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Junwei Zhou - IEEE Xplore Author Profile
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Junwei Zhou

Publications
34
Citations
412
Publications by Year
20112025
Co-Authors:
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Junwei Zhou

Affiliation

School of Computer and Artificial Intelligence
Wuhan University of Technology
Wuhan, China

Publication Topics

Biography

Junwei Zhou received the Ph.D. degree from the Department of Electrical Engineering, The City University of Hong Kong, Hong Kong, in 2014.,Since then, he has been with the Polytechnic University of Turin, Torino, TO, Italy, as a Visiting Scholar, with The City University of Hong Kong, as a Researcher. He is currently an Associate Professor with the School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China, and a Postdoctoral Researcher with Pennsylvania State University, Pennsylvania, PA, USA. His research interests include computer vision, information security, and distributed source coding.,Dr. Zhou has served as a PC Member of top conferences, such as EMNLP 2023, Coling 2022, ACL 2022, and ISSRE...
Publications
34
Citations
412
Publications by Year
20112025
Co-Authors:
Show All Co-Authors (84)
Author's Published Works

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LogDLR: Unsupervised Cross-System Log Anomaly Detection Through Domain-Invariant Latent Representation

Junwei Zhou;Shaowen Ying;Shulan Wang;Dongdong Zhao;Jianwen Xiang;Kaitai Liang;Peng Liu

IEEE Transactions on Dependable and Secure Computing
Year: 2025 | Early Access Article |
Log anomaly detection aims to discover abnormal events from massive log data to ensure the security and reliability of software systems. However, due to the heterogeneity of log formats and syntaxes across different systems, existing log anomaly detection methods often need to be designed and trained for specific systems, lacking generalization ability. To address this challenge, we propose LogDLR...Show More

LogDLR: Unsupervised Cross-System Log Anomaly Detection Through Domain-Invariant Latent Representation

Junwei Zhou;Shaowen Ying;Shulan Wang;Dongdong Zhao;Jianwen Xiang;Kaitai Liang;Peng Liu

Year: 2025 | Early Access Article |

DRLLog: Deep Reinforcement Learning for Online Log Anomaly Detection

Junwei Zhou;Yuyang Gao;Ying Zhu;Xiangtian Yu;Yanchao Yang;Cheng Tan;Jianwen Xiang

IEEE Transactions on Network and Service Management
Year: 2025 | Early Access Article |
System logs record the system’s status and application behavior, providing support for various system management and diagnostic tasks. However, existing methods for log anomaly detection face several challenges, including limitations in recognizing current types of anomalous logs and difficulties in performing online incremental updates to the anomaly detection models. To address these challenges,...Show More

DRLLog: Deep Reinforcement Learning for Online Log Anomaly Detection

Junwei Zhou;Yuyang Gao;Ying Zhu;Xiangtian Yu;Yanchao Yang;Cheng Tan;Jianwen Xiang

Year: 2025 | Early Access Article |

PLGAT: Underwater Plectropomus leopardus Recognition Using Global Attention Mechanism and Transfer Learning

Mengran Liu;Weijian Zhang;Cun Wei;Zhenming Bao;Jingjie Hu;Junwei Zhou

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
The Plectropomus leopardus (P. leopardus), a species found in underwater environments, possesses substantial strategic importance due to its rich underwater resources. However, the natural habitat and industrial breeding environment of P. leopardus is generally dark and complex, which presents notable challenges to object detection and recognition. In this research, we propose Plectropomus leopard...Show More

PLGAT: Underwater Plectropomus leopardus Recognition Using Global Attention Mechanism and Transfer Learning

Mengran Liu;Weijian Zhang;Cun Wei;Zhenming Bao;Jingjie Hu;Junwei Zhou

Year: 2024 | Volume: 12 | Journal Article |

AEDD:Anomaly Edge Detection Defense for Visual Recognition in Autonomous Vehicle Systems

Lihao Wang;Junwei Zhou;Dongdong Zhao;Dongqing Liao;Mengjie Wang;Jianwen Xiang

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2024 | Conference Paper |
Visual recognition algorithms based on deep neural network (DNN) have been widely used in the design of automatic driving to recognize traffic sign images. However, there exists adversarial patches which are essentially the anormal image block that can be locally observed but not noticed by humans. And these visual recognition algorithms often suffer from the effect of adversarial patches, due to ...Show More

AEDD:Anomaly Edge Detection Defense for Visual Recognition in Autonomous Vehicle Systems

Lihao Wang;Junwei Zhou;Dongdong Zhao;Dongqing Liao;Mengjie Wang;Jianwen Xiang

Year: 2024 | Conference Paper |

Pattern-Driven Privacy: Columnar Binary Transformation for Robust Iris Recognition

Wiraj Udara Wickramaarachchi;Dongdong Zhao;Junwei Zhou;Jianwen Xiang

2024 International Research Conference on Smart Computing and Systems Engineering (SCSE)
Year: 2024 | Conference Paper |
Iris is a distinct and strong biometric feature that is being employed as a method of authentication all over the world. Irreversibility, Renewability, and Unlinkability are the most critical aspects required to ensure the privacy of biometrics. Keeping recognition performance at a high level while providing privacy is a critical challenge that many researchers try to overcome. Information distort...Show More

Pattern-Driven Privacy: Columnar Binary Transformation for Robust Iris Recognition

Wiraj Udara Wickramaarachchi;Dongdong Zhao;Junwei Zhou;Jianwen Xiang

Year: 2024 | Volume: 7 | Conference Paper |

Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization

Yulong Ye;Songbai Liu;Junwei Zhou;Qiuzhen Lin;Min Jiang;Kay Chen Tan

IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
Cited by: Papers (1)
In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the prediction strategy have shown promising performance for solving dynamic multiobjective optimization problems (DMOPs), as they can predict environmental changing trends in advance. However, most of them follow a regular change pattern and thus their performance is compromised when solving DMOPs with irregular change...Show More

Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization

Yulong Ye;Songbai Liu;Junwei Zhou;Qiuzhen Lin;Min Jiang;Kay Chen Tan

Year: 2024 | Early Access Article |

Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization

Lingjie Li;Yongfeng Li;Qiuzhen Lin;Songbai Liu;Junwei Zhou;Zhong Ming;Carlos A. Coello Coello

IEEE Transactions on Cybernetics
Year: 2024 | Volume: 54, Issue: 6 | Journal Article |
Cited by: Papers (12)
The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner particles and then uses the winner particles to efficiently guide the search of the loser particles. This approach has very promising performance in solving large-scale multiobjective optimization problems (LMOPs). However, most studies of CSOs ignore the evolution of the winner particles, although their quality...Show More

Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization

Lingjie Li;Yongfeng Li;Qiuzhen Lin;Songbai Liu;Junwei Zhou;Zhong Ming;Carlos A. Coello Coello

Year: 2024 | Volume: 54, Issue: 6 | Journal Article |

Cross-Project Aging-Related Bug Prediction Based on Feature Transfer and Class Imbalance Learning

Wenzhi Xie;Chen Zhang;Kai Jia;Dongdong Zhao;Junwei Zhou;Jing Tian;Jianwen Xiang

2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Software aging refers to system performance degradation and failure due to Aging-Related Bugs (ARBs) in long-running software systems. ARB prediction helps identify ARBs to prevent software aging. However, early project stages often lack ARB data for model training. To address this, cross-project ARB prediction (CPAP) is proposed, where a model is trained using labeled source projects to predict t...Show More

Cross-Project Aging-Related Bug Prediction Based on Feature Transfer and Class Imbalance Learning

Wenzhi Xie;Chen Zhang;Kai Jia;Dongdong Zhao;Junwei Zhou;Jing Tian;Jianwen Xiang

Year: 2023 | Conference Paper |

CBSDI: Cross-Architecture Binary Code Similarity Detection based on Index Table

Longmin Deng;Dongdong Zhao;Junwei Zhou;Zhe Xia;Jianwen Xiang

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Binary code similarity detection for cross-platform is widely used in plagiarism detection, malware detection and vulnerability search, aiming to detect whether two binary functions over different platforms are similar. Existing cross-architecture approaches mainly rely on the approximate matching calculation of complex high-dimensional features, such as graph, which are inevitably slow and unsuit...Show More

CBSDI: Cross-Architecture Binary Code Similarity Detection based on Index Table

Longmin Deng;Dongdong Zhao;Junwei Zhou;Zhe Xia;Jianwen Xiang

Year: 2022 | Conference Paper |

Homomorphic multi-label classification of virus strains

Junwei Zhou;Botian Lei;Huile Lang

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Detecting the gene sequence of virus strains from patients and classifying them into specific strains are very important to provide effective treatment. However, there are significant barriers to sharing the virus strains' gene data in plaintext to the privacy concerns of the patients. Homomorphic encryption is a form of encryption that allows users to calculate encrypted data without decrypting i...Show More

Homomorphic multi-label classification of virus strains

Junwei Zhou;Botian Lei;Huile Lang

Year: 2022 | Conference Paper |

AugLog: System Log Anomaly Detection Based on Contrastive Learning and Data Augmentation

Junwei Zhou;Yijia Qian

2022 5th International Conference on Data Science and Information Technology (DSIT)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
System logs are valuable resources for system main-tenance and troubleshooting since they record run-time status and significant events of computer systems. Detecting anomalies via system logs has been widely researched in recent years. However, current log-based anomaly detection approaches are susceptible to noise introduced by log processing and evolution. In this work, we proposed AugLog, a lo...Show More

AugLog: System Log Anomaly Detection Based on Contrastive Learning and Data Augmentation

Junwei Zhou;Yijia Qian

Year: 2022 | Conference Paper |

Face Template Protection Through Incremental Learning and Error-Correcting Codes

Junwei Zhou;Li Nie;Delong Shang;Xiangbo Yi

2022 7th International Conference on Signal and Image Processing (ICSIP)
Year: 2022 | Conference Paper |
Face template protection are increasingly applied in identity authentication systems in recent years since it can provide high matching performance. Face template protection aims to provide an effective solution to ensure the security of face features. In most of the end-to-end face template protection schemes, the main challenge of the problem is that the unavailability of the enrolled users’ dat...Show More

Face Template Protection Through Incremental Learning and Error-Correcting Codes

Junwei Zhou;Li Nie;Delong Shang;Xiangbo Yi

Year: 2022 | Conference Paper |

DeepSyslog: Deep Anomaly Detection on Syslog Using Sentence Embedding and Metadata

Junwei Zhou;Yijia Qian;Qingtian Zou;Peng Liu;Jianwen Xiang

IEEE Transactions on Information Forensics and Security
Year: 2022 | Volume: 17 | Journal Article |
Cited by: Papers (19)
Anomaly events indicating the unhealthy status of the computer system are recorded in the system log (Syslog). Therefore, Syslog-based anomaly event detection is crucial for diagnosing system issues and problems. However, existing log-based anomaly detection approaches use raw and unstructured log entries independently and incompletely, i.e., without considering the context of each event and event...Show More

DeepSyslog: Deep Anomaly Detection on Syslog Using Sentence Embedding and Metadata

Junwei Zhou;Yijia Qian;Qingtian Zou;Peng Liu;Jianwen Xiang

Year: 2022 | Volume: 17 | Journal Article |

A Lightweight CNN for Large-scale Chinese Character Recognition

Junwei Zhou;Yucheng Liang;Ming Chen

2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The ancient Chinese characters appear in various historical documents and poetry. People tend to use optical character recognition tools to understand these uncommon characters. The current Chinese text recognition interface is restricted to a limited character set, such as GB2312-80 and GB18010-2005 standard. However, the newest HanYu Dictionary contains over 55K characters, much more than the co...Show More

A Lightweight CNN for Large-scale Chinese Character Recognition

Junwei Zhou;Yucheng Liang;Ming Chen

Year: 2022 | Conference Paper |

End-to-end Distributed Video Coding

Junwei Zhou;Ting Lv;XiangBo Yi

2022 Data Compression Conference (DCC)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Existing distributed video coding (DVC) frameworks use manually designed and optimized modules when encoding and decoding video. Each module can set the appropriate parameters as much as possible to achieve its independent optimization. But there is no connection between the modules, and overall end-to-end optimization is not realized. Inspired by the application of neural networks to video coding...Show More

End-to-end Distributed Video Coding

Junwei Zhou;Ting Lv;XiangBo Yi

Year: 2022 | Conference Paper |

Learning to rank software modules for effort-aware defect prediction

Jiqing Rao;Xiao Yu;Chen Zhang;Junwei Zhou;Jianwen Xiang

2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Effort-Aware Software Defect Prediction (EADP) ranks software modules according to the defect density of software modules, which allows testers to find more defects while reviewing a certain amount of code, and allocates testing resources more effectively. However, the recently proposed CBS+ and EASC methods tend to rank the software modules with more LOC (Lines of Code) first. Therefore, there ar...Show More

Learning to rank software modules for effort-aware defect prediction

Jiqing Rao;Xiao Yu;Chen Zhang;Junwei Zhou;Jianwen Xiang

Year: 2021 | Conference Paper |

A Cross-Project Aging-Related Bug Prediction Approach Based on Joint Probability Domain Adaptation and k-means SMOTE

Dimeng Li;Mengting Liang;Bin Xu;Xiao Yu;Junwei Zhou;Jianwen Xiang

2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
In long-running systems, the phenomenon of performance degradation and failure rate increase caused by Aging-Related Bugs (ARBs) is known as software aging. Because of the low presence and reproducing difficulty of ARBs, collecting enough training data to predict ARBs in a project is not easy. Thus, cross-project ARB prediction has been proposed. There are two main challenges in cross-project ARB ...Show More

A Cross-Project Aging-Related Bug Prediction Approach Based on Joint Probability Domain Adaptation and k-means SMOTE

Dimeng Li;Mengting Liang;Bin Xu;Xiao Yu;Junwei Zhou;Jianwen Xiang

Year: 2021 | Conference Paper |

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou;Ke Shu;Peng Liu;Jianwen Xiang;Shengwu Xiong

2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel “LDN-TOP representation followed by ProCRC classification” pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detai...Show More

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou;Ke Shu;Peng Liu;Jianwen Xiang;Shengwu Xiong

Year: 2021 | Conference Paper |

Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis

Bin Xu;Dongdong Zhao;Kai Jia;Junwei Zhou;Jing Tian;Jianwen Xiang

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)
Year: 2020 | Conference Paper |
Cited by: Papers (10)
Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB predi...Show More

Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis

Bin Xu;Dongdong Zhao;Kai Jia;Junwei Zhou;Jing Tian;Jianwen Xiang

Year: 2020 | Conference Paper |

Modified Decoding Metric Of Distributed Arithmetic Coding

Yanchao Yang;Mingwei Qi;Junwei Zhou;Lee-Ming Cheng

2020 IEEE International Conference on Image Processing (ICIP)
Year: 2020 | Conference Paper |
As an alternative implementation of Slepian-Wolf coding, distributed arithmetic coding is very competitive in short and medium block lengths. The existing distributed arithmetic coding decoder uses the maximum a posteriori metric and the M-algorithm to select the decoding sequence by considering the prior information. Since the prior probability distribution has been explored by the encoding proce...Show More

Modified Decoding Metric Of Distributed Arithmetic Coding

Yanchao Yang;Mingwei Qi;Junwei Zhou;Lee-Ming Cheng

Year: 2020 | Conference Paper |

Improved Metric Learning Algorithm for Person Re-Identification Based on Asymmetric Metric

Wenbi Rao;Menghan Xu;Junwei Zhou

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
Person re-identification(re-ID) is becoming a hot research topic because of its value in both machine learning and video surveillance applications. In order to improve the robustness of Metric Learning by Accelerated Proximal Gradient(MLAPG), a person re-ID algorithm, called Asymmetric -MLAPG, is proposed on the basis of asymmetric metric. Unlike traditional metric learning which ignores the takin...Show More

Improved Metric Learning Algorithm for Person Re-Identification Based on Asymmetric Metric

Wenbi Rao;Menghan Xu;Junwei Zhou

Year: 2020 | Conference Paper |

Deep Binarized Convolutional Neural Network Inferences over Encrypted Data

Junwei Zhou;Junjiong Li;Emmanouil Panaousis;Kaitai Liang

2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
Homomorphic encryption provides a way to perform deep learning over encrypted data and permits the user to encrypt the data before uploading, leaving the control of data on the user side. However, operations on encrypted data based on homomorphic encryption are time-consuming, especially in a deep convolutional neural network (CNN), which incorporates a large number of layers and operations. To sp...Show More

Deep Binarized Convolutional Neural Network Inferences over Encrypted Data

Junwei Zhou;Junjiong Li;Emmanouil Panaousis;Kaitai Liang

Year: 2020 | Conference Paper |

Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding

Xing Tang;Junwei Zhou;Shengwu Xiong;Jing Wang;Kunxiao Zhou

IEEE Access
Year: 2018 | Volume: 6 | Journal Article |
Cited by: Papers (25)
In cognitive radio ad hoc networks (CRAHNs), the secondary users' links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing ha...Show More

Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding

Xing Tang;Junwei Zhou;Shengwu Xiong;Jing Wang;Kunxiao Zhou

Year: 2018 | Volume: 6 | Journal Article |

Abnormal driving behavior detection for bus based on the Bayesian classifier

Xinrong Wu;Junwei Zhou;Jinghe An;Yanchao Yang

2018 Tenth International Conference on Advanced Computational Intelligence (ICACI)
Year: 2018 | Conference Paper |
Cited by: Papers (11)
As the primary transportation in the urban traffic, bus plays a significant role in road safety. It would cause severe casualties if an accident occurred. To improve the safety of bus driving, we classify the specific types of latent abnormal driving behavior, which include sudden braking, lane changing casually, quick turn, fast U-turn and long time parking, and propose a method to identify the a...Show More

Abnormal driving behavior detection for bus based on the Bayesian classifier

Xinrong Wu;Junwei Zhou;Jinghe An;Yanchao Yang

Year: 2018 | Conference Paper |

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