Emotion quantification techniques for cognitive reappraisal: a systematic review and scientometric analysis

MA Hamid, J Singh - Artificial Intelligence Review, 2023 - Springer
Cognitive reappraisal intends to study the significance of an event concerning any emotional
reaction. Understanding the efficacy of cognitive reappraisal in emotion regulation requires …

Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review

R **n, J Wang, P Chen, Z Zhao - ACM Computing Surveys, 2025 - dl.acm.org
Performance diagnosis systems are defined as detecting abnormal performance
phenomena and play a crucial role in cloud applications. An effective performance …

Empowering generative AI with knowledge base 4.0: towards linking analytical, cognitive, and generative intelligence

A Beheshti - 2023 IEEE International Conference on Web …, 2023 - ieeexplore.ieee.org
Intelligence refers to the ability to acquire and apply knowledge and skills, which comprises
three fundamental components, namely knowledge, experience, and creativity …

Federated iot interaction vulnerability analysis

G Wang, H Guo, A Li, X Liu… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
IoT devices provide users with great convenience in smart homes. However, the
interdependent behaviors across devices may yield unexpected interactions. To analyze the …

Enhancing federated learning with in-cloud unlabeled data

L Wang, Y Xu, H Xu, J Liu, Z Wang… - 2022 IEEE 38th …, 2022 - ieeexplore.ieee.org
Federated learning (FL) has been widely applied to collaboratively train deep learning (DL)
models on massive end devices (ie, clients). Due to the limited storage capacity and high …

Knowledge base 4.0: Using crowdsourcing services for mimicking the knowledge of domain experts

A Beheshti - 2022 IEEE International Conference on Web …, 2022 - ieeexplore.ieee.org
Intelligence is the ability to learn from experience. Knowledge workers in knowledge-
intensive processes develop invaluable domain-specific expertise and knowledge over time …

Ferrari: A personalized federated learning framework for heterogeneous edge clients

Z Yao, J Liu, H Xu, L Wang, C Qian… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated semi-supervised learning (FSSL) has been proposed to address the insufficient
labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a …

Semi-Supervised Decentralized Machine Learning with Device-to-Device Cooperation

Z Jiang, Y Xu, H Xu, Z Wang, J Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The massive data from mobile and embedded devices have huge potential for training
machine learning models. Decentralized machine learning (DML) can avoid the inherent …

On dynamically pricing crowdsourcing tasks

X Miao, H Peng, Y Gao, Z Zhang, J Yin - ACM Transactions on …, 2023 - dl.acm.org
Crowdsourcing techniques have been extensively explored in the past decade, including
task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms …

Enhancing federated learning with server-side unlabeled data by adaptive client and data selection

Y Xu, L Wang, H Xu, J Liu, Z Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated learning (FL) has been widely applied to collaboratively train deep learning (DL)
models on massive end devices (ie, clients). Due to the limited storage capacity and high …