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ML-Parser:An Eficient and Accurate Online Log Parser

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Journal of computer science & Technology 2022年第6期37卷 1412-1426页

作者： Yu-Qian Zhu Jia-Ying Deng Jia-Chen Pu Peng Wang Shen Liang Wei Wang school of computer science Fudan UniversityShanghai 200082China

A log is a text message that is generated in various services,frameworks,and *** majority of log data mining tasks rely on log parsing as the first step,which transforms raw logs into formatted log *** log parsing approaches often fail to effectively handle the trade-off between parsing quality and *** view of this,in this paper,we present Multi-Layer Parser(ML-Parser),an online log parser that runs in a streaming ***,we present a multi-layer structure in log parsing to strike a balance between efficiency and ***-grained tokenization and a fast similarity measure are applied for efficiency while fine-grained tokenization and an accurate similarity measure are used for *** experiments,we compare ML-Parser with two existing online log parsing approaches,Drain and Spell,on ten real-world datasets,five labeled and five *** the five labeled datasets,we use the proportion of correctly parsed logs to measure the accuracy,and ML-Parser achieves the highest accuracy on four *** the whole ten datasets,we use Loss metric to measure the parsing ***-Parse achieves the highest quality on seven out of the ten datasets while maintaining relatively high efficiency.

关键词： log parsing online approach structure extraction similarity measure

PLQ:An Efficient Approach to Processing Pattern-Based Log Queries

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Journal of computer science & Technology 2022年第5期37卷 1239-1254页

作者： Jia Chen Peng Wang Fan Qiao Shi-Qing Du Wei Wang school of computer science Fudan UniversityShanghai 200082China

As software systems grow more and more complex,extensive techniques have been proposed to analyze the log data to obtain the insight of the system ***,during log data analysis,tedious manual efforts are paid to search interesting or informative log patterns from a huge volume of log data,named pattern-based *** existing log management tools and DMBS systems can also support pattern-based queries,they suffer from a low *** deal with this problem,we propose a novel approach,named PLQ(Pattern-based Log Query).First,PLQ organizes logs into disjoint chunks and builds chunk-wise bitmap indexes for log types and attribute ***,based on bitmap indexes,PLQ finds candidate logs with a set of efficient bit-wise ***,PLQ fetches candidate logs and validates them according to the queried *** experiments are conducted on real-life *** to experimental results,compared with existing log management systems,PLQ is more efficient in querying log patterns and has a higher pruning rate for filtering irrelevant ***,in PLQ,since the ratio of the index size to the data size does not exceed 2.5%for log datasets of different sizes,PLQ has a high scalability.

关键词： pattern query log analysis bitmap index log pattern

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in Engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of computer Engineering Aligarh Muslim UniversityAligarh202002India College of computer science King Khalid UniversityAbha61413Saudi Arabia school of Computing Gachon UniversitySeongnam13120Republic of Korea Department of computer science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

Shapelet Based Two-Step Time Series Positive and Unlabeled Learning

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Journal of computer science & Technology 2023年第6期38卷 1387-1402页

作者：张翰博王鹏张明明汪卫 school of computer science Fudan UniversityShanghai 200438China

In the last decade,there has been significant progress in time series ***,in real-world in-dustrial settings,it is expensive and difficult to obtain high-quality labeled ***,the positive and unlabeled learning(PU-learning)problem has become more and more popular *** current PU-learning approaches of the time series data suffer from low accuracy due to the lack of negative labeled time *** this paper,we propose a novel shapelet based two-step(2STEP)PU-learning *** the first step,we generate shapelet features based on the posi-tive time series,which are used to select a set of negative *** the second step,based on both positive and nega-tive time series,we select the final features and build the classification *** experimental results show that our 2STEP approach can improve the average F1 score on 15 datasets by 9.1%compared with the baselines,and achieves the highest F1 score on 10 out of 15 time series datasets.

关键词： positive unlabeled learning time series shapelet

Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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computer Modeling in Engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam school of computer science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

Spontaneous Orientation Polarization of Anisotropic Equivalent Dipoles Harnessed by Entropy Engineering for Ultra‑Thin Electromagnetic Wave Absorber

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Nano-Micro Letters 2025年第1期17卷 424-438页

作者： Honghan Wang Xinyu Xiao Shangru Zhai Chuang Xue Guangping Zheng Deqing Zhang Renchao Che Junye Cheng Liaoning Key Lab of Lignocellulose Chemistry and BioMaterials Liaoning Collaborative Innovation Center for Lignocellulosic BiorefinerySchool of Light Industry and Chemical EngineeringDalian Polytechnic UniversityDalian 116034People’s Republic of China school of Life science and Biotechnology Dalian University of TechnologyDalian 116024People’s Republic of China Department of Mechanical Engineering Hong Kong Polytechnic UniversityHung HomKowloonHong Kong 999077People’s Republic of China school of Materials science and Engineering Qiqihar UniversityQiqihar 161006People’s Republic of China Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative MaterialsAcademy for Engineering&TechnologyFudan UniversityShanghai 200438People’s Republic of China Department of Materials science Shenzhen MSU-BIT UniversityShenzhen 517182People’s Republic of China

The synthesis of carbon supporter/nanoscale high-entropy alloys(HEAs)electromagnetic response composites by carbothermal shock method has been identified as an advanced strategy for the collaborative competition engineering of conductive/dielectric *** migration modes within HEAs as manipulated by the electronegativity,valence electron configurations and molar proportions of constituent elements determine the steady state and efficiency of equivalent ***,enlightened by skin-like effect,a reformative carbothermal shock method using carbonized cellulose paper(CCP)as carbon supporter is used to preserve the oxygencontaining functional groups(O·)of carbonized cellulose fibers(CCF).Nucleation of HEAs and construction of emblematic shell-core CCF/HEAs heterointerfaces are inextricably linked to carbon metabolism induced by O·.Meanwhile,the electron migration mode of switchable electronrich sites promotes the orientation polarization of anisotropic equivalent *** virtue of the reinforcement strategy,CCP/HEAs composite prepared by 35%molar ratio of Mn element(CCP/HEAs-Mn_(2.15))achieves efficient electromagnetic wave(EMW)absorption of−51.35 dB at an ultra-thin thickness of 1.03 *** mechanisms of the resulting dielectric properties of HEAs-based EMW absorbing materials are elucidated by combining theoretical calculations with experimental characterizations,which provide theoretical bases and feasible strategies for the simulation and practical application of electromagnetic functional devices(e.g.,ultra-wideband bandpass filter).

关键词： High-entropy alloys Carbothermal shock Switchable electron migration modes Emblematic shell-core heterointerfaces Ultra-thin thickness

Text information aggregation with centrality attention

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science China(Information sciences) 2021年第12期64卷 91-103页

作者： Jingjing GONG Hang YAN Yining ZHENG Qipeng GUO Xipeng QIU Xuanjing HUANG school of computer science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Fudan University

A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves an aggregation process of combining the representations of all the words, such as pooling or self-attention. However, these widely used aggregation approaches do not take higher-order relationships among the words into consideration. Hence we propose a new way of obtaining aggregation weights, called eigen-centrality self-attention. More specifically, we build a fully-connected graph for all the words in a sentence, then compute the eigen-centrality as the attention score of each word. The explicit modeling of relationships as a graph is able to capture some higher-order dependency among words, which helps us achieve better results in 5 text classification tasks and one SNLI task than baseline models such as pooling, self-attention, and dynamic routing. Besides, in order to compute the dominant eigenvector of the graph, we adopt a power method algorithm to get the eigen-centrality measure. Moreover, we also derive an iterative approach to get the gradient for the power method process to reduce both memory consumption and computation requirement.

关键词： information aggregation eigen centrality text classification natural language processing deep learning

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Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

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Neural Regeneration Research 2025年第1期20卷 93-106页

作者： Yujie Yang Xinyi Li Jiaying Lu Jingjie Ge Mingjia Chen Ruixin Yao Mei Tian Jian Wang Fengtao Liu Chuantao Zuo Key Laboratory of Arrhythmias Ministry of EducationDepartment of Medical GeneticsShanghai East HospitalSchool of MedicineTongji UniversityShanghaiChina Department of Neurology National Research Center for Aging and MedicineNational Center for Neurological Disordersand State Key Laboratory of Medical NeurobiologyHuashan HospitalFudan UniversityShanghaiChina Department of Nuclear Medicine&PET Center National Center for Neurological Disordersand National Clinical Research Center for Aging and MedicineHuashan HospitalFudan UniversityShanghaiChina International Human Phenome Institutes(Shanghai) ShanghaiChina Human Phenome Institute Fudan UniversityShanghaiChina

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of *** review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest *** conducted a thorough literature search using reputable databases such as PubMed and Web of *** criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical *** findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting ***,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo *** validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic *** summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

关键词： aromatic amino acid decarboxylase brain imaging dopamine transporter Parkinson’s disease parkinsonism positron emission tomography presynaptic dopaminergic function vesicle monoamine transporter type 2

A blockchain-enabled wiki framework based on participatory design

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Digital Communications and Networks 2022年第6期8卷 995-1004页

作者： Zheng Xu Chaofan Liu Peng Zhang Tun Lu Ning Gu school of computer science Fudan UniversityShanghaiChina Shanghai Key Laboratory of Data science Fudan UniversityShanghaiChina

The 5G mobile Internet facilitates contents generation for online communities and platforms through human-to-human ***,a well-known online community,uses wiki technology to build an encyclopedia through collective intelligence and *** wiki systems adopt a centralized implementation,and while existing studies have optimized the efficiency of the wiki systems'centralized implementation,these systems still suffer from a lot of problems,for example,opacity and *** the years,blockchain has brought a flurry of fervour and decentralization to the system architecture,meanwhile giving users a sense of trust and ***,an innovative blockchain-enabled wiki framework called DecWiki is proposed to build one transparent,truthful,collaborative and autonomous *** several participatory design iterations,we present DecWiki's detailed architecture and its implementation in the form of a smart contract and use the interplanetary file system to complement the big data ***,we use the trusted execution environment to secure sensitive information in the wireless ***,the system overhead and the acceptance of the prototype are *** experiments present its significant performance.

关键词： Blockchain Wiki Participatory design Trusted execution envir onment Smart contract