Zhipeng Cai
Biographic Data
| ID | 8547632 |
|---|---|
| NAME | Zhipeng Cai |
| GIVEN NAMES | Zhipeng |
| FAMILY NAME | Cai |
| SIGNATURE | CAI Z |
| AFFILIATIONS | Georgia State University |
| ORCID | 0000-0001-6017-975X |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 12 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
PGCL: Precisely Capturing Propagation Structure Characteristics via Graph Contrastive Learning for Rumor Detection
Has: Hypergraph Adaptive Sampling for Structural Characteristics Preservation
A hypergraph is a mathematical structure capable of representing complex relationships among multiple entities, with widespread applications. However, as the size of hypergraphs continues to grow, their analysis and processing become increasingly challenging. Therefore, how to effectively process large hypergraphs to enable efficient analysis with limited computational resources has become an important research problem. In this article, we propos…
Multiagent Confrontation Method Based on Three-Party Dynamic Multistrategy Evolutionary Game
Unmanned agents represent a significant advancement in unmanned control and constitute an important element in the future agent warfare. Their autonomous decision-making capabilities are integral to accomplishing tasks independently. To address challenges inherent in multiparty game scenarios that traditional method struggle with and enhance the applicability and accuracy of game decision-making, this article proposes a novel multiagent confronta…
Mobile Crowdsourcing Quality Control Method Based on Four-Party Evolutionary Game in Edge Cloud Environment
Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducte…
A Reinforcement Learning-Based Incentive Mechanism for Task Allocation Under Spatiotemporal Crowdsensing
With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results…
Influence blocking maximization under refutation
Fast Core Maintenance in Dynamic Graphs
This article studies the core maintenance problem in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. The core maintenance problem requires updating the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. Previous works focus on the scenario of single-edge updates and process the edges one by one w…
Three-Party Evolutionary Game Model of Stakeholders in Mobile Crowdsourcing
As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This …
Mortality decline, productivity increase, and positive feedback between schooling and retirement choices
The twentieth century has seen a phenomenal decline in mortality and an increase in productivity level. These two important events likely affect people's choices of schooling years and retirement age. We first show that in a standard life-cycle model, positive feedback exists between optimal schooling years and retirement age choices. We then evaluate the impact of a mortality or productivity shock on an endogenous variable (schooling years or re…
Artificial intelligence: A powerful paradigm for scientific research
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promot…
Walrasian Equilibrium-Based Multiobjective Optimization for Task Allocation in Mobile Crowdsourcing
With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges…
Batch Processing for Truss Maintenance in Large Dynamic Graphs
This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously e…
No prominent works on this page.
Walrasian Equilibrium-Based Multiobjective Optimization for Task Allocation in Mobile Crowdsourcing
With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges…
Batch Processing for Truss Maintenance in Large Dynamic Graphs
This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously e…
Artificial intelligence: A powerful paradigm for scientific research
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promot…
Fast Core Maintenance in Dynamic Graphs
This article studies the core maintenance problem in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. The core maintenance problem requires updating the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. Previous works focus on the scenario of single-edge updates and process the edges one by one w…
Three-Party Evolutionary Game Model of Stakeholders in Mobile Crowdsourcing
As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This …
Mortality decline, productivity increase, and positive feedback between schooling and retirement choices
The twentieth century has seen a phenomenal decline in mortality and an increase in productivity level. These two important events likely affect people's choices of schooling years and retirement age. We first show that in a standard life-cycle model, positive feedback exists between optimal schooling years and retirement age choices. We then evaluate the impact of a mortality or productivity shock on an endogenous variable (schooling years or re…
Influence blocking maximization under refutation
Mobile Crowdsourcing Quality Control Method Based on Four-Party Evolutionary Game in Edge Cloud Environment
Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducte…
A Reinforcement Learning-Based Incentive Mechanism for Task Allocation Under Spatiotemporal Crowdsensing
With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results…
Has: Hypergraph Adaptive Sampling for Structural Characteristics Preservation
A hypergraph is a mathematical structure capable of representing complex relationships among multiple entities, with widespread applications. However, as the size of hypergraphs continues to grow, their analysis and processing become increasingly challenging. Therefore, how to effectively process large hypergraphs to enable efficient analysis with limited computational resources has become an important research problem. In this article, we propos…
Multiagent Confrontation Method Based on Three-Party Dynamic Multistrategy Evolutionary Game
Unmanned agents represent a significant advancement in unmanned control and constitute an important element in the future agent warfare. Their autonomous decision-making capabilities are integral to accomplishing tasks independently. To address challenges inherent in multiparty game scenarios that traditional method struggle with and enhance the applicability and accuracy of game decision-making, this article proposes a novel multiagent confronta…
PGCL: Precisely Capturing Propagation Structure Characteristics via Graph Contrastive Learning for Rumor Detection
Computer Science (10 works) · Artificial Intelligence (5 works) · Engineering (5 works) · Computer security (4 works) · Mobile Crowdsensing and Crowdsourcing (4 works) · Algorithm (3 works) · Complex Network Analysis Techniques (3 works) · Crowdsourcing (3 works) · Enhanced Data Rates for GSM Evolution (3 works) · Game theory (3 works)