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Record Nr. |
UNINA9910845094503321 |
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Autore |
Ignatov Dmitry I |
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Titolo |
Analysis of Images, Social Networks and Texts [[electronic resource] ] : 11th International Conference, AIST 2023, Yerevan, Armenia, September 28–30, 2023, Revised Selected Papers / / edited by Dmitry I. Ignatov, Michael Khachay, Andrey Kutuzov, Habet Madoyan, Ilya Makarov, Irina Nikishina, Alexander Panchenko, Maxim Panov, Panos M. Pardalos, Andrey V. Savchenko, Evgenii Tsymbalov, Elena Tutubalina, Sergey Zagoruyko |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (376 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 14486 |
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Altri autori (Persone) |
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KhachayMichael |
KutuzovAndrey |
MadoyanHabet |
MakarovIlya |
NikishinaIrina |
PanchenkoAlexander |
PanovMaxim |
PardalosPanos M |
SavchenkoAndrey V |
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Disciplina |
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Soggetti |
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Data mining |
Machine learning |
Database management |
Natural language processing (Computer science) |
Information storage and retrieval systems |
Application software |
Data Mining and Knowledge Discovery |
Machine Learning |
Database Management |
Natural Language Processing (NLP) |
Information Storage and Retrieval |
Computer and Information Systems Applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Invited Paper: Threatening Expression and Target Identification in under-resource languages using NLP Techniques -- Natural Language Processing: Benchmarking Multi-Label Topic Classification in Kyrgyz Language -- Transformers compression: A study of matrix decomposition methods using Fisher information -- Leveraging Lexical Taxonomy Data in Large Language Models for Hyponymy Prediction -- Content selection in abstractive summarization with biased encoder mixtures -- RuCAM: Comparative Argumentative Machine for the Russian Language -- Paraphrasers and Classifiers: Controllable Text Generation for Text Style Transfer -- Less than Necessary or More than Sufficient: Validating Probing Dataset Size -- Unsupervised Ultra-Fine Entity Typing with Distributionally Induced Word Senses -- Static, dynamic, or contextualized: what is the best approach for discovering semantic shifts in Russian media? -- Controllable Story Generation Based on Perplexity Minimization -- Automatic Detection of Dialectal Features of Pskov Dialects in the Speech of Native Speakers -- Needle in a Haystack: Finding Suitable Idioms Based on Text Descriptions -- Computer Vision: DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images -- MiVOLO: Multi-input Transformer for Age and Gender Estimation -- Handwritten Text Recognition and Browsing in Archive of Prisoners’ Letters from Smolensk Convict Prison -- Greedy Algorithm for Fast Finding Curvilinear Symmetry of Binary Raster Images.-Data Analysis and Machine Learning: Ensemble Clustering with Heterogeneous Transfer Learning -- Detecting design patterns in Android applications with CodeBERT embeddings and CK metrics -- Metamorphic testing for recommender systems -- Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites -- Network Analysis: Visualization-Driven Graph Sampling Strategy for Exploring Large-Scale Networks -- Limit Distributions of Friendship Index in Scale-Free Networks -- Theoretical Machine Learning and Optimization: The Problem of Finding Several Given Diameter Spanning Trees of Maximum Total Weight in a Complete Graph -- Is Canfield Right? On the Asymptotic Coefficients for the Maximum Antichain of Partitions and Related Counting Inequalitie. |
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Sommario/riassunto |
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This book constitutes revised selected papers from the thoroughly refereed proceedings of the 11th International Conference on Analysis of Images, Social Networks and Texts, AIST 2023, held in Yerevan, Armenia, during September 28-30, 2023. The 24 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows: natural language processing; computer vision; data analysis and machine learning; network analysis; and theoretical machine learning and optimization. The book also contains one invited talk in full paper length. . |
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