLOGY 2023年第7期45卷 652-692页

作者： Bair, Jessica L. Patrick, Sarah D. Noyes, Emily T. Hale, Andrew C. Campbell, Elizabeth B. Wilson, Addie M. Ransom, Michael T. Spencer, Robert J. VA Ann Arbor Healthcare Syst Mental Hlth Serv Ann Arbor MI USA Univ Michigan Hlth Syst Dept Psychiat Ann Arbor MI USA Univ Michigan Dept Phys Med & Rehabil Ann Arbor MI USA St Elizabeth Phys Behav Hlth Crestview Hills KY USA VA Ann Arbor Healthcare Mental Hlth 2215 Fuller Rd Ann Arbor MI 48105 USA

IntroductionOn some list-learning tasks, such as the California Verbal Learning Test (CVLT) or Hopkins Verbal Learning Test (HVLT), examinees have the opportunity to group words based on semantically related categories (i.e., semantic clustering). semantic clustering (SC) is often considered the most efficient organizational strategy and adopting SC is presumed to improve learning and memory. In addition, SC is conceptualized as reflecting higher-order executive functioning skills. Although SC measures have intuitive appeal, to date, there are no comprehensive reviews of the SC literature base that summarize its psychometric utility. In this systematic review, we synthesize the literature to judge the validity of SC *** conducted a systematic literature search for empirical articles reporting SC from the CVLT and HVLT. We qualitatively described the relationship of SC with other list-learning and cognitive test scores and clinical diagnoses, contrasting SC with serial clustering and total learning scores when *** was inversely correlated with serial clustering. Higher SC was strongly associated with better learning and memory performances. When compared with cognitive tests, SC tended to have the strongest relationships with other memory measures and modest relationships with tests of executive functioning. SC had negligible to small relationships with most other cognitive domains. Traditional memory scores yielded stronger relationships to cognitive test performances than did SC. SC across clinical groups varied widely, but clinical groups tended to use SC less often than healthy comparison *** comprehensive review of the literature revealed that SC is strongly related to measures of learning and memory on the CVLT and HVLT and is correlated with a wide range of cognitive functions. SC has been understudied in relevant populations and additional research is needed to test the degree to which it adds incremental validity b

关键词： semantic clustering serial clustering memory test psychometrics neuropsychological assessment

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semantic clustering During Verbal List Learning Is Associated With Employment Status in a Community Sample

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PERCEPTUAL AND MOTOR SKILLS 2021年第3期128卷 1235-1251页

作者： Williams, Michael W. Ulrich, Nathalie Woods, Steven Paul Univ Houston Dept Psychol 3695 Cullen BlvdHeyne BldgRoom 126 Houston TX 77204 USA

The ability to learn and remember verbal information is highly relevant to many work roles and environments, but we know little about the underlying cognitive mechanisms of those associations. This study examined the hypothesis that unemployment is associated with decreased spontaneous use of higher-order encoding strategies deployed during list learning and recall. Participants were 120 employed and 59 unemployed community-dwelling adults who completed the California Verbal Learning Test-Second Edition (CVLT-II) as part of a broader neuropsychological assessment. Standardized measures of semantic, serial, and subjective clustering were generated from the CVLT-II. After adjusting for data-driven covariates, a significant interaction emerged between employment status and clustering strategy, whereby participants in the employed group exhibited significantly higher scores on semantic clustering, but not serial or subjective clustering, than the unemployed group. The semantic clustering slope score was higher among the employed group and was positively associated with executive functions and declarative memory. These findings suggest that higher-order semantic organizational strategies during supraspan list learning may be relevant to maintaining gainful employment (e.g., mentally organizing work-related instructions and task lists). Future studies might examine semantic clustering in relation to employment changes and work performance, as well as the potential benefit of metacognitive interventions for learning and employment success.

关键词： neuropsychological testing ecological validity memory semantic clustering verbal learning and employment

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semantic clustering of information systems' users with stochastic techniques

Semantic clustering of information systems' users with stoch...

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International Conference on semantic Computing (ICSC 2007)

作者： Raftopoulos, Konstantinos Papadakis, Nikolaos Ntalianis, Klimis Kollias, Stefanos Natl Tech Univ Athens Elect Engn Bldg1st FloorRoom 1-1-23Iroon Polyt Athens 15780 Greece

ISBN: (纸本)9780769529974

We use a Markovian model to capture the habitual user profiles of an information access system. In this model, the general as well as the individual for each user, profile is captured in the form of a Markovian process where the states are the keywords asked to the system by the users and a transition from state to state corresponds to the order theses keywords appeared in the queries. Under this model the probabilistic locality of the Markovian state space translates to semantical locality of the corresponding keywords in a way that a clustering of the Markovian state space corresponds to a semantic clustering of the keyword space. Since the states represent keywords asked by the users, the state space can grow very large, but at the same time it is partitioned into disjoint subsets such that strong interactions among the states of the same subset exists but weak interactions among states of different subsets. We exploit this structure to effectively cluster the large state space and reveal the corresponding semantic keyword clusters. We then define a semantic distance between the various user profiles that can be used to cluster the user space on the basis of keyword usage and keyword semantic relevance. The resulting clustering achieves high independence from the row data. Users for e.g. that never asked a common keyword may end up very close to each other if their keywords were asked together by many other users.

关键词： user profile clustering semantic clustering Markovian state clustering

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Migrating to an object-oriented database using semantic clustering and transformation rules

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DATA & KNOWLEDGE ENGINEERING 1998年第1期27卷 97-113页

作者： Missaoui, R Godin, R Sahraoui, H Univ Quebec Dept Informat Montreal PQ H3C 3P8 Canada McGill Univ CRIM Montreal PQ H3A 2N4 Canada

This paper presents a methodology for handling an important step of database migration. The methodology is based on a set of techniques: (i) semantic clustering, (ii) metamodeling, and (iii) knowledge-based schema transformation. semantic clustering (i.e., grouping based on semantic cohesion) is mainly used to facilitate the process of translating an extended entity relationship schema into a schema of complex objects. Meta modeling is used to define the data models involved in the process of schema transformation. Finally, transformation rules are defined and used for mapping a schema from a source model into a schema expressed in a target model. In this paper, we limit ourselves to the mapping of an extended entity relationship diagram into an object-oriented database schema using the object model supported by the ODMG standard. (C) 1998 Elsevier Science B.V.

关键词： metamodeling database migration semantic clustering object-oriented databases schema transformation

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Skip-Gram-KR: Korean Word Embedding for semantic clustering

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IEEE ACCESS 2019年 7卷 39948-39961页

作者： Ihm, Sun-Young Lee, Ji-Hye Park, Young-Ho Sookmyung Womens Univ Dept IT Engn Seoul 04310 South Korea

Deep learning algorithms are used in various applications for pattern recognition, natural language processing, speech recognition, and so on. Recently, neural network-based natural language processing techniques use fixed length word embedding. Word embedding is a method of digitizing a word at a specific position into a low-dimensional dense vector with fixed length while preserving the similarity of the distribution of its surrounding words. Currently, the word embedding methods for foreign language are used for Korean words;however, existing word embedding methods are developed for English originally, so they do not reflect the order and structure of the Korean words. In this paper, we propose a word embedding method for Korean, which is called Skip-gram-KR, and a Korean affix tokenizer. Skip-gram-KR creates similar word training data through backward mapping and the two-word skipping method. The experiment results show the proposed method achieved the most accurate performance.

关键词： Word embedding natural language processing Korean word embedding text mining deep learning semantic clustering machine learning

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Verbal fluency in schizophrenia: The role of semantic clustering in category instance generation

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EUROPEAN PSYCHIATRY 1997年第3期12卷 124-129页

作者： Robert, PH Migneco, V Marmod, D Chaix, I Thauby, S Benoit, M Beau, CH Darcourt, G UNIV NICE DEPT PSYCHIAT CMDATA MEM UNIT NICE FRANCE

The aim of this study was to determine whether schizophrenic patients' impairment in semantic verbal fluency tasks is due to difficulties in organizing their search or, in other words, in organizing output in terms of clusters of meaningfully related words. Consecutive association of words belonging to subcategories of the semantic task was defined as semantic clustering. A categorical verbal fluency task was first administered to 100 healthy subjects and then to 22 schizophrenic patients and 22 healthy subjects matched for sex, age and education. In the normal population, semantic clustering was found to be involved in word generation. A large number of semantic clusters indicated efficient organization of semantic knowledge and led to better word production. Schizophrenic patients showed impaired verbal fluency and generated a smaller number of semantic clusters than the control subjects. These findings point to a defect in self-initiation of semantic categorization in schizophrenia.

关键词： schizophrenia verbal fluency semantic clustering cognitive strategy

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"Dirclustering": a semantic clustering approach to optimize website structure discovery during penetration testing

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JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2024年第4期20卷 565-577页

作者： Antonelli, Diego Cascella, Roberta Schiano, Antonio Perrone, Gaetano Romano, Simon Pietro NTT DATA Italia SpA Via Calindri 4 I-20143 Milan Italy Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

Dirbusting is a technique used to brute force directories and file names on web servers while monitoring HTTP responses in order to enumerate server contents. Such a technique uses lists of common words to discover the hidden structure of the target website. Dirbusting typically relies on response codes as discovery conditions to find new pages. It is widely used in web application penetration testing, an activity that allows companies to detect website vulnerabilities. Dirbusting techniques are both time and resource-consuming, and innovative approaches have never been explored in this field. We hence propose "Dirclustering", an advanced technique to optimize the dirbusting process by leveraging semantic clustering. Specifically, we use semantic clustering techniques to organize wordlist items in different groups according to their semantic meaning. The created clusters are used in an ad-hoc implemented next-word intelligent strategy. This paper demonstrates that clustering techniques outperform the commonly used brute-force methods. Performance is evaluated by testing eight different web applications. Results show a performance increase that is up to 50% for each of the conducted experiments.

关键词： Artificial intelligence Dirbusting Network security Penetration testing Performance assessment semantic clustering

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