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Zhemin Wang

dblp:178/3993 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 3 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 61% Approximation and online algorithms · 30% Mathematical optimization · 9%
Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 67% Compilers and program optimization · 33%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › incentive mechanism
mobile crowdsensing
0.912025
A Utility-Optimal Reverse Posted Pricing Mechanism for Online Mobile Crowdsensing Task Allocation · IEEE Trans. Serv. Comput. 2025
Approximation and online algorithms › online allocation
online task assignment
0.912025
A Utility-Optimal Reverse Posted Pricing Mechanism for Online Mobile Crowdsensing Task Allocation · IEEE Trans. Serv. Comput. 2025
Algorithmic game theory and mechanism design › mechanism design › simple mechanisms
posted-price mechanism
0.912025
A Utility-Optimal Reverse Posted Pricing Mechanism for Online Mobile Crowdsensing Task Allocation · IEEE Trans. Serv. Comput. 2025
Programming languages and type systems
aspect-oriented programming
0.812024
Design and Implementation of an Aspect-Oriented C Programming Language · Proc. ACM Program. Lang. 2024
Programming languages and type systems › aspect-oriented programming
aspect weaving
0.812024
Design and Implementation of an Aspect-Oriented C Programming Language · Proc. ACM Program. Lang. 2024
Compilers and program optimization
compiler construction
0.812024
Design and Implementation of an Aspect-Oriented C Programming Language · Proc. ACM Program. Lang. 2024
Mathematical optimization
integer programming
0.312025
A Utility-Optimal Reverse Posted Pricing Mechanism for Online Mobile Crowdsensing Task Allocation · IEEE Trans. Serv. Comput. 2025

Methods — techniques the papers use, named apart from their topics

integer programming · 0.9gradient descent · 0.9aspect-oriented programming · 0.8
YearPublicationVenuePosition
2025 A Utility-Optimal Reverse Posted Pricing Mechanism for Online Mobile Crowdsensing Task Allocation
abstract
In contrast to traditional mechanism design, the posted pricing mechanism can quickly determine the winning user and ensure the revenue of the seller through a predetermined price. Additionally, the posted pricing mechanism inherently possesses economic properties such as truthfulness and individual rationality. These properties make it an ideal method for solving online task allocation problems for mobile crowdsensing services (MCSs). The challenge in posted pricing mechanism design is being able to find reasonable posted prices under complex MCS task constraints. This paper presents an innovative posted pricing mechanism to solve a general point of interest (POI)-based online MCS task allocation problem. We transform the problem into an integer programming model with the goal of maximizing the total utility of the system while satisfying various constraints. We prove that under any user arrival order, there must exist a posted price structure that can ensure that the total utility of the system is approximately optimal, with an approximation ratio of$1/(d+1)$in the worst case. With the support of theoretical analysis, the posted price calculation can be completed using only a simple gradient descent algorithm. Compared with existing methods, our solution achieves very good results in terms of total utility and the task completion ratio, indicating that it can effectively improve the efficiency and service quality of MCSs.
Jixian Zhang 0003, Xuelin Yang, Peng Chen 0056, Zhemin Wang, Weidong Li 0002, Zhenli He, Keqin Li 0001
IEEE Trans. Serv. Comput.4
2024 Design and Implementation of an Aspect-Oriented C Programming Language
abstract
Aspect-Oriented Programming (AOP) is a programming paradigm that implements crosscutting concerns in a modular way. People have witnessed the prosperity of AOP languages for Java and C++, such as AspectJ and AspectC++, which has propelled AOP to become an important programming paradigm with many interesting application scenarios, e.g., runtime verification. In contrast, the AOP languages for C are still poor and lack compiler support. In this paper, we design a new general-purpose and expressive aspect-oriented C programming language, namely Aclang, and implement a compiler for it, which brings fully-fledged AOP support into the C domain. We have evaluated the effectiveness and performance of our compiler against two state-of-the-art tools, ACC and AspectC++. In terms of effectiveness, Aclang outperforms ACC and AspectC++. In terms of performance, Aclang outperforms ACC in execution time and outperforms AspectC++ in both execution time and memory consumption.
Zhe Chen 0011, Zhemin Wang
Proc. ACM Program. Lang.3
2016 Parametric Runtime Verification of C Programs
abstract
Many runtime verification tools are built based on Aspect-Oriented Programming (AOP) tools, most often AspectJ, a mature implementation of AOP for Java. Although already popular in the Java domain, there is few work on runtime verification of C programs via AOP, due to the lack of a solid language and tool support. In this paper, we propose a new general purpose and expressive language for defining monitors as an extension to the C language, and present our tool implementation of the weaver, the Movec compiler, which brings fully-fledged parametric runtime verification support into the C domain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Zhe Chen 0011, Zhemin Wang, Hongwei Xi 0001, Zhibin Yang 0005
TACAS2