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Encrypted traffic classification based on fusion of vision transformer and temporal features

The Journal of China Universities of Posts and Telecommunications 2023年第2期30卷 73-82页

作者： Wang Lanting Hu Wei Liu Jianyi Pang Jin Gao Yating Xue Jingyao Zhang Jie School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Grid Information and Telecommunication Branch Beijing 100761China Information and Telecommunication Company State Grid Shandong Electric Power CorporationJinan 250001China

Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network(CNN),recurrent neural network(RNN),and stacked autoencoder(SAE),and only construct a shallow network to extract features,which leads to the low accuracy of encrypted traffic classification,an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was *** transformer network(BoTNet)was used to extract spatial features and bi-directional long short-term memory(BiLSTM)was used to extract temporal *** the two sub-networks are parallelized,the feature fusion method of early fusion was used in the framework to perform feature ***,the encrypted traffic was identified through the fused *** experimental results show that the BiLSTM and BoTNet fusion transformer(BTFT)model can enhance the performance of encrypted traffic classification by fusing multi-dimensional *** accuracy rate of a virtual private network(VPN)and non-VPN binary classification is 99.9%,and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.

关键词： encrypted traffic classification vision transformer temporal feature

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Information diffusion network inferring and pathway tracking

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Science China(Information Sciences) 2015年第9期58卷 135-149页

作者： ZHOU DongHao HAN WenBao WANG YongJun YUAN BaoDi College of Computer National University of Defense Technology State Key Laboratory of Mathematical Engineering and Advanced Computing College of Computer Science and Technology Fudan University

Network diffusion, such as spread of ideas, rumors, contagious disease, or a new type of behaviors, is one of the fundamental processes within networks. Designing effective strategies for influence spread maximization, rumor spread minimization, or epidemic immunization has attracted considerable research attention. However, a key challenge is that many times we can only observe the trace of contagion spreading across network, but the underlying network structure is unknown and the transmission rates between node pairs are unclear to us. In this paper, given the observed information cascades, we aim to address two problems:diffusion network structure inferring and information diffusion pathways tracking. We propose a novel probabilistic model called Network Inferring from Multidimensional features of Cascades(NIMFC) which takes into account heterogeneous features, including temporal and topological features of cascades, node attributes, and information content, to infer the latent network structure and transmission rates of edges. Also, based on the inferred network structure, we may track diffusion pathways of a cascade in social networks. We use blocked coordinate descent method to learn a sparse estimation of the latent network. Our proposed model NIMFC is evaluated both on large synthetic and real-world data sets, and experimental results show that our method significantly outperforms state-of-the-art models both in terms of recovering the latent network structure and information pathway tracking.

关键词： diffusion network network inferring information pathway tracking temporal feature node attributes topological feature informatio

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How do temporal and spectral features .matter in crop classification in Heilongjiang Province, China？

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Journal of Integrative Agriculture 2017年第2期16卷 324-336页

作者： HU Qiong WU Wen-bin SONG Qian LU Miao CHEN Di YU Qiang-yi TANG Hua-jun Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 P.R.China Remote Sensing Technology Center Heilongjiang Academy of Agricultural Sciences Harbin 150086 PR. China

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.

关键词： crop identification temporal feature spectral feature feature selection MODIS

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教育的时间性

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教育学 2024年第1期

作者：刘铁芳

每个人都活在时间之中，活在过去、现在与未来相统一的整体性时间之中。具有深刻时间意识的孔子真切地意识到自我生命时间的紧迫性，让自己“学而不厌”“诲人不倦”，在“发愤忘食、乐以忘忧”的过程超越时间的流逝，由此凸显个体成人... 详细信息

每个人都活在时间之中，活在过去、现在与未来相统一的整体性时间之中。具有深刻时间意识的孔子真切地意识到自我生命时间的紧迫性，让自己“学而不厌”“诲人不倦”，在“发愤忘食、乐以忘忧”的过程超越时间的流逝，由此凸显个体成人的当下性。一方面，孔子重视现在的时间，充分地活在属于其自身的当代；另一方面，又以斯文的传承与发扬为依托，承接过去、关联未来，保持个体成人的开放性。孔子凭借一以贯之的生命之志达成自我生命时间的统一性，并在创造性地赓续尧舜、文王以来的礼文传统之时，让个体与过去、现在、未来的人们紧密相连，促成个体成人的完整性。重温《论语》，在当下系念过去，其实质就是在重视现在的同时，守护个体成人的历史文化根基，由此而建构一种过去、现在与未来相统一的生活方式与朝向整全的教育方式。

关键词：《论语》时间性个体成人生命共同体整全性教育 The Analects of Confucius temporal feature becoming an individual life community the holistic education

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句子阅读理解过程中句意的建构时间

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心理学 2012年第10期 735-744页

作者：钟伟芳莫雷金花徐贵平

一般语言学理论认为，在句子阅读理解过程中，句子意义建构是基于词汇语义整合的，其反映在N400之上，开始于词语呈现后约250 ms。然而，近年关于语义P600效应的研究却提示句意建构可能存在多通道或方式，读者甚至可能在N400反映的加工... 详细信息

一般语言学理论认为，在句子阅读理解过程中，句子意义建构是基于词汇语义整合的，其反映在N400之上，开始于词语呈现后约250 ms。然而，近年关于语义P600效应的研究却提示句意建构可能存在多通道或方式，读者甚至可能在N400反映的加工出现之前就已建立了初步句意。为探明在句子阅读理解过程中是否存在比N400反映的加工更早的句意建构，以及如果存在这样的句意建构，其是从何时开始的，本研究开展了四个实验。实验1发现被试在句末双字词呈现200 ms后已将其联系到上文语义表征而建立了句意，提示句意建构开始于N400反映的加工出现之前。实验2与3进一步发现，读者开始建立句意的时间约在句末词呈现了150 ms之时。实验4排除了实验1～3效应的其他一些可能解释，提升了实验1～3结果的可靠性。总体上，本研究提示：在句子阅读理解过程中，读者在句末双字词呈现了约150 ms之时就已建立了句意；可能存在比N400反映的加工更早的句意建构。

关键词：句子理解意义建构时间特征 N400 sentence comprehension sentence meaning establishment temporal feature N400

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