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34 results on '"Liu, Shengzong"'

3. Local Core Members Aided Community Structure Detection

Author

Fan, Xiaoping, Chen, Zhijie, Cai, Fei, Wu, Jinsong, Liu, Shengzong, Liao, Zhining, and Liao, Zhifang

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity

Published
2017

4. A Prediction Model of the Project Life-span in Open Source Software Ecosystem

Author

Liao, Zhifang, Zhao, Benhong, Liu, Shengzong, Jin, Haozhi, He, Dayu, Yang, Liu, Wu, Jinsong, and Zhang, Yan

Subjects
Computer Science - Software Engineering
Abstract

In nature ecosystems, animal life-spans are determined by genes and some other biological characteristics. Similarly, the software project life-spans are related to some internal or external characteristics. Analyzing the relations between these characteristics and the project life-span, may help developers, investors, and contributors to control the development cycle of the software project. The paper provides an insight on the project life-span for a free open source software ecosystem. The statistical analysis of some project characteristics in GitHub is presented, and we find that the choices of programming languages, the number of files, the label format of the project, and the relevant membership expressions can impact the life-span of a project. Based on these discovered characteristics, we also propose a prediction model to estimate the project life-span in open source software ecosystems. These results may help developers reschedule the project in open source software ecosystem.

Published
2017

5. DevRank: Mining Influential Developers In Github

Author

Liao, Zhifang, Jin, Haozhi, Li, Yifan, Zhao, Benhong, Wu, Jinsong, and Liu, Shengzong

Subjects
Computer Science - Social and Information Networks, Computer Science - Software Engineering
Abstract

As the social coding is becoming increasingly popular, understanding the influence of developers can benefit various applications, such as advertisement for new projects and innovations. However, most existing works have focused only on ranking influential nodes in non-weighted and homogeneous networks, which are not able to transfer proper importance scores to the real important node. To rank developers in Github, we define developer's influence on the capacity of attracting attention which can be measured by the number of followers obtained in the future. We further defined a new method, DevRank, which ranks the developers by influence propagation through heterogeneous network constructed according to user behaviors, including "commit" and "follow". Our experiment compares the performance between DevRank and some other link analysis algorithms, the results have shown that DevRank can improve the ranking accuracy.

Published
2017

13. Design and Implementation of Wireless Environment Monitoring System Based on STM32

Author

Hu Ying, Xiao Leyi, Qu Xilong, Fu Sha, Liu Shengzong, and He Hong

Subjects
Scheme (programming language), Article Subject, business.industry, Computer science, Serial communication, STM32, Computer Science Applications, Environmental data, QA76.75-76.765, Data acquisition, Wireless, Intelligent environment, Computer software, business, computer, Software, Computer hardware, Server-side, computer.programming_language
Abstract

This paper first designed the overall scheme of a wireless-based intelligent environment monitoring system; this system is comprised of the following four parts: server part, client part, slave computer part, and master computer part. The master computer is equipped with a wireless transmission module and an LCD module. Slave computers are equipped with temperature-humidity sensing modules and wireless transmission modules. The server side has two major interfaces: data display and server establishment. An Android phone as the client can realize the communication between the mobile APP and server through network communication to obtain environmental data in real time. The hardware part of the system consists of wireless transmission module, temperature and humidity module, smoke module, lighting module, flame module, and LCD module. The software implementation part mainly includes the design and implementation of serial communication, the design and implementation of wireless communication, the design and implementation of collection of data acquisition in each sensor module, and the design and implementation of network communication as well as the design and implementation of display unit. With the cooperation of these four parts, we can automatically sense and control the state of the environment to realize human-computer interaction.

Published
2021

27. Hybrid sampling method for autoregressive classification trees under density-weighted curvature distance.

Author

Ye, Hua, Qu, Xilong, Liu, Shengzong, and Li, Guang

Subjects
CURVATURE, SAMPLING methods, DEEP learning, HUMAN behavior, CLASSIFICATION
Abstract

To improve human action recognition performance of visual data, we proposed Hybrid Sampling Ensemble Learning method. 1)define the curvature distance distribution to fit discrete images into continuous expandable surfaces. 2)obtain density-weighted curvature distance of the images through class imbalance adjustment. 3)use auto-regressive classification tree strategy by utilizing the hierarchical regress of the filtered images. The classification performance of the ensemble learning method is not as good as the deep learning framework. However, the parameters are interpretable, and the construction of the classification framework is simple. In short, the proposed ensemble model is robust and controllable. [ABSTRACT FROM AUTHOR]

Published
2021
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34. Robust Target Tracking Algorithm Based on Superpixel Visual Attention Mechanism: Robust Target Tracking Algorithm

Author

Fan, Xiao, Liu, Shengzong, Huang, Lirong, and Hu, Jia

Abstract

As existing target tracking algorithms are prone to drift under complex environments, the authors propose a tracking algorithm based on superpixel segmentation and visual attention mechanism. The algorithm works on a particle filter framework, by conducting a superpixel segmentation first and then building a model of the visual attention mechanism for saliently mapping. After extracting HOG features of salient regions, the authors compared the HOG feature with the target template at last, in order to locate the target region in the new frame. The proposed algorithm is evaluated on a comprehensive test platform, in which the simulation show that our tracker is more efficacious and more efficient than the previous traditional target tracking algorithms to cope with target drift issue.

Published
2019
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