Blockchain and Trustworthy Systems : 7th International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems, BlockSys 2025, Zhuhai, China, May 30–31, 2025, Revised Selected Papers, Part II / edited by Jianguo Chen, Xiaonan Luo, Yuanlong Yu.

Format:

Book

Author/Creator:

Chen, Jianguo.

Contributor:

Luo, Xiaonan.

Yu, Yuanlong.

Series:

Communications in Computer and Information Science, 1865-0937 ; 2638

Language:

English

Subjects (All):

Data protection--Law and legislation.

Data protection.

Artificial intelligence.

Data structures (Computer science).

Information theory.

Operating systems (Computers).

Computers, Special purpose.

Privacy.

Artificial Intelligence.

Data Structures and Information Theory.

Operating Systems.

Special Purpose and Application-Based Systems.

Local Subjects:

Privacy.

Artificial Intelligence.

Data Structures and Information Theory.

Operating Systems.

Special Purpose and Application-Based Systems.

Physical Description:

1 online resource (777 pages)

Edition:

1st ed. 2026.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2026.

Summary:

This three-volume set CCIS 2637-2639, constitutes the refereed proceedings of the 7th International Conference on Blockchain and Trustworthy Systems, BlockSys 2025, held in Zhuhai, China, during May 30–31, 2025. The 110 full papers included in this book were carefully reviewed and selected from 254 submissions. They were organized in topical sections as follows: Part I: Blockchain Security & Attack Mitigation; Identity Authentication & Data Privacy; Blockchain Applications & Industry Solutions; Part II: Deep Learning Architectures & Optimization; Computer Vision & Medical Image Analysis; Natural Language Processing & Multimodal Learning; Reinforcement Learning & Edge Computing Systems; Part III: Blockchain & Smart Contract Trustworthiness; Federated Learning & Edge Computing Security; Industrial & IoT System Reliability; Privacy-Preserving & Secure Data Management; .

Contents:

Deep Learning Architectures & Optimization

Rethinking Oversmoothing Mitigation in Deep Graph Neural Networks.

LPipe: Optimizing Memory Utilization in Pipeline Parallelism for Large Language Model Training with Bubble-Filled Activation Swapping.

A 2.5D Deep Learning Model Based on 3D CNN and 2D Swin Transformer for the Early Diagnosis of Alzheimer's Disease.

MS-UMamba: A Improved Vision Mamba UNet for Fetal Abdominal Medical Image Segmentation.

An Efficient Action Recognition Model Based on Improved Deep Learning Algorithm.

A DeepSeek-based Multimodal Emotion Recognition Algorithm for Elderly-Oriented Cultural Tourism Scenarios.

Matrix Factorization Recommendation System Optimized by Dynamic Latent Factors and Time Weighting.

A Stock Price Prediction Method Based on the DWT-FGRU Model.

An Efficient UAV-view Small Target Detection Algorithm Based on Dynamic Routing Attention and Collaborative Knowledge Distillation.

Computer Vision & Medical Image Analysis

VLME: Vision-Language Model Extraction Attack Through Multi-Modal Information Mapping.

A Multi-Modal and Temporal-Aware Person Re-identification Algorithm for LiDAR-Based Surveillance.

FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement.

Vision Foundation Models in Medical Image Analysis: Advances and Challenges.

A Survey on Crop Disease Prediction Detection: AI Models Trained on Multispectral and Hyperspectral Images for Early Disease Detection.

Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research.

Hybrid Distance-Based K-Means Clustering for Datasets with Numerical, Categorical, and Sequential Attributes.

Building A Cattle Farming System in Industry 4.0.

Underwater fish target detection system integrating multi-scale feature enhancement and multi-target tracking algorithms.

Natural Language Processing & Multimodal Learning

Transferring External Knowledge to Weakly Supervised Temporal Action Localization.

A Contrastive Learning-based Multi-scale Attention Model for Text Recognition.

Low-Resource Project-Specific Code Summarizaiton: On the Importance of Datasets.

Model Inversion Attack Enhancements: A Study in Optimization.

Public Data Pricing Models Leveraging Machine Learning.

An Empirical Study of Test Code Summarization.

Revisting Data-Driven ECG Classification: Definitions, Datasets and Benchmarks.

Intelligent Evaluation Model for Graduate Employment Quality Based on Cross Modal Feature Learning.

A New Timely Dataset for Chinese Rumor Detection: Automatic Generation via SOTA Large Language Models.

Reinforcement Learning & Edge Computing Systems

A Multi-view Hypergraph Contrastive Learning Framework for Item Recommendation.

Trajectory Design for Data Collection under Insufficient UAV Energy: A Staged Actor-Critic Reinforcement Learning Approach.

A Transformer-based Model with Patch and Gate Attention Meachanisms for Price Prediction in the Stock Market.

SEG: Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment.

FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting.

Research on Air Quality Imputation Model Based on Multi-Scale Attention Convolution Network.

A Volatile-Aware Client Selection Method for Federated Learning with Discounted Multi-armed Bandit.

Large-Scale Multiobjective Optimization Based on Gradient Grouping.

QSC: An Edge Server Placement Strategy Based on Spectral Clustering and Q-Learning.

Black-box Target Adversarial Examples against Deep Reinforcement Learning.

EC-MADRL: AI-Enhanced Edge Cooperation Caching Based on Multi-Agent Deep Reinforcement Learning.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9534-83-6

9789819534838

OCLC:

1573146294