Xin Zhang

Work place: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, Jilin, China



Research Interests: Computational Science and Engineering, Computational Engineering, Software Engineering, Engineering


Xin Zhang received the bachelor degree of software engineering from Harbin Institute of Technology at 2006 and then received the master and doctor degrees of computer engineering from University of Bordeaux at 2009 and 2013.
He is an associate professor at School of Computer Science and Technology in Changchun University of Science and Technology. His current research interests include design engineering and intelligent computing.

Author Articles
Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

By Yan Liu Xin Zhang

DOI:, Pub. Date: 8 Dec. 2020

Course evaluation is a critical part of undergraduate curriculum in computer science. Most existing evaluation methods are based on questionnaire by analyzing the satisfaction rate of the respondents. However, there are many indicators such as attendance rate, activity level and average score that can reflect the overall effectiveness of the course. Limited research has taken all those indicators into account during course evaluation. This research chooses an innovative perspective that considers course evaluation as a multiple criteria decision-making problem. A hybrid model is proposed to measure the course effectiveness regarding various indicators. The indicators are first prioritized by a fuzzy Analytic Hierarchical Process (AHP) model which applies fuzzy numbers to deal with the uncertainty brought by subjective judgement. A hierarchical fuzzy inference system (FIS) is then designed to evaluate the course effectiveness, which reduces the number of the fuzzy IF-THEN rules and increases the efficiency compared to the traditional FIS. A numerical example is presented to demonstrate the application. The proposed model helps not only judge an individual course based on a comprehensive view but also rank multiple courses.

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A Multi-agent System-based Method of Detecting DDoS Attacks

By Xin Zhang Ying ZHANG Raees ALTAF Xin FENG

DOI:, Pub. Date: 8 Feb. 2018

Distributed denial of service attacks are the acts aiming at the exhaustion of the limited service resources within a target host and leading to the rejection of the valid user service request. During a DDoS attack, the target host is attacked by multiple, coordinated attack programs, often with disastrous results. Therefore, the effective detection, identification, treatment, and prevention of DDoS attacks are of great significance. Based on the research of DDoS attack principles, features and methods, combined with the possible scenarios of DDoS attacks, a Multi-Agent System-based DDoS attack detection method is proposed in this paper to implement DDoS attack detection for high-load communication scenarios. In this paper, we take the multi-layer communication protocols into consideration to carry out categorizing and analyzing DDoS attacks. Especially given the high-load communication scenarios, we make an effort to exploring a possible DDoS attack detection method with employing a target-driven multi-agent modeling methodology to detect DDoS attacks relying on considering the inherent characteristics of DDoS attacks. According to the experiments verification, the proposed DDoS attack detection method plays a better detection performance and is less relevant with the data unit granularity. Meanwhile, the method can effectively detect the target attacks after the sample training. The detection scheme based on the agent technology can reasonably perform the pre-set behaviors and with good scalability to meet the follow-further requirements of designing and implementing the prototype software.

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A Dynamic Feedback-based Load Balancing Methodology

By Xin Zhang Jinli LI Xin FENG

DOI:, Pub. Date: 8 Dec. 2017

With the recent growth of Internet-based application services, the concurrent accessing requests arriving at the particular servers offering application services are growing significantly. It is one of the critical strategies that employing load balancing to cope with the massive concurrent accessing requests and improve the access performance is. To build up a better online service to users, load balancing solutions achieve to deal with the massive incoming concurrent requests in parallel through assigning and scheduling the work executed by the members within one server cluster. In this paper, we propose a dynamic feedback-based load balancing methodology. The method analyzes the real-time load and response status of each single cluster member through periodically collecting its work condition information to evaluate the current load pressure by comparing the learned load balancing performance with the preset threshold. In this way, since the load arriving at the cluster could be distributed dynamically with the optimized manner, the load balancing performance could thus be maintained so that the service throughput capacity would correspondingly be improved and the response delay to service requests would be reduced. The proposed result is contributed to strengthening the concurrent access capacity of server clusters. According to the experiment report, the overall performance of server system employing the proposed solution is better.

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