Hybrid Soft Computing Systems A Critical Survey with Engineering Applications
计算机学科SCI分区
刊名全称 IEEE TRANSACTIONS ON NEURAL NETWORKS IEEE TRANSACTIONS ON NEURAL NETWORKS Journal of Web Semantics Journal of Web Semantics Journal of Web Semantics IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION MEDICAL IMAGE ANALYSIS MEDICAL IMAGE ANALYSIS ACM TRANSACTIONS ON GRAPHICS IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE VLDB JOURNAL VLDB JOURNAL INTERNATIONAL JOURNAL OF COMPUTING SURVEYS ARTIFICIAL INTELLIGENCE IEEE TRANSACTIONS ON NEURAL NETWORKS HUMAN-COMPUTER INTERACTION ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY JOURNAL OF THE ACM JOURNAL OF THE ACM IEEE Transactions on Information Forensics and Security IEEE Transactions on Dependable and Secure Computing BIOLOGICAL CYBERNETICS ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE IEEE MICRO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS IEEE TRANSACTIONS ON MULTIMEDIA
信息与计算科学的英语
信息与计算科学的英语英文回答：Information and Computing Science (ICS) is an interdisciplinary field that combines the study of information with the study of computing. ICS researchers are interested in how information is created, stored, processed, and transmitted. They also study the design and implementation of computing systems, as well as the applications of computing in various fields.ICS is a relatively new field, but it has already had a major impact on society. The development of the internet, for example, has revolutionized the way people communicate and share information. ICS has also played a major role in the development of new technologies, such as artificial intelligence and robotics.ICS is a rapidly growing field, and there is a high demand for ICS professionals. ICS graduates can work in avariety of fields, including information technology, software development, and data science.Here are some of the specific topics that ICS researchers study:Information theory studies the mathematical properties of information.Computer science studies the design and implementation of computing systems.Data science studies the collection, analysis, and interpretation of data.Artificial intelligence studies the development of computer systems that can perform tasks that typically require human intelligence.Robotics studies the design and construction of robots.ICS is a challenging and rewarding field. ICS graduateshave the opportunity to make a real difference in the world by developing new technologies that solve important problems.中文回答：信息与计算科学（ICS）是一门交叉学科，它将信息的学习与计算的学习结合在一起。
计算语言学新领域
计算语言学新领域计算语言学（Computational Linguistics）是一门学科，旨在研究人类语言和计算机之间的相互关系，并利用计算机技术来处理和分析语言数据。
自从计算语言学领域的诞生以来，它就一直在不断发展。
随着科技和学术的进步，计算语言学也出现了许多新的研究方向和应用，形成了计算语言学的多个新领域。
一、自然语言处理自然语言处理（Natural Language Processing，NLP）是计算语言学的一个重要子领域。
它研究如何使计算机能够理解、处理和生成自然语言。
自然语言处理涉及到语言的语法、语义、语用等多个层面的分析和处理。
现在，随着深度学习技术的发展和大规模语料库的建立，自然语言处理在机器翻译、自动问答、情感分析、文本生成等领域得到了广泛应用。
二、信息抽取信息抽取（Information Extraction）是计算语言学的一个重要应用领域，它的目标是从大规模文本中提取出特定的信息。
信息抽取涉及到实体提取、关系抽取和事件抽取等任务。
例如，从新闻报道中提取出地点、人物和事件等信息，或从科学文献中提取出实验结果和结论等信息。
信息抽取在数据挖掘、情报分析和知识图谱构建等领域具有重要价值。
三、文本挖掘文本挖掘（Text Mining）是计算语言学的另一个重要领域，它结合了自然语言处理和数据挖掘技术，旨在从大规模文本中提取出有用的信息和知识。
文本挖掘涉及到文本分类、文本聚类、情感分析、主题模型等任务。
例如，通过对用户评论进行情感分析，可以推测用户对产品的喜好程度，从而为市场营销提供参考。
四、语言生成语言生成（Natural Language Generation，NLG）是计算语言学的一个重要研究领域，它涉及到如何使用计算机生成自然语言的过程。
语言生成的应用包括文本生成、摘要生成、自动对话系统等。
例如，在自动化新闻报道中，可以通过语言生成技术将数据转化为适当的新闻形式。
此外，语言生成也在人机对话系统中扮演着关键角色，使得机器能够像人类一样进行对话。
机器学习与人工智能领域中常用的英语词汇
机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。
Soft Computing
Dr. Xiao-Zhi Gao Department of Electrical Engineering Helsinki University of Technology gao@cc.hut.fi
Page 1
Institute of Intelligent Power Electronics – IPE
1.2 1
IF x≈0, THEN y ≈ 1 IF x ≈ 5, THEN y ≈ 0.5 IF x ≈ 10, THEN y ≈ 0
0.8
0.6
0.4
0.2
1.2 1
0.8
1 y = 1− (1 + e −2(x −5) )
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
9
10
Page 22
Institute of Intelligent Power Electronics – IPE
Model Construction (Traditional Rules)
Page 18 Institute of Intelligent Power Electronics – IPE
Framework of Soft Computing
Fuzzy Logic
Neuro-Fuzzy
Genetic-Fuzzy
Neural Network Genetic-Neuro
Genetic Algorithm
Estimation of your partner’s age: (40? probability of 40? or about middle-aged?) about middle aged (linguistic term)
计算机专业词汇英语翻译
Guest editora vehicle of 一种手段productivity生产力perceive 感知empirical means：经验方法the prolonged exponential growth：长期的指数增长Fidelity：保真度energy harvesting：能源获取Ubiquitous computing：普适计算Photosynthesis ：光合作用incident light 入射光coated 覆盖的humidity 湿度moisture gradients：湿气梯度semiconductor fabrication：半导体制造Acoustic：声学的Miniaturization:小型化Photons：光子，量子Concentrations：浓度Tailored：定制的Spectrum：光谱sophisticated heterogeneous systems：复杂的异构系统Fusion：融合=aggregationQualitative 定性的Diffusion：扩散duty-cycle：占空比spatial dimension：空间范围Dissemination：散播Pervasive：普遍的Trajectory：轨道Ambient：周围的②leachMicrosensors：微传感器Cluster: 名词：簇动词：分簇Cluster head：簇头Hierarchy 分层Application-Specific 应用相关的In terms of 按照Aggregate聚合Diffusion：传播Dissipated：耗散Timeline 时间轴Backs off：后退Dissipation：耗散spread-spectrum：扩频intra-cluster：簇内Outperform：胜过③pegasisHomogeneous:同质的fusion ：融合aggregationFuse：v. 融合Humidity：湿度Beacon：信标timestamp 时间戳in terms of ：就...而言greedy approach：贪婪算法truncated chain：截断链Critical：关键的propagation delays：传播延迟Dissipate：v.发散SNR：信噪比Joules：焦耳The upper bound：上限tier：等级token ：令牌，象征Dense：密集的Sparse：稀疏的Heuristic：启发式Outperforms：胜过Preliminary：初步的Exponential：指数的traveling salesman problem 旅行商问题tradeoff 代价④z-macLatency：时间延迟Robust：鲁棒性slot assignment：时隙分配multiple access control：多址接入控制Aggregate：聚合duty cycle：占空比the overhead of：开销Vendors：厂商surface-mount：表面贴装hand-soldering：手工焊接Predetermined：预定的Stochastic：随机的Explicit Contention Notification:明确竞争通知Unicast：单播Congestion：拥塞Benchmark：基准Preamble：头部⑤A building。
美国赛博空间作战行动Cyberspace _Operations
CHAPTER II
CYBERSPACE OPERATIONS CORE ACTIVITIES
Introduction................................................................................................................II-1
3.应用
a、本出版物中确立的联合原则适用于联合参谋部、作战司令部指挥官、下属统一司令部、联合特遣部队、这些司令部的下属部门、各军种和作战支持机构。
b、本出版物中的指南具有权威性；因此，除非指挥官认为特殊情况另有规定，否则将遵循这一原则。如果本出版物的内容与出版物的内容发生冲突，则以本出版物为准，除非参谋长联席会议通常与其他参谋长联合会成员协调，提供了更为现行和具体的指导。作为多国（联盟或联盟）军事指挥部一部分的部队指挥官应遵循美国批准的多国原则和程序。对于未经美国批准的条令和程序，指挥官应评估并遵循多国司令部的条令与程序，如果适用并符合美国法律、法规和条令。
•联合职能部门和网络空间运作
第三章权限、角色和职责
•简介III-1
•当局III-2
•角色和职责
•法律考虑因素III-11
第四章规划、协调、执行和评估
•联合规划过程和网络空间运营
•网络空间运营规划考虑因素
•对网络空间的情报和操作分析支持
运营计划IV-6
•针对性IV-8
•网络空间部队的指挥与控制
东北大学论文格式(硕士)
东北大学硕士学位论文排版打印格式1. 引言依据中华人民共和国《科学技术报告、学位论文和学术论文的编写格式》和东北大学学位论文格式改编，专为我校申请硕士、博士学位人员撰写打印论文时使用。
本格式自发布日起实行。
2. 学位论文主要部分学位论文主要部分由前头部分、主体部分和结尾部分（只限必要时采用）组成。
2.1 前头部分（1）封面（2）扉页——题名页（中、英两种）（4）声明（独创性声明）（3）摘要（中、英两种文字）（5）目录（6）插图和附表清单（只限必要时）（7）缩略字、缩写词、符号、单位表（只限必要时）（8）名词术语注释表（只限必要时）2.2 主体部分（1）绪论（前言、引言、绪言）（2）正文（3）讨论、结论和建议2.3 结尾部分（只限必要时采用）（1）参考文献（2）致谢（3）攻读学位期间发表的论著、获奖情况及发明专利等项。
（4）作者从事科学研究和学习经历的简历（5）可供参考的文献题录（只限必要时采用）（6）索引（只限必要时采用）3. 版式纸张大小：纸的尺寸为标准A4复印纸（210mm×297mm）。
版芯（打印尺寸）：160mm×247mm（不包括页眉行、页码行）。
正文字体字号：小4号宋体，全文统一。
每页30～35行，每行35～38字。
装订：双面打印印刷，沿长边装订。
页码：页码用阿拉伯数字连续编页，字号与正文字体相同，页底居中，数字两侧用圆点或一字横线修饰，如·3·或－3－。
页眉：自摘要页起加页眉，眉体可用单线或双线（二等线、文武线），页眉说明5号楷体，左端“东北大学硕士、博士学位论文”，右端“章号章题”。
封面：东北大学研究生（博士或硕士）学位论文标准封面（双A4）。
4. 体例4.1 标题论文正文按章、条、款、项分级，在不同级的章、条、款、项阿拉伯数字编号之间用点“.”（半角实心下圆点）相隔，最末级编号之后不加点。
排版格式见表4.1。
此分级编号法只分至第四级。
再分可用（1）、（2）……；（a）、（b）……等。
智能优化算法英文投稿选类别
智能优化算法英文投稿选类别
智能优化算法的英文投稿在选择类别时，可以考虑以下几个类别：
1. Artificial Intelligence (人工智能)：这个类别涵盖了所有形式的人工智能技术，包括但不限于机器学习、深度学习、强化学习、神经网络等。
如果你的智能优化算法是基于某种人工智能技术，那么这个类别可能非常适合。
2. Optimization Methods (优化方法)：这个类别主要关注各种优化算法和技术，包括但不限于遗传算法、粒子群优化、模拟退火、蚁群优化等。
如果你的智能优化算法是一种新的优化方法，那么这个类别可能非常适合。
3. Computer Science (计算机科学)：这个类别涵盖了计算机科学的各个方面，包括算法设计、数据结构、计算复杂性等。
如果你的智能优化算法是一种新的计算方法或者对现有的计算方法进行了改进，那么这个类别可能非常适合。
4. Engineering (工程)：这个类别主要关注实际应用和工程问题，包括但不限于机械工程、航空航天工程、土木工程等。
如果你的智能优化算法是用于解决某个工程问题，那么这个类别可能非常适合。
需要注意的是，选择类别时还需要考虑期刊或会议的投稿要求和规范。
有些期刊或会议可能对稿件的格式、内容、长度等方面有特定的要求，因此在选择类别时需要仔细阅读投稿指南并遵循相关规定。
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。
计算机专业英语词汇
《计算机专业英语词汇》AActive-matrix主动矩阵Adapter cards适配卡Advanced application高级应用Analytical graph分析图表Analyze分析Animations动画Application software 应用软件Arithmetic operations算术运算Audio-output device音频输出设备Access time存取时间access存取accuracy准确性ad network cookies广告网络信息记录软件Add-ons 插件Address地址Agents代理Analog signals模拟信号Applets程序Asynchronous communications port异步通信端口Attachment附件BBar code条形码Bar code reader条形码读卡器Basic application基础程序Binary coding schemes二进制译码方案Binary system二进制系统Bit比特Browser浏览器Bus line总线Backup tape cartridge units备份磁带盒单元Bandwidth带宽Bluetooth蓝牙Broadband宽带Business-to-business企业对企业电子商务Business-to-consumer企业对消费者Bus总线CCables连线Cell单元箱Chain printer链式打印机Character and recognition device字符标识识别设备Chart图表Chassis支架Chip芯片Clarity清晰度Closed architecture封闭式体系结构Column列Combination key结合键computer competency计算机能力connectivity连接，结点Continuous-speech recognition system连续语言识别系统Control unit操纵单元Cordless or wireless mouse无线鼠标Cable modems有线调制解调器carpal tunnel syndrome腕骨神经综合症CD-ROM可记录光盘CD-RW可重写光盘CD-R可记录压缩光盘Channel信道Chat group谈话群组chlorofluorocarbons(CFCs) ]氯氟甲烷Client客户端Coaxial cable同轴电缆cold site冷网站Commerce servers商业服务器Communication channel信道Communication systems信息系统Compact disc rewritableCompact disc光盘computer abuse amendments act of 19941994计算机滥用法案computer crime计算机犯罪computer ethics计算机道德computer fraud and abuse act of 1986计算机欺诈和滥用法案computer matching and privacy protection act of 1988计算机查找和隐私保护法案Computer network计算机网络computer support specialist计算机支持专家computer technician计算机技术人员computer trainer计算机教师Connection device连接设备Connectivity连接Consumer-to-consumer个人对个人cookies-cutter programs信息记录截取程序cookies信息记录程序cracker解密高手cumulative trauma disorder积累性损伤错乱Cybercash电子现金Cyberspace计算机空间cynic愤世嫉俗者DDatabase数据库database files数据库文件Database manager数据库管理Data bus数据总线Data projector数码放映机Desktop system unit台式电脑系统单元Destination file目标文件Digital cameras数码照相机Digital notebooks数字笔记本Digital bideo camera数码摄影机Discrete-speech recognition system不连续语言识别系统Document文档document files文档文件Dot-matrix printer点矩阵式打印机Dual-scan monitor双向扫描显示器Dumb terminal非智能终端data security数据安全Data transmission specifications数据传输说明database administrator数据库管理员Dataplay数字播放器Demodulation解调denial of service attack拒绝服务攻击Dial-up service拨号服务Digital cash数字现金Digital signals数字信号Digital subscriber line数字用户线路Digital versatile disc数字化通用磁盘Digital video disc数字化视频光盘Direct access直接存取Directory search目录搜索disaster recovery plan灾难恢复计划Disk caching磁盘驱动器高速缓存Diskette磁盘Disk磁碟Distributed data processing system分部数据处理系统Distributed processing分布处理Domain code域代码Downloading下载DVD 数字化通用磁盘DVD-R 可写DVDDVD-RAM DVD随机存取器DVD-ROM 只读DVDdelimiter 定界符号 [定界符]denotation 外延denotic logic 符号逻辑dependency 依存关系Dependency Grammar 依存关系语法dependency relation 依存关系depth-first search 深度优先搜寻derivation 派生derivational bound morpheme 派生性附着语素Descriptive Grammar 描述型语法 [描写语法]Descriptive Linguistics 描述语言学 [描写语言学] desiderative 意愿的determiner 限定词deterministic algorithm 决定型算法 [确定性算法] deterministic finite state automaton 决定型有限状态机deterministic parser 决定型语法剖析器 [确定性句法剖析程序]developmental psychology 发展心理学Diachronic Linguistics 历时语言学diacritic 附加符号dialectology 方言学dictionary database 辞典数据库 [词点数据库]dictionary entry 辞典条目digital processing 数字处理 [数值处理]diglossia 双言digraph 二合字母diminutive 指小词diphone 双连音directed acyclic graph 有向非循环图disambiguation 消除歧义 [歧义消除]discourse 篇章discourse analysis 篇章分析 [言谈分析]discourse planning 篇章规划Discourse Representation Theory 篇章表征理论 [言谈表示理论]discourse strategy 言谈策略discourse structure 言谈结构discrete 离散的disjunction 选言dissimilation 异化distributed 分布式的distributed cooperative reasoning 分布协调型推理distributed text parsing 分布式文本剖析disyllabic 双音节的ditransitive verb 双宾动词 [双宾语动词;双及物动词] divergence 扩散[分化]D-M (Determiner-Measure) construction 定量结构D-N (determiner-noun) construction 定名结构document retrieval system 文件检索系统 [文献检索系统] domain dependency 领域依存性 [领域依存关系]double insertion 交互中插double-base 双基downgrading 降级dummy 虚位duration 音长{语音学}/时段{语法学/语意学}dynamic programming 动态规划Ee-book电子阅读器Expansion cards扩展卡end user终端用户e-cash电子现金e-commerce电子商务electronic cash电子现金electronic commerce电子商务electronic communications privacy act of1986电子通信隐私法案encrypting加密术energy star能源之星Enterprise computing企业计算化environment环境Erasable optical disks可擦除式光盘ergonomics人类工程学ethics道德规范External modem外置调制解调器extranet企业外部网Earley algorithm Earley 算法echo 回声句egressive 呼气音ejective 紧喉音electronic dictionary 电子词典elementary string 基本字符串 [基本单词串] ellipsis 省略EM algorithm EM算法embedding 崁入emic 功能关系的empiricism 经验论Empty Category Principle 虚范畴原则 [空范畴原理] empty word 虚词enclitics 后接成份end user 终端用户 [最终用户]endocentric 同心的endophora 语境照应entailment 蕴涵entity 实体entropy 熵entry 条目episodic memory 情节性记忆epistemological network 认识论网络ergative verb 作格动词ergativity 作格性Esperando 世界语etic 无功能关系etymology 词源学event 事件event driven control 事件驱动型控制example-based machine translation 以例句为本的机器翻译exclamation 感叹exclusive disjunction 排它性逻辑“或”experiencer case 经验者格expert system 专家系统extension 外延external argument 域外论元extraposition 移外变形 [外置转换]FFax machine传真机Field域Find搜索FireWire port火线端口Firmware固件Flash RAM闪存Flatbed scanner台式扫描器Flat-panel monitor纯平显示器floppy disk软盘Formatting toolbar格式化工具条Formula公式Function函数fair credit reporting act of 1970公平信用报告法案Fiber-optic cable光纤电缆File compression文件压缩File decompression文件解压缩filter过滤firewall防火墙firewall防火墙Fixed disk固定硬盘Flash memory闪存Flexible disk可折叠磁盘Floppies磁盘Floppy-disk cartridge磁盘盒Formatting格式化freedom of information act of 1970信息自由法案frequency频率frustrated受挫折Full-duplex communication全双通通信facility value 易度值feature 特征feature bundle 特征束feature co-occurrence restriction 特征同现限制 [特性同现限制]feature instantiation 特征体现feature structure 特征结构 [特性结构]feature unification 特征连并 [特性合一]feedback 回馈felicity condition 妥适条件file structure 档案结构finite automaton 有限状态机 [有限自动机]finite state 有限状态Finite State Morphology 有限状态构词法 [有限状态词法] finite-state automata 有限状态自动机finite-state language 有限状态语言finite-state machine 有限状态机finite-state transducer 有限状态置换器flap 闪音flat 降音foreground information 前景讯息 [前景信息]Formal Language Theory 形式语言理论Formal Linguistics 形式语言学Formal Semantics 形式语意学forward inference 前向推理 [向前推理]forward-backward algorithm 前前后后算法frame 框架frame based knowledge representation 框架型知识表示Frame Theory 框架理论free morpheme 自由语素Fregean principle Fregean 原则fricative 擦音F-structure 功能结构full text searching 全文检索function word 功能词Functional Grammar 功能语法functional programming 函数型程序设计 [函数型程序设计] functional sentence perspective 功能句子观functional structure 功能结构functional unification 功能连并 [功能合一]functor 功能符fundamental frequency 基频编辑本段GGeneral-purpose application通用运用程序Gigahertz千兆赫Graphic tablet绘图板green pc绿色个人计算机Grop by 排序garden path sentence 花园路径句GB (Government and Binding) 管辖约束geminate 重叠音gender 性Generalized Phrase Structure Grammar 概化词组结构语法 [广义短语结构语法]Generative Grammar 衍生语法Generative Linguistics 衍生语言学 [生成语言学]generic 泛指genetic epistemology 发生认识论genetive marker 属格标记genitive 属格gerund 动名词Government and Binding Theory 管辖约束理论GPSG (Generalized Phrase Structure Grammar) 概化词组结构语法 [广义短语结构语法]gradability 可分级性grammar checker 文法检查器grammatical affix 语法词缀grammatical category 语法范畴grammatical function 语法功能grammatical inference 文法推论grammatical relation 语法关系grapheme 字素编辑本段Hhandheld computer手提电脑Hard copy硬拷贝hard disk硬盘hardware硬件Help帮助Host computer主机Home page主页Hyperlink超链接hacker黑客Half-duplex communication半双通通信Hard-disk cartridge硬盘盒Hard-disk pack硬盘组Head crash磁头碰撞header标题help desk specialist帮助办公专家helper applications帮助软件Hierarchical network层次型网络history file历史文件hits匹配记录horizontal portal横向用户hot site热网站Hybrid network混合网络haplology 类音删略head 中心语head driven phrase structure 中心语驱动词组结构 [中心词驱动词组结构]head feature convention 中心语特征继承原理 [中心词特性继承原理]Head-Driven Phrase Structure Grammar 中心语驱动词组结构律heteronym 同形heuristic parsing 经验式句法剖析Heuristics 经验知识hidden Markov model 隐式马可夫模型hierarchical structure 阶层结构 [层次结构]holophrase 单词句homograph 同形异义词homonym 同音异义词homophone 同音词homophony 同音异义homorganic 同部位音的Horn clause Horn 子句HPSG (Head-Driven Phrase Structure Grammar) 中心语驱动词组结构语法human-machine interface 人机界面hypernym 上位词hypertex（转载自第一范文网，请保留此标记。
计算机科学英语词汇大全掌握计算机科学领域的专业术语和常见缩略词
计算机科学英语词汇大全掌握计算机科学领域的专业术语和常见缩略词在计算机科学领域，掌握专业术语和常见缩略词是非常重要的，这有助于更好地理解和沟通。
本文将为您整理一份计算机科学英语词汇大全，以便您学习和掌握这些专业术语。
以下是常见的计算机科学英语词汇及其解释：1. Algorithm（算法）: A set of predefined rules or instructions used to solve a specific problem or perform a specific task in a computer program.2. Binary（二进制）: A numbering system that consists of only two digits, 0 and 1. It is widely used in computer systems as the fundamental language for representing data and performing calculations.3. Compiler（编译器）: A software tool that translates high-level programming languages into machine language or assembly language, which can be directly executed by a computer.4. Database（数据库）: A structured collection of data that is organized and stored in a computer system. It allows users to easily retrieve, update, and manage data efficiently.5. Encryption（加密）: The process of converting data into a form that is unreadable by unauthorized users. Encryption is used to ensure the security and privacy of sensitive information.6. Firewall（防火墙）: A network security device that monitors and controls incoming and outgoing network traffic based on predeterminedsecurity rules. It acts as a barrier between a trusted internal network and untrusted external networks.7. HTML (Hypertext Markup Language)（超文本标记语言）: The standard markup language used for creating and structuring web pages. It defines the structure and layout of the content on a webpage.8. GUI (Graphical User Interface)（图形用户界面）: A visual interface that allows users to interact with a computer or software using graphical elements, such as windows, icons, buttons, and menus.9. Kernel（内核）: The core component of an operating system that manages system resources and provides low-level services to other software applications.10. Machine Learning（机器学习）: A branch of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can analyze and interpret data to make predictions or decisions.11. Network（网络）: A collection of interconnected devices, such as computers, servers, routers, and switches, that allows for the exchange of data and resources.12. Object-Oriented Programming（面向对象编程）: A programming paradigm that organizes software design around objects, rather than logic and procedures. It emphasizes the reusability, modularity, and extensibility of code.13. Protocol（协议）: A set of rules and standards that govern the communication between devices on a network. Protocols ensure that data is transmitted and received correctly.14. Query（查询）: A request for specific information or data from a database using a query language, such as SQL (Structured Query Language).15. RAM (Random Access Memory)（随机存取存储器）: A type of computer memory that stores data that is being actively used by a computer program. It allows for faster access to data compared to other types of storage.16. Software Development（软件开发）: The process of designing, coding, testing, and maintaining software applications and systems. It involves various stages, such as requirements analysis, design, implementation, and deployment.17. TCP/IP (Transmission Control Protocol/Internet Protocol)（传输控制协议/互联网协议）: A set of networking protocols that allows computers to communicate and exchange data over the internet. It provides a reliable and standardized method for transmitting data packets.18. Virtual Reality（虚拟现实）: A computer-generated simulation of a three-dimensional environment that can be interacted with and explored by a user. It typically involves the use of specialized hardware, such as headsets and motion controllers.19. Web Development（网站开发）: The process of creating and maintaining websites and web applications. It includes tasks such as webdesign, web content development, client-side scripting, and server-side scripting.20. XML (eXtensible Markup Language)（可扩展标记语言）: A markup language that is designed to store and transport data. It is widely used for representing and exchanging structured data over the internet.这些是计算机科学中的一些常见英语词汇和术语。
计算机国际会议
International Conference on Hybrid Systems: ACM, Springer / Computation and Control International Conference on Implementation and Springer Application of Automata International SPIN Workshop on Model Checking Springer Software International Conference on Verification, Model Springer Checking, and Abstract Interpretation International Symposium on Formal Methods for Springer Components and Objects International Conference on Formal Methods for Springer Open Object-based Distributed Systems ACM/IEEE International Conference on Formal IEEE Methods and Models for Co-Design / /spin08/ /vmcai08/ http://www-sop.inria.fr/oasis/FMCO/ fmco08.html http://discotec08.ifi.uio.no/FMOODS08/ /memocontest08/
69
动态
第 6 卷 第 10 期 2010 年 10 月
计算机体系结构
一、A类
序号 会议简称 1. 2. 3. ISCA MICRO HPCA 会议全称 主办 网址 International Symposium on Computer ACM SIGARCH, IEEE h t t p://w w w.a c m.o r g/p u b s/c o n t e n t s/ Architecture TCCA proceedings/series/isca/ MICRO High-Performance Computer Architecture IEEE, ACM SIGMICRO / IEEE /~hpca/
CCF推荐国际学术期刊
CCF推荐国际学术期刊中国计算机学会推荐国际学术期刊 (计算机系统与⾼性能计算）⼀、A类序号刊物简称刊物全称出版社⽹址1TOCS ACM Transactions on Computer Systems ACM2TOC IEEE Transactions on Computers IEEE3TPDS IEEE Transactions on Parallel and DistributedSystemsIEEE⼆、B类序号刊物简称刊物全称出版社⽹址1TACO ACM Transactions on Architecture and Code OptimizationACM2TAAS ACM Transactions on Autonomous andAdaptive SystemsACM3TODAES ACM Transactions on Design Automation ofElectronic SystemsACM4TECS ACM Transactions on Embedded ComputingSystemsnACM5TRETS ACM Transactions on ReconfigurableTechnology and SystemsACM6TOS ACM Transactions on Storage ACM7TCAD IEEE Transactions on COMPUTER-AIDEDDESIGN of Integrated Circuits and SystemsIEEE8TVLSI IEEE Transactions on VLSI Systems IEEE 9JPDC Journal of Parallel and Distributed Computing Elsevier 10 PARCO Parallel Computing Elsevier11Performance Evaluation: An InternationalJournalElsevier三、C类序号刊物简称刊物全称出版社⽹址1Concurrency and Computation: Practice andExperienceWiley2DC Distributed Computing Springer 3FGCS Future Generation Computer Systems Elsevier 4Integration Integration, the VLSI Journal Elsevier5 JSA The Journal of Systems Architecture: EmbeddedSoftware DesignElsevier6Microprocessors and Microsystems: EmbeddedHardware DesignElsevier7JGC The Journal of Grid computing Springer 8TJSC The Journal of Supercomputing Springer9JETC The ACM Journal on Emerging Technologies inComputing SystemsACM10JET Journal of Electronic Testing-Theory andApplicationsSpringer中国计算机学会推荐国际学术刊物（计算机⽹络）⼀、A类序号刊物简称刊物全称出版社⽹址1TON IEEE/ACM Transactions on Networking IEEE, ACM2JSAC IEEE Journal of Selected Areas inCommunicationsIEEE3TMC IEEE Transactions on Mobile Computing IEEE⼆、B类序号刊物简称刊物全称出版社⽹址1TOIT ACM Transactions on Internet Technology ACM2TOMCCAP ACM Transactions on Multimedia Computing, Communications and ApplicationsACM3TOSN ACM Transactions on Sensor Networks ACM4CN Computer Networks Elsevier5TOC IEEE Transactions on Communications IEEE6TWC IEEE Transactions on Wireless Communications IEEE三、C类序号刊物简称刊物全称出版社⽹址1Ad hoc Networks Elsevier2CC Computer Communications Elsevier3TNSM IEEE Transactions on Network and ServiceManagementIEEE4IET Communications IET 5JNCA Journal of Network and Computer Applications Elsevier 6MONET Mobile Networks & Applications Springer 7Networks Wiley 8PPNA Peer-to-Peer Networking and Applications Springer9WCMC Wireless Communications & Mobile Computing Wiley.10Wireless Networks Springer中国计算机学会推荐国际学术刊物 (⽹络与信息安全）⼀、A类序号刊物简称刊物全称出版社⽹址1TDSC IEEE Transactions on Dependable and SecureComputingIEEE2TIFS IEEE Transactions on Information Forensicsand SecurityIEEE3 Journal of Cryptology Springer⼆、B类序号刊物简称刊物全称出版社⽹址1TISSEC ACM Transactions on Information and SystemSecurityACM2 Computers & Security Elsevier3 Designs, Codes and Cryptography Springer4JCS Journal of Computer Security IOS Press三、C类序号刊物简称刊物全称出版社⽹址1CLSR Computer Law and Security Reports Elsevier2 EURASIP Journal on Information Security Springer3 IET Information Security IET4IMCS Information Management & Computer Security Emerald5ISTR Information Security Technical Report Elsevier6IJISP International Journal of InformationSecurity and PrivacyIdea GroupInc7IJICS International Journal of Information andComputer SecurityInderscience8SCN Security and Communication Networks Wiley中国计算机学会推荐国际学术刊物 (软件⼯程、系统软件与程序设计语⾔）⼀、A类序号刊物简称刊物全称出版社⽹址1TOPLAS ACM Transactions on ProgrammingLanguages & SystemsACM2TOSEM ACM Transactions on Software Engineering MethodologyACM3TSE IEEE Transactions on Software Engineering IEEE⼆、B类序号刊物简称刊物全称出版社⽹址1ASE Automated Software Engineering Springer2Empirical Software Engineering Springer3 TSC IEEE Transactions on Service Computing IEEE4 IETS IET Software IET5 IST Information and Software Technology Elsevier6JFP Journal of Functional Programming Cambridge University Press7Journal of Software: Evolution and Process Wiley8JSS Journal of Systems and Software Elsevier9RE Requirements Engineering Springer10SCP Science of Computer Programming Elsevier11SoSyM Software and System Modeling Springer12SPE Software: Practice and Experience Wiley13STVR Software Testing, Verification and Reliability Wiley三、C类序号刊物简称刊物全称出版社⽹址1Computer Languages, Systems and Structures Elsevier2IJSEKE International Journal on Software Engineering andKnowledge EngineeringWorld Scientific3STTT International Journal on Software Tools forTechnology TransferSpringer4Journal of Logic and Algebraic Programming Elsevier5JWE Journal of Web Engineering Rinton Press6Service Oriented Computing and Applications Springer 7 SQJ Software Quality Journal Springer8TPLP Theory and Practice of Logic Programming Cambridge University Press中国计算机学会推荐国际学术刊物 (数据库、数据挖掘与内容检索)⼀、A类序号刊物简称刊物全称出版社⽹址1TODS ACM Transactions on Database Systems ACM2TOIS ACM Transactions on Information andSystemsACM3TKDE IEEE Transactions on Knowledge and DataEngineeringIEEE ComputerSociety4VLDBJ VLDB Journal Springer-Verlag⼆、B类序号刊物简称刊物全称出版社⽹址1TKDD ACM Transactions on Knowledge Discoveryfrom DataACM2AEI Advanced Engineering Informatics Elsevier3DKE Data and Knowledge Engineering Elsevier4DMKD Data Mining and Knowledge Discovery Springer5EJIS European Journal of Information Systems The OR Society 6GeoInformatica Springer7IPM Information Processing and Management Elsevier8Information Sciences Elsevier9IS Information Systems Elsevier10JASIST Journal of the American Society for InformationScience and Technology American Society for Information Science andTechnology11JWS Journal of Web Semantics Elsevier12KIS Knowledge and Information Systems Springer13 TWEB ACM Transactions on the Web ACM三、C类序号刊物简称刊物全称出版社⽹址1DPD Distributed and Parallel Databases Springer2I&M Information and Management Elsevier3IPL Information Processing Letters Elsevier4Information Retrieval Springer5IJCIS International Journal of Cooperative InformationSystemsWorld Scientific6IJGIS International Journal of GeographicalInformation ScienceTaylor & Francis7IJIS International Journal of Intelligent Systems Wiley 8IJKM International Journal of Knowledge Management IGI9IJSWIS International Journal on Semantic Web andInformation SystemsIGI10JCIS Journal of Computer Information Systems IACIS 11JDM Journal of Database Management IGI-Global12JGITM Journal of Global Information TechnologyManagementIvy LeaguePublishing13JIIS Journal of Intelligent Information Systems Springer14JSIS Journal of Strategic Information Systems Elsevier中国计算机学会推荐国际学术刊物 (计算机科学理论)⼀、A类序号刊物简称刊物全称出版社⽹址1IANDC Information and Computation Elsevier2SICOMP SIAM Journal on Computing SIAM⼆、B类序号刊物简称刊物全称出版社⽹址1TALG ACM Transactions on Algorithms ACM2TOCL ACM Transactions on ComputationalLogicACM3TOMS ACM Transactions on MathematicalSoftwareACM4Algorithmica Springer 5Computational complexity Springer 6FAC Formal Aspects of Computing Springer 7Formal Methods in System Design Springer 8INFORMS Journal on Computing INFORMS9JCSS Journal of Computer and SystemSciencesElsevier10JGO Journal of Global Optimization Springer 11Journal of Symbolic Computation Elsevier12MSCS Mathematical Structures in ComputerScienceCambridgeUniversityPress13TCS Theoretical Computer Science Elsevier三、C类序号刊物简称刊物全称出版社⽹址1Annals of Pure and Applied Logic Elsevier2Acta Informatica Springer3Discrete Applied Mathematics Elsevier4Fundamenta Informaticae IOS Press5Higher-Order and SymbolicComputationSpringer6Information Processing Letters Elsevier 7JCOMPLEXITY Journal of Complexity Elsevier8LOGCOM Journal of Logic and ComputationOxford University Press9Journal of Symbolic Logic Association for Symbolic Logic10LMCS Logical Methods in Computer Science LMCS11SIDMA SIAM Journal on Discrete Mathematics SIAM12Theory of Computing Systems Springer中国计算机学会推荐国际学术期刊（计算机图形学与多媒体）⼀、A类序号刊物简称刊物全称出版社⽹址1TOG ACM Transactions on Graphics ACM2TIP IEEE Transactions on Image Processing IEEE3TVCG IEEE Transactions on Visualization andComputer GraphicsIEEE⼆、B类序号刊物简称刊物全称出版社⽹址1TOMCCAP ACM Transactions on MultimediaComputing, Communications andApplicationACM2CAD Computer-Aided Design Elsevier 3CAGD Computer Aided Geometric Design Elsevier 4CGF Computer Graphics Forum Wiley 5GM Graphical Models Elsevier6 TCSVT IEEE Transactions on Circuits andSystems for Video TechnologyIEEE7TMM IEEE Transactions on Multimedia IEEE8JASA Journal of The Acoustical Society ofAmericaAIP9SIIMS SIAM Journal on Imaging Sciences SIAM10SpeechComSpeech Communication Elsevier三、C类序号刊物简称刊物全称出版社⽹址1CAVW Computer Animation and Virtual Worlds Wiley2C&G Computers & Graphics-UK Elsevier3CGTA Computational Geometry: Theory andApplicationsElsevier4DCG Discrete & Computational Geometry Springer 5IET Image Processing IET 6IEEE Signal Processing Letter IEEE7JVCIR Journal of Visual Communication and Image RepresentationElsevier8MS Multimedia Systems Springer9MTA Multimedia Tools and Applications Springer10Signal Processing Elsevier11Signal procesing:image communication Elsevier12TVC The Visual Computer Springer中国计算机学会推荐国际学术刊物（⼈⼯智能与模式识别）⼀、A类序号刊物简称刊物全称出版社⽹址1AI Artificial Intelligence Elsevier2TPAMI IEEE Trans on Pattern Analysis and Machine IntelligenceIEEE3IJCV International Journal of Computer Vision Springer4JMLR Journal of Machine Learning Research MIT Press⼆、B类序号刊物简称刊物全称出版社⽹址1TAP ACM Transactions on Applied Perception ACM2TSLP ACM Transactions on Speech andLanguage ProcessingACM3Computational Linguistics MIT Press 4CVIU Computer Vision and Image Understanding Elsevier5DKE Data and Knowledge Engineering Elsevier6Evolutionary Computation MIT Press7TAC IEEE Transactions on Affective Computing IEEE8TASLP IEEE Transactions on Audio, Speech, andLanguage ProcessingIEEE9IEEE Transactions on Cybernetics IEEE10TEC IEEE Transactions on EvolutionaryComputation IEEE11TFS IEEE Transactions on Fuzzy Systems IEEE12TNNLS IEEE Transactions on Neural Networks andlearning systemsIEEE13IJAR International Journal of ApproximateReasoningElsevier14JAIR Journal of AI Research AAAI 15Journal of Automated Reasoning Springer16JSLHR Journal of Speech, Language, and HearingResearchAmericanSpeech-LanguageHearingAssociation17Machine Learning Springer18Neural Computation MIT Press19Neural Networks Elsevier20Pattern Recognition Elsevier三、C类序号刊物简称刊物全称出版社⽹址1TALIP ACM Transactions on Asian LanguageInformation ProcessingACM3Applied Intelligence Springer 4AIM Artificial Intelligence in Medicine Elsevier 5Artificial Life MIT Press6AAMAS Autonomous Agents and Multi-AgentSystemsSpringer7Computational Intelligence Wiley8Computer Speech and Language Elsevier9Connection Science Taylor & Francis10DSS Decision Support Systems Elsevier 11EAAI Engineering Applications of Artificial Intelligence Elsevier 12Expert Systems Blackwell/Wiley 13ESWA Expert Systems with Applications Elsevier 14Fuzzy Sets and Systems Elsevier15T-CIAIG IEEE Transactions on ComputationalIntelligence and AI in GamesIEEE16IET Computer Vision IET 17IET Signal Processing IET 18IVC Image and Vision Computing Elsevier 19IDA Intelligent Data Analysis Elsevier20IJCIA International Journal of ComputationalIntelligence and ApplicationsWorld Scientific21IJDAR International Journal on Document Analysisand RecognitionSpringer22IJIS International Journal of Intelligent Systems Wiley23IJNS International Journal of Neural Systems World Scientific24IJPRAI International Journal of Pattern Recognitionand Artificial IntelligenceWorld Scientific25International Journal of Uncertainty,Fuzziness and KBSWorld Scientific26JETAI Journal of Experimental and TheoreticalArtificial IntelligenceTaylor & Francis27KBS Knowledge-Based Systems Elsevier 28Machine Translation Springer 29Machine Vision and Applications Springer 30Natural Computing Springer31NLE Natural Language Engineering Cambridge University32NCA Neural Computing & Applications Springer 33NPL Neural Processing Letters Springer 34Neurocomputing Elsevier 35PAA Pattern Analysis and Applications Springer 36PRL Pattern Recognition Letters Elsevier 37Soft Computing Springer38WIAS Web Intelligence and Agent Systems IOS Press中国计算机学会推荐国际学术期刊（⼈机交互与普适计算）⼀、A类序号刊物简称刊物全称出版社⽹址1TOCHI ACM Transactions on Computer-HumanInteractionACM2IJHCS International Journal of Human Computer Studies Elsevier⼆、B类序号刊物简称刊物全称出版社⽹址1CSCW Computer Supported Cooperative Work Springer2HCI Human Computer Interaction Taylor & Francis3IWC Interacting with ComputersOxford University Press4UMUAI User Modeling and User-Adapted Interaction Springer三、C类序号刊物简称刊物全称出版社⽹址1BIT Behaviour & Information Technology Taylor & Francis2IJHCI International Journal of Human-ComputerInteractionTaylor & Francis3PMC Pervasive and Mobile Computing Elsevier4PUC Personal and Ubiquitous Computing Springer中国计算机学会推荐国际学术期刊（前沿、交叉与综合）⼀、A类序号刊物简称刊物全称出版社⽹址1Proc. IEEE Proceedings of the IEEE IEEE2JACM Journal of the ACM ACM⼆、B类序号刊物简称刊物全称出版社⽹址1Bioinformatics Oxford UniversityPress2Briefings in Bioinformatics Oxford UniversityPress3Cognition Cognition：International Journal ofCognitive ScienceElsevier4PLOS Computational Biology Public Libraryof Science5TMI IEEE Transactions on Medical Imaging IEEE6TGARS IEEE Transactions on Geoscienceand Remote SensingIEEE7TITS IEEE Transactions on IntelligentTransportation SystemsIEEE8TR IEEE Transactions on Robotics IEEE9TASAE IEEE Transactions on AutomationScience and EngineeringIEEE10JCST Journal of Computer Science andTechnologySCIENCEPRESS/Springer11JAMIA Journal of the American MedicalInformatics AssociationBMJ Journals12Science China Information Sciences Science in China Press/Springer13The Computer Journal Oxford UniversityPress三、C类序号刊物简称刊物全称出版社⽹址1BMC Bioinformatics BioMed Central2Cybernetics and Systems Taylor & Francis3IEEE Geoscience and RemoteSensing LettersIEEE4TITB IEEE Transactions on InformationTechnology in BiomedicineIEEE5TCBB IEEE-ACM Transactions onComputational Biology andBioinformaticsIEEE/ACM6IET Intelligent Transport Systems IET 7Medical Image Analysis Elsevier8FCS Frontiers of Computer Science Higher EducationPress。
柔性工作车间调度问题的多目标优化方法研究
第15卷第8期计算机集成制造系统Vol.15No.82009年8月Computer Integrated Manufacturing SystemsAug.2009文章编号:1006-5911(2009)08-1592-07收稿日期:2008207208;修订日期:2008209201。
Received 08J uly 2008;accepted 01Sep.2008.基金项目:国家863/CIMS 主题资助项目(2007AA04Z190,2008AA042301);国家自然科学基金资助项目(50835008,50875237)。
Found ation i 2tems :Project supported by t he National High 2Tech.R &D Program for CIMS ,China (No.2007AA04Z190,2008AA042301),and t he National Natural Science Foundation ,China (No.50835008,50875237).作者简介:魏 巍(1982-),男,辽宁沈阳人,浙江大学CAD &CG 国家重点实验室博士研究生,主要从事产品配置优化、产品信息建模、多目标优化和先进制造技术等研究。
E 2mail :boyweiwei @ ;+通信作者E 2mail :fyxtv @ 。
柔性工作车间调度问题的多目标优化方法研究魏 巍1,谭建荣1,冯毅雄+1,张 蕊2(1.浙江大学流体传动及控制国家重点实验室,浙江 杭州 310027;2.华晨金杯汽车有限公司,辽宁 沈阳 110044)摘 要:针对各工件目标不同的多目标柔性作业车间调度问题,构建了以加工成本、加工质量及制造工期为目标函数的柔性作业车间调度多目标优化数学模型。
针对传统的加权系数遗传算法不能很好地解决柔性作业车间调度多目标优化问题,提出采用改进的强度Pareto 进化算法,对柔性作业车间调度问题进行多目标优化,从而得出柔性车间调度问题的Pareto 综合最优解。
soft actor-critic 的解释 -回复
soft actor-critic 的解释-回复Soft Actor-Critic (SAC) is a reinforcement learning algorithm that combines the actor-critic framework with maximum entropy reinforcement learning. It is designed to learn policies for continuous action spaces, facilitating robust and flexible control in complex environments. In this article, we will step by step explore the key principles and components of the SAC algorithm.1. Introduction to Reinforcement Learning:Reinforcement learning is a branch of machine learning that focuses on enabling an agent to learn how to make decisions based on its interaction with an environment. The agent receives feedback in the form of rewards or penalties and learns to maximize the cumulative reward over time through trial and error.2. Actor-Critic Framework:The actor-critic framework is a popular approach in reinforcement learning. It combines the advantages of both value-based and policy-based methods. The actor, also known as the policy network, learns to select actions based on the current state of the environment. The critic, on the other hand, estimates the value function or the state-action value function, providing feedback tothe actor's policy learning process.3. Continuous Action Spaces:Many real-world problems, such as robotics control or autonomous driving, involve continuous action spaces. In contrast to discrete action spaces where there are a finite number of actions to choose from, continuous action spaces allow for an infinite number of actions within a specific range. Traditional policy-based methods struggle with continuous actions due to the curse of dimensionality.4. Maximum Entropy Reinforcement Learning:Maximum entropy reinforcement learning aims to learn policies that are not only optimal but also stochastic. Introducing stochasticity in the policy allows for exploration and probabilistic decision-making, enabling the agent to handle uncertainties in the environment. This approach helps prevent the agent from getting trapped in local optima.5. Soft Q-Learning:Soft Q-learning is a variant of the Q-learning algorithm that leverages maximum entropy reinforcement learning principles. Itseeks to learn a soft state-action value function, which combines the typical expected reward with an entropy term. The entropy term encourages exploration by discouraging over-reliance on deterministic policies.6. Policy Optimization with Soft Actor-Critic:In SAC, the actor is responsible for learning the policy distribution, parametrized by a neural network. The critic learns the Q-function, estimating the state-action values. The training procedure consists of sampling actions based on the current policy, collecting trajectories or episodes, and using these samples to update the policy and Q-function.7. Entropy Regularization:SAC utilizes entropy regularization to ensure exploration and stochastic decision-making. The entropy term acts as a regularizer added to the objective function during policy optimization. By maximizing the entropy, the agent strives to maintain a diverse set of actions and explore the full action space.8. Soft Actor-Critic Architecture:The SAC architecture involves three main components: the actornetwork, the critic network, and target networks. The actor network is responsible for learning the policy distribution, while the critic network estimates the Q-function for value estimation. Target networks are used to stabilize the learning process by providing temporally consistent value estimates.9. Experience Replay:Experience replay is a technique employed in SAC to improve sample efficiency and mitigate potential non-stationarity issues. Instead of updating the policy and value function using immediate samples, experience replay stores and replays past experiences. This approach enables the agent to learn from a diverse range of experiences, leading to more robust policy learning.10. Exploration Strategies:Exploration is critical for reinforcement learning, as it allows the agent to discover new and potentially better policies. SAC employs a combination of exploration strategies, including adding noise to the policy parameters or actions. This noise injection encourages the agent to explore different solutions, improving the chance of finding the optimal policy.In conclusion, Soft Actor-Critic is a powerful reinforcement learning algorithm for continuous action spaces. By incorporating maximum entropy reinforcement learning principles, SAC enables robust and flexible control in complex environments. Its actor-critic framework, with entropy regularization, allows for policy optimization and exploration, making it well-suited for real-world problems. Additionally, the use of experience replay and exploration strategies enhances the learning process, leading to better performance and more efficient policy learning.。
贴片机文献
Challenges in Building an Arabic-English GHMT Systemwith SMT ComponentsNizar Habash†,Bonnie Dorr‡,Christof Monz§†Center for Computational Learning Systems,Columbia Universityhabash@‡Department of Computer Science,University of Marylandbonnie@§Department of Computer Science,Queen Mary,University of Londonchristof@AbstractThe research context of this paper is de-veloping hybrid machine translation(MT)systems that exploit the advantages oflinguistic rule-based and statistical MTsystems.Arabic,as a morphologicallyrich language,is especially challengingeven without addressing the hybridiza-tion question.In this paper,we describethe challenges in building an Arabic-English generation-heavy machine trans-lation(GHMT)system and boosting itwith statistical machine translation(SMT)components.We present an extensiveevaluation of multiple system variants andreport positive results on the advantages ofhybridization.1IntroductionThe research context of this work is developing hy-brid machine translation(MT)systems that exploit the advantages of linguistic rule-based and statisti-cal MT systems.Arabic,as an example of a mor-phologically rich language,is especially challeng-ing even without addressing the hybridization ques-tion.In this paper,we describe the challenges in building an Arabic-English generation-heavy ma-chine translation(GHMT)system(Habash,2003a) and extending it with statistical machine translation (SMT)components.A major challenge for working with Arabic is the proliferation of inconsistent morphological repre-sentations in different resources and tools for Arabic natural language processing(NLP)(Habash,2006). This inconsistency is only heightened when trying to interface linguistically-aware MT approaches with surface-based statistical MT approaches,where the level of representation of the phrase(beyond the word)is not consistent.We describe how we ad-dress this issue in our system and present an exten-sive evaluation addressing its various strengths and weaknesses.We show positive improvements when extending our basic GHMT system with SMT com-ponents.The remainder of this paper is organized as fol-lows:the next section(Section2)discusses previ-ous work on hybridization in MT.It is followed by a discussion of Arabic-specific challenges for MT implementations in Section3.Section4describes the Arabic components of our basic GHMT system. Section5describes the extensions we made to in-tegrate SMT components into the GHMT system. Section6presents three evaluations of multiple MT system variants.2Previous WorkWe discuss research related to our approach in the areas of generation-heavy MT and MT hybridiza-tion.2.1Generation-Heavy MTGHMT is an asymmetrical hybrid approach that addresses the issue of MT resource poverty in source-poor/target-rich language pairs by exploiting symbolic and statistical target-language resources (Habash and Dorr,2002;Habash,2003a;Habash, 2003b).Expected source-language resources in-clude a syntactic parser and a simple one-to-many translation dictionary.No transfer rules or complex interlingual representations are used.Rich target-language symbolic resources such as word lexical semantics,categorial variations and subcategoriza-tion frames are used to overgenerate multiple struc-tural variations from a target-language-glossed syn-tactic dependency representation of source-language sentences.This symbolic overgeneration accounts for possible translation divergences,cases where the underlying concept or“gist”of a sentence is dis-tributed differently in two languages such as to put butter and to butter(Dorr,1993).The overgen-eration is constrained by multiple statistical target-language models including surface n-grams and structural n-grams.The source-target asymmetry of systems developed in this approach makes them more easily retargetable to new source languages (provided a source-language parser and translation dictionary).In this paper,we describe these two spe-cific extensions for Arabic in detail(Section4). 2.2MT HybridizationResearch into MT hybrids has increased over the last few years as research in the two main competing paradigms,rule-based MT and corpus-based(statis-tical)MT,is approaching a plateau in performance. In the case of statistical approaches this has recently led to approaches that not just rely on surface forms but also incorporate symbolic knowledge such as morphological information and syntactic structure. In the next two subsections,we review this body of research.Our own research however,differs in that we are approaching the hybridization question from the opposite direction,i.e.,how to incorporate SMT components into rule-based systems(Senel-lart,2006).Nonetheless,the research on SMT-based hybrids has influenced many of our decisions and di-rections.2.2.1Morphology-Based ApproachesThe anecdotal intuition in thefield is that reduc-tion of morphological sparsity often improves trans-lation quality.This reduction can be achieved by in-creasing training data or via morphologically-driven preprocessing(Goldwater and McClosky,2005). Recent investigations of the effect of morphology on SMT quality focused on morphologically rich lan-guages such as German(S.Nießen,2004);Span-ish,Catalan,and Serbian(Popovi´c and Ney,2004); and Czech(Goldwater and McClosky,2005).These studies examined the effects of various kinds of to-kenization,lemmatization and part-of-speech(POS) tagging and showed a positive effect on SMT qual-ity.Lee(2004)investigated the use of automatic alignment of POS tagged English and affix-stem segmented Arabic to determine appropriate tok-enizations of Arabic.Her results showed that mor-phological preprocessing helps,but only for smaller corpora.Habash and Sadat(2006)reached simi-lar conclusions on a much larger set of experiments including various preprocessing schemes and tech-niques.They showed that genre-variation interacts with preprocessing decisions.Within our approach,working with Arabic mor-phology is especially challenging.We discuss this issue in more detail in Section3.2.2.2Syntax-Based ApproachesMore recently a number of statistical MT ap-proaches included syntactic information as part of the preprocessing phase,the decoding phase or the n-best rescoring phase.Collins et al.(2005)incorporate syntactic infor-mation as part of preprocessing the parallel corpus.A series of transformations on the source parse trees are applied to make the order of the source language side closer to that of the target language.The same reordering is done for a new source sentence before decoding.They show a modest statistically signifi-cant improvement over basic phrase-based MT. Quirk et al.(2005)use sub-graphs of dependency trees to deal with word order differences between the source and the target language.During training, dependency graphs on the source side are projected onto the target side by using the alignment links be-tween words in the two languages.The use of syn-tactic information is the main difference between their approach and phrase-based statistical MT ap-proaches.During decoding,the different sub-graphs are combined in order to generate the most likely dependency tree.This approach has been shown to provide significant improvements over a phrase-based SMT system.Och et al.(2004)experimented with a wide range of syntactic features to rescore the n-best lists gener-ated by their statistical MT system.Although some features—e.g.,POS tags and parse-tree to string mappings—lead to slight improvements over the baseline,larger improvements are obtained by using simpler,non-syntactic features,such as IBM Model 1alignments.Similar to Collins et al.(2005)and Quirk et al. (2005),our approach uses source-language syntac-tic(specifically dependency)representations to cap-ture generalizations about the source-language text. Unlike both of them,we do not use or learn specific mappings between the syntactic structure of source and target languages.Instead,our approach maps the source language to a syntactically language-independent representation which forms the basis for target-language generation.3Arabic ChallengesArabic is a morphologically complex language with a large set of morphological features.These fea-tures are realized using both concatenative(affixes and stems)and templatic morphology(root and pat-terns)with a variety of morphological and phono-logical adjustments that appear in word orthography and interact with orthographic variations.As a re-sult,there are many different possible representa-tions of Arabic morphological tokens that have been used in different resources for Arabic NLP(Habash, 2006).For statistical MT,in principle,it does not matter what level of morphological representation is used so long as the input is on the same level as that of the training data.However,in practice,there are certain concerns with issues such as sparsity,ambiguity,and training data size.Symbolic MT approaches tend to capture more abstract generalities about the lan-guages they translate between compared to statisti-cal MT.This comes at a cost of being more com-plex than statistical MT,involving more human ef-fort,and depending on already existing resources for morphological analysis and parsing.This dependence on existing resources highlights the problem of variation in morphological represen-tations for Arabic.In a typical situation,the in-put/output text of an MT system is in simple white-space tokenization.But,a statistical parser(such as (Collins,1997)or(Bikel,2002))trained out-of-the-box on the Penn Arabic Treebank(Maamouri et al., 2004)assumes the same kind of tokenization it uses (4-way normalized segments into conjunction,parti-cle,word and pronominal clitic).This means,a sep-arate tokenizer is needed to convert input text to this representation(Habash and Rambow,2005;Diab et al.,2004).An additional issue with a treebank-trained sta-tistical parser is that its input/output is in normal-ized segmentation that does not contain morpholog-ical information such as features or lexemes that are important for translation:Arabic-English dictionar-ies use lexemes and proper translation of features, such as number and tense,which requires access to these features in both source and target languages. As a result,additional conversion is needed to relate the normalized segmentation to the lexeme and fea-ture levels.Of course,in principle,the treebank and parser could be modified to be at the desired level of representation(i.e.lexeme and features).But this may be a labor-intensive task for researchers inter-ested in MT.4Extending GHMT to ArabicAs described earlier,our English-targeted GHMT system can be used with a new source language given that a dependency parse and a word-based translation lexicon are provided.In the following sub-sections,we describe these two components in our Arabic-English GHMT system.The reusable English generation component is called EXERGE (Expansive Rich Generation for English),which is discussed in detail in(Habash,2003a).4.1Analysis IssuesThis sub-section describes the necessary steps for processing an Arabic input sentence.4.1.1Tokenization and POS TaggingFor tokenization,we use the Penn Arabic Tree-bank(PATB)tokenization scheme,which is most compatible with statistical parsers trained on the PATB(Maamouri et al.,2004).For the POS tagset, we use the collapsed tagset for PATB(24tags).We use the Morphological Analysis and Disambiguation (MADA)tool for Arabic preprocessing(Habash andRambow,2005)together with TOKAN,a general to-kenizer for Arabic(Habash,2006).MADA uses the A LMORGEANA(Arabic Lexeme-based Morpholog-ical Analysis and Generation)which is an alternative engine to Buckwalter’s AraMorph that uses the same lexicalfiles.4.1.2ChunkingWe employ a rule-based segment chunker to ad-dress two issues.First,the Arabic sentence length, which averages over35words with PATB tokeniza-tion(in the news genre),slows down the parser and increases its chances of producing null parses.Sec-ond,the use of punctuation and numbers in by-lines in news requires template handling in analysis and generation,which needs to be updated depending on the genre.Instead,we choose to preserve source-language order for such cases by chunking them out and treating them as special chunk separators that are translated independently.The rules currently im-plemented use the following chunk separators.POS information is used in this process.•Arabic conjunction proclitic w/CC1and •Numbers(CD)and punctuation(PUNC)•The subordinating conjunction An/IN that On average,sentences had3.3chunk separators.4.1.3ParsingFor parsing,we used the Bikel parser(Bikel, 2002)trained on the PATB(Part1).The default out-put of the parser is on unlabeled constituency repre-sentation.The tokens in the parser are surface words in the PATB tokenization scheme.4.1.4PostparsingThe specifications of EXERGE require an in-put dependency tree labeled with minimal syntac-tic relations(subj,obj,obj2,and mod).More-over,the nodes must have lexemes and features from a pre-specified set of feature names and val-ues(Habash,2003a).The output of the parsing step undergoes operations such as relation labeling and 1All Arabic transliterations in this chapter are provided in the Buckwalter transliteration scheme(Buckwalter,2002).node-structure modification.Some of these opera-tions are similar to the Spanish post-parsing process-ing for Matador(Spanish-English GHMT)(Habash, 2003b).Constituency to Dependency We convert con-stituencies to dependencies using modified head-percolation rules from Bikel parser applied with the Const2Dep tool2(Habash and Rambow,2004). Lexeme Selection MADA is only a morpho-logical disambiguation tool that makes no sense-disambiguation choices.Therefore,multiple lex-emes are still available as ambiguous options at the tree nodes.In some cases,the parser overrides the POS tag that was chosen initially by MADA.As a result,we need to re-visit discarded morphologi-cal analyses again.We re-apply the A LMORGEANA system on the tokenized words and thenfilter analy-ses using the following criteria.In case no analysis matches,all analyses are passed on to the nextfilter.•Analyses with PATB tokenizable clitics are ig-nored because the word is already tokenized.•Analyses that match the word’s POS are se-lected.Others are ignored.The POS match-ing is fuzzy since the tagset used by A L-MORGEANA(15tags)is more coarse than the PATB tagset(24tags).Also,since there arecommon cases of mismatch in Arabic,certain seemingly mismatched cases are allowed,e.g., noun,adjective and proper noun.•We use a statistical unigram lexeme and fea-ture model.The model was trained on PATB (part1and part2)and1million words from Arabic Gigaword(Graff,2003)disambiguated using MADA.The lexemes are chosen based on their unigram counts.Ties are broken with feature unigrams.Dependency Tree Restructuring The following operations are applied to the dependency tree:•Idafa Handling:The Idafa construction is a syntactic construction indicating the relation-ship of possession between two nouns,i.e., Noun1of Noun2.Nouns in this construction 2The Const2Dep tool was provided by Rebbecca Hwa.are modified to include an intervening node that has no surface value but is glossed to of/’s/*empty*.•The untokenized prefix Al+the is turned intoa separate node that is a dependent on the wordit is attached to.•Feature mapping:We map Arabic-specific fea-tures to language-independent features used in EXERGE.For example,the untokenized prefix s+will is mapped to the feature TENSE:FUT and the Arabic perfective aspect verb feature is turned into TENSE:PAST.Relation Labeling An Arabic subject may be:(a) pro-dropped(verb conjugated),(b)pre-verbal(full conjugation with verb),or(c)post-verbal(3per-son and gender agreement only).Third mascu-line/feminine singular verbs are often ambiguous as to whether they are case(a),where the adjacent noun is an object,or(c),where the adjacent noun is a subject.A verb can have no,one or two objects. Pronominal objects are always cliticized to the verb, which means they can appear between the verb and the nominal subject.For passive verbs,the sub-ject position is reserved for a*PRO*and the fea-ture is passed along.In principle,Arabic’s rich case system can account for the different configurations and also allow many variations in order,but since most cases are diacritical(and thus optionally writ-ten),that information is not always available.Arabic prose(non-poetry)writers generally avoid such syn-tactic acrobatics.We use heuristics based on Arabic syntax to deter-mine the relation of the verb to its nominal(common and proper),pronominal and relativizing children.4.1.5Subtree Phrase ConstructionEach node in the dependency tree is annotated with the full projection of the subtree it heads.This subtree phrase is later used to interface with the sta-tistical MT component.4.2Lexical Translation IssuesOne of the main challenges in resource usage was the transformation of the lexicon of the Buckwal-ter Arabic morphological analyzer(BAMA)(Buck-walter,2002)into a form that was readily usable by our GHMT system.The original Buckwalter lexi-con contained English glosses to Arabic stem entries used in morphological analysis.Since the glosses are attached to stems,they are sometime inflected for number or voice,although generally they are in lexeme form.Our initial extraction of a translation dictionary from BAMA produced a resource in the following form:[Arabic Word][Tab][POS][Tab][English Word][Tab](comment) For example:$ariyk_1N associates(female)We implemented a lexical reformatting procedure to address several issues with this lexicon.Thefirst issue is the inclusion of plural forms as in the example above—where the singular form ap-pears elsewhere in the lexicon(independently)—or related forms where the entry contains a synonym: $ariyk_1N associate$ariyk_1N partnerNote that,in addition to the redundancy inherent in these related entries,the use of parentheticals is inconsistent,e.g.,the comment“female”appears in only one of the entries above.The lexical reformatting procedure transforms these three entries into the following single line: $ariyk_1N associate/partnerwhere the plural form is assumed to be handled by GHMT during generation of the English surface form.In addition to the redundancy issues above,the material in the parentheses was often combined with a slash(/)in ways that were not uniform throughout the original lexicon.Consider the following exam-ple:<imArAtiy˜_1AJ Emirati(of/from_the_UAE)The material in the parentheses above is shorthand for“of the UAE”and“from the UAE”.Our lexical reformatting procedure transforms this entry into the following single line:<imArAtiy˜_1AJ Emirati(from_the_UAE/ of_the_UAE)Often,this same inconsistency with the slash(/) appeared in the English translation itself,as in the following entry:>a$ad˜_1N more/most_intensewhich was converted by our reformatting procedure into the following:>a$ad˜_1N more_intense/most_intense Beyond depluralizing and making slashes consis-tent,we also addressed the issue of passive conver-sion,where we transform a passive(but not cop-ula/adjective)English translation of an Arabic verb into an active form.Consider the following exam-ples from the original lexicon:>a$Ad_1V be_built>a$Ad_1V be_commended>a$Ad_1V be_praised$Abah_1V be_similarThese entries may be combined with other active forms that occur in the original dictionary:>a$Ad_1V build>a$Ad_1V commend>a$Ad_1V praise$Abah_1V resembleto yield the following two single lines:>a$Ad_1V build/commend/praise$Abah_1V be_similar/resemble5Integration of SMT Components into GHMTThe main challenge for integrating SMT compo-nents into GHMT is that the conception of the phrase (anything beyond a single word)is radically differ-ent.Phrase-based SMT systems take a phrase to be a sequence of words with no hidden underlying structure(Koehn,2004).On the other hand,for sys-tems that use parsers,like GHMT,a phrase has a linguistic structure that defines it and its behavior in a bigger context.Both kinds come with problems. Statistical phrases are created from alignments, which may not be clean.This results in jagged edges to many phrases.For example,the phrase.on the other hand,the(con-taining seven words starting with a period and end-ing with“the”)overlaps multiple linguistic phrase boundaries.Another related phenomenon is that of statistical hallucination,e.g.,the translation of AlswdAn w(literally,Sudan and)into enterprises and banks.Linguistic phrases come with a different set of problems.Since parsing technology for Arabic is still behind English,3many linguistic phrases are 3The parser we used in this paper is among the best avail-able,yet its performance for Arabic is in the lower70s percent mis-parsed creating symbolic hallucinations that af-fect the rest of the system.A common example is in-correctly attaching a prepositional phrase that mod-ifies a complete sentence to one of its noun phrases. We investigate two variants of a basic approach to using statistical phrases in the GHMT system.As phrase-based SMT system we use,Pharaoh(Koehn, 2004).We limit the statistical translation-table phrases used to those that correspond to completely projectable subtrees in the linguistic dependency representation of the input sentence.More complex solutions that use statistical phrases covering parts of a linguistic phrase are left for future work.In thefirst variant,(G HMT+Phrase Table,hence-forth G HMT P H T),we use the phrase table produced by Pharaoh as a multi-word surface dictionary.In the generation process,when a subtree is matched to an entry in this dictionary,an additional path in the generation lattice is created using the phrase-table entry in addition to the basic GHMT generation.In the second variant,(G HMT+Pharoah,hence-forth G HMT P HAROH),we use a phrase-based SMT system(Koehn,2004)to translate the subtree pro-jections for all the subtrees in the input sentence. These translations are added as alternatives to the basic G HMT system.Results comparing these two variants and a few others are described in Section6. The basic idea here is to exploit GHMT’s focus on phrase structure generation(global level)together with a phrase-based SMT system’s robustness(lo-cal phrases).One particular case in Arabic that we investigate later is the position of the subject relative to the verb.When we have a correct parse,moving the subject,which follows the verb in Arabic over 35%of the time,to a preverbal position is easy for GHMT(given a correct parse)but can be hard for a phrase-based SMT system,especially with sub-ject noun phrases exceeding the system’s distortion limit.6EvaluationWe use the standard NIST MTEval datasets for the years2003,2004and2005(henceforth MT03, MT04and MT05,respectively).4The2002MTEval test set was used for Minimum Error Training(Och, (labeled constituency PARSEV AL F-1score).4/speech/tests/mt/Table1:True-cased results of various systems on NIST MTEval test setsTest Set Metric G IST G HMT G HMT P H T G HMT P HAROH P HARAOH B W P HAROAH MT03BLEU0.08110.14790.23620.33790.41280.4162 NIST 5.1846 6.05287.32138.25699.92059.9300 MT04BLEU0.06510.14020.21100.27770.35460.3522 NIST 4.3904 6.09357.09817.58349.20389.1291 MT05BLEU0.06070.1450.23130.32390.39350.3960 NIST 4.7259 6.26367.48368.36879.69809.6615 Table2:Genre-specific true-cased results of various systems on NIST MT04test set Genre Metric G IST G HMT G HMT P H T G HMT P HAROH P HARAOH B W P HAROAH News BLEU0.08170.16170.25820.34340.42660.4244 NIST 4.8989 6.3587.61438.31329.72069.6796 Speech BLEU0.04290.12760.18210.24470.30880.3043 NIST 3.2993 5.3923 6.2022 6.63547.87967.7164 Editorial BLEU0.05750.11440.15420.19140.27040.2703 NIST 3.7633 4.9751 5.4724 5.46087.23447.18122003).All of the training data used here are available from the Linguistic Data Consortium(LDC).We use an Arabic-English parallel corpus of about5mil-lion words to train the translation model.5For Arabic preprocessing the Arabic Treebank scheme was used,see(Habash and Sadat,2006).All sys-tems use the same surface trigram language model, trained on approximately340million words of En-glish newswire text from the English Gigaword cor-pus.6English preprocessing simply included down-casing,separating punctuation from words and split-ting off“’s”.Trigram language models are imple-mented using the SRILM toolkit(Stolcke,2002). Both BLEU(Papineni et al.,2002;Callison-Burch et al.,2006)and NIST(Doddington,2002)metric scores are reported.All scores are computed against four references with n-grams of maximum length four.As a post-processing step,the translations of all systems are true-cased,and all results reported below refer to the case-sensitive BLEU and NIST scores.We conducted three sets of evaluations that ex-plore different aspects of the data sets and the system variants:a full system evaluation,a genre-specific 5The parallel text includes Arabic News,eTIRR,English translation of Arabic Treebank,and Ummah.6Distributed by the Linguistic Data Consortium: .evaluation,and a qualitative evaluation of specific linguistic phenomena.6.1Full EvaluationSix system variants are compared:•G IST is a simple gisting system that produces a sausage lattice from the English glosses in the output of the Buckwalter Arabic morphological analyzer(BAMA).Arabic word order is pre-served and English realization is limited to the variants provided in BAMA.•G HMT is the system described in Section4.The lexical translation is limited to the Buck-walter lexicon.•G HMT P H T is a variant of G HMT that uses a statistical phrase table as support multi-word surface dictionary(see Section5).•G HMT P HAROH is the second variant discussed in Section5.It uses Pharaoh to generate sub-tree phrases.•P HARAOH B W is the phrase-based SMT system Pharaoh trained on the basic training set in ad-dition to the entries in the Buckwalter lexicon.•P HAROAH is the phrase-based SMT system Pharaoh trained only on the basic training set.The results of the full systems are presented in Table1.The lowest performing system is G IST,as expected.G HMT,using only the Buckwalter lexicon and no other training data,more than doubles the G IST score.This indicates that the system is actually making more correct lexical choices and word order realization beyond simple gisting.G HMT P H T and G HMT P HAROH provide substan-tial improvements over G HMT.In G HMT P H T,only 54.6%of subtreesfind a match in the phrase table;as opposed to G HMT P HAROH which guarantees a sta-tistical translation for all subtrees.This accounts for the large difference between the two scores.This is a positive result for improving a non-statistical MT system with SMT components.However,the scores are still lower than the fully statistical system.We discuss the differences further in Section6.3.The primarily statistical systems P HAROAH and P HARAOH B W outperform all other systems. P HAROAH does better than P HARAOH B W for MT03 and MT05but not for MT04.In all three cases,the differences are not statistically significant.As the amount of dependence on training data in-creases,we see a bigger divide between the differ-ent data sets.MT03and MT05behave similarly but MT04lags behind.One of the reason behind this behavior is that MT04is a mixed genre data set.In the next section,we examine the differences in the genres in more detail.6.2Genre EvaluationThe MTEval2004data set is special in that it has a mix of genre(200documents:100news,50 speeches and50editorials).The training data we used is all Arabic news.We wanted to investigate the difference in behavior among variants with different types of symbolic and statistical resources.Table2 presents the scores for genre-specific subsets of the MT04test set.The difference in scores across the different sys-tems is consistent with the full evaluation in Table1. The difference across the genre is very clear,with the news subset performing at a similar score level to that of the MT03and MT05test sets in Table1. Upon examination of the documents in MT04,we see several variations across the genres that explain the differences.Particularly,speeches and editori-als have a much higher rate offirst and second per-son pronouns and verbs,include interrogative sen-tences,and use moreflowery andfiery language than news.Out-of-vocabulary(OOV)rates in the the dif-ferent subsets as measured against the basic train-ing set data is as follows:news(2.02%),speeches (2.01%)and editorials(2.34%).The differences are very small.This confirms that it is style/use differ-ence that is the biggest contributor to the difference in scores.The fact that we see similar differences in G IST and G HMT as in P HAROAH contradicts our hypothe-sis that G HMT is more genre-independent than SMT approaches.We believe this is a result of the Ara-bic linguistic resources we use being biased towards news-genre.For example,the Arabic treebank used for training the parser is only in the news genre.The Buckwalter lexicon potentially also has some inter-nal bias toward news genre because it was developed in tandem with the Arabic treebank.6.3Qualitative EvaluationAutomatic evaluation systems are often criticized for not capturing linguistic subtleties.This is clearly apparent in thefield’s moving back toward using hu-man evaluation metrics such as HTER(Snover et al., 2006).We conducted a small evaluation of verb and subject realization in eight random documents from MT04.The documents contained47sentences and reflect the distribution of genre in the MT04test set. We compare three systems G HMT,G HMT P HAROH and P HAROAH.The evaluation was conducted using one bilingual Arabic-English speaker(native Ara-bic,almost native English).The task is to deter-mine for every verb that appears in the Arabic input whether it is realized or not in the English transla-tion.If realized,we then determine whether its sub-ject is mapped to it correctly.Since translation diver-gences can cause an Arabic verb to appear as a noun in English,a nominalized translation is accepted as a valid realization.The subject of a non-verbal trans-lation is considered correctly assigned if the mean-ing of the relationship of the original subject-verb pair is preserved.Correct realization of the verb ob-ject was not considered here,and neither was non-verbal Arabic translations to verb forms in English. The results are presented in Table3for each genre and also collectively.For each of the three sys-tems studied,two columns are presented.Thefirst。
soft actor-critic 的解释 -回复
soft actor-critic 的解释-回复什么是soft actor-critic算法？Soft actor-critic（SAC）是一种强化学习算法，它是一种基于最大熵的actor-critic框架。
SAC算法的目的是针对连续动作控制的强化学习问题提供一种通用解决方案。
SAC算法的主要思想是最大化系统的熵和期望回报之间的折衷。
在SAC算法中，使用了一个额外的熵项，其目的是确保策略具有更好的探索和稳定性。
同时，SAC算法也允许在不影响策略性能的情况下，提高其鲁棒性和可靠性。
SAC算法的特点和优点是什么？1. Soft actor-critic（SAC）算法是一种基于最大熵强化学习框架的算法。
与其他基于熵的算法不同，SAC算法可以处理连续状态和动作空间的问题。
2. 使用策略熵最大化的方法可以提高策略的鲁棒性和可靠性。
SAC算法使用了一个额外的熵项，其目的是确保策略具有更好的探索和稳定性。
3. SAC算法应用了一种剪枝机制，即使用目标值网络和延迟更新策略，以减少更新时遇到的噪音和不稳定性。
同时，SAC算法还使用了一种经验回放器，以增加算法的样本效率和鲁棒性。
4. SAC算法提供了一种不需要额外的神经网络结构的自动学习速率方法。
这种方法使用一个自适应学习速率和温度系数，以确保算法可以自动调整学习速率，从而提高算法的稳定性和性能。
5. SAC算法可以应用于多种环境和问题，包括机器人控制、策略优化等。
SAC算法的应用场景是什么？Soft actor-critic（SAC）算法可以用于处理多种连续动作控制的强化学习问题，例如机器人控制、自动驾驶和游戏等。
SAC算法的主要应用场景包括以下几种：1. 机器人控制：SAC算法可以用于处理机器人控制问题，例如物体识别、姿态估计、路径规划和移动控制等。
SAC算法在处理机器人控制问题时，可以提高机器人的控制精度、鲁棒性和可靠性。
2. 自动驾驶：SAC算法可以用于处理自动驾驶问题，例如车道保持、智能巡航和自主停车等。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
2 Neural Networks
Arti cial neural systems or neural networks can be considered as a massively parallel distributed model that has a natural propensity for storing experimental knowledge and making it available for use. They represent mathematical models of brain-like systems where knowledge is received through a learning process.
Looking backward, the origins of neural networks can be found in the work of McCulloch and Pitts (1943) 74], where a simple model of neuron as a binary threshold unit was proposed. The next step was Hebb's book (1949): The Organization of Behaviour 35] in
Department of Electrical and Computer Engineering Zographou 157 73, Athens, Greece email: tzafesta@softlab.ece.ntua.gr
Abstract
During the last decade the human behaviour and human imitating processing methods have become of central interest through the scienti c community. The development of methods that mimic the human learning process being able to solve complex engineering problems which are di cult to deal with via conventional approaches, seems to be on an immediate emergency. Concepts such as nervous system, fuzziness and evolution come directly from human resources enclosing attractive properties and reach theory, and as a consequence lead to new scienti c horizons. In this direction, soft computing indicates a new family of computing techniques that accommodate human computing resources and make them being utilized. Neural networks, fuzzy systems and genetic algorithms are mainly the three basic constituents that contribute to this juncture. Starting with the basic features in each one of these partners, this paper is focused on the examination of all the possible combined (hybrid) methods among these units providing their main characteristics under a critical aspect. Moreover, a variety of engineering applications is presented demonstrating the enormous eld of action that soft computing surrounds, as well as proving the importance of dealing with hybrid intelligent methods.
Correspondence should be sent to: Professor S. G. Tzafestas, Intelligent Robotics and Automation Laboratory.
1
is that it focuses on the model of human mind. Soft computing contains many elds such as neural networks, fuzzy logic, probabilistic reasoning, genetic algorithms and chaos theory, that may be seen as complementary.
Hybrid Soft Computing Systems: A Critical Survey with Engineering Applications
Spyros G. Tzafestas and Konstantinos D. Blekas National Technical University of Athens