Biography

I am pursuing my Ph.D. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include city computing and big data.

Research and Interests

• City Computing
• Big Data

Publications

• Jing Fan, Shaowen Gao, Yong Liu, Xinqiang Ma, Jiandang Yang, and Changjie Fan. Semisupervised Game Player Categorization From Very Big Behavior Log Data. IEEE Transactions on Systems Man Cybernetics-Systems, 52(6):3419-3430, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  Extracting the specific category of the players, such as the malignant Bot, from the huge log data of the massive multiplayer online role playing games, denoted as MMORPGs, is an important basic task in game security and personal recommendation. In this article, we propose a parallel semisupervised framework to categorize specific game players with a few labelknown target samples, which are denoted as bait players. Our approach first presents a feature representation model based on the players’ level granularity, which can acquire aligned feature representations in the lower dimensional space from the players’ original action sequences. Then, we propose a semisupervised clustering method, extended from the bisecting k-means model, to extract the specified players with the help of those bait players. Due to massive amounts of game log data, the computation complexity is an extreme challenge to implement our feature representation and semisupervised extraction approaches. We also propose a hierarchical parallelism framework, which allows the data to be computed horizontally and vertically simultaneously and enables varied parallel combinations for the steps of our semisupervised categorization approach. The comparable experiments on real-world MMORPGs’ log data, containing more than 465 Gbytes and million players, are carried out to demonstrate the effectiveness and efficiency of our proposed approach compared with the state-of-the-art methods.

  @article{fan2022sgp,
  title = {Semisupervised Game Player Categorization From Very Big Behavior Log Data},
  author = {Jing Fan and Shaowen Gao and Yong Liu and Xinqiang Ma and Jiandang Yang and Changjie Fan},
  year = 2022,
  journal = {IEEE Transactions on Systems Man Cybernetics-Systems},
  volume = {52},
  number = {6},
  pages = {3419-3430},
  doi = {10.1109/TSMC.2021.3066545},
  abstract = {Extracting the specific category of the players, such as the malignant Bot, from the huge log data of the massive multiplayer online role playing games, denoted as MMORPGs, is an important basic task in game security and personal recommendation. In this article, we propose a parallel semisupervised framework to categorize specific game players with a few labelknown target samples, which are denoted as bait players. Our approach first presents a feature representation model based on the players’ level granularity, which can acquire aligned feature representations in the lower dimensional space from the players’ original action sequences. Then, we propose a semisupervised clustering method, extended from the bisecting k-means model, to extract the specified players with the help of those bait players. Due to massive amounts of game log data, the computation complexity is an extreme challenge to implement our feature representation and semisupervised extraction approaches. We also propose a hierarchical parallelism framework, which allows the data to be computed horizontally and vertically simultaneously and enables varied parallel combinations for the steps of our semisupervised categorization approach. The comparable experiments on real-world MMORPGs’ log data, containing more than 465 Gbytes and million players, are carried out to demonstrate the effectiveness and efficiency of our proposed approach compared with the state-of-the-art methods.}
  }

• Yunliang Jiang, Kang Zhao, Junjie Cao, Jing Fan, and Yong Liu. Asynchronous parallel hyperparameter search with population evolution. Control and Decision, 36:1825–1833, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  In recent years, with the continuous increase of deep learning models, especially deep reinforcement learning models, the training cost, that is, the search space of hyperparameters, has also continuously increased. However, most traditional hyperparameter search algorithms are based on sequential execution of training, which often takes weeks or even months to find a better hyperparameter configuration. In order to solve the problem of the long search time hyperparameters and the difficulty in finding a better hyperparameter of deep reinforcement learning configuration, this paper proposes a new hyper-parameter search algorithm, named asynchronous parallel hyperparameter search with population evolution. This algorithm combines the idea of evolutionary algorithms and uses a fixed resource budget to search the population model and its hyperparameters asynchronously and in parallel, thereby improving the performance of the algorithm. It is realized that a parameter search algorithm can run on the Ray parallel distributed framework. Experiments show that the parametric asynchronous parallel search based on population evolution on the parallel framework is better than the traditional hyperparameter search algorithm, and its performance is stable.

  @article{fan2021aph,
  title = {Asynchronous parallel hyperparameter search with population evolution},
  author = {Yunliang Jiang and Kang Zhao and Junjie Cao and Jing Fan and Yong Liu},
  year = 2021,
  journal = {Control and Decision},
  volume = 36,
  pages = {1825--1833},
  doi = {10.13195/j.kzyjc.2019.1743},
  issue = 8,
  abstract = {In recent years, with the continuous increase of deep learning models, especially deep reinforcement learning models, the training cost, that is, the search space of hyperparameters, has also continuously increased. However, most traditional hyperparameter search algorithms are based on sequential execution of training, which often takes weeks or even months to find a better hyperparameter configuration. In order to solve the problem of the long search time hyperparameters and the difficulty in finding a better hyperparameter of deep reinforcement learning configuration, this paper proposes a new hyper-parameter search algorithm, named asynchronous parallel hyperparameter search with population evolution. This algorithm combines the idea of evolutionary algorithms and uses a fixed resource budget to search the population model and its hyperparameters asynchronously and in parallel, thereby improving the performance of the algorithm. It is realized that a parameter search algorithm can run on the Ray parallel distributed framework. Experiments show that the parametric asynchronous parallel search based on population evolution on the parallel framework is better than the traditional hyperparameter search algorithm, and its performance is stable.}
  }

• Jing Fan, YunLiang Jiang, and Yong Liu. Quick attribute reduction with generalized indiscernibility models. Information Sciences, 397:15–36, 2017.
  [BibTeX] [Abstract] [DOI] [PDF]

  We present a generalized indiscernibility reduction model(GIRM) and a concept of the granular structure in GIRM.We prove that GIRM is compatible with three typical reduction models.We present a generalized attribute reduction algorithm and a generalized positive region computing algorithm based on GIRM.We present acceleration policies on two generalized algorithms and fast positive region computing approaches for three typical reduction models. The efficiency of attribute reduction is one of the important challenges being faced in the field of Big Data processing. Although many quick attribute reduction algorithms have been proposed, they are tightly coupled with their corresponding indiscernibility relations, and it is difficult to extend specific acceleration policies to other reduction models. In this paper, we propose a generalized indiscernibility reduction model(GIRM) and a concept of the granular structure in GIRM, which is a quantitative measurement induced from multiple indiscernibility relations and which can be used to represent the computation cost of varied models. Then, we prove that our GIRM is compatible with three typical reduction models. Based on the proposed GIRM, we present a generalized attribute reduction algorithm and a generalized positive region computing algorithm. We perform a quantitative analysis of the computation complexities of two algorithms using the granular structure. For the generalized attribute reduction, we present systematic acceleration policies that can reduce the computational domain and optimize the computation of the positive region. Based on the granular structure, we propose acceleration policies for the computation of the generalized positive region, and we also propose fast positive region computation approaches for three typical reduction models. Experimental results for various datasets prove the efficiency of our acceleration policies in those three typical reduction models.

  @article{jing2017quickar,
  title = {Quick attribute reduction with generalized indiscernibility models},
  author = {Jing Fan and YunLiang Jiang and Yong Liu},
  year = 2017,
  journal = {Information Sciences},
  volume = 397,
  pages = {15--36},
  doi = {https://doi.org/10.1016/J.INS.2017.02.032},
  abstract = {We present a generalized indiscernibility reduction model(GIRM) and a concept of the granular structure in GIRM.We prove that GIRM is compatible with three typical reduction models.We present a generalized attribute reduction algorithm and a generalized positive region computing algorithm based on GIRM.We present acceleration policies on two generalized algorithms and fast positive region computing approaches for three typical reduction models. The efficiency of attribute reduction is one of the important challenges being faced in the field of Big Data processing. Although many quick attribute reduction algorithms have been proposed, they are tightly coupled with their corresponding indiscernibility relations, and it is difficult to extend specific acceleration policies to other reduction models. In this paper, we propose a generalized indiscernibility reduction model(GIRM) and a concept of the granular structure in GIRM, which is a quantitative measurement induced from multiple indiscernibility relations and which can be used to represent the computation cost of varied models. Then, we prove that our GIRM is compatible with three typical reduction models. Based on the proposed GIRM, we present a generalized attribute reduction algorithm and a generalized positive region computing algorithm. We perform a quantitative analysis of the computation complexities of two algorithms using the granular structure. For the generalized attribute reduction, we present systematic acceleration policies that can reduce the computational domain and optimize the computation of the positive region. Based on the granular structure, we propose acceleration policies for the computation of the generalized positive region, and we also propose fast positive region computation approaches for three typical reduction models. Experimental results for various datasets prove the efficiency of our acceleration policies in those three typical reduction models.}
  }

• Yunliang Jiang, Xiongtao Zhang, Liang Tang, Weicong Liu, Jing Fan, and Yong Liu. Multi-Robot Remote Interaction with FS-MAS. International Journal of Advanced Robotic Systems, 10:141, 2013.
  [BibTeX] [Abstract] [DOI] [PDF]

  The need to reduce bandwidth, improve productivity, autonomy and the scalability in multi-robot teleoperation has been recognized for a long time. In this article we propose a novel finite state machine mobile agent based on the network interaction service model, namely FS-MAS. This model consists of three finite state machines, namely the Finite State Mobile Agent (FS-Agent), which is the basic service module. The Service Content Finite State Machine (Content-FS), using the XML language to define workflow, to describe service content and service computation process. The Mobile Agent computation model Finite State Machine (MACM-FS), used to describe the service implementation. Finally, we apply this service model to the multi-robot system, the initial realization completing complex tasks in the form of multi-robot scheduling. This demonstrates that the robot has greatly improved intelligence, and provides a wide solution space for critical issues such as task division, rational and efficient use of resource and multi-robot collaboration.

  @article{jiang2013multirobotri,
  title = {Multi-Robot Remote Interaction with FS-MAS},
  author = {Yunliang Jiang and Xiongtao Zhang and Liang Tang and Weicong Liu and Jing Fan and Yong Liu},
  year = 2013,
  journal = {International Journal of Advanced Robotic Systems},
  volume = 10,
  pages = 141,
  doi = {https://doi.org/10.5772/54468},
  abstract = {The need to reduce bandwidth, improve productivity, autonomy and the scalability in multi-robot teleoperation has been recognized for a long time. In this article we propose a novel finite state machine mobile agent based on the network interaction service model, namely FS-MAS. This model consists of three finite state machines, namely the Finite State Mobile Agent (FS-Agent), which is the basic service module. The Service Content Finite State Machine (Content-FS), using the XML language to define workflow, to describe service content and service computation process. The Mobile Agent computation model Finite State Machine (MACM-FS), used to describe the service implementation. Finally, we apply this service model to the multi-robot system, the initial realization completing complex tasks in the form of multi-robot scheduling. This demonstrates that the robot has greatly improved intelligence, and provides a wide solution space for critical issues such as task division, rational and efficient use of resource and multi-robot collaboration.}
  }