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Found 6 papers, 0 papers with code
Personality-affected Emotion Generation in Dialog Systems
Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition.
Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process
In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA.
Federated Unlearning for Human Activity Recognition
In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data.
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
FedPick achieves PFL in the low-dimensional feature space by selecting task-relevant features adaptively for each client from the features generated by the global encoder based on its local data distribution.
Unlocking the Potential of Federated Learning for Deeper Models
Our further investigation shows that the decline is due to the continuous accumulation of dissimilarities among client models during the layer-by-layer back-propagation process, which we refer to as "divergence accumulation."
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors
We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.
