Description

Book Synopsis

.- Graph Mining.
.- MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion.
.- Tensor-Fused Multi-View Graph Contrastive Learning.
.- FOG: Interpretable Feature-Oriented Graph Neural Networks for Tabular Data  Prediction.
.- High Resolution Image Classification with Rich Text Information Based on Graph Convolution Neural Network.
.- Time Interval Aware Graph Neural Networks for Session-Based Recommendation.
.- SSGNN: Structure-aware Scoring Graph Neural Network for Molecular Representation.
.- Mint: An Efficient and Robust In-Place Update Approach for Graph-based Vector Index.
.- Machine Learning Applications.
.- Advancing Comprehensive Aspect-Based Sentiment Analysis with Generative Models.
.- A Systematic Evaluation of Generative Models on Tabular Transportation Data.
.- SDF-Guided Multi-modal Big Data Road Extraction.
.- Player Movement Predictions Using Team and Opponent Dynamics for Doubles Badminton.
.- Representation Learning.
.- Late Fusion Ensembles for Speech Recognition on Diverse Input Audio Representations.
.- Text Enhancement-based Multimodal Fusion for Video Sentiment Analysis.
.- Advancing Rubric-based Automated Essay Scoring with Multi-View BERT: A Case Study in New Zealand.
.- A Script Event Prediction Method Based on Multi-Level Joint Pretraining and Prompt Fine-Tuning.
.- Scientific/Business Data Analysis.
.- A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection.
.- Using Pseudo-Synonyms to Generate Embeddings for Clinical Terms.
.- Corporate Carbon Emission Prediction: Combining Structured and Unstructured Data.
.- GDCK: Efficient Large-Scale Graph Distillation utilizing a Model-free Kernelized Approach.
.- Efficient DNA fragment assembly based on Discrete Slime Mould Algorithm.
.- Multi-Scale Control Model for Network Group Behavior.
.- Can Self Supervision Rejuvenate Similarity-Based Link Prediction?.
.- Managing Data Uncertainty in Automatic Mapping of Clinical Classification Systems.
.- Insomnia Detection Based on Brain State Sleep Trajectories.
.- MCA: Multimodal Contrastive Augmentation for Medical Report Generation.
.- Special Track on Large Language Models.
.-Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection.
.- Bot Wars Evolved: Orchestrating Competing LLMs in a Counterstrike Against Phone Scams.
.- Large Language Models with Multi-Faceted Relation Alignment for User Novel Interest Discovery.
.- Estimating Impact of Behavior Change Messages Using Large Language Models.
.- A Meta-Thinking Approach to Mitigating Linguistic Sycophancy in Vision-Language Models.
.- VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models.
.- TRAWL: Tensor Reduced and Approximated Weights for Large Language Models.
.- DAG-Think-Twice: Causal Structure Guided Elicitation of Causal Reasoning in Large Language Model.
.- GRL-Prompt: Towards Prompts Optimization via Graph-empowered Reinforcement Learning using LLMs’ Feedback.

Data Science Foundations and Applications

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    A Paperback by Xintao Wu

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      View other formats and editions of Data Science Foundations and Applications by Xintao Wu

      Publisher: Springer
      Publication Date: 7/27/2025
      ISBN13: 9789819682973, 978-9819682973
      ISBN10: 9819682975
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      .- Graph Mining.
      .- MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion.
      .- Tensor-Fused Multi-View Graph Contrastive Learning.
      .- FOG: Interpretable Feature-Oriented Graph Neural Networks for Tabular Data  Prediction.
      .- High Resolution Image Classification with Rich Text Information Based on Graph Convolution Neural Network.
      .- Time Interval Aware Graph Neural Networks for Session-Based Recommendation.
      .- SSGNN: Structure-aware Scoring Graph Neural Network for Molecular Representation.
      .- Mint: An Efficient and Robust In-Place Update Approach for Graph-based Vector Index.
      .- Machine Learning Applications.
      .- Advancing Comprehensive Aspect-Based Sentiment Analysis with Generative Models.
      .- A Systematic Evaluation of Generative Models on Tabular Transportation Data.
      .- SDF-Guided Multi-modal Big Data Road Extraction.
      .- Player Movement Predictions Using Team and Opponent Dynamics for Doubles Badminton.
      .- Representation Learning.
      .- Late Fusion Ensembles for Speech Recognition on Diverse Input Audio Representations.
      .- Text Enhancement-based Multimodal Fusion for Video Sentiment Analysis.
      .- Advancing Rubric-based Automated Essay Scoring with Multi-View BERT: A Case Study in New Zealand.
      .- A Script Event Prediction Method Based on Multi-Level Joint Pretraining and Prompt Fine-Tuning.
      .- Scientific/Business Data Analysis.
      .- A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection.
      .- Using Pseudo-Synonyms to Generate Embeddings for Clinical Terms.
      .- Corporate Carbon Emission Prediction: Combining Structured and Unstructured Data.
      .- GDCK: Efficient Large-Scale Graph Distillation utilizing a Model-free Kernelized Approach.
      .- Efficient DNA fragment assembly based on Discrete Slime Mould Algorithm.
      .- Multi-Scale Control Model for Network Group Behavior.
      .- Can Self Supervision Rejuvenate Similarity-Based Link Prediction?.
      .- Managing Data Uncertainty in Automatic Mapping of Clinical Classification Systems.
      .- Insomnia Detection Based on Brain State Sleep Trajectories.
      .- MCA: Multimodal Contrastive Augmentation for Medical Report Generation.
      .- Special Track on Large Language Models.
      .-Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection.
      .- Bot Wars Evolved: Orchestrating Competing LLMs in a Counterstrike Against Phone Scams.
      .- Large Language Models with Multi-Faceted Relation Alignment for User Novel Interest Discovery.
      .- Estimating Impact of Behavior Change Messages Using Large Language Models.
      .- A Meta-Thinking Approach to Mitigating Linguistic Sycophancy in Vision-Language Models.
      .- VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models.
      .- TRAWL: Tensor Reduced and Approximated Weights for Large Language Models.
      .- DAG-Think-Twice: Causal Structure Guided Elicitation of Causal Reasoning in Large Language Model.
      .- GRL-Prompt: Towards Prompts Optimization via Graph-empowered Reinforcement Learning using LLMs’ Feedback.

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