A NEURAL-NETWORK APPROACH FOR MOVING OBJECTS RECOGNITION IN COLOR IMAGE SEQUENCES FOR SURVE
专业英语 人工智能 最终版
• 人工智能是计算机科学的前沿，充满机遇和挑战。 • “A student in physics might reasonably feel that all the good ideas have already been taken by Galileo,Newton,Einstein,and the rest,and that it takes many years of study before one can contribute new ideas,AI,on the other hand,still has openings for a full-time Einstein.” • _______《Artificial Intelligence:A modern Approach》 • （一位在物理学领域的学生会理所当然的认为所有的好点子已经 被伽利略，牛顿，爱因斯坦和其他人，它需要许多年前的研究能 做出贡献的新思路，另一方面，AI，仍然作为一个全职的爱因斯 坦。）
Definition 定义
Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Definition in AI textbook :”the study and design of intelligent agents” 人工智能（AI）是机器智能与计算机科学，旨 在创建它的分支。在AI教科书的定义：“学习 的智能代理的设计。”
在解决问题方面：1960纽厄尔编译一个普遍的问题解决者 (GPS),可以解决11个不同类型的问题; 在专家系统：1968的结果费根鲍姆开发DENDRAL“专家系 统”并投入使用；
扭矩电机控制Neural_Approach_for_Induction_Motor_Load_Torque_Identification_in
in their useful life. A study carried out at CEMIG (Electrical Energy
Company of Minas Gerais State – Brazil), with 3425 threephase induction motors, in several industrial sectors, showed that 28.7% of them were over dimensioned and 5.9% of them were under dimensioned. Another study, at COPEL (Electrical Energy Company of Parana State – Brazil) with 6108 three-phase induction motors showed that 37.75% were working over dimensioned [1].
P. J. A. Serni is with the Electrical Engineering Department (DEE) at State University of São Paulo (UNESP), CP 473, CEP 17033-360, Bauru, SP, Brazil, Phone: +55 (14) 31036115; Fax: +55 (14) 31036116; e-mail: paulojas@feb.unesp.br.
This work was supported in part by the FAPESP and CNPQ under Grant (06/56093-3) and (14236/2005-4).
中文电子病历命名实体识别（CNER）研究进展
中⽂电⼦病历命名实体识别（CNER）研究进展中⽂电⼦病历命名实体识别（CNER）研究进展中⽂电⼦病历命名实体识别（Chinese Clinical Named Entity Recognition, Chinese-CNER）任务⽬标是从给定的电⼦病历纯⽂本⽂档中识别并抽取出与医学临床相关的实体提及，并将它们归类到预定义的类别。
最近把之前收集整理的⼀些CNER相关的研究进展放在了github 上。
主要内容包括Chinese-CNER的相关论⽂列表，以及⽬前各个主要数据集上的⼀些先进结果，希望对CNER感兴趣的读者有所帮助。
中⽂电⼦病历实体识别研究相关论⽂在中⽂电⼦病历实体识别任务上，已经有不少研究⽅法被提出，这些研究主要集中在对领域特征的探索上，即在通⽤领域NER⽅法的基础上，研究中⽂汉字特征和电⼦病历知识特征等来提升模型性能。
综述论⽂1. 电⼦病历命名实体识别和实体关系抽取研究综述. 杨锦锋, 于秋滨, 关毅等. ⾃动化学报, 2014, 40(8):1537-1561.2. 中⽂电⼦病历的命名实体识别研究进展. 杨飞洪,张宇,覃露等.中国数字医学,2020,15(02):9-12.3. Overview of CCKS 2018 Task 1: Named Entity Recognition in Chinese Electronic Medical Records. Zhang J, Li J, Jiao Z, et al. InChina Conference on Knowledge Graph and Semantic Computing, Springer, 2019:158-164.4. Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA. Han X, Wang Z, Zhang J, etal. arXiv preprint, 2020, arXiv:2003.03875.⽅法论⽂1. HITSZ_CNER: a hybrid system for entity recognition from Chinese clinical text. Hu J, Shi X, Liu Z, et al. Proceedings of theEvaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2017), Chendu, China, 2017:1-6. .2. Clinical named entity recognition from Chinese electronic health records via machine learning methods. Zhang Y, Wang X, Hou Z, etal. JMIR medical informatics. 2018;6(4):e50.3. A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition. Ji B, Liu R, Li S, et al. In Proceedings ofthe 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018:1-6.4. A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records. Chowdhury S, Dong X,Qian L, et al. BMC bioinformatics. 2018, 19(17):75-84.5. A Conditional Random Fields Approach to Clinical Name Entity Recognition. Yang X, Huang W. Proceedings of the EvaluationTasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2018). Tianjin, China, 2018:1-6.6. DUTIR at the CCKS-2018 Task1: A Neural Network Ensemble Approach for Chinese Clinical Named Entity Recognition. Luo L, Li N,Li S, et al. Proceedings of the Evaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2018). Tianjin, China, 2018:1-6.7. Incorporating dictionaries into deep neural networks for the chinese clinical named entity recognition. Wang Q, Zhou Y, Ruan T, etal. Journal of biomedical informatics, 2019, 92: 103133.8. A hybrid approach for named entity recognition in Chinese electronic medical record. Ji B, Liu R, Li S, et al. BMC medical informaticsand decision making. 2019 Apr;19(2):149-58.9. Chinese Clinical Named Entity Recognition Using Residual Dilated Convolutional Neural Network with Conditional Random Field.Qiu J, Zhou Y, Wang Q, et al. IEEE Transactions on NanoBioscience. 2019, 18(3):306-315.10. An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records. Li L, Zhao J, HouL, et al. BMC medical informatics and decision making. 2019, 19(5):1-1.11. Chinese clinical named entity recognition with word-level information incorporating dictionaries. Lu N, Zheng J, Wu W, et al. In 2019International Joint Conference on Neural Networks (IJCNN), 2019,1-8.12. Fine-tuning BERT for joint entity and relation extraction in Chinese medical text. Xue K, Zhou Y, Ma Z, et al. In 2019 IEEEInternational Conference on Bioinformatics and Biomedicine (BIBM), 2019, 892-897.13. Chinese clinical named entity recognition with radical-level feature and self-attention mechanism. Yin M, Mou C, Xiong K, etal. Journal of biomedical informatics. 2019, 98:103289.14. Adversarial training based lattice LSTM for Chinese clinical named entity recognition. Zhao S, Cai Z, Chen H, et al. Journal ofbiomedical informatics. 2019, 99:103290.15. 基于句⼦级 Lattice-长短记忆神经⽹络的中⽂电⼦病历命名实体识别. 潘璀然, 王青华, 汤步洲等. 第⼆军医⼤学学报. 2019,40(05):497-507.16. 基于BERT与模型融合的医疗命名实体识别. 乔锐，杨笑然，黄⽂亢. Proceedings of the Evaluation Tasks at the China Conference onKnowledge Graph and Semantic Computing (CCKS 2019)17. Noisy Label Learning for Chinese Medical Named Entity Recognition Based on Uncertainty Strategy. Li Z, Gan Z, Zhang B, etal. Proceedings of the Evaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2020) 18. 基于BERT与字形字⾳特征的医疗命名实体识别. 晏阳天, 赵新宇, 吴贤. Proceedings of the Evaluation Tasks at the China Conferenceon Knowledge Graph and Semantic Computing (CCKS 2020)19. Cross domains adversarial learning for Chinese named entity recognition for online medical consultation. Wen G, Chen H, Li H, etal. Journal of Biomedical Informatics. 2020 Dec 1;112:103608.20. Chinese medical named entity recognition based on multi-granularity semantic dictionary and multimodal tree. Wang C, Wang H,Zhuang H, et al. Journal of Biomedical Informatics. 2020, 111:103583.21. Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term MemoryModel With Contextualized Character Representations. Li Y, Wang X, Hui L, et al. JMIR Medical Informatics. 2020;8(9):e19848. 22. Chinese clinical named entity recognition with variant neural structures based on BERT methods. Li X, Zhang H, Zhou XH. Journal ofbiomedical informatics. 2020, 107:103422.23. 融⼊语⾔模型和注意⼒机制的临床电⼦病历命名实体识别. 唐国强,⾼⼤启,阮彤等. 计算机科学,2020,47(03):211-216.24. 基于笔画ELMo和多任务学习的中⽂电⼦病历命名实体识别研究. 罗凌, 杨志豪, 宋雅⽂等. 计算机学报, 2020, 43(10): 1943-1957.中⽂电⼦病历实体识别现存⽅法性能中⽂电⼦病历实体识别任务的数据集以及相应数据集上系统模型性能表现。
人工智能真的了解人类吗 中英互译
People are funny. We’re constantly trying to understand and interpret the world around us. I live in a house with two black cats, and let me tell you, every time I see a black, bunched up sweater out of the corner of my eye, I think it’s a cat. It’s not just the things we see.人是非常有趣的。
我们一直在试图理解和解释我们周围的世界。
我家里有两只黑猫，我可以告诉你，每次我眼角瞥见一件团成一团的黑色毛衣，都会以为那是一只猫。
不只是我们看到的东西。
Sometimes we attribute more intelligence than might actually be there. Maybe you’ve seen the dogs on TikTok. They have these little buttons that say things like “walk”or “treat.”They can push them to communicate some things with their owners, and their owners think they use them to communicate some pretty impressive things.我们有时以为一些东西有超常的智慧，但实际上未必有。
比如，你也许在TikTok 上看到过狗狗的视频。
上面有一些小按钮，写着“要遛遛”或“要吃的”。
这些狗狗能用这些按钮和它们的主人交流，它们的主人也以为用这些按钮就能让狗狗做一些令人惊奇的事情。
But do the dogs know what they’re saying? Or perhaps you’ve heard the story of Clever Hans the horse, and he could do math. And not just like, simple math problems, really complicated ones, like, if the eighth day of the month falls on a Tuesday, what’s the date of the following Friday? It’s like, pretty impressive for a horse.但狗狗知道它们在说什么吗？或许你听过那匹叫作“聪明的汉斯”的马的故事，这匹马居然能做数学题。
数据缺失下的IFCM-Slope One协同过滤推荐算法
D01:10.13546/ki.tjyjc.2020.09.040Ct理送愛］数据缺失下的IFCM-Slope One协同过滤推荐算法张艳菊"，陆畅小(辽宁工程技术大学a.工商管理学院;b.管理科学与工程研究院，辽宁葫芦岛125105)摘要：为了提高数据缺失情况下的推荐准确性，保证服务的质量，给用户提供更加准确与实时的个性化信息，文章将直觉模糊C均值聚类(IFCM)和协同过滤推荐算法相结合，构建了IFCM-Slope One协同过滤推荐算法。
通过引入直觉模糊C均值聚类对用户进行分类，减小邻居用户的搜索范围，降低计算的复杂度，再利用Slope One对用户喜好矩阵缺失数据进行填补，避免由于数据缺失导致推荐偏差，最后基于协同过滤推荐算法计算相似邻居集，并将相似邻居集中的用户喜好隶属度进行从大到小的排序，形成Top-n项目推荐集，生成用户推荐结果。
关键词：直觉模糊C均值聚类(IFCM);协同过滤推荐:Slope One中图分类号:0159文献标识码：A文章编号:1002-6487(2020)09-0185-040引言实时准确的个性化推荐是电子商务行业运营管理水平的体现，是大数据时代发展的重要方面。
但现在互联网信息呈指数增长，全世界现存网站已到达10亿以上，我国网民数量也已经超过7亿，庞大的数据量加大了推荐的难度,如何提高推荐的准确性成为亟待解决的问题'“。
国内学者中，邓爱林等'通过对用户评分项目集中的空缺进行填充，并运用领域最近邻方法进行预测推荐％古凌岚°」针对传统的协同过滤推荐算法的稀疏性问题利用基因表达式预测局部用户一项目的缺失评分。
高灵渲网通过对样本用户利用分类策略进行分类,再对目标用户的具体推荐项目进行预测评分。
李小浩E针对协同过滤推荐算法的缺陷,提出了SCFCM推荐算法，提高推荐精度。
国外学者中Xue等冋通过预估缺失数据进行填充，减小稀疏性问题。
Kim等回利用预测模型,对已有评分预估和实际评分比较得预测偏差,进而进行结果修正。
【2021.03.07】看论文神器知云文献翻译、百度翻译API申请、机器学习术语库
【2021.03.07】看论⽂神器知云⽂献翻译、百度翻译API申请、机器学习术语库最近在看论⽂，因为论⽂都是全英⽂的，所以需要论⽂查看的软件，在macOS上找到⼀款很好⽤的软件叫做知云⽂献翻译知云⽂献翻译界⾯长这样，可以长段翻译，总之很不错百度翻译API申请使⽤⾃⼰的api有两个好处：⼀、更加稳定⼆、可以⾃定义词库，我看的是医疗和机器学习相关的英⽂⽂献，可以⾃定义api申请在上⽅控制台、根据流程申请后可以在这⾥看到⾃⼰的ID和密钥填⼊就可以了⾃定义术语库我看的是机器学习的⽂献，因此在术语库⾥添加，导⼊⽂件（我会把⽂本放在后⾯导⼊后完成，有部分词语不翻译，⽐如MNIST这样的专有词语，就会报错，忽略掉就可以了开启术语库就⾏了机器学习术语库Supervised Learning|||监督学习Unsupervised Learning|||⽆监督学习Semi-supervised Learning|||半监督学习Reinforcement Learning|||强化学习Active Learning|||主动学习Online Learning|||在线学习Transfer Learning|||迁移学习Automated Machine Learning (AutoML)|||⾃动机器学习Representation Learning|||表⽰学习Minkowski distance|||闵可夫斯基距离Gradient Descent|||梯度下降Stochastic Gradient Descent|||随机梯度下降Over-fitting|||过拟合Regularization|||正则化Cross Validation|||交叉验证Perceptron|||感知机Logistic Regression|||逻辑回归Maximum Likelihood Estimation|||最⼤似然估计Newton’s method|||⽜顿法K-Nearest Neighbor|||K近邻法Mahanalobis Distance|||马⽒距离Decision Tree|||决策树Naive Bayes Classifier|||朴素贝叶斯分类器Generalization Error|||泛化误差PAC Learning|||概率近似正确学习Empirical Risk Minimization|||经验风险最⼩化Growth Function|||成长函数VC-dimension|||VC维Structural Risk Minimization|||结构风险最⼩化Eigendecomposition|||特征分解Singular Value Decomposition|||奇异值分解Moore-Penrose Pseudoinverse|||摩尔－彭若斯⼴义逆Marginal Probability|||边缘概率Conditional Probability|||条件概率Expectation|||期望Variance|||⽅差Covariance|||协⽅差Critical points|||临界点Support Vector Machine|||⽀持向量机Decision Boundary|||决策边界Convex Set|||凸集Lagrange Duality|||拉格朗⽇对偶性KKT Conditions|||KKT条件Coordinate ascent|||坐标下降法Sequential Minimal Optimization (SMO)|||序列最⼩化优化Ensemble Learning|||集成学习Bootstrap Aggregating (Bagging)|||装袋算法Random Forests|||随机森林Boosting|||提升⽅法Stacking|||堆叠⽅法Decision Tree|||决策树Classification Tree|||分类树Adaptive Boosting (AdaBoost)|||⾃适应提升Decision Stump|||决策树桩Meta Learning|||元学习Gradient Descent|||梯度下降Deep Feedforward Network (DFN)|||深度前向⽹络Backpropagation|||反向传播Activation Function|||激活函数Multi-layer Perceptron (MLP)|||多层感知机Perceptron|||感知机Mean-Squared Error (MSE)|||均⽅误差Chain Rule|||链式法则Logistic Function|||逻辑函数Hyperbolic Tangent|||双曲正切函数Rectified Linear Units (ReLU)|||整流线性单元Residual Neural Networks (ResNet)|||残差神经⽹络Regularization|||正则化Overfitting|||过拟合Data(set) Augmentation|||数据增强Parameter Sharing|||参数共享Ensemble Learning|||集成学习Dropout|||L2 Regularization|||L2正则化Taylor Series Approximation|||泰勒级数近似Taylor Expansion|||泰勒展开Bayesian Prior|||贝叶斯先验Bayesian Inference|||贝叶斯推理Gaussian Prior|||⾼斯先验Maximum-a-Posteriori (MAP)|||最⼤后验Linear Regression|||线性回归L1 Regularization|||L1正则化Constrained Optimization|||约束优化Lagrange Function|||拉格朗⽇函数Denoising Autoencoder|||降噪⾃动编码器Label Smoothing|||标签平滑Eigen Decomposition|||特征分解Convolutional Neural Networks (CNNs)|||卷积神经⽹络Semi-Supervised Learning|||半监督学习Generative Model|||⽣成模型Discriminative Model|||判别模型Multi-Task Learning|||多任务学习Bootstrap Aggregating (Bagging)|||装袋算法Multivariate Normal Distribution|||多元正态分布Sparse Parametrization|||稀疏参数化Sparse Representation|||稀疏表⽰Student-t Prior|||学⽣T先验KL Divergence|||KL散度Orthogonal Matching Pursuit (OMP)|||正交匹配追踪算法Adversarial Training|||对抗训练Matrix Factorization (MF)|||矩阵分解Root-Mean-Square Error (RMSE)|||均⽅根误差Collaborative Filtering (CF)|||协同过滤Nonnegative Matrix Factorization (NMF)|||⾮负矩阵分解Singular Value Decomposition (SVD)|||奇异值分解Latent Sematic Analysis (LSA)|||潜在语义分析Bayesian Probabilistic Matrix Factorization (BPMF)|||贝叶斯概率矩阵分解Wishart Prior|||Wishart先验Sparse Coding|||稀疏编码Factorization Machines (FM)|||分解机second-order method|||⼆阶⽅法cost function|||代价函数training set|||训练集objective function|||⽬标函数expectation|||期望data generating distribution|||数据⽣成分布empirical risk minimization|||经验风险最⼩化generalization error|||泛化误差empirical risk|||经验风险overfitting|||过拟合feasible|||可⾏loss function|||损失函数derivative|||导数gradient descent|||梯度下降surrogate loss function|||代理损失函数early stopping|||提前终⽌Hessian matrix|||⿊塞矩阵second derivative|||⼆阶导数Taylor series|||泰勒级数Ill-conditioning|||病态的critical point|||临界点local minimum|||局部极⼩点local maximum|||局部极⼤点saddle point|||鞍点local minima|||局部极⼩值global minimum|||全局最⼩点convex function|||凸函数weight space symmetry|||权重空间对称性Newton’s method|||⽜顿法activation function|||激活函数fully-connected networks|||全连接⽹络Resnet|||残差神经⽹络gradient clipping|||梯度截断recurrent neural network|||循环神经⽹络long-term dependency|||长期依赖eigen-decomposition|||特征值分解feedforward network|||前馈⽹络vanishing and exploding gradient problem|||梯度消失与爆炸问题contrastive divergence|||对⽐散度validation set|||验证集stochastic gradient descent|||随机梯度下降learning rate|||学习速率momentum|||动量gradient descent|||梯度下降poor conditioning|||病态条件nesterov momentum|||Nesterov 动量partial derivative|||偏导数moving average|||移动平均quadratic function|||⼆次函数positive definite|||正定quasi-newton method|||拟⽜顿法conjugate gradient|||共轭梯度steepest descent|||最速下降reparametrization|||重参数化standard deviation|||标准差coordinate descent|||坐标下降skip connection|||跳跃连接convolutional neural network|||卷积神经⽹络convolution|||卷积pooling|||池化feedforward neural network|||前馈神经⽹络maximum likelihood|||最⼤似然back propagation|||反向传播artificial neural network|||⼈⼯神经⽹络deep feedforward network|||深度前馈⽹络hyperparameter|||超参数sparse connectivity|||稀疏连接parameter sharing|||参数共享receptive field|||接受域chain rule|||链式法则tiled convolution|||平铺卷积object detection|||⽬标检测error rate|||错误率activation function|||激活函数overfitting|||过拟合attention mechanism|||注意⼒机制transfer learning|||迁移学习autoencoder|||⾃编码器unsupervised learning|||⽆监督学习back propagation|||反向传播pretraining|||预训练dimensionality reduction|||降维curse of dimensionality|||维数灾难feedforward neural network|||前馈神经⽹络encoder|||编码器decoder|||解码器cross-entropy|||交叉熵tied weights|||绑定的权重PCA|||PCAprincipal component analysis|||主成分分析singular value decomposition|||奇异值分解SVD|||SVDsingular value|||奇异值reconstruction error|||重构误差covariance matrix|||协⽅差矩阵Kullback-Leibler (KL) divergence|||KL散度denoising autoencoder|||去噪⾃编码器sparse autoencoder|||稀疏⾃编码器contractive autoencoder|||收缩⾃编码器conjugate gradient|||共轭梯度fine-tune|||精调local optima|||局部最优posterior distribution|||后验分布gaussian distribution|||⾼斯分布reparametrization|||重参数化recurrent neural network|||循环神经⽹络artificial neural network|||⼈⼯神经⽹络feedforward neural network|||前馈神经⽹络sentiment analysis|||情感分析machine translation|||机器翻译pos tagging|||词性标注teacher forcing|||导师驱动过程back-propagation through time|||通过时间反向传播directed graphical model|||有向图模型speech recognition|||语⾳识别question answering|||问答系统attention mechanism|||注意⼒机制vanishing and exploding gradient problem|||梯度消失与爆炸问题jacobi matrix|||jacobi矩阵long-term dependency|||长期依赖clip gradient|||梯度截断long short-term memory|||长短期记忆gated recurrent unit|||门控循环单元hadamard product|||Hadamard乘积back propagation|||反向传播attention mechanism|||注意⼒机制feedforward network|||前馈⽹络named entity recognition|||命名实体识别Representation Learning|||表征学习Distributed Representation|||分布式表征Multi-task Learning|||多任务学习Multi-Modal Learning|||多模态学习Semi-supervised Learning|||半监督学习NLP|||⾃然语⾔处理Neural Language Model|||神经语⾔模型Neural Probabilistic Language Model|||神经概率语⾔模型RNN|||循环神经⽹络Neural Tensor Network|||神经张量⽹络Graph Neural Network|||图神经⽹络Graph Covolutional Network (GCN)|||图卷积⽹络Graph Attention Network|||图注意⼒⽹络Self-attention|||⾃注意⼒机制Feature Learning|||表征学习Feature Engineering|||特征⼯程One-hot Representation|||独热编码Speech Recognition|||语⾳识别DBM|||深度玻尔兹曼机Zero-shot Learning|||零次学习Autoencoder|||⾃编码器Generative Adversarial Network（GAN）|||⽣成对抗⽹络Approximate Inference|||近似推断Bag-of-Words Model|||词袋模型Forward Propagation|||前向传播Huffman Binary Tree|||霍夫曼⼆叉树NNLM|||神经⽹络语⾔模型N-gram|||N元语法Skip-gram Model|||跳元模型Negative Sampling|||负采样CBOW|||连续词袋模型Knowledge Graph|||知识图谱Relation Extraction|||关系抽取Node Embedding|||节点嵌⼊Graph Neural Network|||图神经⽹络Node Classification|||节点分类Link Prediction|||链路预测Community Detection|||社区发现Isomorphism|||同构Random Walk|||随机漫步Spectral Clustering|||谱聚类Asynchronous Stochastic Gradient Algorithm|||异步随机梯度算法Negative Sampling|||负采样Network Embedding|||⽹络嵌⼊Graph Theory|||图论multiset|||多重集Perron-Frobenius Theorem|||佩龙—弗罗贝尼乌斯定理Stationary Distribution|||稳态分布Matrix Factorization|||矩阵分解Sparsification|||稀疏化Singular Value Decomposition|||奇异值分解Frobenius Norm|||F-范数Heterogeneous Network|||异构⽹络Graph Convolutional Network （GCN）|||图卷积⽹络CNN|||卷积神经⽹络Semi-Supervised Classification|||半监督分类Chebyshev polynomial|||切⽐雪夫多项式Gradient Exploding|||梯度爆炸Gradient Vanishing|||梯度消失Batch Normalization|||批标准化Neighborhood Aggregation|||邻域聚合LSTM|||长短期记忆⽹络Graph Attention Network|||图注意⼒⽹络Self-attention|||⾃注意⼒机制Rescaling|||再缩放Attention Mechanism|||注意⼒机制Jensen-Shannon Divergence|||JS散度Cognitive Graph|||认知图谱Generative Adversarial Network(GAN)|||⽣成对抗⽹络Generative Model|||⽣成模型Discriminative Model|||判别模型Gaussian Mixture Model|||⾼斯混合模型Variational Auto-Encoder（VAE）|||变分编码器Markov Chain|||马尔可夫链Boltzmann Machine|||玻尔兹曼机Kullback–Leibler divergence|||KL散度Vanishing Gradient|||梯度消失Surrogate Loss|||替代损失Mode Collapse|||模式崩溃Earth-Mover/Wasserstein-1 Distance|||搬⼟距离/EMD Lipschitz Continuity|||利普希茨连续Feedforward Network|||前馈⽹络Minimax Game|||极⼩极⼤博弈Adversarial Learning|||对抗学习Outlier|||异常值/离群值Rectified Linear Unit|||线性修正单元Logistic Regression|||逻辑回归Softmax Regression|||Softmax回归SVM|||⽀持向量机Decision Tree|||决策树Nearest Neighbors|||最近邻White-box|||⽩盒（测试 etc. ）Lagrange Multiplier|||拉格朗⽇乘⼦Black-box|||⿊盒（测试 etc. ）Robustness|||鲁棒性/稳健性Decision Boundary|||决策边界Non-differentiability|||不可微Intra-technique Transferability|||相同技术迁移能⼒Cross-technique Transferability|||不同技术迁移能⼒Data Augmentation|||数据增强Adaboost|||recommender system|||推荐系统Probability matching|||概率匹配minimax regret|||face detection|||⼈脸检测i.i.d.|||独⽴同分布Minimax|||极⼤极⼩linear model|||线性模型Thompson Sampling|||汤普森抽样eigenvalues|||特征值optimization problem|||优化问题greedy algorithm|||贪⼼算法Dynamic Programming|||动态规划lookup table|||查找表Bellman equation|||贝尔曼⽅程discount factor|||折现系数Reinforcement Learning|||强化学习gradient theorem|||梯度定理stochastic gradient descent|||随机梯度下降法Monte Carlo|||蒙特卡罗⽅法function approximation|||函数逼近Markov Decision Process|||马尔可夫决策过程Bootstrapping|||引导Shortest Path Problem|||最短路径问题expected return|||预期回报Q-Learning|||Q学习temporal-difference learning|||时间差分学习AlphaZero|||Backgammon|||西洋双陆棋finite set|||有限集Markov property|||马尔可夫性质sample complexity|||样本复杂性Cartesian product|||笛卡⼉积Kevin Leyton-Brown|||SVM|||⽀持向量机MNIST|||ImageNet|||Ensemble learning|||集成学习Neural networks|||神经⽹络Neuroevolution|||神经演化object recognition|||⽬标识别Multi-task learning|||多任务学习Treebank|||树图资料库covariance|||协⽅差Hamiltonian Monte Carlo|||哈密顿蒙特卡罗Inductive bias|||归纳偏置bilevel optimization|||双层规划genetic algorithms|||遗传算法Bayesian linear regression|||贝叶斯线性回归ANOVA|||⽅差分析Extrapolation|||外推法activation function|||激活函数CIFAR-10|||Gaussian Process|||⾼斯过程k-nearest neighbors|||K最近邻Neural Turing machine|||神经图灵机MCMC|||马尔可夫链蒙特卡罗Collaborative filtering|||协同过滤AlphaGo|||random forests|||随机森林multivariate Gaussian|||多元⾼斯Bayesian Optimization|||贝叶斯优化meta-learning|||元学习iterative algorithm|||迭代算法Viterbi algorithm|||维特⽐算法Gibbs distribution|||吉布斯分布Discriminative model|||判别模型Maximum Entropy Markov Model|||最⼤熵马尔可夫模型Information Extraction|||信息提取clique|||⼩圈⼦conditional random field|||条件随机场CRF|||条件随机场triad|||三元关系Naïve Bayes|||朴素贝叶斯social network|||社交⽹络Bayesian network|||贝叶斯⽹络SVM|||⽀持向量机Joint probability distribution|||联合概率分布Conditional independence|||条件独⽴性sequence analysis|||序列分析Perceptron|||感知器Markov Blanket|||马尔科夫毯Hidden Markov Model|||隐马尔可夫模型finite-state|||有限状态Shallow parsing|||浅层分析Active learning|||主动学习Speech recognition|||语⾳识别convex|||凸transition matrix|||转移矩阵factor graph|||因⼦图forward-backward algorithm|||前向后向算法parsing|||语法分析structural holes|||结构洞graphical model|||图模型Markov Random Field|||马尔可夫随机场Social balance theory|||社会平衡理论Generative model|||⽣成模型probalistic topic model|||概率语义模型TFIDF|||词频-⽂本逆向频率LSI|||潜在语义索引Bayesian network|||贝叶斯⽹络模型Markov random field|||马尔科夫随机场restricted boltzmann machine|||限制玻尔兹曼机LDA|||隐式狄利克雷分配模型PLSI|||概率潜在语义索引模型EM algorithm|||最⼤期望算法Gibbs sampling|||吉布斯采样法MAP (Maximum A Posteriori)|||最⼤后验概率算法Markov Chain Monte Carlo|||马尔科夫链式蒙特卡洛算法Monte Carlo Sampling|||蒙特卡洛采样法Univariate|||单变量Hoeffding Bound|||Hoeffding界Chernoff Bound|||Chernoff界Importance Sampling|||加权采样法invariant distribution|||不动点分布Metropolis-Hastings algorithm|||Metropolis-Hastings算法Probablistic Inference|||概率推断Variational Inference|||变量式推断HMM|||隐式马尔科夫模型mean field|||平均场理论mixture model|||混合模型convex duality|||凸对偶belief propagation|||置信传播算法non-parametric model|||⾮参模型Gaussian process|||正态过程multivariate Gaussian distribution|||多元正态分布Dirichlet process|||狄利克雷过程stick breaking process|||断棒过程Chinese restaurant process|||中餐馆过程Blackwell-MacQueen Urn Scheme|||Blackwell-MacQueen桶法De Finetti's theorem|||de Finetti定理collapsed Gibbs sampling|||下陷吉布斯采样法Hierarchical Dirichlet process|||阶梯式狄利克雷过程Indian Buffet process|||印度餐馆过程。
计算机期刊大全
计算机期刊大全【前言】随着计算机技术的快速发展，越来越多的人开始关注计算机期刊，以获取最新的科研成果和技术进展。
本文旨在介绍全球范围内主要的计算机期刊，帮助读者了解各期刊的主题范围、影响因子、最新收录论文等信息，以提高论文发表效率和科研成果的质量。
【一、计算机科学顶级期刊】计算机领域的顶级期刊，对于任何一位计算机科学家来说，都是非常重要的。
这些期刊的文章水平高、质量优，其发表文章往往具有一定的权威性和影响力。
以下是全球最著名的计算机科学顶级期刊：1.《ACM Transactions on Computer Systems》（ACM TOCS）主题范围：该期刊关注计算机系统的设计、分析、实现和评估等方面，特别是操作系统、网络、分布式系统、数据库管理系统和存储系统等方面的最新研究成果。
影响因子：3.612发行周期：每年4期最新收录论文：Content-Based Data Placement for Efficient Query Processing on Heterogeneous Storage Systems, A Framework for Evaluating Kernel-Level Detectors, etc.2.《IEEE Transactions on Computers》（IEEE TC）主题范围：该期刊刊登计算机科学领域的创新性研究成果，重点关注计算机系统、组件和软件的设计、分析、实现和评估等方面的最新进展。
影响因子：4.804发行周期：每月1期最新收录论文：A Comprehensive View of Datacenter Network Architecture, Design, and Operations, An Efficient GPU Implementation of Imperfect Hash Tables, etc.3.《IEEE Transactions on Software Engineering》（IEEE TSE）主题范围：该期刊涉及软件工程领域的各个方面，包括软件开发、可靠性、维护、测试等方面的最新研究成果。
distilling the knowledge in a neural network
distilling the knowledge in a neural networkKnowledge Distilling Is a method of model compression, which refers to the method of using a more complex Teacher model to guide a lighter Student model training, so as to maintain the accuracy of the original Teacher model as far as possible while reducing the model size and computing resources. This approach was noticed, mainly due to Hinton's paper Distilling the Knowledge in a Neural Network.Knowledge Distill Is a simple way to make up for the insufficient supervision signal of classification problems. In the traditional classification problem, the goal of the model is to map the input features to a point in the output space, for example, in the famous Imagenet competition, which is to map all possible input images to 1000 points in the output space. In doing so, each of the 1,000 points is a one hot-encoded category information. Such a label can provide only the supervision information of log (class) so many bits. In KD, however, we can use teacher model to output a continuous label distribution for each sample, so that the supervised information is much more available than one hot's. From another perspective, you can imagine that if there is only one goal like label, the goal of the model is to force the mapping of each class in the training sample to the same point, so that the intra-class variance and inter-class distance that are very helpful for training will be lost. However, using the output of teacher model can recover this information. The specific example is like the paper, where a cat and a dog are closer than a cat and a table, and if an animal does look like a cat or a dog, it can provide supervision for both categories. To sum up, the core idea of KD is that "dispersing" is compressed to the supervisory information of a point, so that the output of student model can be distributed as much as the output of match teacher model as possible. In fact, to achieve this goal, it is not necessarily teacher model to be used. The uncertain information retained in the data annotation or collection can also help the training of the model.。
《人工智能英语》试卷(含答案)
参考试卷一、写出以下单词的中文意思（每小题0.5分，共10分）1 accuracy 11 customize2 actuator 12 definition3 adjust 13 defuzzification4 agent 14 deployment5 algorithm 15 effector6 analogy 16 entity7 attribute 17 extract8 backtrack 18 feedback9 blockchain 19 finite10 cluster 20 framework二、根据给出的中文意思，写出英文单词（每小题0.5分，共10分）1 v.收集，搜集11 n.神经元；神经细胞2 adj.嵌入的，内置的12 n.节点3 n.指示器；指标13 v.运转；操作4 n.基础设施，基础架构14 n.模式5 v.合并；集成15 v.察觉，发觉6 n.解释器，解释程序16 n.前提7 n.迭代；循环17 adj.程序的；过程的8 n.库18 n.回归9n.元数据19 adj.健壮的，强健的；结实的10 v.监视；控制；监测20 v.筛选三、根据给出的短语，写出中文意思（每小题1分，共10分）1 data object2 cyber security3 smart manufacturing4 clustered system5 data visualization6 open source7 analyze text8 cloud computing9 computation power10 object recognition四、根据给出的中文意思，写出英文短语（每小题1分，共10分）1 数据结构2 决策树3 演绎推理4 贪婪最佳优先搜索5 隐藏模式，隐含模式6 知识挖掘7 逻辑推理8 预测性维护9 搜索引擎10 文本挖掘技术五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）缩略语完整形式中文意思1 ANN2 AR3 BFS4 CV5 DFS6 ES7 IA8 KNN9 NLP10 VR六、阅读短文，回答问题（每小题2分，共10分）Artificial Neural Network (ANN)An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain.An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs alsouse a set of learning rules called backpropagation, an abbreviation for backward propagation of error, to perfect their output results.An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce — the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backward, going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.A neural network may contain the following 3 layers:Input layer – The activity of the input units represents the raw information that can feed into the network.Hidden layer – To determine the activity of each hidden unit. The activities of the input units and the weights on the connections between the input and the hidden units. There may be one or more hidden layers.Output layer – The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.1. What is an artificial neural network (ANN)?2.What is each neuron made up of？3.Wha do the input units do?4.What does an ANN initially go through?5.How many layers may a neural network contain? What are they?七、将下列词填入适当的位置（每词只用一次）。
语音情感特征提取方法和情感识别研究
西北丁业人学硕十论文第＿章语音信号前端处理寸不同，发出的音的音色不同。
音调是指声音的高低，它取决于声波的频率，而声波频率又与发音体长短、厚薄以及松紧程度有关。
声音的强弱叫做音强，它是由声波振动幅度决定的。
声音的长短叫音长，它取决于发音时间的长短，一个多音节的词，各个音节的轻重不同，其长短就不一样，此外不同音长还可以表达不同的语气和情态。
说话的时候，很自然地一次发出来的、有一个响亮的重心的、听的时候也很自然地感到是一个小的语音片段的，叫做音节。
一个音节可以由一个音素构成，也可以由几个音素构成。
音素是语音的最小单位。
任何语言的语音都有元音和辅音两种音素。
元音是由声带振动发出来的乐音。
每个元音的特点是由声道的形状和尺寸决定的。
辅音是由呼出的声流克服发音器官的阻碍而产生的。
发辅音时，如果声带不振动，发出的辅音就叫清辅音，简称清音。
声带振动发出的辅音叫做浊辅音也叫浊音，它是乐音和清音的混合物。
形成障碍的发音部位和发音的方法不同，发出的辅音就不同。
语音除了具有上述的声音的物理属性外，它还具有另外一个重要的性质，语音总是和一定的意义相联系着。
语音不仅表达了一定的意义和思想内容，而且还能表达出一定的语气、情感，甚至表达许多“言外之意”。
因此，语音中所包含的信息是十分丰富和多种多样的。
２．１．２语音的时间波形和频谱特性语音信号首先是一个时间序列，进行语音分析时，最直观的就是它的时域波形。
图２．２为单词ｓ廿ｅｅｔ中音素［ｓ】、【ｉ：】的时域波形。
【ｓ】的时域波形【Ｉ】的时域玻形图２．２音素【ｓ】、【ｉ：】的信号波形西北工业人学硕Ｉ论文第一章语音信号前端处理从图２．２上可以看出，清音和浊音（包括元音）的波形有很大的不同。
清音的波形类似于白噪声，且具有很弱的振幅。
元音具有明显的周期性，并且具有较强的振幅，它的周期对应的频率就是基音频率。
语音波形是时间的连续函数，语音信号的特性是随时间而变化的。
浊音和清音的激励不同，从浊音改变到清音，相应地要改变激励，语音信号的幅值随时间有明显的变化。
人工神经网络在食品加工过程模拟控制中的应用_史德芳
基金项目：湖北省农业科技创新中心资助项目（２００７－６２０－００１－０３）作者简介：史德芳（１９７９—），男（汉），研究实习员，硕士，研究方向：天然产物活性成分提取与分离工艺优化。
＊通讯作者史德芳，高虹＊，程薇，薛淑静，何建军，熊光权（湖北省农业科技创新中心湖北省农科院农产品加工与核农技术研究所，湖北武汉430064）人工神经网络在食品加工过程模拟控制中的应用APPLICATION OF ARTIFICIAL NEURAL NETWORK IN FOOD PROCESS SIMULATION AND QUALITY CONTROLSHI De-fang,GAO Hong *,CHENG Wei,XUE Shu-jing,HE Jian-jun,XIONG Guang-quan （Center of Hubei Agricultural Science&Technology Innovation Center,Research Institute of Agricultural Products Processing and Nuclear-Agricultural Technology,Hubei Academy of Agricultural Sciences,Wuhan 430064,Hubei,China ）Abstract:Artificial Neural Network (ANN)is able to deal with process optimization,risk evaluation and predic －tion,nonlinear model,etc,which makes it advantageous over other methods to be applied in a few of fields,for example production process control in modern industrial engineering.Its application in recent years,focused on modeling simulation and optimization,quality control of food are reviewed,the prospects of its application was further discussed.Key words:artificial Neural Network;agricultural;process simulation;quality control摘要：由于人工神经网络在处理现代工业工程中工艺优化、风险评估与预测、非线性模式等问题所具有的优势，而被广泛应用于生产过程控制等领域。
小型液晶屏盒内缺陷检测中纹理的消除
小型液晶屏盒内缺陷检测中纹理的消除姚景昭;叶玉堂;刘霖;刘娟秀;罗颖;叶涵;徐伟;李沧海【摘要】提出了一种在小型液晶屏盒内缺陷检测中消除纹理影响的方法,并成功制作了国内首台小型液晶屏盒内缺陷自动光学检测仪.本方法的基本原理是:采集标准液晶屏图像,利用其图像数据建立补偿矩阵,在检测的预处理过程中使用补偿矩阵消除纹理.这种方法有效解决了液晶屏纹理影响检测结果的难题.理论分析及现场实验结果表明,该方法达到消除液晶屏纹理的同时完整保留缺陷的目的,使液晶屏盒内缺陷自动检测的准确性大大提高.%A method of eliminating texture in defects inspection of LC cell of small-size Liquid Crystal Display (LCD) is proposed, and the domestic first LC cell defects Automatic Optical Inspection (AOI) for small-size LCD was made. The basic principle of this method is to eliminate texture in preprocessing of detecting LCD image by compensation matrix which is established from the image of standard LCD. The method of eliminating texture presented solves the problem that the texture which appears in LCD image will makes it more difficult to detect defects. Theoretical analysis and experimental results show that, by using this method, the texture is eliminated and the defects are kept completely, and the detection accuracy of AOI is improved greatly.【期刊名称】《光电工程》【年(卷),期】2012(039)010【总页数】6页(P116-121)【关键词】液晶屏盒内缺陷;纹理消除;缺陷检测;自动光学检测【作者】姚景昭;叶玉堂;刘霖;刘娟秀;罗颖;叶涵;徐伟;李沧海【作者单位】电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054;电子科技大学光电信息学院现代光电测控及仪器实验室,成都610054【正文语种】中文【中图分类】TP391.40 引言液晶屏生产工艺流程多达几十道工序左右，这些工序可分为：ITO图形刻蚀(光刻)、定向排列、空盒制作、液晶灌注和成品检测与包装五个阶段。
基于傅里叶变换和Hough变换的商标图案倾斜校正
基于傅里叶变换和Hough变换的商标图案倾斜校正胡仁伟;张希仁;杨立峰;林道锋;成祎珊【摘要】针织物商标图案加工过程中会产生形变,影响纺织品等级和质量.针对商标图案形变检测过程中出现倾斜现象而降低形变测量精度的问题,采用Hough变换提取商标图案频谱图的旋转角度,以实现图案的倾斜校正.应用结果表明该算法能精确提取商标图案的倾斜角度并进行倾斜校正.算法不改变图案的原始形貌,提高了运行效率.%The appearance and quality of clothing is affected by the deformation of the fabric trademark pattern during manufacturing.But the measurement accuracy of deformation of trademark pattern is decreased due to trademark pattern tilting.In this paper the method based on the Fourier and Hough transform was proposed and used to measure and correct the rotation angle of tilted trademark pattern.The application shows that the method proposed can be used to correct the tilted fabric pattern.The algorithm guarantees the original shape of the pattern and improves the efficiency.【期刊名称】《轻工机械》【年(卷),期】2018(036)001【总页数】4页(P62-65)【关键词】针织物商标图案;傅里叶变换;Hough变换;倾斜校正【作者】胡仁伟;张希仁;杨立峰;林道锋;成祎珊【作者单位】电子科技大学光电信息学院,四川成都 610054;电子科技大学光电信息学院,四川成都 610054;电子科技大学光电信息学院,四川成都 610054;昆山联滔电子有限公司,江苏昆山 215324;电子科技大学光电信息学院,四川成都 610054【正文语种】中文【中图分类】TP391.41商标是产品的身份证，具有独特的代表意义。
基于极限学习神经网络的短时交通流预测
120交通科技与管理智慧交通与信息技术0 引言 短时交通流预测是智能交通系统(Intelligent Transportation System, ITS)[1]中的一个关键技术，通过分析当前交通流的变化规律，提前感知交通系统状态的变化情况，为主动式交通管理和控制提供支撑。
为此，准确、快速和可靠是实施短时交通流预测的基本要求。
短时交通流预测的研究至今已有近60年的研究历程，国内外专家学者已经提出了众多的预测模型和方法。
传统的预测方法如历史平均[2]和指数平滑[3]，基于参数的预测方法如随机时间序列[4]、卡尔曼滤波[5]；基于非参数的预测方法如神经网络[6]、支持向量机[7]、非参数回归[8]、小波理论[9]等；基于组合预测的方法如多个神经网络预测结果的组合[10]、神经网络与卡尔曼滤波的组合[11]。
这些预测方法基本上都是数据驱动，利用历史的交通流数据进行预测模型标定或训练，以获得高精度的预测结果。
对于基于非参数的预测方法来说，特别是广泛应用的神经网络，主要存在三个方面的问题，训练速度慢、容易陷入局部极小点和学习效率选择的敏感性。
为此，本文研究一个针对单隐含层前馈网络的算法，即极限学习。
该算法随机产生输入层与隐含层的连接权值及隐含层神经元的阈值，且在训练过程中无需调整，只需要设置隐含层神经元个数，就可以获得唯一的最优解。
与传统的训练方法相比，具有学习速度快、泛化性能好等优点。
1 基于极限学习的前馈神经网络1.1 单隐含层前馈神经网络图1 典型的单隐含层前馈神经网络 典型的单隐含层前馈神经网络结构如图1所示，由输入层、隐含层和输出层组成，输入层与隐含层、隐含层与输出层神经元间全连接。
其中，输入层有n 个神经元，对应n 个输入变量，隐含层有l 个神经元，输出层有m 个神经元，对应m 个输出变量。
在短时交通流预测建模过程中，利用已有的交通流数据进行模型训练，假设有N 个训练数据样本(X i , Y i )，X i =[x i1, x i2, …, x in ]T ，Y i =[y i1, y i2, …, y im ]T ，i=1,2,…,N，其中X i 为神经网络的输入数据样本，Y i 为神经网络的输出数据样本，有l 个隐含层节点和激励函数g(x)，则图1所示的神经网络数学模型可以表示为： （1） 式中，w i =[w i1, w i2,…, w il ,]T 表示第i 个隐含层节点和输入层节点之间的权向量，βi =[βi1, βi2,…, βim ,]T 表示第i 个隐含层节点和输出层节点之间的权向量，b i 表示第i 个隐含层节点的阈值，w i ·x i 表示权向量w i 和样本x i 的内积。
基于深度学习的网络流量预测研究综述
20215710根据2020年4月中国互联网信息中心发布的《第45次中国互联网络发展状况统计报告》显示，截至2020年3月份，我国共有网民9.04亿，与2018年底相比，增长7 508万人，普及率增至64.5%。
不仅如此，我国网民平均每周每人上网30.8小时，与2018年底相比，增长3.2小时[1-2]。
网民数量的增长及上网时间的延长带来网络流量的激增。
随着5G、边缘计算、NFV等技术的发展，对网络进行精细化、自动化、智能化运维及管理将成为新的挑战。
为了应对这一挑战，需要对边缘网络、城域网、骨干网等多个层级的应用级网络流量进行精准感知。
而网络流量预测能力则是核心技术之一。
精准的网络流量预测技术能够实现如下功能：（1）帮助改善通信网络管理。
在分配网络资源的过程中，传统方法仅仅依靠网络基于深度学习的网络流量预测研究综述康梦轩1，2，宋俊平1，范鹏飞1，高博文3，周旭1，李琢1，21.中国科学院计算机网络信息中心，北京1001902.中国科学院大学，北京1000493.中国联合网络通信有限公司北京市分公司，北京100038摘要：精准地预判网络流量变化趋势可以帮助运营商准确预估网络的使用情况，合理分配并高效利用网络资源，以满足日益增长且多样化的用户需求。
以深度学习算法在网络流量预测领域的进展为线索，阐述了网络流量预测的评价指标和目前公开的网络流量数据集及应用，具体分析了网络流量预测中常用的深度信念网络、卷积神经网络、循环神经网络和长短时记忆网络共四种深度学习方法，并重点介绍了近年来针对不同问题所提出的改进神经网络模型，总结了各模型特点及应用场景。
最后对网络流量预测未来发展进行了展望。
关键词：深度学习；网络流量预测；深度信念网络；卷积神经网络；长短时记忆网络文献标志码：A中图分类号：TP393doi：10.3778/j.issn.1002-8331.2101-0402Survey of Network Traffic Forecast Based on Deep LearningKANG Mengxuan1，2,SONG Junping1,FAN Pengfei1,GAO Bowen3,ZHOU Xu1,LI Zhuo1，2puter Network Information Center,Chinese Academy of Sciences,Beijing100190,China2.University of Chinese Academy of Sciences,Beijing100049,China3.China United Network Communications Co.,Ltd.,Beijing Branch,Beijing100038,ChinaAbstract：Precisely predicting the trend of network traffic changes can help operators accurately predict network usage, correctly allocate and efficiently use network resources to meet the growing and diverse user needs.Taking the progress of deep learning algorithms in the field of network traffic prediction as a clue,this paper firstly elaborates the evaluation indi-cators of network traffic prediction and the current public network traffic data sets.Secondly,this paper specifically ana-lyzes four deep learning methods commonly used in network traffic prediction：deep belief networks,convolutional neural network,recurrent neural network,and long short term memory network,and focuses on the integrated neural network models used in recent years for different problems.The characteristics and application scenarios of each model are sum-marized.Finally,the future development of network traffic forecast is prospected.Key words：deep learning;network traffic prediction;deep belief networks;convolutional neural network;long short term memory network⦾热点与综述⦾基金项目：国家自然科学基金（U1909204）。
网络安全技术论文参考文献
网络安全技术论文参考文献1. Abbas, A., & Michael, K. (2017). Cryptocurrencies - Blockchain Technology & Forensics: A Bibliometric Analysis. International Conference on Internet Science. Springer, Cham.2. AbuKhousa, E., & Mahmoud, Q. H. (2015). The impact of DDoS attacks on cloud computing. Future Generation Computer Systems, 49, 16-24.3. Amin, R., & Mahanti, A. (2017). Towards detecting malware in the cloud: A machine learning approach. Computers & Security, 67, 120-137.4. Anderson, R. (2001). Charting the global information infrastructure. Telecommunications Policy, 25(5), 331-349.5. Angin, P., Naserzadeh, M., & Gunes, M. H. (2018). A survey on data mining techniques for malware detection. Journal of Information Security and Applications, 38, 1-13.6. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512.7. Buchanan, S., Paine, C., Jones, T., & Turbett, C. (2016). Data breaches: Crisis and opportunity. Strategic Direction, 32(9), 26-28.8. Choo, K. K. R., & Ross, B. (2006). Comparing computer intrusion detection using neural networks with other statistical methods. Computers & security, 25(5), 334-345.9. Cornwall, J. R., & Perl, R. (2011). Cybercrime and the Law: Challenges, Issues, and Outcomes. In The Oxford handbook of internet studies (pp. 405-425). Oxford University Press.10. El-Mousa, S., Iqbal, M. A., & Jalab, H. (2011). Intrusion Detection System Based on Neural Network Untitled- 1. IBIMA Business Review Journal, 2011, 1-10.11. Hu, Y., Ahn, G. J., & Ke, J. (2016). Survey of network traffic monitoring and analysis techniques. IEEE Communications Surveys & Tutorials, 18(1), 63-93.12. Ou, X. (2015). Intrusion Detection Systems (p. 87). CRC Press.13. Parvez, I. M., Hong, W., & Kim, D. S. (2015). A classification-based survey of malware detection methods. ACM Computing Surveys (CSUR), 48(2), 1-34.14. Ray, S., Thomas, R., & Mallick, A. (2017). A Deep Learning Approach for Network Intrusion Detection System Using Restricted Boltzmann Machine. In Proceedings of the International Conference on Data Engineering and Communication Technology (pp. 343-355). Springer, Singapore.15. Wang, H., Atkison, R., Wolfe, R., & Gu, G. (2012). Malware data clustering using call graph structural information. In Proceedings of the Eighth Annual IFIP WG 11.10 International Conference on Critical Infrastructure Protection (pp. 123-137). Springer, Berlin, Heidelberg.(Note: These references do not contain the titles as requested but you may need to modify them appropriately in your thesis to maintain consistency and clarity.)。
《青蛙的启示》小学四年级英语作文
《青蛙的启示》小学四年级英语作文One day, I went downstairs to play and saw a little frog catching insects.A bug flew past its eyes, and the little frog stretched out its tongue and ate the bug. I thought to myself: Why can a little frog catch fast flying insectsI ran home with this question and asked my mother, and she said, "Just do it yourself and you'll understand!" I puzzled and caught a frog and a bug, putting them in a transparent box. The bug kept flying, and the frog looked at the bug with its eyes open. With its tongue extended, the bug was eaten by the frog. My mother said to me, "You catch another bug and tie it to a rope to try. I did it honestly, and I thought to myself: the bound bug will definitely be eaten by the frog. Who knew, the frog didn't eat the bug. I was curious, why didn't the bug be eaten by the frog? I accidentally moved the rope, and the frog immediately stretched out its tongue and ate the bug. Oh! So, that's how it is. The frog only eats moving bugs, not those that can't move. So why is that?I can't wait to search for answers online. So, the frog's eyes and retina... Neural cells are divided into five categories, one only responds to colors, and the other four only respond to moving targets, so they can eat fast-moving insects, while frogs do not respond to motionless insects.Mom said that humans mimic the eyes of frogs and make something called an electronic frog eye. The electronic frog eye is used for military purposes to help missiles hit moving targets.Nature is really wonderful. As long as you observe carefully, you will discover all kinds of strange things。
动态追击移动物体的路径算法
动态追击移动物体的路径算法英文回答：As an AI, I can provide you with an algorithm for dynamically tracking the path of a moving object. One common approach is to use a predictive algorithm that anticipates the future position of the object based on its current trajectory and speed. This algorithm continuously updates the predicted position of the object and adjusts the tracking path accordingly.One popular method for dynamic path tracking is the Proportional-Derivative (PD) controller. This controller calculates the error between the predicted position of the object and its actual position, and then adjusts the tracking path based on this error. The proportional term controls the immediate response to the error, while the derivative term helps to dampen oscillations and improve stability.Another approach is to use a Kalman filter, which is an optimal estimator that combines measurements of theobject's position with predictions of its future position. The Kalman filter continuously updates its estimate of the object's position and velocity, allowing for smoother and more accurate tracking.In addition to these methods, machine learning algorithms such as neural networks can also be used for dynamic path tracking. By training a neural network on a dataset of object trajectories, the network can learn to predict the future path of the object and adjust the tracking path accordingly.Overall, the key to successful dynamic path tracking is to continuously update the predicted position of the object and adjust the tracking path in real-time. By using predictive algorithms such as the PD controller, Kalman filter, or neural networks, it is possible to accurately track the path of a moving object in a dynamic environment.中文回答：作为一个AI，我可以为您提供一个动态追踪移动物体路径的算法。
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messages and suggestions) and intelligent logging capabilities (which allows variables of interest to be automatically extracted, coded and memorized, by saving recording resources). In this way, possible human failures are expected to be overcome and better surveillance performances are obtained.
Department of Biophysical and Electronic Engineering – University of Genoa (Italy) Via all’Opera Pia11a – I-16145 Genova (Italy) e-mail: andretes@dibe.unige.it
A NEURAL-NETWORK APPROACH FOR MOVING OBJECTS RECOGNITION IN COLOR IMAGE
SEQUENCES FOR SURVEILLANCE APPLICATIONS
Andrea Teschioni, Franco Oberti and Carlo Regazzoni
task.
Modern automatic visual surveillance systems provide the operator with both attention focusing information (which allows important events to be signalled by means of user-friendly
The number of monitors is usually smaller than the number of video sensors and automatic processing multisensorial information can be used, for example, to select the subset of cameras whose subset must be displayed. The restriction to a temporary selection of scenes from different cameras is devoted to help the human operator to concentrate his decision capabilities on possible danger situations, by means of significant focus-of-attention messages. Other tasks such as information logging from image sequences should require a continuous analysis of visual information. In this case the most practical solution was to perform off-line analysis of recorded sequences.
In this field, some surveillance systems are used to detect dangerous situations, to get statistical knowledge for traffic activities (maintenance schedule, traffic flow plans and simulator, etc.) and to provide users with information about accidents or traffic jams so that they may travel safe and comfortable. In particular, the aim of the proposed work is to develop an algorithm for the discrimination, within the wide class of moving objects present in a road traffic scene, of pedestrians and vehicles in order to provide some possible alarm situations to a remote operator who has in charge to monitor the examined scene.
As further functionality, the system must be also able, within the class of detected people, to discriminate between the municipality personnel village, who is dressed in a coloured particular way, and civilian people, who must be counted for statistical purposes, and to provide a different kind of alarm depending on the class of detected people.
In this work, a neural networks based approach for image understanding using a Multilayer Perceptron [1] is presented dealing with a particular surveillance application in the transport field.
3. SYSTEM DESCRIPTION
Figure 3 shows the general architecture of the proposed surveillaபைடு நூலகம்ce system.
The following assumptions are made: (a) stationary and precalibrated camera, (b) ground-plane hypothesis, (c) known set of object and behaviour models. The system is composed by 5 modules: image acquisition (IA), background updating (BU), mobile object detection (MOD), object tracking (OT), object recognition (OR) and dynamic scene interpretation (DSI).
2. THE PROBLEM
The system that we are considering must be applied to the surveillance of dangerous situations in the entrance access of a touristic village: in particular such system must be able to detect, as main functionality, the presence of people walking in zones of the road normally reserved for vehicles and to provide an alarm to a remote operator whenever such an event appears.
This solution has two major disadvantages:
·
it requires selection and storage of large amount of data;
·
it charges human operator of an annoying, and repetitive
The work is organized in such a way: section II provides a little overview of the faced problem, section III presents in general the examined proposed system, section IV examines in detail the neural based approach while section V presents the obtained results in moving objects classification.
ABSTRACT
Monitoring systems of outdoor environments for surveillance applications need real time solutions for complex computer vision problems. However, advanced visual surveillance systems not only need to detect and track moving objects but also to interpret their pattern of behaviour. This work aims at presenting a method based on the use of neural networks for classification of moving tracked objects. Application scenario consists of an entrance access of a touristic village crossed by vehicles and the main aim of the method is to recognise possible presence of pedestrians in the zone.