Yueshen Xu
Biographic Data
| ID | 10054652 |
|---|---|
| NAME | Yueshen Xu |
| GIVEN NAMES | Yueshen |
| FAMILY NAME | Xu |
| SIGNATURE | XU Y |
| AFFILIATIONS | Xidian University |
| ORCID | 0000-0001-7210-0543 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Resource-Aware Microservice Deployment in Cyber–Physical–Social Systems via Distributed Intelligence
Microservice-based architecture has become prevalent in the development of diverse types of applications and systems within cyber–physical–social systems (CPSSs), leading to the development and deployment of microservices that provide a range of functionalities in CPSSs. An effective deployment solution for microservices holds promise for enhancing response times in CPSSs. However, prior research typically overlooks several important elements, fo…
Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences
The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies …
Adversarial Learning-Based Sentiment Analysis for Socially Implemented IoMT Systems
Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classific…
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Adversarial Learning-Based Sentiment Analysis for Socially Implemented IoMT Systems
Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classific…
Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences
The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies …
Resource-Aware Microservice Deployment in Cyber–Physical–Social Systems via Distributed Intelligence
Microservice-based architecture has become prevalent in the development of diverse types of applications and systems within cyber–physical–social systems (CPSSs), leading to the development and deployment of microservices that provide a range of functionalities in CPSSs. An effective deployment solution for microservices holds promise for enhancing response times in CPSSs. However, prior research typically overlooks several important elements, fo…
Computer Science (3 works) · Artificial Intelligence (2 works) · Computer security (2 works) · Machine learning (2 works) · Advanced Data and IoT Technologies (1 work) · Advanced Graph Neural Networks (1 work) · Adversarial system (1 work) · Artificial neural network (1 work) · Cloud Computing and Resource Management (1 work) · Collaborative filtering (1 work)