Jiuxin Cao
Datos Biográficos
| ID | 8819299 |
|---|---|
| NOMBRE | Jiuxin Cao |
| NOMBRES | Jiuxin |
| APELLIDO | Cao |
| FIRMA | CAO J |
| AFILIACIONES | Southeast University |
| ORCID | 0000-0002-2448-6717 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 9 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 9 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2022 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
Behavior-Driven Detection of Social Bot Groups Through Coordination Patterns and Stance Consistency
No Place to Hide
Online social network has emerged as a prominent place for the propagation of fake news due to its low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance…
Adversarial Reinforcement Learning for Enhanced Decision-Making of Evacuation Guidance Robots in Intelligent Fire Scenarios
In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in comp…
Community Detection for Heterogeneous Multiple Social Networks
The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This article prese…
Response Generation in Social Network With Topic and Emotion Constraints
Response generation is the task of automatically generating human-like content based on the provided context. One of its prominent applications is to simulate realistic response content for social network posts. In the digital age, social network platforms play a vital role in information exchange and social interaction. This study focuses on response generation techniques for the platform of public opinion evolution simulation that simulate real…
In Your Eyes
With the dramatic growth of various short video platforms, users are more likely to share their social stream online and make their social connections stronger. To better understand their preferences, personality analysis has attracted more attention. Unlike single modal data such as text or images, which is hard to comprehensively uncover one’s personal traits, personality analysis on short video is verified to be much more accurate but also mor…
Lpp2kl
When user-centric location privacy protections are used by the public (e.g., published as a mobile application to Mac app store), they are found to be vulnerable to a novel kind of potential inference attacks. These attacks can utilize the open-use protection’s input–output pairs to closely study the protection’s structure and mechanism. We name this kind of attacks as knowing-and-learning (KL) attacks. Targeted at such potential but threatening …
Attention-Fused Deep Relevancy Matching Network for Clickbait Detection
Clickbait is a sort of news that contains low-quality content but intriguing or overstated titles. Most of the existing studies usually utilize titles to detect clickbait and suffer from poor performance. However, from the perspective of user cognition, the essence of clickbait news is due to the fact that the body contents fall short of the titles’ expectations. So, in addition to the explicit features of titles, many extra informative signals c…
A Multi-Agent System for Fine-Grained Opinion Dynamics Analysis in Online Social Networks
The influence and dissemination of users’ opinions are the essence of the opinion dynamics in online social networks (OSNs). Understanding the process of users’ opinion formation and propagation can provide better service for public opinion monitoring and product advertising. However, most opinion dynamics research typically do not distinguish between user opinion formation and propagation, instead focusing on the process of mass opinion polariza…
Sin obras prominentes en esta página.
A Multi-Agent System for Fine-Grained Opinion Dynamics Analysis in Online Social Networks
The influence and dissemination of users’ opinions are the essence of the opinion dynamics in online social networks (OSNs). Understanding the process of users’ opinion formation and propagation can provide better service for public opinion monitoring and product advertising. However, most opinion dynamics research typically do not distinguish between user opinion formation and propagation, instead focusing on the process of mass opinion polariza…
In Your Eyes
With the dramatic growth of various short video platforms, users are more likely to share their social stream online and make their social connections stronger. To better understand their preferences, personality analysis has attracted more attention. Unlike single modal data such as text or images, which is hard to comprehensively uncover one’s personal traits, personality analysis on short video is verified to be much more accurate but also mor…
Lpp2kl
When user-centric location privacy protections are used by the public (e.g., published as a mobile application to Mac app store), they are found to be vulnerable to a novel kind of potential inference attacks. These attacks can utilize the open-use protection’s input–output pairs to closely study the protection’s structure and mechanism. We name this kind of attacks as knowing-and-learning (KL) attacks. Targeted at such potential but threatening …
Attention-Fused Deep Relevancy Matching Network for Clickbait Detection
Clickbait is a sort of news that contains low-quality content but intriguing or overstated titles. Most of the existing studies usually utilize titles to detect clickbait and suffer from poor performance. However, from the perspective of user cognition, the essence of clickbait news is due to the fact that the body contents fall short of the titles’ expectations. So, in addition to the explicit features of titles, many extra informative signals c…
Community Detection for Heterogeneous Multiple Social Networks
The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This article prese…
Response Generation in Social Network With Topic and Emotion Constraints
Response generation is the task of automatically generating human-like content based on the provided context. One of its prominent applications is to simulate realistic response content for social network posts. In the digital age, social network platforms play a vital role in information exchange and social interaction. This study focuses on response generation techniques for the platform of public opinion evolution simulation that simulate real…
No Place to Hide
Online social network has emerged as a prominent place for the propagation of fake news due to its low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance…
Adversarial Reinforcement Learning for Enhanced Decision-Making of Evacuation Guidance Robots in Intelligent Fire Scenarios
In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in comp…
Behavior-Driven Detection of Social Bot Groups Through Coordination Patterns and Stance Consistency
Computer Science (8 obras) · Artificial Intelligence (7 obras) · Computer security (4 obras) · Misinformation and Its Impacts (3 obras) · Spam and Phishing Detection (3 obras) · Adversarial system (2 obras) · Complex Network Analysis Techniques (2 obras) · Data science (2 obras) · Psychology (2 obras) · Telecommunications (2 obras)