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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jin Qian Wei Jin Xuanyuan Wang Ying Yu Mingchen Zheng Duoqian Miao School of Software East China Jiaotong University Nanchang Jiangxi School of Transportation Engineering East China Jiaotong University Nanchang Jiangxi Department Computer Science and Technology Tongji University Shanghai China

In the existing multi-attribute decision making researches, most of the hesitant fuzzy linguistic multi-attribute decision making methods assume the decision makers use the linguistic term set under the same granularity to express their linguistic evaluation informations. However, in actual decision making, due to different cultural and educational backgrounds, decision makers may use different sets of linguistic terms at different granularities, which leads to the mismatch of decision information and inaccuracy of decision results. To address this problem, this paper proposes VIKOR decision making method based on multi-granularity hesitant furzy linguistic term sets, which consider both the overall benefits of the group and the regret values of individuals to select the optimal solution. Specifically, a VIKOR decision making method based on the multi-granularity hesitant fuzzy linguistic term set is first proposed, which employs to convert the multi-granularity hesitant fuzzy linguistic term set of each decision maker to the same granularity for aggregation, and obtains the corresponding membership level linguistic term set. Secondly, using the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked based on their proximity to the positive ideal solution. This ranking facilitates the selection of the optimal solution. Secondly, combined with the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked in order of merit according to the magnitude of the closeness of the preference value setting of each alternative to the positive ideal solution, so as to achieve the selection of the optimal solution. Finally, an arithmetic example is used to prove the accuracy, rationality and feasibility of the method, and the stability of the proposed method is further illustrated by experimentally analyzing the decision mechanism coefficients.

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Semi-Asynchronous Hierarchical Federated Deep Learning for Intrusion Detection in Ami Networks

SSRN

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SSRN 2023年

作者： Xia, Zhuoqun Zhou, Hongmei Hu, Zhenzhen Jiang, Qisheng Zhou, Kaixin School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Software University of South China Hengyang421001 China

The emergence of smart grid brings great convenience to users and power companies, but also brings many new problems, among which the most prominent one is network attack security. Although federated learning works well in dealing with smart grid network attacks, it suffers from gradient leakage, client node failure and a single type of training model. Therefore, this paper proposes a new federated deep ensemble learning approach for advanced metering infrastructure (AMI) intrusion detection to protect data privacy and security. First, we design a hierarchical federated learning framework based on chained secure multi-party computation, which allows multiple concentrators to collaboratively train hierarchical intrusion detection models to protect local gradients. Second, we adapt the framework to the AMI network structure characteristics and intrusion detection real-time properties and design a semi-asynchronous model distribution protocol. Finally, we build an integrated model based on temporal convolutional networks and gated recursive units (TCN-GRU) to accurately detect AMI network attacks. The experimental results show that the proposed method can achieve 99.23% detection accuracy and 40% faster convergence than existing methods. © 2023, The Authors. All rights reserved.

关键词： Intrusion detection

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Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing

arXiv

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arXiv 2024年

作者： Xue, Pengyu Wu, Linhao Yang, Zhen Li, Xinyi Yu, Zhongxing Jin, Zhi Li, Ge Xiao, Yan Wu, Jingwen The School of Computer Science and Technology Shandong University Qingdao China The Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China The School of Computer Science Peking University Beijing China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The School of Cyber Science and Technology Sun Yat-sen University Shenzhen China

In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and studied in both industry and academia. Nonetheless, LLMs were proved to be highly sensitive to input prompts, with slight differences in the expressions of semantically equivalent programs potentially causing repair failures. Therefore, it is crucial to conduct robustness testing on LAPR techniques before their practical deployment. However, related research is scarce. To this end, we propose MT-LAPR, a Metamorphic Testing framework exclusively for LAPR techniques, which summarizes nine widely-recognized Metamorphic Relations (MRs) by developers across three perturbation levels: token, statement, and block. Afterward, our proposed MRs are applied to buggy codes to generate test cases, which are semantically equivalent yet to affect the inference of LAPR. Experiments are carried out on two extensively examined bug-fixing datasets, i.e., Defect4J and QuixBugs, and four bug-fixing abled LLMs released recently, demonstrating that 34.4%∼48.5% of the test cases expose the instability of LAPR techniques on average, showing the effectiveness of MT-LAPR and uncovering a positive correlation between code readability and the robustness of LAPR techniques. Inspired by the above findings, this paper uses the test cases generated by MT-LAPR as samples to train a CodeT5-based code editing model aiming at improving code readability and then embeds it into the LAPR workflow as a data preprocessing step. Extensive experiments demonstrate that this approach significantly enhances the robustness of LAPR by 49.32% at most. Copyright © 2024, The Authors. All rights reserved.

关键词： software testing

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

arXiv

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arXiv 2025年

作者： Yang, Ziyuan Chen, Yingyu Wang, Zhiwen Shan, Hongming Chen, Yang Zhang, Yi College of Computer Science Sichuan University China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University China School of Cyber Science and Engineering Sichuan University China School of Computer Science and Software Engineering Southwest Petroleum University China Institute of Science and Technology for Brain-inspired Intelligence Fudan University China School of Computer Science and Engineering Southeast University China

Reducing radiation doses benefits patients, however, the resultant low-dose computed tomography (LDCT) images often suffer from clinically unacceptable noise and artifacts. While deep learning (DL) shows promise in LDCT reconstruction, it requires large-scale data collection from multiple clients, raising privacy concerns. Federated learning (FL) has been introduced to address these privacy concerns;however, current methods are typically tailored to specific scanning protocols, which limits their generalizability and makes them less effective for unseen protocols. To address these issues, we propose SCAN-PhysFed, a novel SCanning- and ANatomy-level personalized Physics-Driven Federated learning paradigm for LDCT reconstruction. Since the noise distribution in LDCT data is closely tied to scanning protocols and anatomical structures being scanned, we design a dual-level physics-informed way to address these challenges. Specifically, we incorporate physical and anatomical prompts into our physics-informed hypernetworks to capture scanning- and anatomy-specific information, enabling dual-level physics-driven personalization of imaging features. These prompts are derived from the scanning protocol and the radiology report generated by a medical large language model (MLLM), respectively. Subsequently, client-specific decoders project these dual-level personalized imaging features back into the image domain. Besides, to tackle the challenge of unseen data, we introduce a novel protocol vector-quantization strategy Copyright © 2025, The Authors. All rights reserved.

关键词： Computerized tomography

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

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2nd EAI International Conference on Applied Cryptography in Computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Computational linguistics

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MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting

arXiv

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arXiv 2023年

作者： Zhao, Tianlong Ma, Xiang Li, Xuemei Zhang, Caiming School of Software Shandong University China Shandong Provincial Laboratory of Future Intelligence and Financial Engineering China Digital Media Technology Key Lab of Shandong Province China

Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effective patterns in historical series and applying them to future forecasting. Advanced models based on point-wise connected MLP and Transformer architectures have strong fitting power, but their secondary computational complexity limits practicality. Additionally, those structures inherently disrupt the temporal order, reducing the information utilization and making the forecasting process uninterpretable. To solve these problems, this paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable. By carrying out sufficient experiments on more than ten real data sets of both short and long term forecasting tasks, MPR-Net achieves the state of the art forecasting performance, as well as good generalization and robustness performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Log Anomaly Detection Based on Bi-LSTM Feature Extraction 5

Log Anomaly Detection Based on Bi-LSTM Feature Extraction

引用

5th International Conference on Data Science and Information technology, DSIT 2022

作者： Zhang, Yue Zhang, Dong Guo, Feng Wang, Xiaotong Duan, Yihai Zhang, Xiaonan School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Inspur Electronic Information Industry Co. Ltd. System Software R&D Dept. Jinan China Inspur Electronic Information Industry Co. Ltd. System Software R&D Dept. Zhengzhou China Compute Science and Technology Harbin Institute of Technology Weihai Weihai China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the development of the Internet and the digital age continues to advance, traditional operation and maintenance methods can no longer meet the maintenance of large-scale distributed systems, AIOps emerges as the times require. In AIOps, finding anomalies is a top priority. PCA (Principal Component Analysis) is a traditional machine learning algorithm, which is used for log anomaly detection using the dimensionality reduction feature of PCA algorithm, the advantages of this detection method are: fast detection and simple model. However, the anomaly detection effect is not satisfactory, because when extracting the features of log data, the sequence features and semantic features cannot be sufficiently mined, resulting in a lower accuracy rate. For this problem, we propose a feature extraction improvement model. This model is based on Bi-LSTM (Bidirectional Long Short Term Memory) deep network to improve the feature extraction method of PCA model, extracting semantic features of log sequences by exploiting the ability of long short-term memory network to grasp contextual information, and feed the vector representation matrix containing the semantic features into the PCA anomaly detection network for anomaly detection. The model is validated on HDFS open source dataset and Inspur server dataset, and the results show that the F1-measure of this model is 36.3 % higher than that of the traditional PCA model, and 1.1 % higher than that of the single Bi-LSTM model. © 2022 IEEE.

关键词： Long short-term memory

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Survey on Lie Group Machine Learning

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Big Data Mining and Analytics 2020年第4期3卷 235-258页

作者： Mei Lu Fanzhang Li the School of Software Engineering Jinling Institute of TechnologyNanjing 211169China the School of Computer Science and Technology Jiangsu Normal UniversityXuzhou 221000China the School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Lie group machine learning is recognized as the theoretical basis of brain intelligence,brain learning,higher machine learning,and higher artificial *** sets of Lie group matrices are widely available in practical *** group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed *** study aims to provide a comprehensive survey on recent advances in Lie group machine *** introduce Lie group machine learning techniques in three major categories:supervised Lie group machine learning,semisupervised Lie group machine learning,and unsupervised Lie group machine *** addition,we introduce the special application of Lie group machine learning in image *** work covers the following techniques:Lie group machine learning model,Lie group subspace orbit generation learning,symplectic group learning,quantum group learning,Lie group fiber bundle learning,Lie group cover learning,Lie group deep structure learning,Lie group semisupervised learning,Lie group kernel learning,tensor learning,frame bundle connection learning,spectral estimation learning,Finsler geometric learning,homology boundary learning,category representation learning,and neuromorphic synergy ***,this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine *** will enable researchers to comprehensively understand the state of the field,identify the most appropriate tools for particular applications,and identify directions for future research.

关键词： Lie group machine learning Lie group subspace orbit generation learning quantum group learning symplectic group learning Lie group fiber bundle learning

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A New Identity-Based Encryption Scheme with Accountable Authority Based on SM9 11

A New Identity-Based Encryption Scheme with Accountable Auth...

引用

11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Wang, Ke Zhao, Yuan Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643682402

Accountable authority identity-based encryption (A-IBE) is an extension of identity-based encryption (IBE) in which private key's source can be traced, i.e., whether the key comes from a private key generator or a user. SM9 is an official cryptography standard of China which defines a practical IBE scheme. In this paper, we construct a practical A-IBE scheme from the SM9-IBE scheme. Our A-IBE scheme has public traceability and is proven secure if the based SM9-IBE scheme is secure. Compared with other A-IBE schemes, our A-IBE scheme has better efficiency in encryption and decryption. © 2022 The authors and IOS Press.

关键词： Cryptography

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