Knowledge Science, Engineering and Management

The five-volume set LNCS 14884, 14885, 14886, 14887 & 14888 constitutes the refereed deadline proceedings of the 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024, held in Birmingham, UK, during August 1618, 2024.

The 160 full papers presented in these proceedings were carefully reviewed and selected from 495 submissions. The papers are organized in the following topical sections:

Volume I: Knowledge Science with Learning and AI (KSLA)

Volume II: Knowledge Engineering Research and Applications (KERA)

Volume III: Knowledge Management with Optimization and Security (KMOS)

Volume IV: Emerging Technology

Volume V: Special Tracks

Klappentext

The five-volume set LNCS 14884, 14885, 14886, 14887 & 14888 constitutes the refereed deadline proceedings of the 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024, held in Birmingham, UK, during August 16 18, 2024. The 160 full papers presented in these proceedings were carefully reviewed and selected from 495 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS) Volume IV: Emerging Technology Volume V: Special Tracks

Inhalt

.- Knowledge Management with Optimization and Security (KMOS).

.- Knowledge Enhanced Zero-Shot Visual Relationship Detection.

.- WGGAL: A Practical Time Series Forecasting Framework for Dynamic Cloud Environments.

.- Dynamic Splitting of Diffusion Models for Multivariate Time Series Anomaly Detection in A JointCloud Environment.

.- VulCausal: Robust Vulnerability Detection Using Neural Network Models from a Causal Perspective.

.- LLM-Driven Ontology Learning to Augment Student Performance Analysis in Higher Education.

.- DA-NAS: Learning Transferable Architecture for Unsupervised Domain Adaptation.

.- Optimize rule mining based on constraint learning in knowledge graph.

.- GC-DAWMAR: A Global-Local Framework for Long-Term Time Series Forecasting.

.- An improved YOLOv7 based prohibited item detection model in X-ray images.

.- Invisible Backdoor Attacks on Key Regions Based on Target Neurons in Self-Supervised Learning.

.- Meta learning based Rumor Detection by Awareness of Social Bot.

.- Financial FAQ Question-Answering System Based on Question Semantic Similarity.

.- An illegal website family discovery method based on association graph clustering.

.- Different Attack and Defense Types for AI Cybersecurity.

.-An Improved Ultra-Scalable Spectral Clustering Assessment with Isolation Kernel.

.- A Belief Evolution Model with Non-Axiomatic Logic.

.- Lurking in the Shadows: Imperceptible Shadow Black-Box Attacks against Lane Detection Models.

.- Multi-mode Spatial-Temporal Data Modeling with Fully Connected Networks.

.- KEEN: Knowledge Graph-enabled Governance System for Biological Assets.

.- Cop: Continously Pairing of Heterogeneous Wearable Devices based on Heartbeat.

.- DFDS: Data-Free Dual Substitutes Hard-Label Black-Box Adversarial Attack.

.- Logits Poisoning Attack in Federated Distillation.

.- DiVerFed: Distribution-Aware Vertical Federated Learning for Missing Information.

.- Prompt Based CVAE Data Augmentation for Few-shot Intention Detection.

.- Reentrancy Vulnerability Detection Based On Improved Attention Mechanism.

.- Knowledge-Driven Backdoor Removal in Deep Neural Networks via Reinforcement Learning.

.- AI in Healthcare Data Privacy-preserving: Enhanced Trade-off between Security and Utility.

.- Traj-MergeGAN: A Trajectory Privacy Preservation Model Based on Generative Adversarial Network.

.- Adversarial examples for Preventing Diffusion Models from Malicious Image Edition.

.- ReVFed: Representation-based Privacy-preserving Vertical Federated Learning with Heterogeneous Models.

.- Logit Adjustment with Normalization and Augmentation in Few-shot Named Entity Recognition.

.- New Indicators and Optimizations for Zero-Shot NAS Based on Feature Maps.