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Found 7 papers, 1 papers with code
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition
As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services.
Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition.
Using neural networks to predict icephobic performance
Artificial neural network models were developed to predict the icephobic performance of concrete.
Machine learning model to cluster and map tribocorrosion regimes in feature space
Tribocorrosion maps serve the purpose of identifying operating conditions for acceptable rate of degradation.
