My direction

My teaching course range from Computer Engineering, Software Engineering, and Network Engineering.

CoursesTOP

Postgraduates

2017-*, 大数据处理技术 (For Academic Postgraduates & Professional Postgraduates)

2017-*, 高级软件工程 (For Academic Postgraduates & Professional Postgraduates)

2012-2016, 软件架构设计 (For Academic Postgraduates & Professional Postgraduates)

Undergraduates

2024-*, 云计算与大数据基础 (For Undergraduate)

2017-*, IT新技术专题 (For Undergraduate)

2017-*, 计算机专业导论 (For Undergraduate)

2021-*, 软件工程 (For Undergraduate)

2012-2023, 现代软件工程技术（2024年开始改名为软件工程） (For Undergraduate)

2016-2017, 云计算与物联网基础 (For Undergraduate)

2012-2022, 企业软件开发流程 (For Undergraduate, outsanding engineers)

2012, Web系统设计 (For Undergraduate)

2012, UNIX系统管理 (For Undergraduate)

Journal Papers (Postgraduate Student)TOP

2026

2025

Hui Liu, Ke Ji, Kun Ma, Zhenxiang Chen, Jin Zhou, Jun Wu, "Robust recommendation-oriented malicious attack detection method," Information Sciences, 2025, 715 (10): 122213 (SCI: )
ISSN: 0020-0255, Date:
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Abstract
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邱艳芳，赵振宇，孙志杰，马坤*，纪科，陈贞翔, "外部知识与内部上下文语义聚合的短文本新闻虚假检测模型," 济南大学学报（自然科学版）, 2025, 39 (4): 1-7
ISSN: , Date:
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Abstract
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Xiaoyun Liu, Kun Ma*, Weijuan Zhang, Yanfang Qiu, Zhenxiang Chen, Ke Ji, Bo Yang, "EDCIN: Enhanced Dual-channel Interaction Network for Multi-label Text Classification," International Journal of Web Information Systems, 2025, 21 (): (EI: )
ISSN: 1744-0084, Date:
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Yanfang Qiu, Kun Ma*, Weijuan Zhang, Run Pan, Zhenxiang Chen, "DSEN-EK: Dual-layer Semantic Information Extraction Network with External Knowledge for Fake News Detection," International Journal of Web Information Systems, 2025, 21 (2): 139-157 (EI: )
ISSN: 1744-0084, Date:
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2024

Xinyu Liu, Kun Ma*, Ke Ji, Zhenxiang Chen, and Bo Yang, "Graph-based Multi-information Integration Network with External News Environment Perception for Propaganda Detection," International Journal of Web Information Systems, 2024, 20 (2): 195-212 (EI: 20240815605997)
ISSN: 1744-0084, Date: 2024/02/14
Abstract BiBTeX

Abstract

Purpose
Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

Design/methodology/approach
G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

Findings
G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

Originality/value
An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

BiBTeX

Xinyu Liu, Kun Ma*, Qiang Wei, Ke Ji, Bo Yang, and Ajith Abraham, "G-HFIN: Graph-based Hierarchical Feature Integration Network for Propaganda Detection of We-media News Articles," Engineering Applications of Artificial Intelligence, 2024, 132 (): 1-16 (EI: 20240515464076, WOS: 001170111600001; IF: 7.802, CCF-C, Q2, Top期刊)
ISSN: 0952-1976, Date: 2024/1/23

Code and data Abstract BiBTeX
Abstract
BiBTeX

Qiang Wei, Kun Ma*, Xinyu Liu, Ke Ji, Bo Yang, and Ajith Abraham, "DIMN: Dual Integrated Matching Network for Multi-Choice Reading Comprehension," Engineering Applications of Artificial Intelligence, 2024, 130 (): 1-11 (EI: 20240115321602, WOS: 001149734800001; IF: 7.802, CCF-C, Q2, Top期刊)
ISSN: 0952-1976, Date: 2023/12/28

Code and data Abstract BiBTeX

Abstract

Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: 1) generating the contextualized representations, namely, drawing associated information, and 2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69.34%), DREAM (68.45%) and MCTest (71.81% on MCTest160 and 78.83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https://github.com/vqiangv/DIMN}{https://github.com/vqiangv/DIMN.

BiBTeX

@article{WEI2024107694,
title = {DIMN: Dual Integrated Matching Network for multi-choice reading comprehension},
journal = {Engineering Applications of Artificial Intelligence},
volume = {130},
pages = {107694},
year = {2024},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107694},
url = {https://www.sciencedirect.com/science/article/pii/S095219762301878X},
author = {Qiang Wei and Kun Ma and Xinyu Liu and Ke Ji and Bo Yang and Ajith Abraham},
keywords = {Multi-choice reading comprehension, Contextualized representation, Global interactive relationship, Attention, Convolution},
abstract = {Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: (1) generating the contextualized representations, namely, drawing associated information, and (2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69.34%), DREAM (68.45%) and MCTest (71.81% on MCTest160 and 78.83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https://github.com/vqiangv/DIMN.}
}

2023

Benkuan Cui, Kun Ma*, Leping Li, Weijuan Zhang, Ke Ji, Zhenxiang Chen, Ajith Abraham, "Intra-graph and Inter-graph Joint Information Propagation Network with Third-order Text Graph Tensor for Fake News Detection," Applied Intelligence, 2023, 53 (16): 18971–18988 (WOS: 000933342800002, IF: 5.3, EI: 20230813600742, CCF-C, Q2)
ISSN: 0924-669X, Date: 2023/02/15

Code and data Abstract BiBTeX
Abstract
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Jing Chen, Kun Ma, Ke Ji, Zhenxiang Chen, "TM-HOL: Topic Memory model for Detection of Hate Speech and Offensive Language," Concurrency and Computation: Practice and Experience, 2023, 35 (14): e6754 (WOS: 000727115900001, IF: 2, EI: 20214911294843, CCF-C, Q3)
ISSN: 1532-0634, Date: 2021/12/06

Code and data Abstract BiBTeX
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2022

Changhao Tang, Kun Ma, Benkuan Cui, Ke Ji, Ajith Abraham, "Long Text Feature Extraction Network with Data Augmentation," Applied Intelligence, 2022, 52 (12): 17652–17667 (WOS: 000777881900008, IF: 5.019, EI: 20221511941186, CCF-C, Q2)
ISSN: 0924-669X, Date: 2022/04/04

Code and data Abstract BiBTeX
Abstract
BiBTeX
```
@article{tang2022long,
  title={Long text feature extraction network with data augmentation},
  author={Tang, Changhao and Ma, Kun and Cui, Benkuan and Ji, Ke and Abraham, Ajith},
  journal={Applied Intelligence},
  pages={1--16},
  year={2022},
  publisher={Springer}
}
```

2021

吕晓琦, 纪科, 陈贞翔, 孙润元, 马坤, 邬俊, 李浥东, "结合注意力与循环神经网络的专家推荐算法," 计算机科学与探索, 2021, 16 (9): 2068-2077 (CCF B类)
ISSN: 1673-9418, Date: 2021/03/26
Abstract BiBTeX

Abstract

在线问答社区（Community Question Answering， CQA）已经成为互联网最重要的知识分享交流平台，将用户提出的海量问题有效推荐给可能解答的用户，挖掘用户感兴趣的问题是此类平台最核心功能。一些针对问答社区的专家推荐算法已经被提出用来提高平台解答效率，但是现有工作大多关注于用户兴趣与问题信息匹配，忽视了用户兴趣动态变化问题，可能会严重影响推荐质量。本文提出了结合注意力与循环神经网络的专家推荐算法，不仅实现了问题信息的深度特征编码，而且还能捕获动态变化的用户兴趣。首先，问题编码器在预训练词嵌入基础上结合CNN卷积神经网络和Attention注意力机制实现了问题标题与绑定标签的深度特征联合表示。然后，用户编码器在用户历史回答问题的时间序列上利用长短期记忆神经网络Bi-GRU模型捕捉动态兴趣，并结合用户固定标签信息表征长期兴趣。最后，根据两个编码器输出向量的相似性计算产生用户动态兴趣与长期兴趣相结合的推荐结果。我们在来自于知乎问答社区的真实数据上进行了不同参数配置及不同算法的对比实验，表明该算法性能要明显优于目前比较流行的深度学习专家推荐算法。

BiBTeX

2020

Yufeng Wang, Kun Ma*, Laura Garcia-Hernandez, Jing Chen, Zhihao Hou, Ke Ji, Zhenxiang Chen, Ajith Abraham, "A CLSTM-TMN for Marketing Intention Detection," Engineering Applications of Artificial Intelligence, 2020, 91 (): 103595: 1-9 (EI: 20201108304140, WOS: 000528195100023, IF: 6.212, CCF-C, Q2, Top期刊)
ISSN: 0952-1976, Date: 2020/5/1

Code and data Abstract BiBTeX

Abstract

In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models.

BiBTeX

@article{WANG2020103595,
title = "A CLSTM-TMN for marketing intention detection",
journal = "Engineering Applications of Artificial Intelligence",
volume = "91",
pages = "103595",
year = "2020",
issn = "0952-1976",
doi = "https://doi.org/10.1016/j.engappai.2020.103595",
url = "http://www.sciencedirect.com/science/article/pii/S0952197620300671",
author = "Yufeng Wang and Kun Ma and Laura Garcia-Hernandez and Jing Chen and Zhihao Hou and Ke Ji and Zhenxiang Chen and Ajith Abraham",
keywords = "Text classification, Marketing intention, Topic memory, News",
abstract = "In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models."
}

2019

李浩、马坤、陈贞翔、赵川, "基于网络流量分析的未知恶意软件检测," 济南大学学报(自然科学版), 2019, 33 (6): 500-505
ISSN: 1671-3559, Date: 2019/12/1
Abstract BiBTeX

Abstract

为了有效检测移动端的未知恶意软件,提出一种基于机器学习算法,并结合提取的具有鲁棒性的网络流量统计特征,训练出具有未知移动恶意网络流量识别能力的检测模型;该模型主要包括Android恶意软件样本数据预处理、网络流量数据自动采集以及机器学习检测模型训练;通过对不同时间节点的零日恶意软件检测的实验,验证模型的有效性。结果表明,所提出的方法对未知恶意样本的检测精度可以超过90%,并且F度量值为80%。

BiBTeX

@article{李浩2019基于网络流量分析的未知恶意软件检测,
  title={基于网络流量分析的未知恶意软件检测},
  author={李浩 and 马坤 and 陈贞翔 and 赵川},
  journal={济南大学学报(自然科学版)},
  number={6},
  year={2019},
}

段吉东，刘双荣，马坤，孙润元, "基于集成学习的情感分类方法," 济南大学学报(自然科学版), 2019, 33 (6): 483-488
ISSN: 1671-3559, Date: 2019/12/1
Abstract BiBTeX

Abstract

针对自然语言处理的文本情感分类问题,提出一种基于集成学习的文本情感分类方法;基于微博数据的特殊性,首先对微博数据进行分词等预处理,结合词频-逆文档频率(TF-IDF)和奇异值分解(SVD)方法进行特征提取和降维,再通过堆叠泛化(stacking)集成学习的方式进行分类模型融合。结果表明,模型融合对文本情感分析的准确率达到93%,可以有效地判别微博文本的情感极性。

BiBTeX

@article{段吉东2019基于集成学习的文本情感分类方法,
  title={基于集成学习的文本情感分类方法},
  author={段吉东 and 刘双荣 and 马坤 and 孙润元},
  journal={济南大学学报(自然科学版)},
  year={2019},
}

Yufeng Wang, Shuangrong Liu, Songqian Li, Jidong Duan, Zhihao Hou, Jia Yu, and Kun Ma*, "Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection," Future Internet, 2019, 11 (7): 155 (EI: 20193207277459; WOS: 000478637600017)
ISSN: 1999-5903, Date: 2019/7/10

Code and data Abstract BiBTeX

Abstract

Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

BiBTeX

@Article{fi11070155,
AUTHOR = {Wang, Yufeng and Liu, Shuangrong and Li, Songqian and Duan, Jidong and Hou, Zhihao and Yu, Jia and Ma, Kun},
TITLE = {Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection},
JOURNAL = {Future Internet},
VOLUME = {11},
YEAR = {2019},
NUMBER = {7},
ARTICLE-NUMBER = {155},
URL = {https://www.mdpi.com/1999-5903/11/7/155},
ISSN = {1999-5903},
ABSTRACT = {Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.},
DOI = {10.3390/fi11070155}
}

2018

2017

2016

2015

Yu, Z. and Ma, K., "Toward Core Point Evolution Using Water Ripple Model," WSEAS Transactions on Computers, 2015, 14 (Art. #79): 819-825
ISSN: 1109-2750, Date:

Abstract BiBTeX

Abstract

This article presents software library for the Arduino platform which significantly improves the speed of the functions for digital input and output. This allows the users to apply these functions in whole range of applications, without being forced to resort to direct register access or various 3rd party libraries when the standard Arduino functions are too slow for given application. The method used in this library is applicable also to other libraries which aim to abstract the access to general purpose pins of a microcontroller.

BiBTeX

@article {YuToward2015, title={Toward Core Point Evolution Using Water Ripple Model}, author={Zhibing Yu and Kun Ma}, journal={WSEAS Transactions on Computers}, pages={819-825}, year={2015}, volume={14}, number={Art. #79}}

Dong, F., Yang, B., Ma, K., and Wang, W., "Incremental duplicate data detection method with MapReduce," Journal of University of Jinan (Science and Technology), 2015, 29 (4): 241-245
ISSN: 1671-3559, Date: 2015/8/1
Abstract BiBTeX

Abstract

针对重复数据检测过程中增量数据重复值检测问题进行分析,在基本近邻排序算法基础上,提出增量近邻排序比较算法。该算法通过跳动窗口形式比较相邻数据,大大减少了数据比较次数;同时引入MapReduce模型对该算法加以改进以提高其海量数据处理的能力。实验表明,改进后的增量近邻排序比较算法在保证检则结果准确的前提下,能够有效提高增量数据重复检测的速度,并且算法具有较高的稳定性,更适应海量数据环境中重复数据检测任务。

BiBTeX

@article{董富森2015mapreduce,  title={MapReduce 模型下增量重复数据检测方法},  author={董富森 and 杨波 and 马坤 and 王文华},  journal={济南大学学报 (自然科学版)},  volume={4},  pages={001},  year={2015}}

Dong, F., Ma, K., and Yang, B., "Cache System for Frequently Updated Data in the Cloud," WSEAS Transactions on Computers, 2015, 14 (Art. #17): 163-170
ISSN: 1109-2750, Date:

Abstract BiBTeX

Abstract

Maintaining data indexes and query cache becomes the bottleneck of the database, especially in the context of frequently updated data. In order to reduce the burden of the database, a cache system for frequently updated data has been proposed in this paper. In the system, update statements are parsed firstly. Then updated data are saved as key-value pairs in the cache and they are synchronized into the database at idle time. Experimental results show that the proposed cache system cannot only accelerate the data updating rate, but also improve the data writing ability in maintaining indexes and consistency of cache data greatly.

BiBTeX

@article {DongCache2015, title={Cache System for Frequently Updated Data in the Cloud}, author={Fusen Dong and Kun Ma and Bo Yang}, journal={WSEAS Transactions on Computers}, pages={163-170}, year={2015}, volume={14}, number={Art. #17}}

2014

Tang, Z. and Ma, K., "RSSCube: A Content Syndication and Recommendation Architecture," International Journal of Database Theory and Application, 2014, 7 (4): 237-248 (EI: 20143718151244)
ISSN: 2005-4270, Date: 2014/8/31
Abstract BiBTeX

Abstract

Content syndication is the process of pushing the information out into third-party information providers. The idea is to drive more engagement with your content by wiring it into related digital contexts. However, there are some shortages of current related products, such as search challenges on massive feeds, synchronization performance, and user experience. To address these limitations, we aim to propose an improved architecture of content syndication and recommendation. First, we design a source listener to extract feed changes from different RSS sources, and propagate the incremental changes to target schema-free document stores to improve the search performance. Second, the proposed recommendation algorithm is to tidy, filter, and sort all the feeds before pushing them to the users automatically. Third, we provide some OAuth2-authorization RESTful feed sharing APIs for the integration with the third-party systems. The experimental result shows that this architecture speeds up the search and synchronization process, and provides friendlier user experience.

BiBTeX

@article {TangRSSCube2014, title={RSSCube: A Content Syndication and Recommendation Architecture}, author={Zijie Tang and Kun Ma}, journal={International Journal of Database Theory and Application}, pages={237-248}, year={2014}, volume={7}, number={4}}

2013

2012

Chen, G., Yang, B., Ma, K., "Universal data interactive interface model based on publish/subscribe," Journal of Huazhong University of Science and Technology (Natural Science Edition), 2012, 40 (S1): 141-145 (EI: 20131816300570)
ISSN: 1671-4512, Date:

Scopus Document Scopus Citeby Abstract BiBTeX
Abstract
BiBTeX
Wu, J., Yang, B., Sun, R., and Ma, K., "Design of virtualization based application services monitoring software," Shandong Science, 2012, 25 (5): 78-82
ISSN: 1002-4026, Date: 2012/10/20
Abstract BiBTeX
Abstract
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Journal Papers (Undergraduate Student)TOP

2026

2025

2024

Jiaqi Fang, Kun Ma*, Yanfang Qiu, Ke Ji, Zhenxiang Chen, Bo Yang, "SEN-CTD: Semantic enhancement Network with Content-title Discrepancy for Fake News Detection," International Journal of Web Information Systems, 2024, 20 (6): 603-620 (EI: 20244517331485)
ISSN: 1744-0084, Date: 2024/11/04
Abstract BiBTeX
Abstract
BiBTeX

2023

YinNan Yao, ChangHao Tang, Kun Ma*, "Toward Stance Parameter Algorithm with Aggregate Comments for Fake News Detection," International Journal of Grid and Utility Computing, 2023, 14 (5): 443-454 (EI: 20234014828930, ESCI)
ISSN: 1741-847X, Date: 2023/06/25

Code and data Abstract BiBTeX

Abstract

In the detection of fake news, the stance of comments usually contains evidence supporting false news that can be used to corroborate the detected results of the fake news. However, due to the misleading content of fake news, there is also the possibility of fake comments. By analyzing the position of comments and considering the falseness of comments, comments can be used more effectively to detect fake news. In response to this problem, we proposed Bipolar Argumentation Frameworks of Reset Comments Stance (BAFs-RCS) and Average Parameter Aggregation of Comments (APAC) to use the stance of comments to correct the prediction results of the Roberta model. We use the Fakeddit dataset for experiments. Our macro-F1 results on 2way and 3way are improved by 0.0029 and 0.0038 compared to the baseline RoBERTa model's macro-F1 results at Fakeddit dataset. The results show that our method can effectively use the stance of comments to correct the results of model prediction errors.

BiBTeX

2022

Nan Su, Zhishuo Lin, Wenlong You, Nan Zheng, and Kun Ma*, "RMGCS: Real-time Multimodal Garbage Classification System for Recyclability," Journal of Intelligent & Fuzzy Systems, 2022, 42 (4): 3963-3973 (EI: 20221411883329, WOS: 000771196600074; IF: 2)
ISSN: 1875-8967, Date: 2021/10/27

Code and data Abstract BiBTeX
Abstract
BiBTeX

2021

Zhihao Hou, Kun Ma*, Yufeng Wang, Jia Yu, Ke Ji, Zhenxiang Chen, and Ajith Abraham, "Attention-based learning of self-media data for marketing intention detection," Engineering Applications of Artificial Intelligence, 2021, 98 (): 104118: 1-9 (EI: 20204909594046, WOS: 000606752400010, IF: 7.802, CCF-C, Q2, Top期刊)
ISSN: 0952-1976, Date: 2021/2/1

Code and data Abstract BiBTeX

Abstract

In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.6

BiBTeX

@article{HOU2021104118,
title = {Attention-based learning of self-media data for marketing intention detection},
journal = {Engineering Applications of Artificial Intelligence},
volume = {98},
pages = {104118},
year = {2021},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2020.104118},
url = {https://www.sciencedirect.com/science/article/pii/S0952197620303572},
author = {Zhihao Hou and Kun Ma and Yufeng Wang and Jia Yu and Ke Ji and Zhenxiang Chen and Ajith Abraham},
keywords = {Marketing intention detection, Attention model, Convolutional neural network, Feature extraction},
abstract = {In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.68% in contrast with ATT-CNN. It illustrates that our A-CNN can effectively address semantic and feature selection for marketing intention detection.}
}

2020

Zhe Yang, Kun Ma, Xiaoli Zhang, Lizhen Cui, Bo Yang, "RSCVC: Row-based Semantic Cache With Incremental Versioning Consistency," Concurrency and Computation: Practice and Experience, 2020, 32 (17): e5672:1-14 (WOS: 000563945600001, IF: 1.536, EI: 20201508392927, CCF-C)
ISSN: 1532-0634, Date: 2020/3/24

Abstract BiBTeX

Abstract

In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row‐based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version‐based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version‐based consistency enhances the stability of the system especially in high‐concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state‐of‐the‐art methods.

BiBTeX

@article{doi:10.1002/cpe.5672,
author = {Yang, Zhe and Ma, Kun and Zhang, Xiaoli and Cui, Lizhen and Yang, Bo},
title = {RSCVC: Row-based semantic cache with incremental versioning consistency},
journal = {Concurrency and Computation: Practice and Experience},
volume = {n/a},
number = {n/a},
pages = {e5672},
keywords = {cache loading, cache penetration, cache snowslide, data consistency, query optimization, semantic cache},
doi = {10.1002/cpe.5672},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5672},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5672},
abstract = {Summary In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row-based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version-based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version-based consistency enhances the stability of the system especially in high-concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state-of-the-art methods.}
}

2019

2018

2017

Tianren Luo, Xueyong Li, Xiaoying Luo, Kun Ma*, "Toward Mobile Smart Data File Protection Box," International Journal of Autonomic Computing, 2017, 2 (3): 282-309
ISSN: 1741-8569, Date: 2017/9/27
Abstract BiBTeX

Abstract

Aiming at this urgent need of the security protection of mobile intelligent terminal data file, we design a file security box to protect and manage important files in smartphones. The main functions of this file security box are: 1) fingerprint verification; 2) file management; 3) efficient encryption and decryption; 4) adaptive cipher algorithms; 5) separate logical document library; 6) updating the secret key regularly; 7) storing the key securely; 8) reinforcing the safety in enterprise level. Functional tests and performance tests show that the file security box we designed can not only achieve all of the above functionality with excellent user transparency and friendliness but also ensure the safety of important files without effecting users' experience.

BiBTeX

@article{luo2017toward,
  title={Toward mobile smart data file protection box},
  author={Luo, Tianren and Li, Xueyong and Ma, Kun and Luo, Xiaoying},
  journal={International Journal of Autonomic Computing},
  volume={2},
  number={3},
  pages={282--309},
  year={2017},
  publisher={Inderscience Publishers (IEL)}
}

Conference Papers (Postgraduates)TOP

2026

2025

Yanfang Qiu, Weijuan Zhang, Kun Ma*, Xiaoyun Liu, Ke Ji, Bo Yang, Entity-aware Multi-perspective Semantic Fusion Network for Fact-checking Fake News Detection, Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024), Compiègne, France, May 5-7, 2025, (EI: )
Date:
Acceptance Rate: 432/580=74.5%
Xiaoyun Liu, Weijuan Zhang, Kun Ma*, Yanfang Qiu, Ke Ji, Bo Yang, LMFN: Label-aware Multi-semantic Fusion Network for Multi-label Text Classification, Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024), Compiègne, France, May 5-7, 2025, (EI: )
Date:
Acceptance Rate: 432/580=74.5%
Ruxin Wang, Ke Ji, Yuan Gao, Kun Ma, Chong Ma and Xiaofan Zhao, Fake News Detection Based on Cross-Semantic Multimodal Data Fusion, 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, June 30- July 5, 2025, (EI: )
Date:

2024

Hui Liu, Ke Ji, Zhenxiang Chen, Kun Ma, Xiaofan Zhao, Malicious Attack Detection Method for Recommendation Systems Based on Meta-pseudo Labels and Dynamic Features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 14964, 8th International Joint Conference on Web and Big Data (APWeb-WAIM 2024), Jinhua, Zhejiang, Aug. 28-30, 2024, 379-393 (EI: )
Date:
Yongxin Yu, Ke Ji, Yuan Gao, Zhenxiang Chen, Kun Ma, Jun Wu, MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection, Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science 14649, Taipei, Taiwan, May 5-10, 2024, 28-39 (EI: )
Date: 2024/4/25

2023

Leping Li, Kun Ma*, Benkuan Cui, Multi-Information Filter Encoding Network for Multi-Label Text Classification, Proceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2023), Rio de Janeiro, Brazil, May 24-26, 2023, 612-617 (EI: 20232914401943)
Date: 2023/6/23

2022

Jiaxuan Zhang, Kun Ma*, EDPS: Early Dropout Prediction System of MOOC Courses, Proceedings of the 2022 29th Asia-Pacific Software Engineering Conference (APSEC 2022), Online, Dec. 6-9, 2022, 562-563 (EI: 20231013670567)
Date: 2023/2/16
Acceptance Rate: 128/261=49%
Yanqiang Li, Ke Ji, Kun Ma, Zhenxiang Chen, Jin Zhou, Jun Wu, Fake news detection based on the Correlation Extension of Multimodal information, Proceedings of the 2022 The 6th APWeb-WAIM International Joint Conference on Web and Big Data, Nanjing, China, Aug. 11-13, 2022, 443–450 (EI: 20231413824477)
Date: 2023/2/10
Yanqi Li, Ke Ji, Kun Ma, Zhenxiang Chen, Jun Wu, Yidong Li, Guandong Xu, HACK: A Hierarchical Model for Fake News Detection, Proceedings of the WISE 2021: Web Information Systems Engineering, Lecture Notes in Computer Science, 13080, Melbourne, Australian, Oct. 26-29, 2021, 565-572 (EI: 20220111412980)
Date: 2021/12/2
Xiaoqi Lv, Ke Ji, Zhenxiang Chen, Kun Ma, Jun Wu, Yidong Li, Guandong Xu, Expert Recommendations with Temporal Dynamics of User Interest in CQA, Proceedings of the WISE 2021: Web Information Systems Engineering, Lecture Notes in Computer Science, 13080, Melbourne, Australian, Oct. 26-29, 2021, 645-652 (EI: 20220111412986)
Date: 2021/12/2

2021

Yanqi Li, Ke Ji, Kun Ma, Zhenxiang Chen, Jun Wu, Yidong Li, Enhancing Fake News Classification by Generating Neural Fake Samples, Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), Beijing, China, July 15-August 15, 2021, 152-155 (EI: 20214010978824)
Date: 2021/9/22
Yue Lu, Kun Ma and Jidong Duan, Influence Model of Paper Citation Networks with Integrated Weighted PageRank and HITS, Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2021), Dalian, China, May 5-7, 2021, 1081-1086 (EI: 20212410506468, CCF-C)
Date: 2021/5/28
Acceptance Rate: 221/(171+146)=69.7%

2020

Xiaoqian Zhang and Kun Ma*, Toward Sliding Time Window of Low Watermark to Detect Delayed Stream Arrival, Proceedings of the 2020 16th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020), Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 350), Shanghai, China, Oct. 16-18, 2020, 444-454 (EI: 20210909990969, CCF C)
Date: 2021/1/22
Acceptance Rate: 77/211=36.5%.
Yahan Yuan, Ke Ji, Runyuan Sun, Kun Ma, Zhenxiang Chen, Lin Wang, An Integration Method Of Classifiers For Abnormal Phone Detection, Proceedings of the 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC 2019), Beijing, China, Oct. 28-30, 2019, (EI: 20200608146581)
Date: 2019/10/1
Ying Pang, Zhenxiang Chen, Lizhi Peng, Kun Ma, Chuan Zhao, Ke Ji, A Signature-Based Assistant Random Oversampling Method for Malware Detection, Proceedings of the 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), Rotorua, New Zealand, Aug. 5-8, 2019, 256-263 (EI: 20194707716364, CCF C)
Date: 2019/8/1
Yahan Yuan, Ke Ji, Runyuan Sun, Kun Ma, Zhenxiang Chen, Lin Wang, An Integration Method Of Classifiers For Abnormal Phone Detection, Proceedings of the 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC 2019), Beijing, China, Oct. 28-30, 2019, 1-6 (EI: 20200608146581)
Date: 2019/10/1

2019

Wenshuang Zhao, Kun Liu and Kun Ma, Design of Student Capability Evaluation System Merging Blockchain Technology, Journal of Physics: Conference Series, Volume 1168, Issue 3, Daqing, China, March 12, 2019, 1-7 (EI: 20191506743365)
Date: 2019/2/1
Yahan Yuan, Ke Ji, Runyuan Sun, Kun Ma, Zhenxiang Chen, Lin Wang, An Abnormal Phone Identification Model with Meta-learning Two-layer Framework Based on PCA Dimension Reduction, Proceedings of the 2019 11th International Conference on Machine Learning and Computing (ICMLC 2019), Zhuhai, China, Feb. 22-24, 2019, 511-515 (EI: 20192307006445)
Date: 2019/2/22
Jian Liu, Ke Ji, Runyuan Sun, Kun Ma, Zhenxiang Chen, Lin Wang, Abnormal Phone Analysis Based on Learning to Rank and Ensemble Learning in Environment of Telecom Big Data, Proceedings of the 2019 11th International Conference on Machine Learning and Computing (ICMLC 2019), Zhuhai, China, Feb. 22-24, 2019, 301-305 (EI: 20192307006408)
Date: 2019/2/22
Jidong Duan, Kun Ma, and Runyuan Sun, Unbalanced data sentiment classification method based on ensemble learning, Proceedings of 2019 2nd International Conference on Big Data Technologies (ICBDT2019), Jinan, China, August 28-30, 2019, 34-38 (EI: 20194607685450)
Date: 2018/8/28
Data
Qi He, Zhenxiang Chen, Ke Ji, Lin Wang, Kun Ma, Chuan Zhao, Yuliang Shi, Cluster Center Initialization and Outlier Detection Based on Distance and Density for the K-Means Algorithm, Proceedings of 2018 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018), Advances in Intelligent Systems and Computing, Vellore, India, Dec. 06-08, 2018, 530-539 (EI: 20192306999255)
Date: 2019/4/12
Ning Yu, Kun Liu, Kun Ma, Analysis of Public Opinion Heat Trend in Universities on The Basis of Markov Chain, Proceedings of 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE 2018), Xi'An, China, Oct. 12-14, 2018, 218-222 (EI: 20190706511756)
Date: 2018/10/1

2018

Qun Li, Zhenxiang Chen, Qiben Yan, Shanshan Wang, Kun Ma, Yuliang Shi and Lizhen Cui, MulAV: Multilevel and Explainable Detection of Android Malware with Data Fusion, Proceedings of 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2018), Lecture Notes in Computer Science, Guangzhou, China, Nov. 15-17, 2018, 166-177 (EI: 20185106273858, CCF-C)
Date: 2018/12/7
Jian Liu, Ke Ji, Runyuan Sun, Kun Ma, Learning to rank-based abnormal phone analysis in environment of telecommunication big data, Proceedings of Third International Workshop on Pattern Recognition (IWPR 2018), Volume 10828, Jinan, China, May 26-28, 2018, 1-9 (EI: 20183505762760)
Date: 2018/7/26
Yahan Yuan, Kei Ji, Runyuan Sun, Kun Ma, An abnormal telephone identification model based on ensemble algorithm, Proceedings of Third International Workshop on Pattern Recognition (IWPR 2018), Volume 10828, Jinan, China, May 26-28, 2018, 1-9 (EI: 20183505762743)
Date: 2018/7/26
ChuanShi Chen, Kun Liu and Kun Ma, Research on Evaluation of College Students' Professional Ability Based on K-means Clustering, Proceedings of 2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018), MATEC Web Conf. Volume 173, 2018, Nanjing, China, May 24-26, 2018, 1-5 (EI: 20183205673827)
Date: 2018/6/19

2017

Yamin Han, Kun Liu*, Kun Ma, Research and Realization of News Gathering and Editing System Based on Distributed Search Engine, Proceedings of 2017 International Conference on Intelligent Networking and Collaborative Systems (INCoS 2017), Lecture Notes on Data Engineering and Communications Technologies, 8, Ryerson University, Canada, Aug. 24-26, 2017, 349-354 (WOS: 000434865700031)
Date: 2017/8/17

2016

Wu, J., Yang, B., Wang, L., Ma, K., Zhao, X., Zhou, J., MRF parameter estimation based on weighted Least Squares Fit method, Proceedings of 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), Jin Zhou, China, Aug. 26-28, 2016, 164-169 (EI: 20164603016571)
Date: 2016/8/1

2015

Yao, S., Ma, K.*, Yang, Z., and Abraham, A., Toward a Multi-tenant College Sustained Team Seeking System, Proceedings of the 2015 5th World Congress on Information and Communication Technologies (WICT 2015), Marrakesh, Morocco, Dec. 14-16, 2015, 18-22 (EI: 20163102671086, INSPEC:16072436, WOS:000383221800004)
Date: 2015/12/1
Yang, Z., Ma, K.*, Yao, S., Exploring Mobile Instant Messaging to Leverage Management of College Association, Proceedings of 2015 IEEE International Conference on e-Business Engineering (ICEBE 2015), Beijing, China, Oct. 23-25, 2015, 233-238 (EI: 20161902347415, INSPEC:15662628, WOS:000377382000034)
Date: 2015/10/1
Cheng, Q., Ma, K., Yang, B., Stream-based Particle Swarm Optimization for Data Migration Decision, Proceedings of the 2015 Seventh International Conference of Soft Computing and Pattern Recognition (SoCPaR 2015), Fukuoka, Japan, November 13-15, 2015, 264-269 (EI: 20163002649793, INSPEC:16090028, WOS:000383091300044)
Date: 2015/11/1
Yu, Z., Ma, K., Yang, B., Core Point Evolution: A Waterwave Model of Software Development Process, Proceedings of 2015 IEEE Fifth International Conference on Big Data and Cloud Computing (BDCloud 2015), Dalian, China, August 26-29, 2015, 338-343 (EI: 20161502230341, WOS:000367074900033)
Date: 2015/8/1
Acceptance Rate: 23%

2014

Tang, Z., Ma, K., An Online Content Syndication Tool with Built-In Search and Social Sharing, Proceedings of the 1st International Conference on Intelligent Computing, Communication & Devices (ICCD 2014), Advances in Intelligent Systems and Computing, Volume 1, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India, April 18-19, 2014, 308: 17-20 (EI: 20143718158217, Inspec: , ISI: )
Date: 2014/8/26

Conference Papers (Graduates)TOP

2026

2025

2024

Jiaqi Fang, Kun Ma*, SIGAN: Self-Inhibited Graph Attention Network FOR TEXT Classification, Proceedings of 2023 23th International Conference on Intelligent Systems Design and Applications (ISDA 2023), Olten, Switzerland, Dec. 11-13, 2023, 127-136 (EI: 20243216843570)
Date: 2024/7/15

2023

Baiheng Wu, Shengcai Zhou, Jing Ye, Yiyang Li, Heyao Zhou, Kun Ma*, NATOrderMonitoring: On-site Order Monitoring System of Nucleic Acid Testing with Raspberry PI, Proceedings of the 2023 6th International Conference on Big Data and Education (ICBDE 2023), Jinan, China, June 16-18, 2023, 125–130 (EI: 20233814750224)
Date: 2023/8/24
Yilin Zhao, Biao Zhang, and Kun Ma*, RMSLRS: Real-time Multi-terminal Sign Language Recognition System, Innovations in Bio-Inspired Computing and Applications: Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022) held during December 15-17, 2022, Lecture Notes in Networks and Systems 649, Online, Dec. 15-17, 2022, 575-585 (EI: 20231613899312)
Date: 2023/3/27

2022

2021

Fanglve Zhang, Jia Yu, and Kun Ma*, IAS: Intelligent Attendance System based on Hybrid Criteria Matching, Proceedings of 2020 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020), Advances in Intelligent Systems and Computing 1351, On the World Wide Web, Dec. 12-15, 2020, 1-7 (EI: )
Date: 2021/6/2

2020

Fanghan Liu, Wenzheng Cai, and Kun Ma*, PLRS: Personalized literature hybrid recommendation system with paper influence, Proceedings of the 2020 20th International Conference, ICA3PP 2020, New York City, NY, USA, October 2–4, 2020, Proceedings, Part III, Lecture Notes in Computer Science 12454, New York, USA, Oct. 2-4, 2020, 703-705 (EI: ,WOS: 000719296800049, CCF-C)
Date: 2020/9/29
Acceptance Rate: 147/507=29%.
Yinnan Yao, Nan Su, Kun Ma*, UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers, Proceedings of the 2020 14th International Workshop on Semantic Evaluation (SemEval 2020), Barcelona, Spain, Dec. 12-13, 2020, 2203–2208 (EI: 20220611589508, Importance of papers in SemEval conference is just below 3 top NLP conferences (ACL/EMNLP/NAACL) in the field of Computational Linguistics.)
Date: 2021/10/20
This is a system paper on SemEval 2020 Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. This task is here. Our team has got the ranking 16/85. SemEval (Semantic Evaluation) is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval Word sense evaluation series. SemEval-2020 will be the 14th workshop on semantic evaluation. Importance of papers in SemEval conference is just below 3 top NLP conferences (ACL/EMNLP/NAACL) in the field of Computational Linguistics.
Yinnan Yao, Xiunan Zheng, and Kun Ma*, ILFS: Intelligent Lost and Found System using Multidimensional Matching Model, Proceedings of 2019 IEEE International Conference on Ubiquitous Intelligence & Computing (UIC 2019), Leicester, UK, August 19-23, 2019, 1205-1208 (EI: 20201708533975, CCF-C)
Date: 2019/8/1
Acceptance Rate: 64/145=44.14%, Demo details from here.
Xiaonan Ji, Kun Ma*, Toward Automatic Scheduling Algorithm with Hash-based Priority Selection Strategy, Proceedings of 2019 8th International Conference Soft Computing for Problem Solving (SocProS 2019), Advances in Intelligent Systems and Computing, 1057, Tamil Nadu, India, Dec 17-19, 2018, 35-42 (EI: 20195207924588, WOS: 000631850100004)
Date: 2019/11/28

2019

Songqian Li, Kun Ma*, Xuewei Niu, Yufeng Wang, Ke Ji, Ziqiang Yu, and Zhenxiang Chen, Stacking-based Ensemble Learning on Low Dimensional Features for Fake News Detection, Proceedings of 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS 2019), Zhangjiajie, China, August 10-12, 2019, 2730-2735 (EI: 20194307564087, CCF-C)
Date: 2019/8/1
Hao Qu and Kun Ma*, WebSocket-Based Real-Time Single-Page Application Development Framework, Proceedings of 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2018), Lecture Notes on Data Engineering and Communications Technologies, Taichung, Taiwan, Oct. 27-29, 2018, 36-47 (EI:, WOS:000674927000004)
Date: 2018/10/17

2018

Xuewei Niu, Kun Ma*, Toward An Efficient Cache Management Framework, Proceedings of 2018 IEEE International Conference on Ubiquitous Intelligence & Computing (UIC 2018), Guangzhou, China, Oct. 8-12, 2018, 1491-1496 (EI: 20190406418313, CCF-C)
Date: 2018/10/1
Acceptance Rate: 101/297=34%
Hengyue Shi, Kun Ma*, Toward An Individual Mobile Nursery Service Platform, Proceedings of 2018 International Conference on Computing, Power and Communication Technologies(GUCON 2018), Galgotias University, India, Sep. 28-29, 2018, 732-737 (EI: 20191606806584)
Date: 2018/9/1
Acceptance Rate: 31.9%
Hao Qu, Kun Ma*, Zhe Yang, Xuewei Niu, Ajith Abraham, Toward Real-time High-Frequency Stock Monitoring System using Node.js, Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Advances in Intelligent Systems and Computing, 614, VIT University, India, Dec. 19-21, 2016, 1-10 (EI: 20173604120249)
Date: 2017/8/19

2017

2016

Yang, Z., Ma, K.*, Zhong, J., Toward a Semantic Cache Supporting Version-based Consistency, Proceedings of 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2016), Fukuoka, Japan, Jul. 6-8, 2016, 367-372 (EI: 20170603321487, WOS:000391528700056)
Date: 2016/7/1

2015

Student Projects TOP

2026

2025

2024

2024-2025, 913, 基于注意力机制机器阅读理解研究与应用, 陈明炀, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 911, 鼓动宣传意图识别及其在自媒体新闻软文检测方面的应用, 袁颢真, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 907, “镜”界——基于多模态AI融合与AR智能交互的全场景影像创作系统, 亢博斐, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 904, 多信息融合的云端协同安全驾驶监测系统研发, 李瑛琦, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 902, 基于STM32的智能花卉栽培系统, 付鹏翔, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 901, 基于YOLOv8与DLIB的学生行为分析系统, 路苗苗, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2024-2025, 981, 英语阅读理解多项选择题自动答题系统设计与实现, 刘博, 国家级大学生创新创业训练计划项目（创新训练项目，省级）, 立项经费：0.5万元.
2024-2025, 943, “谣”无音讯——内容标题差异图网络研究及其在早期假新闻检测中的应用, 方家琪, 国家级大学生创新创业训练计划项目（创新训练项目，省级）, 立项经费：0.5万元.

2023

2023-2024, 2810, 融合外部知识的图注意力网络在假新闻识别中的研究, 方家琪, 国家级大学生创新创业训练计划项目（省级）, 立项经费：0万元.
2023-2024, 889, 基于低代码开发技术的社区防疫平台的设计与实现, 徐乐轩, 济南大学大学生创新创业训练计划项目（校级重点项目）, 立项经费：0万元.
2023-2024, 872, MOOC学习中学生退课行为预测研究与应用, 樊承鑫, 济南大学大学生创新创业训练计划项目（校级重点项目）, 立项经费：0万元.

2022

2022-2023, 202210427017, 手语心声, 赵怡琳, 国家级大学生创新创业训练计划项目（创新训练项目，国家级）, 立项经费：1万元.
2022-2023, 202210427021, 基于GCN的虚假新闻识别及其系统研发, 齐世豪, 国家级大学生创新创业训练计划项目（创新训练项目，国家级）, 立项经费：1万元.

2021

2021-2022, , 新闻Propaganda鼓吹宣传检测, 王阳, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2021-2022, , 软件工程实践中学生行为跟踪及评价, 周诗栩, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2021-2022, , 基于GCN的虚假新闻识别研究, 齐世豪, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2021-2022, , MOOC平台学生退课行为预测方法及其系统研发, 刘宇, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.

2020

2020-2021, , 基于Paddlehub的口罩佩戴识别方法研究与应用, 张国辉, 济南大学大学生研究训练（SRT）计划项目（校筹）, 立项经费：0万元.
2020-2021, , 基于深度学习的互联网虚假新闻识别研究, 苏南, 济南大学大学生研究训练（SRT）计划项目（校筹）, 立项经费：0万元.
2020-2021, , 基于迁移学习的攻击性语言鉴别和跨度检测, 郑坤昌, 济南大学大学生研究训练（SRT）计划项目（校筹）, 立项经费：0万元.
2020-2021, S202010427054, 基于多模态内容识别的垃圾分类系统, 尤文龙, 国家级大学生创新创业训练计划（省级）, 立项经费：0.5万元.

2019

2019.1-2020.3, , 基于Transformer的社交媒体攻击性语言检测模型设计与实现, 姚胤楠, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2019.1-2020.3, , 基于集成学习的情感分析方法研究及其在舆情检测系统中的应用, 吴磊, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2019.1-2020.3, , 基于短文本的中英文立场检测, 王艳艳, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2019.1-2020.3, , 基于机器学习的垃圾分类小程序设计与实现, 苏南, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2019.1-2020.3, , 基于ISBN码识别的二手图书交易平台设计与实现, 侯志浩, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2019-2020, , 失物招领智能匹配系统, 姚胤楠, 微信小程序“U”计划（腾讯创新创业训练项目）, 立项经费：1万元.
2019-2020, , 基于场景智能识别的通用签到系统, 张方略, 微信小程序“U”计划（腾讯创新创业训练项目）, 立项经费：5万元.
2019-2020, 201910427027, 基于WiFi检测与图像特征识别的多模式签到系统, 张方略, 国家级大学生创新创业训练计划项目（创新训练项目，国家级）, 立项经费：1万元.
2019-2020, 201910427029, 失物招领智能匹配系统, 姚胤楠, 国家级大学生创新创业训练计划项目（创新训练项目，国家级）, 立项经费：1万元.

2018

2018.11-2019.4, , 基于主题模型的舆情潜在语义获取方法的研究, 段平碧, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2018.11-2019.4, , 基于文本分析下的营销意图识别, 侯志浩, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2018.11-2019.4, , 失物招领智能匹配系统, 姚胤楠, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2018.11-2019.4, , 基于WiFi检测与图像特征识别的多模式签到系统, 张方略, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2018.11-2019.4, , 献文-文献推荐与管理系统, 刘方涵, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.

2017

2017.06-2018.5, SY20170737, 叮咚APP的设计与实现, 张家豪, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2017.06-2018.5, SY20170716, 高校社团兴趣部落的设计与实现, 蔡钟晟, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2017.06-2018.5, SX20170679, 基于Vue.js的在线知识分享平台设计与实现, 李松谦, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.
2017.06-2018.5, SX20170678, 概率主题模型研究及其在舆情监测中的应用, 段平碧, 济南大学大学生研究训练（SRT）计划项目（校级）, 立项经费：0万元.

2016

2016.1-2017.05, S2015644, 自动排课(班)算法研究及其在学生组织管理系统中的应用, 纪笑难, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2016.1-2017.05, S2015620, 云开发协助平台的设计与实现, 李易君, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2016.1-2017.05, S2015615, 云盘大文件分块上传设计与iOS客户端适配, 牛学蔚, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2016.1-2017.05, S2015593, 社交化数据舆情监测系统设计与实现, 李昶昕, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2016.06-2016.1, 201613, 校乡汇——高校老乡社平台, 瞿浩, 国家级大学生创新创业训练计划项目（创新训练项目，腾讯创新创业训练项目）, 立项经费：1万元.

2015

2015.1-2016.06, S2015105, 穆宝网——民族特色与清真食品交易平台, 杨震, 济南大学学生科技立项, 立项经费：0万元.
2015.1-2016.06, S2015103, 基于多用户的大学生在线组队平台, 姚树巍, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2015.1-2016.06, S2015102, 海量数据缓存一致性方法研究及其在020电子云商系统应用, 杨哲, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2015.1-2016.06, S2015101, 同乡社交微信服务平台, 瞿浩, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2015.11-2017.06, 2014210229, 基于核心点演化水纹软件开发过程模型的研究, 余智兵, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.

2014

2014.09-2015.09, 2014339, 面向服务的多用户大规模开放在线课程平台研究, 房敬超, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2014.09-2015.09, 2014335, 面向服务的多租户通用微信公众平台, 杨哲, 济南大学大学生研究训练（SRT）计划项目, 立项经费：0万元.
2014.06-2015.06, 201410427030, 多承租海量数据自适应存储与缓存关键技术研究及其在互助社交系统的应用, 唐子杰, 国家级大学生创新创业训练计划项目（创新训练项目，国家级）, 立项经费：1万元.