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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. Then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. Then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. The experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. The proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Qatar University Kindi Center for Computing Research College of Engineering Doha2713 Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks. © 2025 IEEE.

关键词： Contrastive Learning

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Multi-channel set polynomial based label regularized graph neural networks against extreme data scarcity

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Pattern Recognition 2025年 166卷

作者： Zhang, Jingxiao Ding, Shifei Zhang, Jian Guo, Lili Ding, Ling School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Mine Digitization Engineering Research Center of Ministry of Education Xuzhou221116 China College of Intelligence and Computing Tianjin University Tianjin300350 China

Graph Neural Networks (GNNs) are one of the commonly used methods for semi-supervised node classification. Their advantage lies in modeling the relational information in the data and propagating the feature information of labeled nodes to unlabeled nodes in the graph, thereby predicting their labels. However, current research results indicate that existing models perform poorly when labeled data are extremely limited. To address this problem, we introduce a label regularization method and propose a multi-channel set polynomial based label regularized graph neural network against extreme data scarcity (MSP-LR). It consists of two components: a basic learning module based on multi-channel set polynomials and a label regularization module. Specifically, we use the basic module to expand the model's receptive field and obtain pseudo-labels for all nodes. For labeled nodes, we replace the obtained pseudo-label information with their initial label information. In the label regularization module, we impose regularization constraints on unlabeled nodes based on the clustering assumption to improve the reliability of labels. Experimental results on two homogeneous graphs and four heterogeneous graphs with different labeling rates demonstrate the effectiveness of this model. © 2025 Elsevier Ltd

关键词： Self-supervised learning

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chiming Duan Tong Jia Yong Yang Guiyang Liu Jinbu Liu Huxing Zhang Qi Zhou Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Industries Systems operation Large language models Retrieval augmented generation Active learning Noise Software systems Speech processing Anomaly detection

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A novel unsupervised method for new word extraction

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Science China(Information Sciences) 2016年第9期59卷 11-21页

作者： Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Department of Computer Science and TechnologyBeijing Institute of Technology

New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even appear with the new meanings of existing words. To tackle these problems, this paper proposes a novel method to extract new words. It not only considers domain specificity, but also combines with multiple statistical language knowledge. First, we perform a filtering algorithm to obtain a candidate list of new words. Then, we employ the statistical language knowledge to extract the top ranked new words. Experimental results show that our proposed method is able to extract a large number of new words both in Chinese and English corpus, and notably outperforms the state-of-the-art methods. Moreover, we also demonstrate our method increases the accuracy of Chinese word segmentation by 10% on corpus containing new words.

关键词： new word extraction word segmentation domain specificity statistical language knowledge domain word extraction

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A novel secure group RFID authentication protocol

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The Journal of China Universities of Posts and Telecommunications 2014年第1期21卷 94-103页

作者： GUO Cong ZHANG Zi-jian ZHU Lie-huang TAN Yu-an YANG Zhen Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of ComputerBeijing Institute of Technology

The trend of researching group radio frequency identification devices（RFID） authentication protocol has become increasingly popular in recent years. One of the newest work in this area is from Batina and Lee, they presented a privacy-preserving multi-players grouping-proof protocol based on the elliptic curve cryptography（ECC）, and claimed their protocol have the ability to resist five potential attacks, including compromised tag attack, man-in-the-middle attack, colluding tags attack, etc. In this paper, we first take a counterexample to demonstrate their protocol is vulnerable to compromised tag attack. Then we propose a novel secure RFID authentication protocol, and analyze its security by merging formal analysis, provable security, and mathematical inductive method, so as to solve the weakness of Batina and Lee＇s work. Furthermore, compared with another two classic protocols（secure ownership transfer protocol（SOTP） and secure multiple group ownership transfer protocol（SMGOTP））, the performance analysis show that our protocol provides not only a lower tags＇ communication cost at about 50.0% and 14.3%, but also a lower reader＇s computation cost（approximate 14.5% and 55.1% respectively）, when transferring a large number of tags.

关键词： group RFID authentication compromised attack elliptic curve RFID internet of things(IOT)

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An end-to-end deep neural network for truth discovery 1

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17th International Conference on Web Information Systems and Applications, WISA 2020

作者： Chen, Huafeng Dong, Yongquan Gu, Qing Liu, Yali School of Computer Science and Technology Jiangsu Normal University Xuzhou221000 China Xuzhou Engineering Research Center of Cloud Computing Xuzhou221000 China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

With the rapid growth of web data, information about the same target gathered from multiple sources often exhibits conflicts. This problem motivates the need for truth discovery, which is to automatically resolve conflicts and find the truth from multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates or probability models. A common limitation of these methods is that their models are complex to be built. In this paper, we propose a concise end-to-end deep neural network for truth discovery, which regards the task as a classification problem. Firstly, for each target, we extract a unique claim, and for each unique claim, we construct a source-unique-claim vector depending on whether the source provides this value. Then on the training dataset, we label the vector as true/false according to the ground truth. Finally, we use a deep neural network to build a classification model for each target to judge which claim is the truth. Experimental results on two real-world datasets show that our proposed model has better performance than existing state-of-the-art methods. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Scalable protocol for cross-domain group password-based authenticated key exchange

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Frontiers of Computer Science 2015年第1期9卷 157-169页

作者： Cong GUO Zijian ZHANG Liehuang ZHU Yu-an TAN Zhen YANG Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

Cross-domain password-based authenticated key exchange （PAKE） protocols have been studied for many years. However, these protocols are mainly focusing on multi-participant within a single domain in an open network environment. This paper proposes a novel approach for designing a cross-domain group PAKE protocol, that primarily handles with the setting of multi-participant in the multi- domain. Moreover, our protocol is proved secure against active adversary in the Real-or-Random （ROR） model. In our protocol, no interaction occurs between any two domain authentication servers. They are regarded as ephemeral certificate authorities （CAs） to certify key materials that participants might subsequently use to exchange and agree on group session key. We further justify the computational complexity and measure the average computation time of our protocol. To the best of our knowledge, this is the first work to analyze and discuss a provably secure multi-participant cross-domain group PAKE protocol.

关键词： cross-domain password-based authenticated key exchange (PAKE) group key exchange Real-Or-Random (ROR) model provable secure

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Blockchain-assisted Puncturable Signcryption for cloud and Fog-Hosted Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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