Autor Chakraborty, Tanmoy
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Documentos disponibles escritos por este autor (5)
Hacer una sugerencia Refinar búsqueda25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I / Karlapalem, Kamal ; Cheng, Hong ; Ramakrishnan, Naren ; Agrawal, R. K. ; Reddy, P. Krishna ; Srivastava, Jaideep ; Chakraborty, Tanmoy
TÃtulo : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I Tipo de documento: documento electrónico Autores: Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXV, 834 p. 30 ilustraciones ISBN/ISSN/DL: 978-3-030-75762-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Inteligencia artificial Procesamiento de datos Ciencias sociales Red de computadoras Algoritmos MinerÃa de datos y descubrimiento de conocimientos Aplicación informática en ciencias sociales y del comportamiento Redes de comunicación informática Diseño y Análisis de Algoritmos Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Applications of Knowledge Discovery -- Fuzzy World:A Tool Training Agent from Concept Cognitive to Logic Inference -- Collaborative Reinforcement Learning Framework to Model Evolution of Cooperation in Sequential Social Dilemmas -- SIGTRAN: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks -- VOA*: Fast Angle-Based Outlier Detection Over High-Dimensional Data Streams -- Learning Probabilistic Latent Structure for Outlier Detection from Multi-View Data -- GLAD-PAW: Graph-based Log Anomaly Detection by Position Aware Weighted Graph Attention Network -- CubeFlow: Money Laundering Detection with Coupled Tensors -- Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection -- Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency -- A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations -- Autonomous Vehicle Path Prediction using Conditional Variational Autoencoder Networks -- Heterogeneous Graph Attention Network for Small and Medium-sized Enterprises Bankruptcy Prediction -- Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data -- Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-Training -- Attack Is the Best Defense: A Multi-Mode Poisoning PUF against Machine Learning Attacks -- Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets -- TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions -- Lifelong Learning based Disease Diagnosis on Clinical Notes -- GrabQC: Graph based Query Contextualization for automated ICD coding -- Deep Gaussian Mixture Model on Multiple Interpretable Features of Fetal Heart Rate for Pregnancy Wellness -- Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines -- Adaptive Graph Co-Attention Networks for Traffic Forecasting -- Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting -- AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life -- Data Mining of Specialized Data -- Analyzing Topic Transitions in Text-based Social Cascades using Dual-Network Hawkes Process -- HiPaR: Hierarchical Pattern-Aided Regression -- Improved Topology Extraction using Discriminative Parameter Mining of Logs -- Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion -- A k-MCST based Algorithm for Discovering Core-Periphery Structures in Graphs -- Detecting Sequentially Novel Classes with Stable Generalization Ability -- Learning-based Dynamic Graph Stream Sketch -- Discovering Dense Correlated Subgraphs in Dynamic Networks -- Fake News Detection with Heterogenous Deep Graph Convolutional Network -- Incrementally Finding the Vertices Absent from the Maximum Independent Sets -- Neighbours and Kinsmen: HatefulUsers Detection with Graph Neural Network -- Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs -- A Meta-path based Graph Convolutional Network with Multi-Scale Semantic Extractions for Heterogeneous Event Classification -- Noise-Enhanced Unsupervised Link Prediction -- Weak Supervision Network Embedding for Constrained Graph Learning -- RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment -- Graph Attention Networks with Positional Embeddings -- Unified Robust Training for Graph Neural Networks against Label Noise -- Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs -- A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention -- Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads. -Reliably Calibrated Isotonic Regression -- Multiple Instance Learning for Unilateral Data -- An Online Learning Algorithm for Non-Stationary Imbalanced Data by Extra-Charging Minority Class -- Locally Linear Support Vector Machines for Imbalanced Data Classification. - Low-Dimensional Representation Learning from Imbalanced Data Streams -- PhotoStylist: Altering the Style of Photos based on the Connotations of Texts -- Gazetteer-Guided Keyphrase Generation from Research Papers -- Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns -- T^3N: Harnessing Text and Temporal Tree Network for Rumor Detection on Twitter -- AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection -- SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction -- Content matters: A GNN-based Model Combined with Text Semantics for Social Network Cascade Prediction -- TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting -- Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-hailing Demand Forecasting -- A Proximity Forest for Multivariate Time Series Classification -- C²-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction -- Simultaneous multiple POI population patternanalysis system with HDP mixture regression -- Interpretable Feature Construction for Time Series Extrinsic Regression -- SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I [documento electrónico] / Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXV, 834 p. 30 ilustraciones.
ISBN : 978-3-030-75762-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Inteligencia artificial Procesamiento de datos Ciencias sociales Red de computadoras Algoritmos MinerÃa de datos y descubrimiento de conocimientos Aplicación informática en ciencias sociales y del comportamiento Redes de comunicación informática Diseño y Análisis de Algoritmos Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Applications of Knowledge Discovery -- Fuzzy World:A Tool Training Agent from Concept Cognitive to Logic Inference -- Collaborative Reinforcement Learning Framework to Model Evolution of Cooperation in Sequential Social Dilemmas -- SIGTRAN: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks -- VOA*: Fast Angle-Based Outlier Detection Over High-Dimensional Data Streams -- Learning Probabilistic Latent Structure for Outlier Detection from Multi-View Data -- GLAD-PAW: Graph-based Log Anomaly Detection by Position Aware Weighted Graph Attention Network -- CubeFlow: Money Laundering Detection with Coupled Tensors -- Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection -- Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency -- A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations -- Autonomous Vehicle Path Prediction using Conditional Variational Autoencoder Networks -- Heterogeneous Graph Attention Network for Small and Medium-sized Enterprises Bankruptcy Prediction -- Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data -- Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-Training -- Attack Is the Best Defense: A Multi-Mode Poisoning PUF against Machine Learning Attacks -- Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets -- TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions -- Lifelong Learning based Disease Diagnosis on Clinical Notes -- GrabQC: Graph based Query Contextualization for automated ICD coding -- Deep Gaussian Mixture Model on Multiple Interpretable Features of Fetal Heart Rate for Pregnancy Wellness -- Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines -- Adaptive Graph Co-Attention Networks for Traffic Forecasting -- Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting -- AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life -- Data Mining of Specialized Data -- Analyzing Topic Transitions in Text-based Social Cascades using Dual-Network Hawkes Process -- HiPaR: Hierarchical Pattern-Aided Regression -- Improved Topology Extraction using Discriminative Parameter Mining of Logs -- Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion -- A k-MCST based Algorithm for Discovering Core-Periphery Structures in Graphs -- Detecting Sequentially Novel Classes with Stable Generalization Ability -- Learning-based Dynamic Graph Stream Sketch -- Discovering Dense Correlated Subgraphs in Dynamic Networks -- Fake News Detection with Heterogenous Deep Graph Convolutional Network -- Incrementally Finding the Vertices Absent from the Maximum Independent Sets -- Neighbours and Kinsmen: HatefulUsers Detection with Graph Neural Network -- Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs -- A Meta-path based Graph Convolutional Network with Multi-Scale Semantic Extractions for Heterogeneous Event Classification -- Noise-Enhanced Unsupervised Link Prediction -- Weak Supervision Network Embedding for Constrained Graph Learning -- RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment -- Graph Attention Networks with Positional Embeddings -- Unified Robust Training for Graph Neural Networks against Label Noise -- Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs -- A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention -- Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads. -Reliably Calibrated Isotonic Regression -- Multiple Instance Learning for Unilateral Data -- An Online Learning Algorithm for Non-Stationary Imbalanced Data by Extra-Charging Minority Class -- Locally Linear Support Vector Machines for Imbalanced Data Classification. - Low-Dimensional Representation Learning from Imbalanced Data Streams -- PhotoStylist: Altering the Style of Photos based on the Connotations of Texts -- Gazetteer-Guided Keyphrase Generation from Research Papers -- Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns -- T^3N: Harnessing Text and Temporal Tree Network for Rumor Detection on Twitter -- AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection -- SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction -- Content matters: A GNN-based Model Combined with Text Semantics for Social Network Cascade Prediction -- TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting -- Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-hailing Demand Forecasting -- A Proximity Forest for Multivariate Time Series Classification -- C²-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction -- Simultaneous multiple POI population patternanalysis system with HDP mixture regression -- Interpretable Feature Construction for Time Series Extrinsic Regression -- SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i
TÃtulo : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part II Tipo de documento: documento electrónico Autores: Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXVI, 774 p. 30 ilustraciones ISBN/ISSN/DL: 978-3-030-75765-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Inteligencia artificial Ciencias sociales Software de la aplicacion Procesamiento de imágenes Visión por computador Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Aplicaciones informáticas y de sistemas de información Imágenes por computadora visión reconocimiento de patrones y gráficos Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Classical Data Mining,. Mining Frequent Patterns from Hypergraph Databases -- Discriminating Frequent Pattern based Supervised Graph Embedding for Classification -- Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure -- Similarity Forest Revisited: a Swiss Army Knife for Machine Learning -- Discriminative Representation Learning for Cross-domain Sentiment Classification -- SAGCN: Towards Structure-Aware Deep Graph Convolutional Networks on Node Classification -- Hierarchical Learning of Dependent Concepts for Human Activity Recognition -- Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge -- A Novel Method for Offline Handwritten Chinese Character Recognition under the Guidance of Print -- Upgraded Attention-based Local FeatureLearning Block for speech emotion recognition -- Memorization in Deep Neural Networks: Does the Loss Function matter -- Gaussian Soft Decision Trees for Interpretable Feature-Based Classification -- Efficient Nodes Representation Learning with Residual Feature Propagation -- Progressive AutoSpeech: An efficient and general framework for automatic speech classification -- CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data -- Effective and Adaptive Multi-metric Refined Similarity Graph Fusion for Multi-view Clustering -- aHCQ: Adaptive Hierarchical Clustering based Quantization Framework for Deep Neural Networks -- Maintaining Consistency with Constraints: a Constrained Deep Clustering method -- Data Mining Theory and Principles -- Towards multi-label Feature selection by Instance and Label Selections -- FARF: A Fair and Adaptive Random Forests Classifier -- Sparse Spectrum Gaussian Process for Bayesian Optimization -- Densely Connected Graph Attention Network based on Iterative Path Reasoning for Document-level Relation Extraction -- Causal Inference Using Global Forecasting Models for Counterfactual Prediction. -CED-BGFN: Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network -- Learning Finite Automata with Shuffle -- Active Learning based Similarity Filtering for Efficient and Effective Record Linkage -- Stratified Sampling for Extreme Multi-Label Data -- Vertical Federated Learning for Higher-order Factorization Machines -- dK-Projection: Publishing Graph Joint degree distribution with Node Differential Privacy -- Recommender Systems -- Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks -- Exploring Implicit Relationships in Social Network for Recommendation Systems -- Transferable Contextual Bandits with Prior Observations -- Modeling Hierarchical Intents and Selective Current Interest for Session-based Recommendation -- A Finetuned language model for Recommending cQA-QAs for enriching Textbooks -- XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction -- Learning Multiclass Classifier Under Noisy Bandit Feedback -- Diversify orNot: Dynamic Diversification for Personalized Recommendation -- Multi-criteria and Review-based Overall Rating Prediction -- W2FM: The Doubly-Warped Factorization Machine -- Causal Combinatorial Factorization Machines for Set-wise Recommendation -- Transformer-based Multi-task Learning for Queuing Time Aware Next POI Recommendation -- Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation -- Box4Rec: Box Embedding for Sequential Recommendation -- UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering -- IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation -- Nonlinear Matrix Factorization via Neighbor Embedding -- Deconfounding representation learning based on user interactions in Recommendation Systems -- Personalized Regularization Learning for Fairer Matrix Factorization -- Instance Selection for Online Updating in Dynamic Recommender Environments -- Text Analytics.-Fusing Essential Knowledge for Text-Based Open-Domain Question Answering. - TSSE-DMM: Topic Modeling for Short Texts based on Topic Subdivision and Semantic Enhancement -- SILVER: Generating Persuasive Chinese Product Pitch -- Capturing SQL Query Overlapping via SubtreeCopy for Cross-domain Context-dependent SQLGeneration -- HScodeNet: Combining Hierarchical Sequential and Global Spatial Information of Text for Commodity HS Code Classification -- PLVCG: A Pretraining Based Model for Live Video Comment Generation -- Inducing Rich Interaction Structures between Words for Document-level Event Argument Extraction -- Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking -- TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training -- Semantic-syntax Cascade Injection Model for Aspect Sentiment Triple Extraction -- Modeling Inter-Aspect Relationship with Conjunction for Aspect-based Sentiment Analysis. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part II [documento electrónico] / Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXVI, 774 p. 30 ilustraciones.
ISBN : 978-3-030-75765-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Inteligencia artificial Ciencias sociales Software de la aplicacion Procesamiento de imágenes Visión por computador Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Aplicaciones informáticas y de sistemas de información Imágenes por computadora visión reconocimiento de patrones y gráficos Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Classical Data Mining,. Mining Frequent Patterns from Hypergraph Databases -- Discriminating Frequent Pattern based Supervised Graph Embedding for Classification -- Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure -- Similarity Forest Revisited: a Swiss Army Knife for Machine Learning -- Discriminative Representation Learning for Cross-domain Sentiment Classification -- SAGCN: Towards Structure-Aware Deep Graph Convolutional Networks on Node Classification -- Hierarchical Learning of Dependent Concepts for Human Activity Recognition -- Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge -- A Novel Method for Offline Handwritten Chinese Character Recognition under the Guidance of Print -- Upgraded Attention-based Local FeatureLearning Block for speech emotion recognition -- Memorization in Deep Neural Networks: Does the Loss Function matter -- Gaussian Soft Decision Trees for Interpretable Feature-Based Classification -- Efficient Nodes Representation Learning with Residual Feature Propagation -- Progressive AutoSpeech: An efficient and general framework for automatic speech classification -- CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data -- Effective and Adaptive Multi-metric Refined Similarity Graph Fusion for Multi-view Clustering -- aHCQ: Adaptive Hierarchical Clustering based Quantization Framework for Deep Neural Networks -- Maintaining Consistency with Constraints: a Constrained Deep Clustering method -- Data Mining Theory and Principles -- Towards multi-label Feature selection by Instance and Label Selections -- FARF: A Fair and Adaptive Random Forests Classifier -- Sparse Spectrum Gaussian Process for Bayesian Optimization -- Densely Connected Graph Attention Network based on Iterative Path Reasoning for Document-level Relation Extraction -- Causal Inference Using Global Forecasting Models for Counterfactual Prediction. -CED-BGFN: Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network -- Learning Finite Automata with Shuffle -- Active Learning based Similarity Filtering for Efficient and Effective Record Linkage -- Stratified Sampling for Extreme Multi-Label Data -- Vertical Federated Learning for Higher-order Factorization Machines -- dK-Projection: Publishing Graph Joint degree distribution with Node Differential Privacy -- Recommender Systems -- Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks -- Exploring Implicit Relationships in Social Network for Recommendation Systems -- Transferable Contextual Bandits with Prior Observations -- Modeling Hierarchical Intents and Selective Current Interest for Session-based Recommendation -- A Finetuned language model for Recommending cQA-QAs for enriching Textbooks -- XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction -- Learning Multiclass Classifier Under Noisy Bandit Feedback -- Diversify orNot: Dynamic Diversification for Personalized Recommendation -- Multi-criteria and Review-based Overall Rating Prediction -- W2FM: The Doubly-Warped Factorization Machine -- Causal Combinatorial Factorization Machines for Set-wise Recommendation -- Transformer-based Multi-task Learning for Queuing Time Aware Next POI Recommendation -- Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation -- Box4Rec: Box Embedding for Sequential Recommendation -- UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering -- IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation -- Nonlinear Matrix Factorization via Neighbor Embedding -- Deconfounding representation learning based on user interactions in Recommendation Systems -- Personalized Regularization Learning for Fairer Matrix Factorization -- Instance Selection for Online Updating in Dynamic Recommender Environments -- Text Analytics.-Fusing Essential Knowledge for Text-Based Open-Domain Question Answering. - TSSE-DMM: Topic Modeling for Short Texts based on Topic Subdivision and Semantic Enhancement -- SILVER: Generating Persuasive Chinese Product Pitch -- Capturing SQL Query Overlapping via SubtreeCopy for Cross-domain Context-dependent SQLGeneration -- HScodeNet: Combining Hierarchical Sequential and Global Spatial Information of Text for Commodity HS Code Classification -- PLVCG: A Pretraining Based Model for Live Video Comment Generation -- Inducing Rich Interaction Structures between Words for Document-level Event Argument Extraction -- Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking -- TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training -- Semantic-syntax Cascade Injection Model for Aspect Sentiment Triple Extraction -- Modeling Inter-Aspect Relationship with Conjunction for Aspect-based Sentiment Analysis. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i
TÃtulo : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III Tipo de documento: documento electrónico Autores: Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXIII, 434 p. 142 ilustraciones, 117 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-75768-7 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Inteligencia artificial Ciencias sociales Algoritmos Informática Visión por computador Aplicación informática en ciencias sociales y del comportamiento Diseño y Análisis de Algoritmos Computadoras y Educación Matemáticas de la Computación Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Representation Learning and Embedding -- Episode Adaptive Embedding Networks for Few-shot Learning -- Universal Representation for Code -- Self-supervised Adaptive Aggregator Learning on Graph -- A Fast Algorithm for Simultaneous Sparse Approximation -- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning -- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification -- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models -- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network -- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning -- Self-supervised Graph Representation Learning with Variational Inference -- Manifold Approximation and Projection by Maximizing Graph Information -- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping -- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction -- Human-Understandable Decision Making for Visual Recognition -- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding -- Transferring Domain Knowledge with an Adviser in Continuous Tasks -- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach -- Quality Control for Hierarchical Classification with Incomplete Annotations -- Learning from Data -- Learning Discriminative Features using Multi-label Dual Space -- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering -- BanditRank: Learning to Rank Using Contextual Bandits -- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach -- Meta-Context Transformers for Domain-Specific Response Generation -- A Multi-task Kernel Learning Algorithm for Survival Analysis -- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection -- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction -- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning -- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition -- Reinforced Natural Language Inference for Distantly Supervised Relation Classification -- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction -- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function -- Incorporating Relational Knowledge in Explainable Fake News Detection -- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III [documento electrónico] / Karlapalem, Kamal, ; Cheng, Hong, ; Ramakrishnan, Naren, ; Agrawal, R. K., ; Reddy, P. Krishna, ; Srivastava, Jaideep, ; Chakraborty, Tanmoy, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXIII, 434 p. 142 ilustraciones, 117 ilustraciones en color.
ISBN : 978-3-030-75768-7
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Inteligencia artificial Ciencias sociales Algoritmos Informática Visión por computador Aplicación informática en ciencias sociales y del comportamiento Diseño y Análisis de Algoritmos Computadoras y Educación Matemáticas de la Computación Ãndice Dewey: 006.3 Inteligencia artificial Resumen: El conjunto de tres volúmenes LNAI 12712-12714 constituye las actas de la 25.ª Conferencia de Asia PacÃfico sobre avances en el descubrimiento de conocimientos y la minerÃa de datos, PAKDD 2021, que se celebró del 11 al 14 de mayo de 2021. Los 157 artÃculos incluidos en las actas fueron cuidadosamente revisado y seleccionado de un total de 628 presentaciones. Se organizaron en secciones temáticas de la siguiente manera: Parte I: Aplicaciones del descubrimiento de conocimientos y extracción de datos especializados; Parte II: MinerÃa de datos clásica; teorÃa y principios de minerÃa de datos; sistemas de recomendación; y análisis de texto; Parte III: Aprendizaje e incorporación de representaciones, y aprendizaje a partir de datos. Nota de contenido: Representation Learning and Embedding -- Episode Adaptive Embedding Networks for Few-shot Learning -- Universal Representation for Code -- Self-supervised Adaptive Aggregator Learning on Graph -- A Fast Algorithm for Simultaneous Sparse Approximation -- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning -- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification -- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models -- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network -- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning -- Self-supervised Graph Representation Learning with Variational Inference -- Manifold Approximation and Projection by Maximizing Graph Information -- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping -- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction -- Human-Understandable Decision Making for Visual Recognition -- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding -- Transferring Domain Knowledge with an Adviser in Continuous Tasks -- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach -- Quality Control for Hierarchical Classification with Incomplete Annotations -- Learning from Data -- Learning Discriminative Features using Multi-label Dual Space -- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering -- BanditRank: Learning to Rank Using Contextual Bandits -- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach -- Meta-Context Transformers for Domain-Specific Response Generation -- A Multi-task Kernel Learning Algorithm for Survival Analysis -- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection -- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction -- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning -- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition -- Reinforced Natural Language Inference for Distantly Supervised Relation Classification -- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction -- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function -- Incorporating Relational Knowledge in Explainable Fake News Detection -- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i
TÃtulo : Combating Online Hostile Posts in Regional Languages during Emergency Situation : First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers Tipo de documento: documento electrónico Autores: Chakraborty, Tanmoy, ; Shu, Kai, ; Bernard, H. Russell, ; Liu, Huan, ; Akhtar, Md Shad, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XI, 258 p. 19 ilustraciones ISBN/ISSN/DL: 978-3-030-73696-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Gestión de base de datos Inteligencia artificial Ciencias sociales Software de la aplicacion Sistema de administración de base de datos Aplicación informática en ciencias sociales y del comportamiento Aplicaciones informáticas y de sistemas de información Ãndice Dewey: 005.7 Datos en sistemas de computadoras Resumen: Este libro constituye artÃculos seleccionados y revisados ​​del Primer Taller Internacional sobre la lucha contra publicaciones hostiles en lÃnea en idiomas regionales durante situaciones de emergencia, CONSTRAINT 2021, ubicado junto con AAAI 2021, celebrado como evento virtual, en febrero de 2021. El 14 completo Los artÃculos y 9 artÃculos breves presentados fueron revisados ​​minuciosamente y seleccionados entre 62 presentaciones calificadas. Los artÃculos presentan enfoques interdisciplinarios sobre análisis de redes sociales multilingües y formas no convencionales de combatir publicaciones hostiles en lÃnea. Nota de contenido: Identifying Offensive Content in Social Media Posts -- Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation -- Fighting an Infodemic: COVID-19 Fake News Dataset -- Revealing the Blackmarket Retweet Game: A Hybrid Approach -- Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts -- LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT -- Extracting latent information from datasets in The CONSTRAINT-2020 shared task on the hostile post detection -- Fake news and hostile posts detection using an ensemble learning model -- Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection -- Tackling the infodemic : Analysis using Transformer based models -- Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English -- g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection -- Model Generalization on COVID-19 Fake News Detection -- ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information -- Evaluating Deep Learning Approaches for Covid19 Fake News Detection -- A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection -- Identification of COVID-19 related Fake News via Neural Stacking -- Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task -- Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings -- Hostility Detection in Hindi leveraging Pre-Trained Language Models -- Stacked embeddings and multiple fine-tuned XLM-RoBERTa models for Enhanced hostility identification -- Task Adaptive Pretraining of Transformers for Hostility Detection -- Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i Combating Online Hostile Posts in Regional Languages during Emergency Situation : First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers [documento electrónico] / Chakraborty, Tanmoy, ; Shu, Kai, ; Bernard, H. Russell, ; Liu, Huan, ; Akhtar, Md Shad, . - 1 ed. . - [s.l.] : Springer, 2021 . - XI, 258 p. 19 ilustraciones.
ISBN : 978-3-030-73696-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Gestión de base de datos Inteligencia artificial Ciencias sociales Software de la aplicacion Sistema de administración de base de datos Aplicación informática en ciencias sociales y del comportamiento Aplicaciones informáticas y de sistemas de información Ãndice Dewey: 005.7 Datos en sistemas de computadoras Resumen: Este libro constituye artÃculos seleccionados y revisados ​​del Primer Taller Internacional sobre la lucha contra publicaciones hostiles en lÃnea en idiomas regionales durante situaciones de emergencia, CONSTRAINT 2021, ubicado junto con AAAI 2021, celebrado como evento virtual, en febrero de 2021. El 14 completo Los artÃculos y 9 artÃculos breves presentados fueron revisados ​​minuciosamente y seleccionados entre 62 presentaciones calificadas. Los artÃculos presentan enfoques interdisciplinarios sobre análisis de redes sociales multilingües y formas no convencionales de combatir publicaciones hostiles en lÃnea. Nota de contenido: Identifying Offensive Content in Social Media Posts -- Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation -- Fighting an Infodemic: COVID-19 Fake News Dataset -- Revealing the Blackmarket Retweet Game: A Hybrid Approach -- Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts -- LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT -- Extracting latent information from datasets in The CONSTRAINT-2020 shared task on the hostile post detection -- Fake news and hostile posts detection using an ensemble learning model -- Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection -- Tackling the infodemic : Analysis using Transformer based models -- Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English -- g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection -- Model Generalization on COVID-19 Fake News Detection -- ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information -- Evaluating Deep Learning Approaches for Covid19 Fake News Detection -- A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection -- Identification of COVID-19 related Fake News via Neural Stacking -- Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task -- Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings -- Hostility Detection in Hindi leveraging Pre-Trained Language Models -- Stacked embeddings and multiple fine-tuned XLM-RoBERTa models for Enhanced hostility identification -- Task Adaptive Pretraining of Transformers for Hostility Detection -- Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i
TÃtulo : Data Science for Fake News : Surveys and Perspectives Tipo de documento: documento electrónico Autores: P, Deepak, Autor ; Chakraborty, Tanmoy, Autor ; Long, Cheng, Autor ; G, Santhosh Kumar, Autor Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XIV, 302 p. 70 ilustraciones, 17 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-62696-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Sistemas de almacenamiento y recuperación de información Protección de datos Procesamiento de datos Comunicación Almacenamiento y recuperación de información Seguridad de datos e información MinerÃa de datos y descubrimiento de conocimientos Media y comunicación Ãndice Dewey: 025.04 Sistemas de almacenamiento y recuperación de información Resumen: Este libro proporciona una descripción general de la detección de noticias falsas, tanto a través de una variedad de artÃculos de encuestas de estilo tutorial que capturan los avances en el campo desde diversas facetas como en una dirección un tanto única a través de perspectivas de expertos de diversas disciplinas. El enfoque se basa en la idea de que avanzar en la frontera de los enfoques de ciencia de datos para las noticias falsas es un esfuerzo interdisciplinario, y que las perspectivas de los expertos en el campo son cruciales para dar forma a la próxima generación de métodos y herramientas. El desafÃo de las noticias falsas abarca varios subcampos de la ciencia de datos, como el análisis de gráficos, la extracción de datos espacio-temporales, la recuperación de información, el procesamiento del lenguaje natural, la visión por computadora y el procesamiento de imágenes, por nombrar algunos. Este libro presentará una serie de encuestas de estilo tutorial que resumen una variedad de trabajos recientes en este campo. Como caracterÃstica única, este libro incluye notas perspectivas de expertos en disciplinas como la lingüÃstica, la antropologÃa, la medicina y la polÃtica que ayudarán a dar forma a la próxima generación de investigación cientÃfica de datos en noticias falsas. Los principales grupos objetivo de este libro son investigadores académicos e industriales que trabajan en el área de la ciencia de datos y con intereses en diseñar y aplicar tecnologÃas de ciencia de datos para la detección de noticias falsas. Para investigadores jóvenes, como estudiantes de doctorado, se proporciona una revisión del trabajo de ciencia de datos sobre noticias falsas, proporcionándoles conocimientos suficientes para comenzar a participar en investigaciones en el área. Para los investigadores experimentados, las descripciones detalladas de los enfoques les permitirán tomar decisiones experimentadas para identificar direcciones prometedoras para futuras investigaciones. Nota de contenido: A Multifaceted Approach to Fake News -- Part I: Survey -- On Unsupervised Methods for Fake News Detection -- Multi-modal Fake News Detection -- Deep Learning for Fake News Detection -- Dynamics of Fake News Diffusion -- Neural Language Models for (Fake?) News Generation -- Fact Checking on Knowledge Graphs -- Graph Mining Meets Fake News Detection -- Part II: Perspectives -- Fake News in Health and Medicine -- Ethical Considerations in Data-Driven Fake News Detection -- A Political Science Perspective on Fake News -- A Political Science Perspective on Fake News -- Fake News and Social Processes: A Short Review -- Misinformation and the Indian Election: Case Study -- STS, Data Science, and Fake News: Questions and Challenges -- Linguistic Approaches to Fake News Detection. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i Data Science for Fake News : Surveys and Perspectives [documento electrónico] / P, Deepak, Autor ; Chakraborty, Tanmoy, Autor ; Long, Cheng, Autor ; G, Santhosh Kumar, Autor . - 1 ed. . - [s.l.] : Springer, 2021 . - XIV, 302 p. 70 ilustraciones, 17 ilustraciones en color.
ISBN : 978-3-030-62696-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Sistemas de almacenamiento y recuperación de información Protección de datos Procesamiento de datos Comunicación Almacenamiento y recuperación de información Seguridad de datos e información MinerÃa de datos y descubrimiento de conocimientos Media y comunicación Ãndice Dewey: 025.04 Sistemas de almacenamiento y recuperación de información Resumen: Este libro proporciona una descripción general de la detección de noticias falsas, tanto a través de una variedad de artÃculos de encuestas de estilo tutorial que capturan los avances en el campo desde diversas facetas como en una dirección un tanto única a través de perspectivas de expertos de diversas disciplinas. El enfoque se basa en la idea de que avanzar en la frontera de los enfoques de ciencia de datos para las noticias falsas es un esfuerzo interdisciplinario, y que las perspectivas de los expertos en el campo son cruciales para dar forma a la próxima generación de métodos y herramientas. El desafÃo de las noticias falsas abarca varios subcampos de la ciencia de datos, como el análisis de gráficos, la extracción de datos espacio-temporales, la recuperación de información, el procesamiento del lenguaje natural, la visión por computadora y el procesamiento de imágenes, por nombrar algunos. Este libro presentará una serie de encuestas de estilo tutorial que resumen una variedad de trabajos recientes en este campo. Como caracterÃstica única, este libro incluye notas perspectivas de expertos en disciplinas como la lingüÃstica, la antropologÃa, la medicina y la polÃtica que ayudarán a dar forma a la próxima generación de investigación cientÃfica de datos en noticias falsas. Los principales grupos objetivo de este libro son investigadores académicos e industriales que trabajan en el área de la ciencia de datos y con intereses en diseñar y aplicar tecnologÃas de ciencia de datos para la detección de noticias falsas. Para investigadores jóvenes, como estudiantes de doctorado, se proporciona una revisión del trabajo de ciencia de datos sobre noticias falsas, proporcionándoles conocimientos suficientes para comenzar a participar en investigaciones en el área. Para los investigadores experimentados, las descripciones detalladas de los enfoques les permitirán tomar decisiones experimentadas para identificar direcciones prometedoras para futuras investigaciones. Nota de contenido: A Multifaceted Approach to Fake News -- Part I: Survey -- On Unsupervised Methods for Fake News Detection -- Multi-modal Fake News Detection -- Deep Learning for Fake News Detection -- Dynamics of Fake News Diffusion -- Neural Language Models for (Fake?) News Generation -- Fact Checking on Knowledge Graphs -- Graph Mining Meets Fake News Detection -- Part II: Perspectives -- Fake News in Health and Medicine -- Ethical Considerations in Data-Driven Fake News Detection -- A Political Science Perspective on Fake News -- A Political Science Perspective on Fake News -- Fake News and Social Processes: A Short Review -- Misinformation and the Indian Election: Case Study -- STS, Data Science, and Fake News: Questions and Challenges -- Linguistic Approaches to Fake News Detection. En lÃnea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i
