Recent Trends in Analysis of Images, Social Networks and Texts : 11th International Conference, AIST 2023, Yerevan, Armenia, September 28-30, Revised Selected Papers

• Record Nr.: UNINA9910878069703321
• Autore: Ignatov Dmitry I
• Pubbl/distr/stampa: Cham : , : Springer International Publishing AG, , 2024; ©2024
• ISBN: 9783031670084; 9783031670077
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (332 pages)
• Collana: Communications in Computer and Information Science Series ; ; v.1905
• Altri autori (Persone): KhachayMichael; KutuzovAndrey; MadoyanHabet; MakarovIlya; NikishinaIrina; PanchenkoAlexander; PanovMaxim; M. PardalosPanos; SavchenkoAndrey V
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Organisation -- Abstracts of Posters -- Time Series Analysis and Complexity Theory -- Theoretical Guarantees for Neural Control Variates in MCMC -- Guaranteed Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution -- Analyzing Local Representative Power of Vision Transformers -- Towards Robust Entity Matching: Cases Studies for the Pharmaceutical Databases -- Evaluating Naturalness of the Text Using Quantitative Features -- Smaller3D: Smaller Models for 3D Semantic Segmentation Using Minkowski Engine and Knowledge Distillation Methods -- Machine Learning Pipelines Synthesis with Large Language Models -- Assessing Proficiency Levels of Russian Language Learners: Evaluation of Lexical ; Skills -- Genetic Variability and Disease Resistance Analysis of Grapevine from Armenia -- Raw Operational Data Visualization -- Using Physics-Informed Neural Networks to Predict Spatiotemporal Dynamical Processes Driven by Complex Ginzburg-Landau Equation -- Contents -- Natural Language Processing -- Determination of the Number of Topics Intrinsically: Is It Possible? -- 1 Introduction -- 2 Related Work -- 3 Intrinsic Quality Metrics -- 4 Methodology -- 4.1 Topic Models Studied -- 4.2 Corpora Used -- 5 Results and Discussion -- 5.1 Common Issues Encountered -- 5.2 Properties of the Studied Metrics -- 6 Conclusion -- References -- Sentence Difficulty in Three Languages: Russian Dataset Compared to Italian and English -- 1 Introduction -- 2 Related Work -- 2.1 Datasets and Models -- 3 Datasets -- 3.1 Sentence Complexity Dataset for Russian -- 3.2 Comparing Russian Data to English, Italian and Arabic -- 4 Modeling Sentence Complexity -- 4.1 Decision Trees, SVMs, and Linear Regression -- 4.2 Graph Neural Networks -- 4.3 Pre-trained Language Models -- 5 Results and Discussion -- 6 Conclusion -- References.; Machine Translation Models Stand Strong in the Face of Adversarial Attacks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 General Description of a Machine Translation Model -- 3.2 Gradient Machine Translation Attack -- 3.3 BLEUER Attack -- 3.4 MBART Attack -- 3.5 Synthetic Attacks -- 4 Experiments -- 4.1 Metrics -- 4.2 Baselines -- 4.3 Attack Examples -- 4.4 Experiment Setup -- 4.5 Main Results -- 4.6 Performance with Respect to Different Metrics -- 5 Conclusion -- References -- Whether Large Language Models Learn at the Inference Stage? Effects of Active Learning and Labelling with LLMs on their Reasoning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experiment Results -- 5 Conclusion -- 6 Future Directions -- References -- Machine Translation for Russian-Khakas Language Pair: Translation Results in Low-Resource Setting -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 Choosing the Language for Parent Model -- 3.2 Data for Child Model -- 4 Methods -- 5 Experiments -- 6 Results -- 6.1 Metrics -- 6.2 Case and Error Analysis -- 7 Conclusion and Future Work -- References -- Automatic Aspect Extraction from Scientific Texts -- 1 Introduction -- 2 Related Work -- 2.1 Background -- 2.2 Datasets -- 2.3 Methods -- 3 Dataset Creation -- 4 Automatic Aspect Extraction -- 4.1 Model -- 5 Experiments and Results -- 5.1 Evaluation Method -- 5.2 Experiments with Models -- 5.3 Cross Domain Experiments -- 5.4 Analysis of Automatic Annotation -- 6 Discussion -- 7 Conclusion -- References -- User Review Summarization in Russian -- 1 Introduction -- 2 Related Works -- 3 Data -- 4 Methodology -- 4.1 Abstractive Methods -- 4.2 Extractive Methods -- 5 Experiments -- 5.1 Metrics -- 5.2 Data Preprocessing -- 5.3 Seed Words Extraction -- 5.4 Review Summarization -- 5.5 Manual Analysis -- 6 Conclusion -- References.; Tuning-Free Discriminative Nearest Neighbor Few-Shot Intent Detection via Consecutive Knowledge Transfer -- 1 Introduction -- 2 Background -- 2.1 OOS Detection -- 3 Proposed Method -- 3.1 Pre-training -- 3.2 Paraphrases Augmentation -- 4 Experimental Settings -- 4.1 Evaluation Datasets -- 4.2 Evaluation Metrics -- 4.3 Model Configurations -- 5 Experimental Results -- 5.1 Pre-training Results -- 5.2 Comparison with Existing Solutions -- 6 Conclusion -- A Appendix -- References -- Prompt-Tuning for Targeted Sentiment Analysis in Russian -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Dataset -- 4.1 Dataset for the RuSentNE-2023 Competition -- 4.2 Evaluation Metrics -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Manual Prompts -- 5.3 Prompt-Tuning with Mixed Template -- 5.4 Prompt-Tuning with Rules -- 5.5 Knowledgeable Verbalizer -- 6 Results and ; Discussion -- 6.1 Cross-validation -- 6.2 RuSentNE-2023 Post-evaluation -- 7 Conclusion -- References -- On the Way to Controllable Text Summarization in Russian -- 1 Introduction -- 2 Related Work -- 2.1 Extractive Summarization -- 2.2 Abstractive Summarization -- 2.3 Summarization in Russian -- 3 HydraSum Method -- 4 Experimental Setup -- 4.1 Models -- 4.2 Data -- 4.3 Metrics -- 5 Results -- 6 Conclusion -- References -- Document-Level Relation Extraction in Russian -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Approaches -- 4.1 BiLSTM and Context-Aware LSTM -- 4.2 DocuNET -- 4.3 DREEAM -- 5 Experiments -- 5.1 Data Processing -- 5.2 DREEAM and DocuNET Model -- 6 Conclusion -- References -- The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends -- 1 Introduction -- 2 Contribution -- 3 Related Work -- 4 Methods -- 4.1 Data Preparation -- 4.2 Predictive Model Parameters -- 4.3 Loss Function -- 5 Results -- 5.1 Daily Return Analysis.; 5.2 Relation Between Tweet Sentiments and Stock Market -- 5.3 Connection of Sentiments and Embeddings -- 5.4 Price Prediction Results and Sensitivity Analysis -- 6 Conclusions -- 7 Statement on Computational Resources and Environmental Impact -- References -- Computer Vision -- Interactive Image Segmentation with Superpixel Propagation -- 1 Introduction -- 2 Interactive Segmentation Method -- 2.1 Overview of the Method -- 2.2 Fast Dashing Method and Arrival Time Redistribution -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Quantitative and Qualitative Comparison -- 3.3 Comparison with Naive Approach -- 4 Conclusions -- 4.1 Limitations -- References -- Gesture Recognition on Video Data -- 1 Introduction -- 2 Related Work -- 2.1 Datasets -- 2.2 Current SOTA Methods -- 3 Experiments and Results -- 3.1 Hand Keypoints Approaches -- 3.2 Image-Based Approaches -- 4 Proposed Approach -- 4.1 Training -- 4.2 Inference -- 5 Conclusion -- 5.1 Future Work -- References -- Semantic-Aware GAN Manipulations for Human Face Editing -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Comparable Methods -- 3.2 Latent Space Manipulation Improvements -- 3.3 Experiment Settings -- 4 Results -- 4.1 Manipulations in GAN Latent Space -- 4.2 Manipulations with Real Images -- 4.3 Improving Quality Manipulation with Translation in Extended Latent Space -- 5 Conclusion -- References -- Learning Facial Expression Recognition In-the-Wild from Synthetic Data Based on an Ensemble of Lightweight Neural Networks -- 1 Introduction -- 2 Research Methods -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Ensemble Approach for Facial Expressions Recognition -- 2.4 End-to-End Video Analytics Application -- 3 Experimental Results -- 4 Conclusion -- References -- Acne Recognition: Training Models with Experts -- 1 Introduction -- 2 Related Work -- 3 Data and Problem Statement.; 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- Data Analysis and Machine Learning -- Application of Multimodal Machine Learning for Image Recommendation Systems -- 1 Introduction -- 1.1 Recommender System -- 1.2 Multimodality -- 2 Related Work -- 3 Model -- 3.1 Dataset Preparation -- 3.2 Model -- 4 Experiment Settings -- 4.1 Experimental Datasets -- 4.2 Experiments with Yandex Dataset -- 4.3 Experiments with Flickr Image Dataset -- 5 Results -- 6 Conclusion -- References -- Date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 4 Results -- 5 Conclusions and Future Directions -- References -- Network Analysis -- Approximate Density Computation for OA-Biclustering -- 1 Introduction -- 2 Basic Notions ; for Two-Mode Network Analysis -- 2.1 Basic Definition of FCA -- 2.2 Basic Definition of Bicluster and Its Properties -- 3 Search Algorithm for Biclusters, Complexity Assessment of Algorithm -- 4 Approximate Density Estimation for the Large Biclusters -- 5 Search for the Optimal Set of Biclusters in the Context Coverage Problem -- 5.1 A Greedy Approach to Cover a Context -- 6 Experiments -- 6.1 Description of Data -- 6.2 Approximation of Formal Concepts by Biclusters -- 6.3 Approximate Density Estimation -- 6.4 Genome Association Study -- 6.5 Community Detection Cases -- 7 Conclusion -- References -- Theoretical Machine Learning and Optimization -- Distributed Bayesian Coresets -- 1 Introduction -- 2 Background -- 2.1 Bayesian Coresets -- 2.2 Sensitivity -- 2.3 Related Work -- 3 Distributed Setting -- 4 Experiments -- 4.1 Multivariate Gaussian -- 4.2 Gaussian Mixture -- 4.3 Classification -- 4.4 Number of Processors -- 5 Conclusion -- References -- Demo Paper.; Development of a Visualization Tool for the Occurrence of Life Events on the Demographic Lexis Grid.