Knowledge Science, Engineering and Management

15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part II

AvGerard Memmi,Baijian Yang

Häftad, Engelska, 2022

Del 13369 i serien Lecture Notes in Computer Science

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Beskrivning

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6–8, 2022.

Produktinformation

Utgivningsdatum:2022-07-31
Mått:155 x 235 x 39 mm
Vikt:1 072 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:701
Förlag:Springer International Publishing AG
ISBN:9783031109850

Utforska kategorier

Innehållsförteckning

Knowledge Engineering Research and Applications (KERA).- Multi-View Heterogeneous Network Embedding.- A Multi-level Attention-based LSTM Network for Ultra-short-term Solar Power Forecast using Meteorological Knowledge.- Unsupervised Person Re-ID via Loose-Tight Alternate Clustering.- Sparse Dense Transformer Network for Video Action Recognition.- Deep User Multi-Interest Network for Click-Through Rate Prediction.- Open Relation Extraction via Query-based Span Prediction.- Relational Triple Extraction with Relation-Attentive Contextual Semantic Representations.- Mario Fast Learner: Fast and Efficient solutions for Super Mario Bros.- Few-shot Learning with Self-supervised Classifier for Complex Knowledge Base Question Answering.- Data-driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel.- Deep-to-bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation.- Topic and Reference Guided KeyphraseGeneration from Social Media.- DISEL: A Language for Specifying DIS-based Ontologies.- MSSA-FL:High-Performance Multi-Stage Semi-Asynchronous Federated Learning with Non-IID Data.- A GAT-based Chinese Text Classification Model: Using of Redical Guidance and Association Between Characters Across Sentences.- Incorporating Explanation to Balance the Exploration and Exploitation of Deep Reinforcement Learning.