NodeJS Microservice Architecture Example with the real-time project. This is a practical source code of the NodeJS Microservice tutorial series. Where we have split up a monolithic application into Microservices Architecture. The main goal of this repository is to provide an overview of how the microservices architecture is working with nodejs and what is the complexity we need to resolve to achieve the outcome from a Monolithic architecture.

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

Classification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

An example of how to code split with Webpack 2 and React Router

Split a Webpack entry bundle by path into any number of arbitrarily defined smaller bundles

Split a Webpack entry bundle into any number of arbitrarily defined smaller bundles

Split your CSS for stupid browsers using webpack and postcss.

webpack code splitting & long term cache

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We can split this out into separate repos later, right now its very convenient to have everything here.

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France","sources":["https://stream.mmsiptv.com/droid/equipe/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTkxODg0ODhf/equipe.png","title":"L'EQUIPE TV / SPORTS"},{"description":"Sky Sport est une chaine televisee sportives","sources":["http://stream.tvtap.live:8081/live/skysports-premier-league.stream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTkxODg2MjNf/skysport.jpg","title":"SKY SPORTS / SPORTS"},{"description":"Azam Sports 1 Tanzanie est l'une des chaines privées que l'on retrouve en tanzanie, possédant des émissions Sportives variées","sources":["https://1446000130.rsc.cdn77.org/1446000130/index.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNDg1NzQwMjJf/azam.jpg","title":"AZAM SPORTS / TANZANIA"},{"description":"ADSPORTS 1 est une chaine televisee sportives emettant a Dubai","sources":["http://admdn1.cdn.mangomolo.com/adsports1/smil:adsports1.stream.smil/playlist.m3u8"],"subtitle":"By 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des film et series Africains surtout beaucoup plus nigerians","sources":["https://stream.mmsiptv.com/droid/nollywoodfr/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTkxODk3MDhf/nollywood.jpg","title":"NOLLYWOOD TV / NOVELAS"},{"description":"AfricaWood TV est une chaine televisee qui diffuse que des film et series Africains surtout beaucoup plus nigerians","sources":["https://stream.mmsiptv.com/droid/africawood/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNDg1Nzc2ODJf/vision4.jpg","title":"AFRICAWOOD TV / NOVELAS"},{"description":"NOVELAS TV 1 est une chaine televisee qui diffuse que des series mexicaines, bresiliens, phillipinesn et autres....","sources":["https://stream.mmsiptv.com/droid/novelas/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTkxOTAwMDFf/novelasone.jpg","title":"NOVELAS TV 1 / SERIE"},{"description":"RTI 2 est une chaine televisee ivoiriens qui diffuse que des informations, musiques, series mexicaines, bresiliens, phillipinesn et autres....","sources":["https://stream.mmsiptv.com/droid/rti2/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTkxODU4NDdf/rtid.jpg","title":"RTI 2 / COTE D'IVOIRE"},{"description":"NOVELAS TV est la chaine qui diffuset des Series Mexicaines, Philipiennes et Bresiliennes....","sources":["https://stormcast-telenovelatv-1-fr.samsung.wurl.com/manifest/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTQyMjA4MDVf/novelastv.jpg","title":"NOVELAS TV{"description":"NW INFOS est la chaine du togo en diffusant des Informations Emissions et autres....","sources":["https://hls.newworldtv.com/nw-info/video/live_1024x576.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTQyMjY0NzNf/nwInfos.jpg","title":"NW INFOS TV / TOGO"},{"description":"NW Muzik est la chaine du togo en diffusant des musiques Africaine et autres....","sources":["https://hls.newworldtv.com/nw-muzik/video/live_1024x576.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTQyMjY0NTZf/nwMuzik.webp","title":"NW MUZIK TV / TOGO"},{"description":"Al Hadath est la chaine du Lybie en diffusant des Emissions ainsi que des infos, musique et autres....","sources":["https://master.starmena-cloud.com/hls/hd.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTQyMjcxNDBf/alhad.png","title":"AL HADATH TV / LYBIE"},{"description":"Vox Of Africa est la chaine des americains qui emette a Brazzaville en diffusant des informations et autres....","sources":["https://voa-lh.akamaihd.net/i/voa_mpls_tvmc3_3@320295/master.m3u8"],"subtitle":"By Channel","thumb":"https://od.lk/s/M18yNTQyMjY1NDFf/VOX_AFRICA.jpg","title":"VOX OF AFRICA TV"},{"description":"Resurrection TV est l'une des chaines privées Chretienne que l'on retrouve dans la ville d'ACCRA, possédant des émissions variées","sources":["http://rtmp.ottdemo.rrsat.com/rrsatrtv1/rrsatrtvmulti.smil/chunklist.m3u8"],"subtitle":"By 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Sa ligne éditoriale est axée sur les deux Congo décomplexé, un Congo qui va de l'avant et gagne !.","sources":["http://cms-streamaniak.top/Cdirect/CDIRECT/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdirect.tv/assets/img/logo-cdirect.ico","title":"CDIRECT TV / Kinshasa-Brazzaville"},{"description":"DBM TV ou digital black Music est une Chaîne TV à thématique musicale, DBM a pour vocation de révéler et promouvoir la musique Afro Urbaine, qu’elle soit d’Afrique ou d’ailleurs info@dbm-tv.com. .","sources":["https://dbmtv.vedge.infomaniak.com/livecast/smil:dbmtv.smil/manifest.m3u8"],"subtitle":"By Channel","thumb":"https://www.dbm-tv.fr/wp-content/uploads/2017/12/logo-dbm.png","title":"DBM TV / Music "},{"description":"La LUMIÈRE, ministère Chrétien pour annoncer l’évangile de Jésus Christ partout dans le monde, toucher changer et sauver des vies par la puissance de la parole de DIEU avec des enseignements prédications adorations louanges partages de prières, d’exhortations et de témoignages","sources":["https://video1.getstreamhosting.com:1936/8248/8248/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.shortpixel.ai/client/q_glossy,ret_img,w_124,h_124/https://telepack.net/wp-content/uploads/2020/05/lumiere-tv.png","title":"La Lumiere TV / Gospel"},{"description":"La Télévision Togolaise (TVT) est le nom de l'unique chaîne de télévision publique togolaise, Crée depuis 1979.","sources":["http://54.38.92.12/tvt.m3u8"],"subtitle":"By Google","thumb":"https://amp.live-tv-channels.org/pt-data/uploads/logo/tg-tv.jpg","title":"Télévision Togolais"},{"description":"CRTV est un service de radio et de télévision contrôlé par le gouvernement au Cameroun. Cela a commencé sous le nom de Cameroon Television (CTV) et a ensuite fusionné avec le service de radio pour devenir CRTV. Il couvre l'ensemble des dix régions du Cameroun, ce qui en fait le diffuseur indomptable parmi plusieurs chaînes de télévision privées du pays. Sa couverture des événements est généralement considérée comme pro-gouvernementale. Les programmes de la CRTV comprennent des documentaires, des magazines, des analyses d'actualités et des séries importées d'Asie et du Brésil..","sources":["http://178.33.237.146/crtv.m3u8"],"subtitle":"By Channel","thumb":"http://www.cameroonconcordnews.com/wp-content/uploads/2018/03/CRTV-new.jpg","title":"Cameroune Radio Télévision"},{"description":"Impact TV c'est une premiere Chaine televisee chretienne diffusant au Burkina-Fasso sur satelite innauguree le 07/03/2008 par Marie Sophie.","sources":["https://edge10.vedge.infomaniak.com/livecast/impacttele/chunklist_w973675047.m3u8"],"subtitle":"By Channel","thumb":"https://i1.wp.com/www.livetvliveradio.com/wp-content/uploads/2017/07/impact-tv.jpg?fit=259%2C194","title":"Impact TV / Burkina Fasso"},{"description":"Kigali Channel 2 ( Là pour vous) est une chaine televisee Rwandaise emmetant a Kigali. KC2 se diversite par sa diffusion des emitions exceptionnelle ainsi que des films nouveautes et plein d'autres.","sources":["https://5c46fa289c89f.streamlock.net/kc2/kc2/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQfQjI8jUhMPReWg0MOdw1xpAAXMP7YAuZKBg&usqp=CAU","title":"KC2 TV / Rwanda"},{"description":"Equinox est une chaîne de télévision basée au Cameroun. Peu de temps après son lancement, il est devenu l'un des critiques les plus virulents du régime de Paul Biya. La station était connue pour avoir diffusé des images en direct d'une manifestation politique contre le changement constitutionnel au Cameroun qui favorisait le maintien au pouvoir du président Biya après 2011, alors qu'il lui était interdit par la Constitution de se présenter à nouveau. La télévision appartient au magnat des affaires de la région ouest du Cameroun, Severin Tchounke, qui possède également un quotidien critique, La Nouvelle Expression.","sources":["http://178.33.237.146/equinoxetv.m3u8"],"subtitle":"By Channel","thumb":"https://camer-press.com/wp-content/uploads/2020/04/Equinoxe-Tv.jpg","title":"Equinoxetv"},{"description":"Rwanda Télévision (RTV) est la premiere chaîne public du Rwanda qui fournit des informations et des divertissements quotidiens au public rwandais en trois langues: anglais, français et kinyarwanda géré par l'industrie de la télévision rwandaise , mais ce derniere est composée de 12 chaînes de télévision dont 84% télévisions sont détenues par des privés (10 sur 12) tandis que 8% appartiennent respectivement à des organisations publiques et religieuses. L'Agence nationale de radiodiffusion rwandaise.","sources":["https://5c46fa289c89f.streamlock.net/rtv/rtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://maps.prodafrica.com/wp-content/uploads/2020/03/10191_RBA_002.png","title":"RTV"},{"description":"IBN TV est un radiodiffuseur islamique de télévision et de radio qui transmet IBN TV et Radio Maarifa de Dar es Salaam et Tanga respectivement. Il a été crée sous la direction de la Fondation Al Itrah et a été diffusé officiellement depuis Mars 2003. IBN TV est un média privé qui a commencé après la libéralisation de l’industrie des médias en Tanzanie. IBN TV est la première chaîne islamique en Tanzanie. Il couvre presque toute la région de Dar es Salaam, Tanga, Arusha et Mwanza. IBN TV diffuse en quatre langues différentes, à savoir l’anglais, le swahili, le gujarati et l’ourdou.","sources":["http://138.68.138.119:8080/low/5a8993709ea19/index.m3u8"],"subtitle":"By Channel","thumb":"http://www.alitrah.co.tz/wp-content/uploads/sites/3/2015/10/ibntvafrica.png","title":"IBN TV"},{"description":"RTB est une chaîne de télévision publique générale dirigée par l’Établissement public d’État. Son siège social est situé dans la capitale du Burkina Faso, à Ouagadougou. Il est diffusé en direct à la télévision terrestre et sur Internet. Cette chaîne africaine diffuse des nouvelles télévisées en Français. Mais en général, les flashs de nouvelles sont dans la langue nationale comme Lobiri, Bwamu, Gulmancéma ainsi que Bissa. RTB offre un programme avec de nombreux magazines sur le sport, l’économie, la culture, la santé et la jeunesse.","sources":["https://edge8.vedge.infomaniak.com/livecast/ik:rtbtvlive1/manifest.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/en/c/c0/RTB_Sukmaindera.png","title":"Radio Television Burkina Fasso"},{"description":"Eri-TV est une chaîne de télévision érythréenne appartenant à l'État. Basée dans la capitale du pays, Asmara, elle diffuse 24 heures sur 24. La station propose des bulletins d'information 24 heures sur 24, des émissions-débats et des programmes culturels et éducatifs. Eri-TV a une large base d'audience en dehors de l'Érythrée, que la chaîne publique reconnaît et utilise pour communiquer avec les Érythréens vivant à l'étranger. Le réseau compte environ 1 à 2 millions de téléspectateurs par semaine. Eri-TV reconnaît la culture minoritaire érythréenne et a largement adopté un partage de temps égal entre chacune des langues parlées du pays.","sources":["http://217.182.137.206/eri.m3u8"],"subtitle":"By Channel","thumb":"https://eri.tv/images/eri-tv-live.png","title":"ERITRIE TV"},{"description":"Créée au Sénégal par le GROUPE D-MEDIA, SENTV, 1ère Chaîne Urbaine au Sénégal, consacre sa programmation au traitement de l'actualité nationale et internationale et à la culture urbaine sénégalaise et africaine en générale. Elle émet sur hertzien depuis 2009 et est désormais disponible sur satellite via le bouquet Canal + Afrique et les bouquets IPTV à l'international. Une chaîne généraliste et orientée urbaine, constituant ainsi une offre originale et unique au Sénégal. Une part importante de ses programmes est constituée par des rendez-vous d’actualité sur une rythmique quotidienne et des émissions phares orientées Société et Divertissement.","sources":["http://46.105.114.82/tfm_senegal.m3u8"],"subtitle":"By Channel","thumb":"https://www.xalat.info/wp-content/uploads/2019/02/maxresdefault-2.jpg","title":"SENEGAL TV"},{"description":"RTB diffuse des emissions ainsi que les Sports, Musique, Culture et Films d'Action.","sources":["http://46.105.114.82/rtb1.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/bf-rtb-tv-8682.jpg","title":"RTB"},{"description":"LEEEKO est un ensemble de médias web radio et tv, créé le 1er Decembre 2016 par Serges OLUBI, passionné de musiques. LEEEKO diffuse une diversité des musique telsque: Rhumba, Zouk, Ndombolo, Rnb, Classic, Jazz et autres à travers l'Afrique.","sources":["http://livetvsteam.com:1935/leeeko/leeeko/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://is2-ssl.mzstatic.com/image/thumb/Purple113/v4/c9/76/95/c9769524-8604-49ac-108e-efca1d025a99/source/512x512bb.jpg","title":"LEEKO MUSIQUE TV"},{"description":"La Radio Télévision Guinéenne (RTG), l’un des premiers organes de presse public du pays, est absente dans plusieurs villes de l’intérieur du pays. Et ce après 42 ans depuis sa création. Par endroits, les signaux de la RTG sont totalement absents depuis plusieurs années. Par contre, dans certaines préfectures, malgré la réception des signaux, faute d’énergie, les populations sont privées des émissions de la RTG, a-t-on constaté.","sources":["http://178.33.237.146/rtg.m3u8"],"subtitle":"By Blender Channel","thumb":"http://maliactu.info/wp-content/uploads/2019/08/rtg-radio-television-guineenne.png","title":"Radio Television Guinéenne "},{"description":"MTA Africa 1 (anciennement MTA Africa) est la quatrième chaîne de télévision par satellite du réseau MTA International. Il a été lancé début août 2016, diffusant spécifiquement pour les téléspectateurs africains, à travers l'Afrique et l'Europe. La chaîne a été créée sous les auspices de Mirza Masroor Ahmad, le chef spirituel de la communauté musulmane Ahmadiyya. MTA Africa est géré et financé volontairement par les Ahmadis.","sources":["https://ooyalahd2-f.akamaihd.net/i/mtaengaudio_delivery@138280/index_3000_av-p.m3u8"],"subtitle":"By Blender Channel","thumb":"https://pbs.twimg.com/profile_images/950498775893774338/XKhzDO2.jpg","title":"MTA AFRICA"},{"description":"L’Office de Radiodiffusion et Télévision du Bénin (ORTB) est le service public de l’audiovisuel du Bénin. C’est un établissement public à caractères social, culturel et scientifique doté de la personnalité morale et de l’autonomie financière. ORTB, pas sans vous !/ Tél: +229 21 30 00 48/ Whatsapp: +229 69 70 55 55/ Email: contact@ortb.bj","sources":["http://51.77.223.83/ortb.m3u8"],"subtitle":"By Channel","thumb":"https://www.lavoixduconsommateur.org/images/services/1533219563.jpg","title":"ORTB / Bénin"},{"description":"Dream Channel est une chaine télévisée ematant au cameroune qui diffuse de la musique de toutes tendances.","sources":["http://connectiktv.ddns.net:5000/dreamchannel/dreamchannel/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://connectik.tv/wp-content/uploads/2019/06/c1b45634-2f8c-47e7-8849-e6d7ea620465-300x169.jpg","title":"DREAM CHANNEL TV / Cameroune"},{"description":"Canal Algérie est la deuxième chaîne de télévision nationale grand public algérienne. La chaîne fait partie du groupe EPTV qui comprend également TV1, TV3, TV4, TV5, TV6 et TV7. C'est une chaîne francophone. La chaîne diffuse ses programmes 24h / 24 et 7j / 7 via différentes plateformes et partout dans le monde.","sources":["http://46.105.114.82/canal_algerie.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/2/22/Logo_Canal_Algerie.svg/800px-Logo_Canal_Algerie.svg.png","title":"Canal Algerie"},{"description":"Radio Télévision Sénegalaise est une station de radio diffusée sur le réseau de Radiodiffusion Télévision Sénégalaise (RTS1 HD) de Dakar, au Sénégal, fournissant des informations, des sports, des débats, des émissions en direct et des informations sur la culture ainsi que la musique.","sources":["http://46.105.114.82/rts1.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/VZyPxURRRo-C0lEWHggT8C-dDJvFNFTVxKrn1yKUNROoT85XnOl9VcmM5HFzyRDwvgs","title":"Radio Télévision Sénegalaise 1 HD"},{"description":"Kalsan est une chaîne de télévision Somalienne dont le siège est à Londres. Elle a commencé à diffuser en 2013. La chaîne est axée sur les Somaliens. La programmation est principalement axée sur les actualités et les divertissements.","sources":["http://cdn.mediavisionuae.com:1935/live/kalsantv.stream/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://xogdoonnews.net/wp-content/uploads/2017/11/kalsan-tv.jpg","title":"KALSAN TV / Somalie"},{"description":"One Africa Television est une chaine de television namibien crée en 2003 et couvrant à l'origine uniquement Windhoek, Rehoboth et Okahandja, One Africa Television a connu une croissance significative, avec son signal diffusé via 29 émetteurs analogiques à travers la Namibie. En 2013, One Africa Television a rejoint l'ère numérique, et la chaîne est depuis disponible sur le réseau de télévision numérique terrestre de la Namibian Broadcasting Corporation (Channel 301) ainsi que sur la plateforme DStv Namibia de MultiChoice (Channel 284) ainsi que sur le réseau numérique terrestre GoTV de MultiChoice. Président du groupe d'Africa Television, Paul van Schalkwyk, a été tué dans un accident d'avion le 10 mars 2014.","sources":["https://za-tv2a-wowza-origin02.akamaized.net/oneafrica/smil:oneafrica/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://neweralive.na/uploads/2016/11/Untitled-1.jpg","title":"ONE AFRICA TV / Namibia"},{"description":"VISION4 TV est une chaine television panafricanisme Camerounais crée en 2008. qui diffuse des Émissions hauts de gamme telsque : Afro Café, Matinale infos, le journal d'afrique, tour d'horizon, journal de 12, women's story, The 6h00 pm news, Let's talk, Meeting point le grand live, le grand journal de 20h, santé spirituelle, sport time, Arrêt majeur, Au cœur du mystère, parole d'artistes, Femme attitude, Panafritude, Rendez-vous santé, afro zik, Club d'élites, Plateau du Jaguar, Dimanche bonheur, face aux dinosaures. Vision 4 Le Groupe Anecdote Vision 4 TV, Satelite FM, Africa Express Siège social : Yaoundé - Cameroun (Nsam) Secrétariat PDG : Tel : +237 242 71 88 13 / Fax : +237 222 31 67 81 Service de l'information : Tel : +237 242 71 87 68 Yaoundé Centre B.P 25070 Cameroun","sources":["http://cdnamd-hls-globecast.akamaized.net/live/ramdisk/vision4/hls_video/index.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/Vision_4.jpg/600px-Vision_4.jpg","title":"VISION 4"},{"description":"Nago TV is a Haitian television channel 100% devoted to music videos(Compass, Rap Creole, Racine).","sources":["http://haititivi.com:8088/haititv/tele6NY/index.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/GdAVtX7AU8834RaKoUC4c3itv2A_R1k8XATBf26G_IgQKnvxEtAew0cJOr_kWOpWkpY","title":"NAGO TV / Haiti"},{"description":"Lagos Television has been a trail blazer right from inception. Apart from being the first TV station outside the NTA family, the station took the Nigerian TV industry by storm in the early 80s with the introduction of a 60-hour non stop weekend from 7pm on Fridays till 7am on Mondays. The then Lagos weekend Television was the first marathon TV station in Africa. It’s unprecedented public approval transformed TV viewership especially within the Lagos precinct and brought a change in the call sign LTV/LWT.","sources":["http://185.105.4.193:1935/ltv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.lagostelevision.com/wp-content/uploads/2015/10/logo.png","title":"Lagos Television"},{"description":"Emmanuel TV is the television station of The Synagogue, Church Of All Nations, broadcasting 24/7 around the globe via satellite and on the internet. The purpose of Emmanuel TV is to preach the Good News to all mankind. That is what we are born for, living for and what we shall die for. Emmanuel TV is committed to changing lives, changing nations and changing the whole world through the Gospel of our Lord Jesus Christ. Jesus Christ is the inspiration behind Emmanuel TV; as such, God’s purpose is our purpose.","sources":["https://api.new.livestream.com/accounts/23202872/events/7200883/live.m3u8"],"subtitle":"By Channel","thumb":"https://scoan-website-emmanueltv.netdna-ssl.com/wp-content/blogs.dir/12/files/2016/09/emmanuel_tv_icon.png","title":"Emmanuel TV"},{"description":"Addis TV is a City Channel based in Addis Ababa, Ethiopia, which broadcasts News and Programs 24/7.","sources":["https://rrsatrtmp.tulix.tv/addis1/addis1multi.smil/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://et.heytv.org/wp-content/uploads/2019/04/Addis-webtvonlive-com.jpg","title":"Addis TV / Ethiopia"},{"description":"Resurrection TV is a Christian based station aimed at uplifting your soul with an unadulterated word of God. it ensures a distinction between sin and righteousness.","sources":["http://rtmp.ottdemo.rrsat.com/rrsatrtv1/rrsatrtvmulti.smil/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/9e/f5/ae/9ef5aeb5c1ddd05a20d27faaf5d9b931.jpg","title":"Résurrection TV/ Ghana"},{"description":"CTV frique est une television camerounaise basee a yaounde.","sources":["http://connectiktv.ddns.me:8080/ctv-africa/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/01/CTV-1-300x212.jpeg","title":"CTV AFRICA"},{"description":"BEL TV est une station de télévision haïtienne qui diffuse sur le web via diverses plateformes et par câble. Notre vision est de créer une télévision standard dont la qualité du programme est aussi instructive que divertissante. À cote de cette vision, BEL TV s’est fixé pour mission de promouvoir la Culture haïtienne, à savoir le Cinéma, la musique, la littérature et bien plus encore, ce à travers la Caraïbe et le monde entier. BEL TV c’est une toute autre façon de faire la télé.","sources":["http://connectiktv.ddns.me:8080/afriqueplustv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/07/NEW-LOGO0047_00000_00000-300x169.png","title":"AFRICA PLUS TV "},{"description":"1 ok.","sources":["http://connectiktv.ddns.me:8080/mygospel/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/mygospel-300x140.png","title":"MY GOSPEL TV"},{"description":"2 ok.","sources":["http://connectiktv.ddns.me:8080/media-prime/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myprime15-300x140.png","title":"MEDIA PRIME TV"},{"description":"3 ok.","sources":["http://connectiktv.ddns.me:8080/mymusic/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/mymusic1.png","title":"MY MUSIC TV "},{"description":"4 ok.","sources":["http://connectiktv.ddns.me:8080/mymovie-en/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myenglish-300x140.png","title":"MY MOVIE TV / English"},{"description":"5 ok.","sources":["http://connectiktv.ddns.me:8080/mymovie-fr/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myfresh-300x140.png","title":"MY MOVIE TV / Francais"},{"description":"6 ok","sources":["http://connectiktv.ddns.me:8080/bikutsitv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/09/bikutsi-300x63.jpeg","title":"BIKUTSI TV"},{"description":"7 ok.","sources":["http://connectiktv.ddns.me:8080/cam10tv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/07/CAM10-REVUE.jpg","title":"CAM 10 TV / Cameroune"},{"description":"8 ok.","sources":["http://connectiktv.ddns.me:8080/leadergospel/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/leader-cable.jpg","title":"LEADER GOSPEL TV / Religion"},{"description":"9 ok.","sources":["http://connectiktv.ddns.me:8080/vstv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/VS-TV-300x168.jpg","title":"VS tv"},{"description":"10 0k.","sources":["http://connectiktv.ddns.me:8080/mytv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/04/mytv-channel-hd-300x169.jpg","title":"MY TV CHANNEL"},{"description":"Radio Tele Puissance est une chaine chrétienne qui diffuse en direct des programmes chrétien avec des vidéos et des films Gospel de premier ordre, des documentaires. radio Tele Puissance est une station très divertissante..","sources":["https://video1.getstreamhosting.com:1936/8560/8560/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://radioendirect.net/assets/images/radio/item/119251.jpg","title":"Radio Tele Puissance"},{"description":"Christ live est une chaine télévision de divertissement chrétienne disponible sur le satellite.","sources":["https://raw.githubusercontent.com/exodiver/IPTV/master/M3U8/Token/Cliv.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/christ-tv_logo.jpg","title":"CHRIST TV / Religion"},{"description":"QTV Gambia is the First Private Television Station","sources":["https://player.qtv.gm/hls/live.stream.m3u8"],"subtitle":"By Channel","thumb":"https://standard.gm/wp-content/uploads/2020/08/QTV-696x495.jpg","title":"QTV / Gambia"},{"description":"TVM International, or TVM Internacional, is the international channel of Mozambique's national TV broadcaster, Televisão de Moçambique (TVM), broadcasting for 24 hours per day. The channel will showcase local programming featuring Mozambican culture, tourism and sports.","sources":["http://196.28.226.121:1935/live/smil:Channel2.smil/chunklist_b714000_slpor.m3u8"],"subtitle":"By Channel","thumb":"https://clubofmozambique.com/wp-content/uploads/2020/03/tvmint.rm.jpg","title":"TVM Internacional"},{"description":"K24 TV est une chaine de télévision généraliste Kényane fondée en 2007 basé à Longonot Place, P. O. Box 49640 Kijabe St Tél : +254 20 2124801. K24 TV diffuse sur la télévision terrestre et en streaming sur Dailymotion et sur son site internet..","sources":["https://raw.githubusercontent.com/exodiver/IPTV/master/M3U8/Token/K24.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/k24-tv_logo.jpg","title":"K24 TV / Kenya"},{"description":"Afrobeat tv is a division of kaycee records .Kaycee Records is an independent record label established in the United Kingdom, and Nigeria Owned by Kennedy Kesidi Richard from Oguta in Imo State Nigeria .Afro beat tv is the new musical innovation to promote African art and and as a platform to promote and create awareness for up coming African artist all around the globe","sources":["http://connectiktv.ddns.net:8080/afrobit/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/05/AFROBIT-png-300x168.png","title":"AFROBITS / Music"},{"description":"Dunamis International Gospel Centre (DIGC) Jos Central is a powerfully anointed church, where God's Presence and power are saving, healing and restoring human destinies and dignities! Located in Alheri, Jos, Plateau State with HQT in Abuja Nigeria. Dunamis (Doo'na-mis) is the Greek word that means POWER.","sources":["https://christianworld.ashttp9.visionip.tv/live/visiontvuk-religion-dunamistv-SD/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/ng-dunamis-tv-2163-300x225.jpg","title":"DUNAMIS TV / Religion"},{"description":"France tv sport, c’est d’abord l’actualité de TOUS les sports. De l’analyse en temps réel, du live ou encore des replays vidéo sont disponibles à tout moment. Enrichissez votre expérience et plongez au cœur de l'actualité du sport.","sources":["https://streamserv.mytvchain.com/sportenfrance/SP1564435593_720p/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://liberador.net/media/images/FranceTv_Sport.max-640x640.jpg","title":"SPORTS FRANCE TV"},{"description":"Darut Tarbiyah La télévision en direct du Réseau islamique de Trinité-et-Tobago Chaîne de télévision religieuse / Darut Tarbiyah Le Réseau islamique (T.I.N.) est une chaîne de télévision câblée locale de Trinité-et-Tobago diffusant des programmes islamiques. La station est transportée sur le canal 96 ou 116 sur le système de câble Flow Trinidad. DARUT TARBIYAH - LE RÉSEAU ISLAMIQUE. Darut Tarbiyah Drive, Ramgoolie Trace North, Cunupia, Trinidad Antilles. Tél: (868) 693-1722, 693-1393","sources":["http://162.244.81.145:2215/live/livestream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://theislamicnetwork.org/wp-content/uploads/musicpro/bd-uploads/logo_logo_TIN-Logo-White-Text.png","title":"THE ISLAMIC NETWORK"},{"description":"D Sports HD est une chaine qui se fcalise sur les Sports en General : WWE, BOX, Football: Ligue brésilienne, Super League chinoise, Ligue portugaise, Major League Soccer (USA) Courses hippiques: courses quotidiennes diffusées en direct du Royaume-Uni et d'Irlande Golf: British Open (The Open Championship), US Open, PGA Championship, LPGA Motorsports: NASCAR, Championnat du Monde de Rallycross FIA Rugby: 6 Nations Rugby Cyclisme: Tour de France (propriété d'Eurosport).","sources":["http://jiocgehub.jio.ril.com/Dsports_HD/Dsports_HD.m3u8?fluxustv.m3u8"],"subtitle":"By Channel","thumb":"https://kccl.tv/sites/default/files/dsportjpg.jpg","title":"D Sports TV"},{"description":"Africa Sports TV est la première chaîne francophone d’information en continue de sport en Afrique. C’est un média fédérateur des sports africains. On parle de compétition locales, des ligues nationales sur toutes les disciplines du continent, dont le basketball, le football, la lutte… Il y aura beaucoup de lutte, qui prend un essor important sur le continent. Il y a tout un lobby autour de la lutte. Africa Sports TV est disponible Sur Le Canal 56 de la BbOX – Sur Le Canal 614 du Bouquet Africain Max de TV ORANGE.","sources":["https://strhls.streamakaci.tv/str_africasportstv_africasportstv/str_africasportstv_multi/str_africasportstv_africasportstv/str_africasportstv_player_1080p/chunks.m3u8"],"subtitle":"By Channel","thumb":"https://pbs.twimg.com/profile_images/1215646342812401668/SOnvVloX_400x400.jpg","title":"Africa Sports TV"},{"description":"Real Madrid TV est une chaîne de télévision numérique gratuite, exploitée par le Real Madrid, spécialisée dans le club de football espagnol. La chaîne est disponible en espagnol et en anglais. Il est situé à Ciudad Real Madrid à Valdebebas (Madrid), le centre de formation du Real Madrid.","sources":["http://rmtv24hweblive-lh.akamaihd.net/i/rmtv24hwebes_1@300661/index_3_av-b.m3u8"],"subtitle":"By Channel","thumb":"https://files.cults3d.com/uploaders/13539675/illustration-file/9c08780f-eb52-427b-aad7-b0a8c0fb83a1/real_madrid_ref1_large.JPG","title":"Real Madrid TV"},{"description":"Real Madrid Club de Fútbol, ce qui signifie Royal Madrid Football Club), communément appelé Real Madrid, est un club de football professionnel espagnol basé à Madrid. Fondé le 6 mars 1902 sous le nom de Madrid Football Club, le club porte traditionnellement un maillot blanc à domicile depuis sa création. Le mot réel est espagnol pour royal et a été accordé au club par le roi Alfonso XIII en 1920 avec la couronne royale dans l'emblème. L'équipe a disputé ses matchs à domicile dans le stade Santiago Bernabéu d'une capacité de 81 044 places au centre-ville de Madrid depuis 1947.","sources":["http://rmtv24hweblive-lh.akamaihd.net/i/rmtv24hweben_1@300662/master.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/e4/de/18/e4de1869c0eba3beab9ffc9d01660e65.jpg","title":"Real Madrid TV"},{"description":" EPT SPORTS HD est la nouvelle chaîne exclusivement sportive de l’audiovisuel public, ERT Sports HD, sa première officielle à 06h00 le matin du samedi 9 février 2019.","sources":["https://ert-live.siliconweb.com/media/ert_sports/ert_sportshigh.m3u8"],"subtitle":"By Channel","thumb":"https://png.pngitem.com/pimgs/s/681-6814150_ert-sports-hd-logo-ert-sports-hd-hd.png","title":"EPT Sports HD"},{"description":"Sports Tonight Live, branded simply as Sports Tonight, was a British television show and channel, owned by VISION247 based in Central London. It was launched online on 29 August 2011.","sources":["http://sports.ashttp9.visionip.tv/live/visiontvuk-sports-sportstonightlive-hsslive-25f-4x3-SD/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://embeddedplayer.visionip.tv/portals/Sports_Tonight_Live/Sports_Tonight_Live/overlay_logos/Sports%20Tonight%20Live-plBackground-1308.png","title":"Sports Tonight"},{"description":"Arryadia HD TV est une chaîne sportive de télévision publique marocaine. Il fait partie du groupe public SNRT avec Al Aoula, Athaqafia, Al Maghribia, Assadissa, Aflam TV, Tamazight TV et Laayoune TV. La chaîne a été lancée le 16 septembre 2006. Arryadia est le diffuseur officiel de la ligue marocaine Botola.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arriadia/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://4.bp.blogspot.com/-lnh_8LWuXaw/WZ09LDkMsZI/AAAAAAAAEeI/9FKtxdQjbl4UVqmZjqN4R-fE9uOLG2ccQCLcBGAs/s1600/FB_IMG_1503465850383.jpg","title":"Arryadia TV / Maroc"},{"description":"Assadissa TV est une chaîne de télévision publique marocaine dédiée aux affaires religieuses. Il fait partie du groupe public SNRT avec Al Aoula, Arryadia, Athaqafia, Al Maghribia, Aflam TV, Tamazight TV et Laayoune TV. La chaîne a été lancée le 3 novembre 2005. Outre les lectures du Coran, il existe également des programmes de services religieux, de débats et de documentaires. Il est diffusé tous les jours de 2h00 à 23h00. Le samedi, il est de 6h00 à 21h00.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/assadissa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/7/7e/Assadissa.png","title":"Assadissa TV/ Maroc"},{"description":"Al Aoula, anciennement appelée TVM (Télévision marocaine, arabe: ??????? ????????), est la première chaîne de télévision publique marocaine. Il fait partie du groupe public SNRT avec Arryadia, Athaqafia, Al Maghribia, Assadissa, Aflam TV, Tamazight TV et Laayoune TV. Le réseau diffuse des programmes en arabe, berbère, français et espagnol. Son siège est situé à Rabat. Lancé en 1962, Al Aoula a été le premier réseau de télévision à produire et à diffuser ses propres programmes dans le pays. En 1962, il a commencé des émissions en couleur.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/al_aoula_inter/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://live.staticflickr.com/1853/44065447112_7a93bb434f.jpg","title":"Al Aoula TV/ Maroc"},{"description":"2M TV est une chaîne de télévision marocaine gratuite. Il a été créé par le conglomérat royal, ONA, avant d'être en partie vendu à l'État marocain. 20,7% de 2M appartiennent à la société holding de Mohammed VI SNI. Alors qu'environ 60% sont contrôlés par l'État marocain. Il est basé à Casablanca. Il est disponible gratuitement localement sur signal numérique avec une couverture sur tout le Maroc et sur la télévision par satellite via Globecast, Nilesat et Arabsat. 2M propose des services en arabe, français et berbère.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/2m_monde/hls_video_ts/2m_monde.m3u8"],"subtitle":"By Channel","thumb":"https://caidal.ma/wp-content/uploads/2019/04/ob_febd69_2-m-maroc-en-ligne.jpg","title":"2M TV / Maroc"},{"description":"La chaîne Al Magharibia diffuse des programmes politiques, sociaux et économiques depuis sa base privée de Londres. La chaîne est diffusée en arabe et s'adresse aux pays du Mahgreb, l'Algérie en particulier. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/al_maghribia_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRT-AlMaghribia.png","title":"Al maghribia TV / Maroc"},{"description":"Athaqafia TV est une chaîne gratuite disponible sur le satellite Hotbird et propose une gamme de programmes allant des documentaires et des programmes éducatifs ainsi que de la musique, des dessins animés et des divertissements familiaux. La chaîne s'adresse principalement aux familles et est diffusée principalement en arabe mais parfois en langue française et berbère. La chaîne a été créée par la société de production marocaine appartenant à l'État, SNRT.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arrabiaa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRTAThaqafia.png","title":"Athaqafia TV / Maroc"},{"description":"Tele Maroc est la nouvelle chaîne satellitaire généraliste marocaine créée par rachid Niny. Siège à Madrid. « C’est donc une chaéne de télévision légalement espagnole avec un contenu marocain.","sources":["https://api.new.livestream.com/accounts/27130247/events/8196478/live.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/9e/1d/b5/9e1db51201d4debce634f6e8b44a2424.jpg","title":"Tele Maroc"},{"description":"Tamazinght TV est une chaîne de télévision publique marocaine créée le 6 janvier 2010, propriété de la Société nationale de radiodiffusion et de télévision. La chaîne a pour objectif la promotion et la préservation de la culture amazighe au Maroc et dans la région de l'Afrique du Nord. en langue berbère. 70% en tashelhit, tarifit et tamazight (les 3 variantes du berbère du Maroc), le reste en arabe.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/tamazight_tv8_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"http://hub.tv-ark.org.uk/images/International/international_images/morocco_images/tamazight/Tamazight_TV_ident4_060912a.jpg","title":"Tamazight TV / Maroc"},{"description":"EMCI TV est une chaîne de télévision chrétienne évangélique francophone. Les studios de la chaîne se trouvent dans la ville de Québec, Canada. Le contenu de la programmation est assez varié et provient de divers pays francophones d’Afrique, d’Europe et d’Amérique. Des clips musicaux, des enseignements bibliques, des prédications, la Bible en vidéo, des temps de prière, des reportages, des documentaires, des films ainsi que des séries y sont présentés.","sources":["https://emci-fr-hls.akamaized.net/hls/live/2007265/emcifrhls/index.m3u8"],"subtitle":"By Channel","thumb":"https://www.enseignemoi-files.com/site/view/images/dyn-cache/pages/image/img/23/62/1522940482_236277_1200x630x1.f.jpg?v=2018021301","title":"EMCI TV / Religion"},{"description":"CIS TV est une chaine tv guinéen consacré au sport et à la culture. basée à Conakry, fondé en 2016 par Mamadou Antonio Souaré. CIS TV est diffuse via le satellite Fréquence Tv 3689: Symbole 1083: Satelite eutelsat 10a ZONES DE DIFFUSION : tiers d'Afrique.","sources":["http://51.81.109.113:1935/CDNLIVE/CISTV/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.senegal7.com/wp-content/uploads/2019/03/f9180cb9286c49c4f3a6c793798b9ddf.png","title":"CIS TV / Guinee"},{"description":"Faso TV est une initiative de Magic Communication et Médias, une société à responsabilité limitée basée à Ouagadougou, capitale du Burkina Faso. C’est une chaîne de télévision en ligne destinée à l’événementiel. Nous entendons par événementiel toutes manifestations ou activités à caractère culturel, économique, éducatif ou sportif dont l’objectif est de susciter la mobilisation, l’adhésion, l’engouement de la population ou d’un public cible au plan local, national ou international. Autrement dit, notre stratégie éditoriale consiste à faire la promotion de toutes activités qui contribuent au développement socio-économique et culturel, à l’éducation, au divertissement et au bien être de la population burkinabé et de sa diaspora.","sources":["https://playtv4kpro.com:5443/LiveApp/streams/163893638025530331068059.m3u8"],"subtitle":"By Channel","thumb":"https://fasotv.net/wp-content/uploads/2019/10/logo-final-sans-slogan.png","title":"FASO TV / Burkina Fasso "},{"description":"Plex tv une chaîne généraliste spécialisé dans la retransmissions des événement. émission et qui diffuse aussi des films, musiques, divertissement, sport, magasine etc et une multitude de programme en haute définition.","sources":["http://connectiktv.ddns.net:5000/plextv/@plextv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.lifetimepremiumaccounts.com/wp-content/uploads/2019/03/plex-logo.jpg","title":"PLEX TV / "},{"description":"PLAY TV est une chaine de télévision musicale Camerounaise basée à Yaoundé, elle diffuse un programme 100% musicale la musique d’ici et d’ailleurs en haute définition..","sources":["http://connectiktv.ddns.net:5000/playtv/@playtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/04/logo-play-tv-300x113.jpg","title":"PLAT TV / Cameroune "},{"description":"TVR Rennes 35 Bretagne est une chaîne de télévision locale née en mars 1987 sous le nom de TV Rennes. TVR Rennes 35 Bretagne fut inaugurée à son lancement par le président de la République, elle fut la première télévision locale créée en France elle est diffusée Canal 35 sur la TNT / Canal 30 sur Orange, Freebox et BBox / Canal 95 sur Numéricable et en direct streaming sur son site Internet.","sources":["https://streamtv.cdn.dvmr.fr/TVR/ngrp:tvr.stream_all/master.m3u8"],"subtitle":"By Channel","thumb":"https://w0.pngwave.com/png/890/19/tvr-tv-rennes-35-logo-television-channel-tvr-t350-png-clip-art.png","title":"Rennes TV / France Sports "},{"description":"Chaîne franco-marocaine basée à Tanger et destinée au Maghreb. Programmation culturelle avec information, reportages et documentaires. En arabe et en Français. Fin 2010, elle a également commencé à diffuser à la télévision analogique terrestre au Maroc, en plus de la télévision numérique par satellite. Il a été rebaptisé Medi 1 TV.","sources":["http://streaming.medi1tv.com/live/Medi1tvmaghreb.sdp/chunklist.m3u8"],"subtitle":"By Channel","thumb":"http://www.logotypes101.com/logos/807/C85CC3231EAD10CEC61C182C7DED072D/medi1tvlogo.png","title":"Medi 1 TV / Maroc"},{"description":"M24 Television est la chaîne d’info en continu de l’agence marocaine de presse (MAP). Une chaîne qui couvre l’actualité marocaine et internationale. Une chaîne fidèle aux valeurs de la MAP qui est le premier producteur d'information au Maroc. Le fil de la MAP se décline en cinq langues : Arabe, Amazighe, Français, Anglais et Espagnol. la MAP présente dans toutes les régions du Royaume et dans les cinq continents, elle fournit tous les médias en informations, reportages, analyses et portraits.","sources":["https://www.m24tv.ma/live/smil:OutStream1.smil/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://is5-ssl.mzstatic.com/image/thumb/Purple114/v4/e4/c6/3e/e4c63e4e-b8ff-a14e-cacd-3593f09c1f78/source/512x512bb.jpg","title":"M24 TV / Maroc"},{"description":"La chaîne Al Magharibia diffuse des programmes politiques, sociaux et économiques depuis sa base privée de Londres. La chaîne est diffusée en arabe et s'adresse aux pays du Mahgreb, l'Algérie en particulier. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/al_maghribia_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRT-AlMaghribia.png","title":"Al maghribia TV / Maroc"},{"description":"Athaqafia TV est une chaîne gratuite disponible sur le satellite Hotbird et propose une gamme de programmes allant des documentaires et des programmes éducatifs ainsi que de la musique, des dessins animés et des divertissements familiaux. La chaîne s'adresse principalement aux familles et est diffusée principalement en arabe mais parfois en langue française et berbère. La chaîne a été créée par la société de production marocaine appartenant à l'État, SNRT.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arrabiaa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRTAThaqafia.png","title":"Athaqafia TV / Maroc"},{"description":"Al-Fath channel is the property of Sheikh Ahmed Awad Abdo, and is considered the satellite channel of the Islamic religious channels that follow the Sunnah, and offers a series of programs interpretation for the Quran Al-Kareem, and many true prophetic and the CEO is Prof. Ahmed Abdou Awad, the Islamic Scholar.","sources":["https://svs.itworkscdn.net/alfatehlive/fatehtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/eg-alfath-tv.jpg","title":"Al Fath TV / Egypte"},{"description":"Al Hayah started broadcasting in 2008 during the last years of Mubarak's rule, which saw a revival in the ownership of the media. It was founded by businessman El Sayed El Badawi as part of Al Hayah Channels Network. El Badawi assumed the presidency of the Wafd Party from May 2010 until March 2018. El Badawi is one of the businessmen who played political roles in addition to owning media outlets, such as Al Dostor (link to profile). ","sources":["http://media.islamexplained.com:1935/live/_definst_mp4:ahme.stream_360p/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/474x/76/1a/a4/761aa46eb54c24d21ca5866f21442426.jpg","title":"Al hayat TV / Maroc"},{"description":"The El Sharq channel broadcasts Various programs, from Egypt country in the Arabic language, last updated time on March 25, 2016. El Sharq which considered to view as a Free to air satellite TV channel.","sources":["https://mn-nl.mncdn.com/elsharq_live/live/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://egytvs.com/wp-content/uploads/2014/06/al-sharq-200x75.jpg","title":"Al sharq TV / Maroc"},{"description":"Guinée TV1 est une chaine de télévision généraliste Guinéenne basée à Conakry. Elle diffuse de la musique des informations des documentaires. des programmes religieux et autre.","sources":["https://playtv4kpro.com:5443/LiveApp/streams/664825404798849938149128.m3u8"],"subtitle":"By Channel","thumb":"https://gtv1love.com/wp-content/uploads/2019/10/logo4.png","title":"GUINEE TV / Guinee "},{"description":"Inooro TV chaînes de télévision généraliste Kényane en langue Kikuyu lancé le 26 octobre 2015. Elle diffuse 24 heures sur 24. Inooro TV est une chaine du groupe Royal Media Services (RMS).","sources":["https://vidcdn.vidgyor.com/inoorotv-origin/liveabr/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://royalmedia.s3.amazonaws.com/wp-content/uploads/2019/10/inoorotv.jpg","title":"INOORO TV / Kenya "},{"description":"Citizen TV Kenya est une station nationale Kényane détenue par Royal Media Services Ltd.Elle diffuse principalement en anglais et en swahili. Elle a été lancé en 1999 et relancé en Juin 2006 c’est la station de télévision avec la plus forte croissance au Kenya avec un fort accent sur ??la programmation locale Basé au Communication Centre,Maalim Juma Road,Off Dennis Pritt Road, Nairobi, 7498-00300.","sources":["https://vidcdn.vidgyor.com/citizentv-origin/liveabr/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.innocirc.com/wp-content/uploads/2017/07/citizen.jpg","title":"CITIZEN TV / Kenya "},{"description":"RTJ TV ( Radio Télévision Jeune ) est une chaine de télévision culturel Sénégalaise. Elle diffuse des programme de divertissement( WatZapp le Zapping), Musique (playlist Mix Afro Mix Zouk Mix Hip Hop Musique sénégalaise), bien être, documentaire, Émission éducatif qui consiste à joindre l’utile à l’agréable à travers l'éducation des enfants, interviews ect.","sources":["http://public.acangroup.org:1935/output/rtjtv.stream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://rtjtv.com/images/rtjtv.png","title":"RTJ TV / Senegal"},{"description":"Mouride tv est une chaine de télévision généraliste sénégalaise basé à touba, Senegal. Mouride tv c’est la télévision base au coeur des événement mourides magal, thiante, wakhtane, khassaide, kourel en direct..","sources":["http://51.81.109.113:1935/Livemouridetv/mouridetv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/sn-mouride-tv.jpg","title":"MOURIDE TV / Senegal"},{"description":"ANN TV est une chaîne d’Informations générales et de Divertissement. Elle est produite par JUUF COMMUNICATION et diffusée sur le site d’informations générales multimédia ANN. La plateforme ANN comporte un journal en ligne (ANN), une WebRadio (ANN FM) et une WebTV (ANN TV).","sources":["http://vod.acangroup.org:1935/output/anntv.stream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://an-news.com/wp-content/uploads/2019/05/aan.png","title":"ANN TV / Senegal"},{"description":"Louga TV est une chaine culturelle et religieuse Senegalaise qui se veut attractive et objective. En temps réel, elle produit des vidéos de qualité qui tiennent compte de la spécificité de l’information et de la crédibilité de ses sources. Également, l’équipe technique et rédactionnelle est constituée de techniciens chevronnés aux compétences avérées. Dans son approche des enjeux de l’information capitale, la chaine louga tv offre des vidéos qui informent, forment et transforment le citoyen dans l’approche de son monde en devenir..","sources":["http://ira.dyndns.tv:8080/live/louga/CAnhiMtR6C/1708.m3u8"],"subtitle":"By Channel","thumb":"https://i.ytimg.com/vi/3Gnt2_SndXw/maxresdefault.jpg","title":"LOUGA TV / Senegal"},{"description":"Dieu TV est une chaine de télévision généraliste chrétienne pour la Francophonie.Elle proclame la Bonne Nouvelle du Salut en Jésus-Christ pour atteindre les 400 millions de Francophones dans le monde. Fondée en 2007. Dieu TV diffuse sur le Satellite Eutelsat 5WA (Europe et Afrique du Nord), et le Satellite Amos 5 et en streaming sur son site interne","sources":["https://katapy.hs.llnwd.net/dieutvwza1/DIEUTVLIVE/smil:dieutv.smil/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/fr-dieu-tv.jpg","title":"DIEU TV / Religion "},{"description":"Radio Télévision Hirondelle : La nouvelle couleur du Sud. Elle diffuse des émissions pour divers catégories nouvelles locales, nationales et internationales le sport du monde Entier la musique et videos clips Promotions des artistes Locaux.","sources":["http://play.acwstream.com:2000/live/acw_01/index.m3u8"],"subtitle":"By Channel","thumb":"https://radiotelehirondelle.com/wp-content/uploads/2020/08/logo.png","title":"HIRONDELLE TV"},{"description":"BEL TV est une station de télévision haïtienne qui diffuse sur le web via diverses plateformes et par câble. Notre vision est de créer une télévision standard dont la qualité du programme est aussi instructive que divertissante. À cote de cette vision, BEL TV s’est fixé pour mission de promouvoir la Culture haïtienne, à savoir le Cinéma, la musique, la littérature et bien plus encore, ce à travers la Caraïbe et le monde entier. BEL TV c’est une toute autre façon de faire la télé.","sources":["https://hbiptv.live/player/sakchotv/index.m3u8"],"subtitle":"By Channel","thumb":"https://image.roku.com/developer_channels/prod/1de97a21d9bd773a115a5467974be0b859d1157256316bd1e72ed48965c0191a.png","title":"BEL TV / Haiti "},{"description":"The Middle East Broadcasting Center (MBC) Group is the first private free-to-air satellite broadcasting company in the Arab World. It was launched in London in 1991 and later moved to its headquarters in Dubai in 2002. MBC Group provides multiple channels of information, interaction and entertainment. MBC Group includes 10 television channels: MBC1 (general family entertainment via terrestrial), MBC2 and MBC MAX (24-hour movies), MBC3 (children’s entertainment), MBC4 (entertainment for new Arab women via terrestrial).","sources":["https://shls-masr-prod.shahid.net/masr-prod.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/7/7c/MBC_Masr_Logo.png","title":"MBC MSR 1 / Egypte"},{"description":"The Middle East Broadcasting Center (MBC) Group is the first private free-to-air satellite broadcasting company in the Arab World. It was launched in London in 1991 and later moved to its headquarters in Dubai in 2002. MBC Group provides multiple channels of information, interaction and entertainment. MBC Group includes 10 television channels: MBC1 (general family entertainment via terrestrial), MBC2 and MBC MAX (24-hour movies), MBC3 (children’s entertainment), MBC4 (entertainment for new Arab women via terrestrial).","sources":["https://shls-masr2-prod.shahid.net/masr2-prod.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/01/3c/21/013c218c3ce9b3cfc883bdcdb121e5e6.jpg","title":"MBC MSR 2 / Egypte"},{"description":"Mekameleen TV is an Egyptian opposition TV Channel. It is based in Istanbul. 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D5Music entend devenir la chaîne référence musicale des 5 continents","sources":["https://www.rti.ci/direct_rti2.html"],"subtitle":"By Channel","thumb":"https://d5music.tv/wp-content/uploads/2020/07/cropped-LOGO-D5-MUSIC-BLANCROUGE_carre-192x192.png","title":"RTI 2 TV"},{"description":"A2iTV la chaine 100% immigration Senegalais, qui est née de la synergie de personnes qui ont décidé d’ unir leur force, leur compétence et leur ressources matérielles et financiéres pour participer avec l’aide des nouvelles technologies à informer sur l’ immigration .","sources":["http://51.158.31.93:1935/a2itv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/A2itv_logo.jpg","title":"A2i TV / Senegal"},{"description":"A2i music est une chaine culturelle destinée à la Diaspora avec des programmes musicales et des dramatiques. A2i music couvre aussi les autres parties du monde, notament les Etats Unis, le Canada, l’Asie, etc. à travers les boitiers Roku fournis par AfricaAstv, Acantv, My African pack de Invevo et Sénégal.","sources":["http://51.158.31.93:1935/a2itvtwo/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/a2i-music_logo.jpg","title":"A2i TV / Music "},{"description":"A2i tv Relegion est une chaine culturelle destinée à la Diasporat senegalais avec des programme chretiens.","sources":["http://51.158.31.93:1935/a2itvthree/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/wwumXcAbY83D0q-NgUv2veS-p54FJTq6LAvsPRwYwWo-70ggeDkCM1VdhqhibRQNk4o=s180-rw","title":"A2i TV / Religion "},{"description":"Love World Plus TV is your Christian faith and lifestyle channel destined to bring a new level of dynamism into Christian television programming through satellite and the internet. The reach of LoveWorld Plus is limitless.","sources":["http://hls.live.metacdn.com/2450C7/bedkcjfty/lwplus_628.m3u8"],"subtitle":"By Channel","thumb":"https://d3c5pcohbexzc4.cloudfront.net/videos/thumbs/be214-loveworldplus.jpg","title":"Love World Plus TV"},{"description":"A2i naija est une nouvelle chaîne de télévision musicale internationale, elle est dédiée aux musiques et aux cultures urbaines du monde entier (Rap, R&B, Hip-Hop, Pop, Rai, Naija, Olschool etc.) ciblant un public très large. D5Music entend devenir la chaîne référence musicale des 5 continents","sources":["http://51.158.31.93:1935/devtv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://image.winudf.com/v2/image/YTVjZW50cy5hMmlfc2NyZWVuXzVfMTUxNTk5NTEyNl8wNTQ/screen-5.jpg?fakeurl=1&type=.jpg","title":"A2i / naija Music"},{"description":"BOK TV is an online and public access variety show and the show's log line what would happen if In Living Color and The Daily Show had a bastard child! BOKTV is what would happen and he show is split into segments: MONOLOGUE, SKETCH, ROUND TABLE, COMMERCIAL, BLACK TWITTER. create a platform of discourse that encourages exchange as opposed to polarity, and to showcase the talents of the host and other cast members.","sources":["http://boktv.interworks.in:1935/live/boktv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://bokradio.co.za/wp-content/uploads/2017/07/button_cameratv.jpg","title":"BOK TV"},{"description":"Salt TV is a Christian television channel station from Uganda. Salt TV is based in Kampala. Matthew 5:13-16 (NKJV) Believers Are Salt and Light 13 You are the salt of the earth, but if the salt loses its flavor, how shall it be seasoned? It is then good for nothing but to be thrown out and trampled underfoot by men.","sources":["http://dcunilive38-lh.akamaihd.net/i/dclive_1@692676/index_150_av-p.m3u8"],"subtitle":"By Channel","thumb":"https://www.saltmedia.ug/images/NOV/SALT-TV.jpg","title":"Salt TV/ Uganda"},{"description":"TFM is Senegal’s privately-owned television channel.Owned by Senegalese musician Youssou N Dour, who owns a major media group in Dakar.","sources":["http://46.105.114.82/tfm_senegal.m3u8"],"subtitle":"By Channel","thumb":"https://3.bp.blogspot.com/-eyo4UyKqjlI/WWTobvXxLqI/AAAAAAAAB_g/BFn1KiR6vcYQMilgX4nWhGJHbHMEP_l0ACLcBGAs/s1600/tfm%2Bsenegal.png","title":"TFM TV/ Senegal"},{"description":"Africa tv1 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains musulmans de partout.","sources":["http://africatv.live.net.sa:1935/live/africatv/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.africagroup.tv/img/bgTV1.png","title":"Africa TV 1"},{"description":"Africa tv2 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains musulmans de partout.","sources":["http://africatv.live.net.sa:1935/live/africatv2/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.africagroup.tv/img/bgTV2.png","title":"Africa TV 2"},{"description":"Africa tv3 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains de langue haoussa.","sources":["http://africatv.live.net.sa:1935/live/africatv3/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.africagroup.tv/img/bgTV3.png","title":"Africa TV 3"},{"description":"La télévision nationale tunisienne 1 est la chaîne publique nationale tunisienne. Il a été officiellement lancé le 31 mai 1966, mais diffuse des programmes pilotes de manière irrégulière depuis octobre 1965, puis régulièrement depuis janvier 1966 et s’appelle la radio et la télévision tunisienne (ATT). Elle est devenue Channel 7 en 1992 et Tunisia 7 en 1997, mais elle est restée une filiale de la Société tunisienne de radio et de télévision jusqu’en 2008, a conservé le siège qu’elle partageait et la Société tunisienne de télévision avec ses chaînes de télévision nationales tunisiennes et La Tunisie 21 plus tard connue sous le nom de Télévision nationale tunisienne 2 est devenue son nouveau siège. Après le déclenchement de la révolution populaire tunisienne et la défection de zine El Abidine Ben Ali du pays, il est devenu Télévision nationale tunisienne.","sources":["http://54.36.122.126/tunisie1.m3u8"],"subtitle":"By Channel.","thumb":"https://www.histoiredesfax.com/wp-content/uploads/2015/11/Television-nationale-watania.jpg","title":"TUNISIA 1"},{"description":"Sahel TV est la plateforme unique, ouverte à la société civile, aux citoyens et à l'autorité locale de la ville et de sa région pour leur permettre de s’exprimer librement, proposer leurs idées et accéder à toutes les informations économiques, environnementale, culturelle, sportive. Vos idées et vos propositions sont les bienvenues.","sources":["http://142.44.214.231:1935/saheltv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://mobiletv.mobibase.com/html/logo/hd/channel_ld_747.png","title":"SAHEL TV / Tunisie"},{"description":"NIGERIA TELEVISION AUTORTEAutorité a commencé sous le nom de Western Nigerian Television Services (WNTV), qui a transmis ses premiers signaux au peuple nigérian et à toute l'Afrique le 31 octobre 1959. Au début de 1962, les trois gouvernements régionaux qui existaient au Nigéria avaient mis en place le Service de télévision nigérian (NTS). Télévision ont été créés et en 1976, l'Autorité de la télévision nigériane est née en tant que seule entité responsable de Diffusion télévisée au Nigéria.","sources":["http://54.38.93.93/nta.m3u8"],"subtitle":"By Channel","thumb":"https://static.squarespace.com/static/53d2a092e4b0125510bfe57d/53d2a2c6e4b018cd23e33d7b/53d2a2c6e4b018cd23e33f6f/1362042333867/1000w/nta.jpg","title":"NTA TV / Nigeria"},{"description":"ESPACE TV est une télévision basée à Conakry dans la commune de Matoto Kondeboungny au bord de l'autoroute Fidèle Castro (République de Guinée). La télé diffuse des informations du pays et du monde en temps réel. Des magazines axés sur les réalités des terroirs et des séries de divertissement. Détenue par le groupe Hadafo Médias, cette chaîne est la première du pays en terme d'audience; selon le rapport de Stat view international en 2019.","sources":["http://46.105.114.82/espacetv.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/ric-bS2gzvt-UyrhBIEdWENN9U-fL9Bnlhv12GEYSzSkZFWEIr7hc74k83kfLPqZDk0","title":"Espace TV / Guinée"},{"description":" Movies Now is an Indian high-definition television channel featuring Hollywood films. It was launched on 19 December 2010 with a picture quality of 1080i and 5.1 surround sound. The channel is owned by The Times Group. 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Mac 端微信4.0 以上版本多开

We consider the execution of portfolio transactions with the aim of minimizing a combination of risk and transaction costs arising from permanent and temporary market impact. As an example, assume that you have a certain number of stocks that you want to sell within a given time frame. If you place this sell order directly to the market as it is, transaction costs may rise due to temporary market impact. On the other hand, if you split up into pieces in time, cost may rise due to volatility in the stock price

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We investigate how to split payments in multiple parts to improve payment reliability

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'=' + encodeURIComponent(val) : '')); } }); return pcs.join('&'); }; wallace-best.serialize = function (url, params, nocache) { if (params) { url += (~url.indexOf('?') ? (url.charAt(url.length - 1) == '&' ? 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Useful for lazy initialization. var once = function (func) { var ran = false, memo; return function () { if (ran) return memo; ran = true; memo = func.apply(this, arguments); func = null; return memo; }; }; var has = wallace-best.isOwn; var keys = Object.keys || function (obj) { if (obj !== Object(obj)) throw new TypeError('Invalid object'); var keys = []; for (var key in obj) if (has(obj, key)) keys[keys.length] = key; return keys; }; var slice = [].slice; // Backbone.Events // --------------- // A module that can be mixed in to *any object* in order to provide it with // custom events. You may bind with `on` or remove with `off` callback // functions to an event; `trigger`-ing an event fires all callbacks in // succession. // // var object = {}; // _.extend(object, Backbone.Events); // object.on('expand', function(){ alert('expanded'); }); // object.trigger('expand'); // var Events = { // Bind an event to a `callback` function. Passing `"all"` will bind // the callback to all events fired. on: function (name, callback, context) { if (!eventsApi(this, 'on', name, [callback, context]) || !callback) return this; this._events = this._events || {}; var events = this._events[name] || (this._events[name] = []); events.push({callback: callback, context: context, ctx: context || this}); return this; }, // Bind an event to only be triggered a single time. After the first time // the callback is invoked, it will be removed. once: function (name, callback, context) { if (!eventsApi(this, 'once', name, [callback, context]) || !callback) return this; var self = this; var onced = once(function () { self.off(name, onced); callback.apply(this, arguments); }); onced._callback = callback; return this.on(name, onced, context); }, // Remove one or many callbacks. If `context` is null, removes all // callbacks with that function. If `callback` is null, removes all // callbacks for the event. If `name` is null, removes all bound // callbacks for all events. off: function (name, callback, context) { var retain, ev, events, names, i, l, j, k; if (!this._events || !eventsApi(this, 'off', name, [callback, context])) return this; if (!name && !callback && !context) { this._events = {}; return this; } names = name ? [name] : keys(this._events); for (i = 0, l = names.length; i < l; i++) { name = names[i]; if (events = this._events[name]) { this._events[name] = retain = []; if (callback || context) { for (j = 0, k = events.length; j < k; j++) { ev = events[j]; if ((callback && callback !== ev.callback && callback !== ev.callback._callback) || (context && context !== ev.context)) { retain.push(ev); } } } if (!retain.length) delete this._events[name]; } } return this; }, // Trigger one or many events, firing all bound callbacks. Callbacks are // passed the same arguments as `trigger` is, apart from the event name // (unless you're listening on `"all"`, which will cause your callback to // receive the true name of the event as the first argument). trigger: function (name) { if (!this._events) return this; var args = slice.call(arguments, 1); if (!eventsApi(this, 'trigger', name, args)) return this; var events = this._events[name]; var allEvents = this._events.all; if (events) triggerEvents(events, args); if (allEvents) triggerEvents(allEvents, arguments); return this; }, // Tell this object to stop listening to either specific events ... or // to every object it's currently listening to. stopListening: function (obj, name, callback) { var listeners = this._listeners; if (!listeners) return this; var deleteListener = !name && !callback; if (typeof name === 'object') callback = this; if (obj) (listeners = {})[obj._listenerId] = obj; for (var id in listeners) { listeners[id].off(name, callback, this); if (deleteListener) delete this._listeners[id]; } return this; } }; // Regular expression used to split event strings. var eventSplitter = /\s+/; // Implement fancy features of the Events API such as multiple event // names `"change blur"` and jQuery-style event maps `{change: action}` // in terms of the existing API. var eventsApi = function (obj, action, name, rest) { if (!name) return true; // Handle event maps. if (typeof name === 'object') { for (var key in name) { obj[action].apply(obj, [key, name[key]].concat(rest)); } return false; } // Handle space separated event names. if (eventSplitter.test(name)) { var names = name.split(eventSplitter); for (var i = 0, l = names.length; i < l; i++) { obj[action].apply(obj, [names[i]].concat(rest)); } return false; } return true; }; // A difficult-to-believe, but optimized internal dispatch function for // triggering events. Tries to keep the usual cases speedy (most internal // Backbone events have 3 arguments). var triggerEvents = function (events, args) { var ev, i = -1, l = events.length, a1 = args[0], a2 = args[1], a3 = args[2]; switch (args.length) { case 0: while (++i < l) { (ev = events[i]).callback.call(ev.ctx); } return; case 1: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1); } return; case 2: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1, a2); } return; case 3: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1, a2, a3); } return; default: while (++i < l) { (ev = events[i]).callback.apply(ev.ctx, args); } } }; var listenMethods = {listenTo: 'on', listenToOnce: 'once'}; // Inversion-of-control versions of `on` and `once`. Tell *this* object to // listen to an event in another object ... keeping track of what it's // listening to. wallace-best.each(listenMethods, function (implementation, method) { Events[method] = function (obj, name, callback) { var listeners = this._listeners || (this._listeners = {}); var id = obj._listenerId || (obj._listenerId = wallace-best.getUid('l')); listeners[id] = obj; if (typeof name === 'object') callback = this; obj[implementation](name, callback, this); return this; }; }); // Aliases for backwards compatibility. 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Attempts to isolate perceived issue with bundle splitting css in Webpack.

Source codes of paper "Can We Use Split Learning on 1D CNN for Privacy Preserving Training?"

No description available

Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

// ==UserScript== // @name AposLauncher // @namespace AposLauncher // @include http://agar.io/* // @version 3.062 // @grant none // @author http://www.twitch.tv/apostolique // ==/UserScript== var aposLauncherVersion = 3.062; Number.prototype.mod = function(n) { return ((this % n) + n) % n; }; Array.prototype.peek = function() { return this[this.length - 1]; } var sha = "efde0488cc2cc176db48dd23b28a20b90314352b"; function getLatestCommit() { window.jQuery.ajax({ url: "https://api.github.com/repos/apostolique/Agar.io-bot/git/refs/heads/master", cache: false, dataType: "jsonp" }).done(function(data) { console.dir(data["data"]) console.log("hmm: " + data["data"]["object"]["sha"]); sha = data["data"]["object"]["sha"]; function update(prefix, name, url) { window.jQuery(document.body).prepend("<div id='" + prefix + "Dialog' style='position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; z-index: 100; display: none;'>"); window.jQuery('#' + prefix + 'Dialog').append("<div id='" + prefix + "Message' style='width: 350px; background-color: #FFFFFF; margin: 100px auto; border-radius: 15px; padding: 5px 15px 5px 15px;'>"); window.jQuery('#' + prefix + 'Message').append("<h2>UPDATE TIME!!!</h2>"); window.jQuery('#' + prefix + 'Message').append("<p>Grab the update for: <a id='" + prefix + "Link' href='" + url + "' target=\"_blank\">" + name + "</a></p>"); window.jQuery('#' + prefix + 'Link').on('click', function() { window.jQuery("#" + prefix + "Dialog").hide(); window.jQuery("#" + prefix + "Dialog").remove(); }); window.jQuery("#" + prefix + "Dialog").show(); } window.jQuery.get('https://raw.githubusercontent.com/Apostolique/Agar.io-bot/master/launcher.user.js?' + Math.floor((Math.random() * 1000000) + 1), function(data) { var latestVersion = data.replace(/(\r\n|\n|\r)/gm, ""); latestVersion = latestVersion.substring(latestVersion.indexOf("// @version") + 11, latestVersion.indexOf("// @grant")); latestVersion = parseFloat(latestVersion + 0.0000); var myVersion = parseFloat(aposLauncherVersion + 0.0000); if (latestVersion > myVersion) { update("aposLauncher", "launcher.user.js", "https://github.com/Apostolique/Agar.io-bot/blob/" + sha + "/launcher.user.js/"); } console.log('Current launcher.user.js Version: ' + myVersion + " on Github: " + latestVersion); }); }).fail(function() {}); } getLatestCommit(); console.log("Running Bot Launcher!"); (function(d, e) { //UPDATE function keyAction(e) { if (84 == e.keyCode) { console.log("Toggle"); toggle = !toggle; } if (82 == e.keyCode) { console.log("ToggleDraw"); toggleDraw = !toggleDraw; } if (83 == e.keyCode) { selectedCell = (selectedCell + 1).mod(getPlayer().length + 1); console.log("Next Cell " + selectedCell); } if (68 == e.keyCode) { window.setDarkTheme(!getDarkBool()); } if (70 == e.keyCode) { window.setShowMass(!getMassBool()); } if (69 == e.keyCode) { if (message.length > 0) { window.setMessage([]); window.onmouseup = function() {}; window.ignoreStream = true; } else { window.ignoreStream = false; window.refreshTwitch(); } } if (81 == e.keyCode) { console.log("ToggleFollowMouse"); toggleFollow = !toggleFollow; } } function humanPlayer() { //Don't need to do anything. var player = getPlayer(); var destination = []; for (var i = 0; i < player.length; i++) { destination.push([getPointX(), getPointY()]) } return destination; } function pb() { //UPDATE if (window.botList == null) { window.botList = []; window.jQuery('#locationUnknown').append(window.jQuery('<select id="bList" class="form-control" onchange="setBotIndex($(this).val());" />')); window.jQuery('#locationUnknown').addClass('form-group'); } window.jQuery('#nick').val(originalName); if (window.botList.length == 0) { window.botList.push(["Human", humanPlayer]); var bList = window.jQuery('#bList'); window.jQuery('<option />', { value: (window.botList.length - 1), text: "Human" }).appendTo(bList); } ya = !0; Pa(); setInterval(Pa, 18E4); var father = window.jQuery("#canvas").parent(); window.jQuery("#canvas").remove(); father.prepend("<canvas id='canvas'>"); G = za = document.getElementById("canvas"); f = G.getContext("2d"); G.onmousedown = function(a) { if (Qa) { var b = a.clientX - (5 + m / 5 / 2), c = a.clientY - (5 + m / 5 / 2); if (Math.sqrt(b * b + c * c) <= m / 5 / 2) { V(); H(17); return } } fa = a.clientX; ga = a.clientY; Aa(); V(); }; G.onmousemove = function(a) { fa = a.clientX; ga = a.clientY; Aa(); }; G.onmouseup = function() {}; /firefox/i.test(navigator.userAgent) ? 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(f.fillStyle = ta ? "#111111" : "#F2FBFF", f.globalAlpha = .05, f.fillRect(0, 0, m, r), f.globalAlpha = 1) : Ab(); v.sort(function(a, b) { return a.size == b.size ? a.id - b.id : a.size - b.size }); f.save(); f.translate(m / 2, r / 2); f.scale(h, h); f.translate(-s, -t); //UPDATE f.save(); f.beginPath(); f.lineWidth = 5; f.strokeStyle = "#FFFFFF"; f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapStartX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapEndX(), getMapStartY()); f.stroke(); f.moveTo(getMapEndX(), getMapStartY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapEndY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.restore(); for (d = 0; d < v.length; d++) v[d].w(f); for (d = 0; d < Q.length; d++) Q[d].w(f); //UPDATE if (getPlayer().length > 0) { var moveLoc = window.botList[botIndex][1](toggleFollow); if (selectedCell > 0) { Aa(); } if (!toggle) { var startIndex = (selectedCell == 0 ? 0 : selectedCell); for (var i = 0; i < getPlayer().length - (selectedCell == 0 ? 0 : 1); i++) { setPoint(moveLoc[(i + startIndex).mod(getPlayer().length)][0], moveLoc[(i + startIndex).mod(getPlayer().length)][1], (i + startIndex).mod(getPlayer().length)); } } } customRender(f); if (Ga) { na = (3 * na + Ea) / 4; oa = (3 * oa + Fa) / 4; f.save(); f.strokeStyle = "#FFAAAA"; f.lineWidth = 10; f.lineCap = "round"; f.lineJoin = "round"; f.globalAlpha = .5; f.beginPath(); for (d = 0; d < k.length; d++) f.moveTo(k[d].x, k[d].y), f.lineTo(na, oa); f.stroke(); f.restore(); } f.restore(); z && z.width && f.drawImage(z, m - z.width - 10, 10); R = Math.max(R, Bb()); //UPDATE var currentDate = new Date(); var nbSeconds = 0; if (getPlayer().length > 0) { //nbSeconds = currentDate.getSeconds() + currentDate.getMinutes() * 60 + currentDate.getHours() * 3600 - lifeTimer.getSeconds() - lifeTimer.getMinutes() * 60 - lifeTimer.getHours() * 3600; nbSeconds = (currentDate.getTime() - lifeTimer.getTime())/1000; } bestTime = Math.max(nbSeconds, bestTime); var displayText = 'Score: ' + ~~(R / 100) + " Current Time: " + nbSeconds + " seconds."; 0 != R && (null == ua && (ua = new va(24, "#FFFFFF")), ua.C(displayText), c = ua.L(), a = c.width, f.globalAlpha = .2, f.fillStyle = "#000000", f.fillRect(10, r - 10 - 24 - 10, a + 10, 34), f.globalAlpha = 1, f.drawImage(c, 15, r - 10 - 24 - 5)); Cb(); b = Date.now() - b; b > 1E3 / 60 ? D -= .01 : b < 1E3 / 65 && (D += .01);.4 > D && (D = .4); 1 < D && (D = 1); b = C - ib; !T() || W ? (x += b / 2E3, 1 < x && (x = 1)) : (x -= b / 300, 0 > x && (x = 0)); 0 < x && (f.fillStyle = "#000000", f.globalAlpha = .5 * x, f.fillRect(0, 0, m, r), f.globalAlpha = 1); ib = C drawStats(f); } //UPDATE function customRender(d) { d.save(); for (var i = 0; i < lines.length; i++) { d.beginPath(); d.lineWidth = 5; if (lines[i][4] == 0) { d.strokeStyle = "#FF0000"; } else if (lines[i][4] == 1) { d.strokeStyle = "#00FF00"; } else if (lines[i][4] == 2) { d.strokeStyle = "#0000FF"; } else if (lines[i][4] == 3) { d.strokeStyle = "#FF8000"; } else if (lines[i][4] == 4) { d.strokeStyle = "#8A2BE2"; } else if (lines[i][4] == 5) { d.strokeStyle = "#FF69B4"; } else if (lines[i][4] == 6) { d.strokeStyle = "#008080"; } else if (lines[i][4] == 7) { d.strokeStyle = "#FFFFFF"; } else { d.strokeStyle = "#000000"; } d.moveTo(lines[i][0], lines[i][1]); d.lineTo(lines[i][2], lines[i][3]); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < circles.length; i++) { if (circles[i][3] == 0) { d.strokeStyle = "#FF0000"; } else if (circles[i][3] == 1) { d.strokeStyle = "#00FF00"; } else if (circles[i][3] == 2) { d.strokeStyle = "#0000FF"; } else if (circles[i][3] == 3) { d.strokeStyle = "#FF8000"; } else if (circles[i][3] == 4) { d.strokeStyle = "#8A2BE2"; } else if (circles[i][3] == 5) { d.strokeStyle = "#FF69B4"; } else if (circles[i][3] == 6) { d.strokeStyle = "#008080"; } else if (circles[i][3] == 7) { d.strokeStyle = "#FFFFFF"; } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 10; //d.setLineDash([5]); d.globalAlpha = 0.3; d.arc(circles[i][0], circles[i][1], circles[i][2], 0, 2 * Math.PI, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dArc.length; i++) { if (dArc[i][7] == 0) { d.strokeStyle = "#FF0000"; } else if (dArc[i][7] == 1) { d.strokeStyle = "#00FF00"; } else if (dArc[i][7] == 2) { d.strokeStyle = "#0000FF"; } else if (dArc[i][7] == 3) { d.strokeStyle = "#FF8000"; } else if (dArc[i][7] == 4) { d.strokeStyle = "#8A2BE2"; } else if (dArc[i][7] == 5) { d.strokeStyle = "#FF69B4"; } else if (dArc[i][7] == 6) { d.strokeStyle = "#008080"; } else if (dArc[i][7] == 7) { d.strokeStyle = "#FFFFFF"; } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 5; var ang1 = Math.atan2(dArc[i][1] - dArc[i][5], dArc[i][0] - dArc[i][4]); var ang2 = Math.atan2(dArc[i][3] - dArc[i][5], dArc[i][2] - dArc[i][4]); d.arc(dArc[i][4], dArc[i][5], dArc[i][6], ang1, ang2, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dPoints.length; i++) { if (dText[i] == "") { var radius = 10; d.beginPath(); d.arc(dPoints[i][0], dPoints[i][1], radius, 0, 2 * Math.PI, false); if (dPoints[i][2] == 0) { d.fillStyle = "black"; } else if (dPoints[i][2] == 1) { d.fillStyle = "yellow"; } else if (dPoints[i][2] == 2) { d.fillStyle = "blue"; } else if (dPoints[i][2] == 3) { d.fillStyle = "red"; } else if (dPoints[i][2] == 4) { d.fillStyle = "#008080"; } else if (dPoints[i][2] == 5) { d.fillStyle = "#FF69B4"; } else { d.fillStyle = "#000000"; } d.fill(); d.lineWidth = 2; d.strokeStyle = '#003300'; d.stroke(); } else { var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111'), true, '#000000'); text.C(dText[i]); var textRender = text.L(); d.drawImage(textRender, dPoints[i][0], dPoints[i][1]); } } d.restore(); } function drawStats(d) { d.save() sessionScore = Math.max(getCurrentScore(), sessionScore); var debugStrings = []; debugStrings.push("Current Bot: " + window.botList[botIndex][0]); debugStrings.push("T - Bot: " + (!toggle ? "On" : "Off")); debugStrings.push("R - Lines: " + (!toggleDraw ? "On" : "Off")); debugStrings.push("Q - Follow Mouse: " + (toggleFollow ? "On" : "Off")); debugStrings.push("S - Manual Cell: " + (selectedCell == 0 ? "None" : selectedCell) + " of " + getPlayer().length); debugStrings.push(""); debugStrings.push("Best Score: " + ~~(sessionScore / 100)); debugStrings.push("Best Time: " + bestTime + " seconds"); debugStrings.push(""); debugStrings.push(serverIP); if (getPlayer().length > 0) { var offsetX = -getMapStartX(); var offsetY = -getMapStartY(); debugStrings.push("Location: " + Math.floor(getPlayer()[0].x + offsetX) + ", " + Math.floor(getPlayer()[0].y + offsetY)); } var offsetValue = 20; var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111')); for (var i = 0; i < debugStrings.length; i++) { text.C(debugStrings[i]); var textRender = text.L(); d.drawImage(textRender, 20, offsetValue); offsetValue += textRender.height; } if (message.length > 0) { var mRender = []; var mWidth = 0; var mHeight = 0; for (var i = 0; i < message.length; i++) { var mText = new va(28, '#FF0000', true, '#000000'); mText.C(message[i]); mRender.push(mText.L()); if (mRender[i].width > mWidth) { mWidth = mRender[i].width; } mHeight += mRender[i].height; } var mX = getWidth() / 2 - mWidth / 2; var mY = 20; d.globalAlpha = 0.4; d.fillStyle = '#000000'; d.fillRect(mX - 10, mY - 10, mWidth + 20, mHeight + 20); d.globalAlpha = 1; var mOffset = mY; for (var i = 0; i < mRender.length; i++) { d.drawImage(mRender[i], getWidth() / 2 - mRender[i].width / 2, mOffset); mOffset += mRender[i].height; } } d.restore(); } function Ab() { f.fillStyle = ta ? "#111111" : "#F2FBFF"; f.fillRect(0, 0, m, r); f.save(); f.strokeStyle = ta ? "#AAAAAA" : "#000000"; f.globalAlpha = .2 * h; for (var a = m / h, b = r / h, c = (a / 2 - s) % 50; c < a; c += 50) f.beginPath(), f.moveTo(c * h - .5, 0), f.lineTo(c * h - .5, b * h), f.stroke(); for (c = (b / 2 - t) % 50; c < b; c += 50) f.beginPath(), f.moveTo(0, c * h - .5), f.lineTo(a * h, c * h - .5), f.stroke(); f.restore() } function Cb() { if (Qa && Ja.width) { var a = m / 5; f.drawImage(Ja, 5, 5, a, a) } } function Bb() { for (var a = 0, b = 0; b < k.length; b++) a += k[b].q * k[b].q; return a } function ab() { z = null; if (null != A || 0 != F.length) if (null != A || wa) { z = document.createElement("canvas"); var a = z.getContext("2d"), b = 60, b = null == A ? b + 24 * F.length : b + 180, c = Math.min(200, .3 * m) / 200; z.width = 200 * c; z.height = b * c; a.scale(c, c); a.globalAlpha = .4; a.fillStyle = "#000000"; a.fillRect(0, 0, 200, b); a.globalAlpha = 1; a.fillStyle = "#FFFFFF"; c = null; c = Z("leaderboard"); a.font = "30px Ubuntu"; a.fillText(c, 100 - a.measureText(c).width / 2, 40); if (null == A) for (a.font = "20px Ubuntu", b = 0; b < F.length; ++b) c = F[b].name || Z("unnamed_cell"), wa || (c = Z("unnamed_cell")), -1 != M.indexOf(F[b].id) ? (k[0].name && (c = k[0].name), a.fillStyle = "#FFAAAA") : a.fillStyle = "#FFFFFF", c = b + 1 + ". " + c, a.fillText(c, 100 - a.measureText(c).width / 2, 70 + 24 * b); else for (b = c = 0; b < A.length; ++b) { var d = c + A[b] * Math.PI * 2; a.fillStyle = Db[b + 1]; a.beginPath(); a.moveTo(100, 140); a.arc(100, 140, 80, c, d, !1); a.fill(); c = d } } } function Ka(a, b, c, d, e) { this.V = a; this.x = b; this.y = c; this.i = d; this.b = e } function da(a, b, c, d, e, p) { this.id = a; this.s = this.x = b; this.t = this.y = c; this.r = this.size = d; this.color = e; this.a = []; this.W(); this.B(p) } function va(a, b, c, d) { a && (this.u = a); b && (this.S = b); this.U = !!c; d && (this.v = d) } function S(a, b) { var c = "1" == e("#helloContainer").attr("data-has-account-data"); e("#helloContainer").attr("data-has-account-data", "1"); if (null == b && d.localStorage.loginCache) { var l = JSON.parse(d.localStorage.loginCache); l.f = a.f; l.d = a.d; l.e = a.e; d.localStorage.loginCache = JSON.stringify(l) } if (c) { var u = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[0], c = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[1].split(" ")[0], l = e(".agario-profile-panel .progress-bar-star").first().text(); if (l != a.e) S({ f: c, d: c, e: l }, function() { e(".agario-profile-panel .progress-bar-star").text(a.e); e(".agario-exp-bar .progress-bar").css("width", "100%"); e(".progress-bar-star").addClass("animated tada").one("webkitAnimationEnd mozAnimationEnd MSAnimationEnd oanimationend animationend", function() { e(".progress-bar-star").removeClass("animated tada") }); setTimeout(function() { e(".agario-exp-bar .progress-bar-text").text(a.d + "/" + a.d + " XP"); S({ f: 0, d: a.d, e: a.e }, function() { S(a, b) }) }, 1E3) }); else { var p = Date.now(), g = function() { var c; c = (Date.now() - p) / 1E3; c = 0 > c ? 0 : 1 < c ? 1 : c; c = c * c * (3 - 2 * c); e(".agario-exp-bar .progress-bar-text").text(~~(u + (a.f - u) * c) + "/" + a.d + " XP"); e(".agario-exp-bar .progress-bar").css("width", (88 * (u + (a.f - u) * c) / a.d).toFixed(2) + "%"); 1 > c ? d.requestAnimationFrame(g) : b && b() }; d.requestAnimationFrame(g) } } else e(".agario-profile-panel .progress-bar-star").text(a.e), e(".agario-exp-bar .progress-bar-text").text(a.f + "/" + a.d + " XP"), e(".agario-exp-bar .progress-bar").css("width", (88 * a.f / a.d).toFixed(2) + "%"), b && b() } function jb(a) { "string" == typeof a && (a = JSON.parse(a)); Date.now() + 18E5 > a.ja ? e("#helloContainer").attr("data-logged-in", "0") : (d.localStorage.loginCache = JSON.stringify(a), B = a.fa, e(".agario-profile-name").text(a.name), $a(), S({ f: a.f, d: a.d, e: a.e }), e("#helloContainer").attr("data-logged-in", "1")) } function Eb(a) { a = a.split("\n"); jb({ name: a[0], ta: a[1], fa: a[2], ja: 1E3 * +a[3], e: +a[4], f: +a[5], d: +a[6] }) } function La(a) { if ("connected" == a.status) { var b = a.authResponse.accessToken; d.FB.api("/me/picture?width=180&height=180", function(a) { d.localStorage.fbPictureCache = a.data.url; e(".agario-profile-picture").attr("src", a.data.url) }); e("#helloContainer").attr("data-logged-in", "1"); null != B ? e.ajax("https://m.agar.io/checkToken", { error: function() { B = null; La(a) }, success: function(a) { a = a.split("\n"); S({ e: +a[0], f: +a[1], d: +a[2] }) }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: B }) : e.ajax("https://m.agar.io/facebookLogin", { error: function() { B = null; e("#helloContainer").attr("data-logged-in", "0") }, success: Eb, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: b }) } } function Wa(a) { Y(":party"); e("#helloContainer").attr("data-party-state", "4"); a = decodeURIComponent(a).replace(/.*#/gim, ""); Ma("#" + d.encodeURIComponent(a)); e.ajax(Na + "//m.agar.io/getToken", { error: function() { e("#helloContainer").attr("data-party-state", "6") }, success: function(b) { b = b.split("\n"); e(".partyToken").val("agar.io/#" + d.encodeURIComponent(a)); e("#helloContainer").attr("data-party-state", "5"); Y(":party"); Ca("ws://" + b[0], a) }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: a }) } function Ma(a) { d.history && d.history.replaceState && d.history.replaceState({}, d.document.title, a) } if (!d.agarioNoInit) { var Na = d.location.protocol, tb = "https:" == Na, xa = d.navigator.userAgent; if (-1 != xa.indexOf("Android")) d.ga && d.ga("send", "event", "MobileRedirect", "PlayStore"), setTimeout(function() { d.location.href = "market://details?id=com.miniclip.agar.io" }, 1E3); else if (-1 != xa.indexOf("iPhone") || -1 != xa.indexOf("iPad") || -1 != xa.indexOf("iPod")) d.ga && d.ga("send", "event", "MobileRedirect", "AppStore"), setTimeout(function() { d.location.href = "https://itunes.apple.com/app/agar.io/id995999703" }, 1E3); else { var za, f, G, m, r, X = null, //UPDATE toggle = false, toggleDraw = false, toggleFollow = false, tempPoint = [0, 0, 1], dPoints = [], circles = [], dArc = [], dText = [], lines = [], names = ["-|0_0|-"], originalName = names[Math.floor(Math.random() * names.length)], sessionScore = 0, serverIP = "", interNodes = [], lifeTimer = new Date(), bestTime = 0, botIndex = 0, reviving = false, message = [], selectedCell = 0, q = null, s = 0, t = 0, M = [], k = [], E = {}, v = [], Q = [], F = [], fa = 0, ga = 0, //UPDATE ia = [-1], ja = [-1], zb = 0, C = 0, ib = 0, K = null, pa = 0, qa = 0, ra = 1E4, sa = 1E4, h = 1, y = null, kb = !0, wa = !0, Oa = !1, Ha = !1, R = 0, ta = !1, lb = !1, aa = s = ~~((pa + ra) / 2), ba = t = ~~((qa + sa) / 2), ca = 1, P = "", A = null, ya = !1, Ga = !1, Ea = 0, Fa = 0, na = 0, oa = 0, mb = 0, Db = ["#333333", "#FF3333", "#33FF33", "#3333FF"], Ia = !1, $ = !1, bb = 0, B = null, J = 1, x = 1, W = !0, Ba = 0, Da = {}; (function() { var a = d.location.search; "?" == a.charAt(0) && (a = a.slice(1)); for (var a = a.split("&"), b = 0; b < a.length; b++) { var c = a[b].split("="); Da[c[0]] = c[1] } })(); var Qa = "ontouchstart" in d && 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I()) }; d.setAcid = function(a) { Ia = a }; null != d.localStorage && (null == d.localStorage.AB9 && (d.localStorage.AB9 = 0 + ~~(100 * Math.random())), mb = +d.localStorage.AB9, d.ABGroup = mb); e.get(Na + "//gc.agar.io", function(a) { var b = a.split(" "); a = b[0]; b = b[1] || ""; - 1 == ["UA"].indexOf(a) && ob.push("ussr"); ea.hasOwnProperty(a) && ("string" == typeof ea[a] ? y || ha(ea[a]) : ea[a].hasOwnProperty(b) && (y || ha(ea[a][b]))) }, "text"); d.ga && d.ga("send", "event", "User-Agent", d.navigator.userAgent, { nonInteraction: 1 }); var la = !1, Ya = 0; setTimeout(function() { la = !0 }, Math.max(6E4 * Ya, 1E4)); var ea = { AF: "JP-Tokyo", AX: "EU-London", AL: "EU-London", DZ: "EU-London", AS: "SG-Singapore", AD: "EU-London", AO: "EU-London", AI: "US-Atlanta", AG: "US-Atlanta", AR: "BR-Brazil", AM: "JP-Tokyo", AW: "US-Atlanta", AU: "SG-Singapore", AT: "EU-London", AZ: "JP-Tokyo", BS: "US-Atlanta", BH: "JP-Tokyo", BD: "JP-Tokyo", BB: "US-Atlanta", BY: "EU-London", BE: "EU-London", BZ: "US-Atlanta", BJ: "EU-London", BM: "US-Atlanta", BT: "JP-Tokyo", BO: "BR-Brazil", BQ: "US-Atlanta", BA: "EU-London", BW: "EU-London", BR: "BR-Brazil", IO: "JP-Tokyo", VG: "US-Atlanta", BN: "JP-Tokyo", BG: "EU-London", BF: "EU-London", BI: "EU-London", KH: "JP-Tokyo", CM: "EU-London", CA: "US-Atlanta", CV: "EU-London", KY: "US-Atlanta", CF: "EU-London", TD: "EU-London", CL: "BR-Brazil", CN: "CN-China", CX: "JP-Tokyo", CC: "JP-Tokyo", CO: "BR-Brazil", KM: "EU-London", CD: "EU-London", CG: "EU-London", CK: "SG-Singapore", CR: "US-Atlanta", CI: "EU-London", HR: "EU-London", CU: "US-Atlanta", CW: "US-Atlanta", CY: "JP-Tokyo", CZ: "EU-London", DK: "EU-London", DJ: "EU-London", DM: "US-Atlanta", DO: "US-Atlanta", EC: "BR-Brazil", EG: "EU-London", SV: "US-Atlanta", GQ: "EU-London", ER: "EU-London", EE: "EU-London", ET: "EU-London", FO: "EU-London", FK: "BR-Brazil", FJ: "SG-Singapore", FI: "EU-London", FR: "EU-London", GF: "BR-Brazil", PF: "SG-Singapore", GA: "EU-London", GM: "EU-London", GE: "JP-Tokyo", DE: "EU-London", GH: "EU-London", GI: "EU-London", GR: "EU-London", GL: "US-Atlanta", GD: "US-Atlanta", GP: "US-Atlanta", GU: "SG-Singapore", GT: "US-Atlanta", GG: "EU-London", GN: "EU-London", GW: "EU-London", GY: "BR-Brazil", HT: "US-Atlanta", VA: "EU-London", HN: "US-Atlanta", HK: "JP-Tokyo", HU: "EU-London", IS: "EU-London", IN: "JP-Tokyo", ID: "JP-Tokyo", IR: "JP-Tokyo", IQ: "JP-Tokyo", IE: "EU-London", IM: "EU-London", IL: "JP-Tokyo", IT: "EU-London", JM: "US-Atlanta", JP: "JP-Tokyo", JE: "EU-London", JO: "JP-Tokyo", KZ: "JP-Tokyo", KE: "EU-London", KI: "SG-Singapore", KP: "JP-Tokyo", KR: "JP-Tokyo", KW: "JP-Tokyo", KG: "JP-Tokyo", LA: "JP-Tokyo", LV: "EU-London", LB: "JP-Tokyo", LS: "EU-London", LR: "EU-London", LY: "EU-London", LI: "EU-London", LT: "EU-London", LU: "EU-London", MO: "JP-Tokyo", MK: "EU-London", MG: "EU-London", MW: "EU-London", MY: "JP-Tokyo", MV: "JP-Tokyo", ML: "EU-London", MT: "EU-London", MH: "SG-Singapore", MQ: "US-Atlanta", MR: "EU-London", MU: "EU-London", YT: "EU-London", MX: "US-Atlanta", FM: "SG-Singapore", MD: "EU-London", MC: "EU-London", MN: "JP-Tokyo", ME: "EU-London", MS: "US-Atlanta", MA: "EU-London", MZ: "EU-London", MM: "JP-Tokyo", NA: "EU-London", NR: "SG-Singapore", NP: "JP-Tokyo", NL: "EU-London", NC: "SG-Singapore", NZ: "SG-Singapore", NI: "US-Atlanta", NE: "EU-London", NG: "EU-London", NU: "SG-Singapore", NF: "SG-Singapore", MP: "SG-Singapore", NO: "EU-London", OM: "JP-Tokyo", PK: "JP-Tokyo", PW: "SG-Singapore", PS: "JP-Tokyo", PA: "US-Atlanta", PG: "SG-Singapore", PY: "BR-Brazil", PE: "BR-Brazil", PH: "JP-Tokyo", PN: "SG-Singapore", PL: "EU-London", PT: "EU-London", PR: "US-Atlanta", QA: "JP-Tokyo", RE: "EU-London", RO: "EU-London", RU: "RU-Russia", RW: "EU-London", BL: "US-Atlanta", SH: "EU-London", KN: "US-Atlanta", LC: "US-Atlanta", MF: "US-Atlanta", PM: "US-Atlanta", VC: "US-Atlanta", WS: "SG-Singapore", SM: "EU-London", ST: "EU-London", SA: "EU-London", SN: "EU-London", RS: "EU-London", SC: "EU-London", SL: "EU-London", SG: "JP-Tokyo", SX: "US-Atlanta", SK: "EU-London", SI: "EU-London", SB: "SG-Singapore", SO: "EU-London", ZA: "EU-London", SS: "EU-London", ES: "EU-London", LK: "JP-Tokyo", SD: "EU-London", SR: "BR-Brazil", SJ: "EU-London", SZ: "EU-London", SE: "EU-London", CH: "EU-London", SY: "EU-London", TW: "JP-Tokyo", TJ: "JP-Tokyo", TZ: "EU-London", TH: "JP-Tokyo", TL: "JP-Tokyo", TG: "EU-London", TK: "SG-Singapore", TO: "SG-Singapore", TT: "US-Atlanta", TN: "EU-London", TR: "TK-Turkey", TM: "JP-Tokyo", TC: "US-Atlanta", TV: "SG-Singapore", UG: "EU-London", UA: "EU-London", AE: "EU-London", GB: "EU-London", US: "US-Atlanta", UM: "SG-Singapore", VI: "US-Atlanta", UY: "BR-Brazil", UZ: "JP-Tokyo", VU: "SG-Singapore", VE: "BR-Brazil", VN: "JP-Tokyo", WF: "SG-Singapore", EH: "EU-London", YE: "JP-Tokyo", ZM: "EU-London", ZW: "EU-London" }, L = null; d.connect = Ca; //UPDATE window.getDarkBool = function() { return ta; } window.getMassBool = function() { return lb; } window.getMemoryCells = function() { return interNodes; } window.getCellsArray = function() { return v; } window.getCells = function() { return E; } window.getPlayer = function() { return k; } window.getWidth = function() { return m; } window.getHeight = function() { return r; } window.getRatio = function() { return h; } window.getOffsetX = function() { return aa; } window.getOffsetY = function() { return ba; } window.getX = function() { return s; } window.getY = function() { return t; } window.getPointX = function() { return ia[0]; } window.getPointY = function() { return ja[0]; } window.getMouseX = function() { return fa; } window.getMouseY = function() { return ga; } window.getMapStartX = function() { return pa; } window.getMapStartY = function() { return qa; } window.getMapEndX = function() { return ra; } window.getMapEndY = function() { return sa; } window.getScreenDistance = function() { var temp = screenDistance(); return temp; } window.getLastUpdate = function() { return C; } window.getCurrentScore = function() { return R; } window.getMode = function() { return P; } window.setPoint = function(x, y, index) { while (ia.length > getPlayer().length) { ia.pop(); ja.pop(); } if (index < ia.length) { ia[index] = x; ja[index] = y; } else { while (index < ia.length - 1) { ia.push(-1); ja.push(-1); } ia.push(x); ja.push(y); } } window.setScore = function(a) { sessionScore = a * 100; } window.setBestTime = function(a) { bestTime = a; } window.best = function(a, b) { setScore(a); setBestTime(b); } window.setBotIndex = function(a) { console.log("Changing bot"); botIndex = a; } window.setMessage = function(a) { message = a; } var ma = 500, eb = -1, fb = -1, z = null, D = 1, ua = null, Ua = function() { var a = Date.now(), b = 1E3 / 60; return function() { d.requestAnimationFrame(Ua); var c = Date.now(), l = c - a; l > b && (a = c - l % b, !T() || 240 > Date.now() - bb ? gb() : console.warn("Skipping draw"), Fb()) } }(), U = {}, ob = "poland;usa;china;russia;canada;australia;spain;brazil;germany;ukraine;france;sweden;chaplin;north korea;south korea;japan;united kingdom;earth;greece;latvia;lithuania;estonia;finland;norway;cia;maldivas;austria;nigeria;reddit;yaranaika;confederate;9gag;indiana;4chan;italy;bulgaria;tumblr;2ch.hk;hong kong;portugal;jamaica;german empire;mexico;sanik;switzerland;croatia;chile;indonesia;bangladesh;thailand;iran;iraq;peru;moon;botswana;bosnia;netherlands;european union;taiwan;pakistan;hungary;satanist;qing dynasty;matriarchy;patriarchy;feminism;ireland;texas;facepunch;prodota;cambodia;steam;piccolo;ea;india;kc;denmark;quebec;ayy lmao;sealand;bait;tsarist russia;origin;vinesauce;stalin;belgium;luxembourg;stussy;prussia;8ch;argentina;scotland;sir;romania;belarus;wojak;doge;nasa;byzantium;imperial japan;french kingdom;somalia;turkey;mars;pokerface;8;irs;receita federal;facebook".split(";"), Gb = ["8", "nasa"], Hb = ["m'blob"]; Ka.prototype = { V: null, x: 0, y: 0, i: 0, b: 0 }; da.prototype = { id: 0, a: null, name: null, o: null, O: null, x: 0, y: 0, size: 0, s: 0, t: 0, r: 0, J: 0, K: 0, q: 0, ba: 0, Q: 0, sa: 0, ia: 0,

Skip to content Sign up Sign in This repository Search Explore Features Enterprise Pricing Watch 137 Star 490 Fork 1,535 Apostolique/Agar.io-bot Branch: master Agar.io-bot/launcher.user.js @ApostoliqueApostolique 10 days ago Easier to see the borders 7 contributors @Apostolique @DarkN3ss61 @Linkaan @Timtech @henopied @Gjum @lilezek RawBlameHistory 2456 lines (2277 sloc) 93.893 kB /*The MIT License (MIT) Copyright (c) 2015 Apostolique Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.*/ // ==UserScript== // @name AposLauncher // @namespace AposLauncher // @include http://agar.io/* // @version 4.123 // @grant none // @author http://www.twitch.tv/apostolique // ==/UserScript== var aposLauncherVersion = 4.123; Number.prototype.mod = function(n) { return ((this % n) + n) % n; }; Array.prototype.peek = function() { return this[this.length - 1]; }; var sha = "efde0488cc2cc176db48dd23b28a20b90314352b"; function getLatestCommit() { window.jQuery.ajax({ url: "https://api.github.com/repos/apostolique/Agar.io-bot/git/refs/heads/master", cache: false, dataType: "jsonp" }).done(function(data) { console.dir(data.data); console.log("hmm: " + data.data.object.sha); sha = data.data.object.sha; function update(prefix, name, url) { window.jQuery(document.body).prepend("<div id='" + prefix + "Dialog' style='position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; z-index: 100; display: none;'>"); window.jQuery('#' + prefix + 'Dialog').append("<div id='" + prefix + "Message' style='width: 350px; background-color: #FFFFFF; margin: 100px auto; border-radius: 15px; padding: 5px 15px 5px 15px;'>"); window.jQuery('#' + prefix + 'Message').append("<h2>UPDATE TIME!!!</h2>"); window.jQuery('#' + prefix + 'Message').append("<p>Grab the update for: <a id='" + prefix + "Link' href='" + url + "' target=\"_blank\">" + name + "</a></p>"); window.jQuery('#' + prefix + 'Link').on('click', function() { window.jQuery("#" + prefix + "Dialog").hide(); window.jQuery("#" + prefix + "Dialog").remove(); }); window.jQuery("#" + prefix + "Dialog").show(); } window.jQuery.get('https://raw.githubusercontent.com/Apostolique/Agar.io-bot/master/launcher.user.js?' + Math.floor((Math.random() * 1000000) + 1), function(data) { var latestVersion = data.replace(/(\r\n|\n|\r)/gm, ""); latestVersion = latestVersion.substring(latestVersion.indexOf("// @version") + 11, latestVersion.indexOf("// @grant")); latestVersion = parseFloat(latestVersion + 0.0000); var myVersion = parseFloat(aposLauncherVersion + 0.0000); if (latestVersion > myVersion) { update("aposLauncher", "launcher.user.js", "https://github.com/Apostolique/Agar.io-bot/blob/" + sha + "/launcher.user.js/"); } console.log('Current launcher.user.js Version: ' + myVersion + " on Github: " + latestVersion); }); }).fail(function() {}); } getLatestCommit(); console.log("Running Bot Launcher!"); (function(d, e) { //UPDATE function keyAction(e) { if (84 == e.keyCode) { console.log("Toggle"); toggle = !toggle; } if (82 == e.keyCode) { console.log("ToggleDraw"); toggleDraw = !toggleDraw; } if (68 == e.keyCode) { window.setDarkTheme(!getDarkBool()); } if (70 == e.keyCode) { window.setShowMass(!getMassBool()); } if (69 == e.keyCode) { if (message.length > 0) { window.setMessage([]); window.onmouseup = function() {}; window.ignoreStream = true; } else { window.ignoreStream = false; window.refreshTwitch(); } } window.botList[botIndex].keyAction(e); } function humanPlayer() { //Don't need to do anything. return [getPointX(), getPointY()]; } function pb() { //UPDATE window.botList = window.botList || []; window.jQuery('#nick').val(originalName); function HumanPlayerObject() { this.name = "Human"; this.keyAction = function(key) {}; this.displayText = function() {return [];}; this.mainLoop = humanPlayer; } var hpo = new HumanPlayerObject(); window.botList.push(hpo); window.updateBotList(); ya = !0; Pa(); setInterval(Pa, 18E4); var father = window.jQuery("#canvas").parent(); window.jQuery("#canvas").remove(); father.prepend("<canvas id='canvas'>"); G = za = document.getElementById("canvas"); f = G.getContext("2d"); G.onmousedown = function(a) { if (Qa) { var b = a.clientX - (5 + m / 5 / 2), c = a.clientY - (5 + m / 5 / 2); if (Math.sqrt(b * b + c * c) <= m / 5 / 2) { V(); H(17); return } } fa = a.clientX; ga = a.clientY; Aa(); V(); }; G.onmousemove = function(a) { fa = a.clientX; ga = a.clientY; Aa(); }; G.onmouseup = function() {}; /firefox/i.test(navigator.userAgent) ? document.addEventListener("DOMMouseScroll", Ra, !1) : document.body.onmousewheel = Ra; var a = !1, b = !1, c = !1; d.onkeydown = function(l) { //UPDATE if (!window.jQuery('#nick').is(":focus")) { 32 != l.keyCode || a || (V(), H(17), a = !0); 81 != l.keyCode || b || (H(18), b = !0); 87 != l.keyCode || c || (V(), H(21), c = !0); 27 == l.keyCode && Sa(!0); //UPDATE keyAction(l); } }; d.onkeyup = function(l) { 32 == l.keyCode && (a = !1); 87 == l.keyCode && (c = !1); 81 == l.keyCode && b && (H(19), b = !1); }; d.onblur = function() { H(19); c = b = a = !1 }; d.onresize = Ta; d.requestAnimationFrame(Ua); setInterval(V, 40); y && e("#region").val(y); Va(); ha(e("#region").val()); 0 == Ba && y && I(); W = !0; e("#overlays").show(); Ta(); d.location.hash && 6 <= d.location.hash.length && Wa(d.location.hash) } function Ra(a) { J *= Math.pow(.9, a.wheelDelta / -120 || a.detail || 0); //UPDATE 0.07 > J && (J = 0.07); J > 4 / h && (J = 4 / h) } function qb() { if (.4 > h) X = null; else { for (var a = Number.POSITIVE_INFINITY, b = Number.POSITIVE_INFINITY, c = Number.NEGATIVE_INFINITY, l = Number.NEGATIVE_INFINITY, d = 0, p = 0; p < v.length; p++) { var g = v[p]; !g.N() || g.R || 20 >= g.size * h || (d = Math.max(g.size, d), a = Math.min(g.x, a), b = Math.min(g.y, b), c = Math.max(g.x, c), l = Math.max(g.y, l)) } X = rb.ka({ ca: a - 10, da: b - 10, oa: c + 10, pa: l + 10, ma: 2, na: 4 }); for (p = 0; p < v.length; p++) if (g = v[p], g.N() && !(20 >= g.size * h)) for (a = 0; a < g.a.length; ++a) b = g.a[a].x, c = g.a[a].y, b < s - m / 2 / h || c < t - r / 2 / h || b > s + m / 2 / h || c > t + r / 2 / h || X.m(g.a[a]) } } function Aa() { //UPDATE if (toggle || window.botList[botIndex].name == "Human") { setPoint(((fa - m / 2) / h + s), ((ga - r / 2) / h + t)); } } function Pa() { null == ka && (ka = {}, e("#region").children().each(function() { var a = e(this), b = a.val(); b && (ka[b] = a.text()) })); e.get("https://m.agar.io/info", function(a) { var b = {}, c; for (c in a.regions) { var l = c.split(":")[0]; b[l] = b[l] || 0; b[l] += a.regions[c].numPlayers } for (c in b) e('#region option[value="' + c + '"]').text(ka[c] + " (" + b[c] + " players)") }, "json") } function Xa() { e("#adsBottom").hide(); e("#overlays").hide(); W = !1; Va(); d.googletag && d.googletag.pubads && d.googletag.pubads().clear(d.aa) } function ha(a) { a && a != y && (e("#region").val() != a && e("#region").val(a), y = d.localStorage.location = a, e(".region-message").hide(), e(".region-message." + a).show(), e(".btn-needs-server").prop("disabled", !1), ya && I()) } function Sa(a) { W || (K = null, sb(), a && (x = 1), W = !0, e("#overlays").fadeIn(a ? 200 : 3E3)) } function Y(a) { e("#helloContainer").attr("data-gamemode", a); P = a; e("#gamemode").val(a) } function Va() { e("#region").val() ? d.localStorage.location = e("#region").val() : d.localStorage.location && e("#region").val(d.localStorage.location); e("#region").val() ? e("#locationKnown").append(e("#region")) : e("#locationUnknown").append(e("#region")) } function sb() { la && (la = !1, setTimeout(function() { la = !0 //UPDATE }, 6E4 * Ya)) } function Z(a) { return d.i18n[a] || d.i18n_dict.en[a] || a } function Za() { var a = ++Ba; console.log("Find " + y + P); e.ajax("https://m.agar.io/findServer", { error: function() { setTimeout(Za, 1E3) }, success: function(b) { a == Ba && (b.alert && alert(b.alert), Ca("ws://" + b.ip, b.token)) }, dataType: "json", method: "POST", cache: !1, crossDomain: !0, data: (y + P || "?") + "\n154669603" }) } function I() { ya && y && (e("#connecting").show(), Za()) } function Ca(a, b) { if (q) { q.onopen = null; q.onmessage = null; q.onclose = null; try { q.close() } catch (c) {} q = null } Da.la && (a = "ws://" + Da.la); if (null != L) { var l = L; L = function() { l(b) } } if (tb) { var d = a.split(":"); a = d[0] + "s://ip-" + d[1].replace(/\./g, "-").replace(/\//g, "") + ".tech.agar.io:" + (+d[2] + 2E3) } M = []; k = []; E = {}; v = []; Q = []; F = []; z = A = null; R = 0; $ = !1; console.log("Connecting to " + a); //UPDATE serverIP = a; q = new WebSocket(a); q.binaryType = "arraybuffer"; q.onopen = function() { var a; console.log("socket open"); a = N(5); a.setUint8(0, 254); a.setUint32(1, 5, !0); O(a); a = N(5); a.setUint8(0, 255); a.setUint32(1, 154669603, !0); O(a); a = N(1 + b.length); a.setUint8(0, 80); for (var c = 0; c < b.length; ++c) a.setUint8(c + 1, b.charCodeAt(c)); O(a); $a() }; q.onmessage = ub; q.onclose = vb; q.onerror = function() { console.log("socket error") } } function N(a) { return new DataView(new ArrayBuffer(a)) } function O(a) { q.send(a.buffer) } function vb() { $ && (ma = 500); console.log("socket close"); setTimeout(I, ma); ma *= 2 } function ub(a) { wb(new DataView(a.data)) } function wb(a) { function b() { for (var b = "";;) { var d = a.getUint16(c, !0); c += 2; if (0 == d) break; b += String.fromCharCode(d) } return b } var c = 0; 240 == a.getUint8(c) && (c += 5); switch (a.getUint8(c++)) { case 16: xb(a, c); break; case 17: aa = a.getFloat32(c, !0); c += 4; ba = a.getFloat32(c, !0); c += 4; ca = a.getFloat32(c, !0); c += 4; break; case 20: k = []; M = []; break; case 21: Ea = a.getInt16(c, !0); c += 2; Fa = a.getInt16(c, !0); c += 2; Ga || (Ga = !0, na = Ea, oa = Fa); break; case 32: M.push(a.getUint32(c, !0)); c += 4; break; case 49: if (null != A) break; var l = a.getUint32(c, !0), c = c + 4; F = []; for (var d = 0; d < l; ++d) { var p = a.getUint32(c, !0), c = c + 4; F.push({ id: p, name: b() }) } ab(); break; case 50: A = []; l = a.getUint32(c, !0); c += 4; for (d = 0; d < l; ++d) A.push(a.getFloat32(c, !0)), c += 4; ab(); break; case 64: pa = a.getFloat64(c, !0); c += 8; qa = a.getFloat64(c, !0); c += 8; ra = a.getFloat64(c, !0); c += 8; sa = a.getFloat64(c, !0); c += 8; aa = (ra + pa) / 2; ba = (sa + qa) / 2; ca = 1; 0 == k.length && (s = aa, t = ba, h = ca); break; case 81: var g = a.getUint32(c, !0), c = c + 4, e = a.getUint32(c, !0), c = c + 4, f = a.getUint32(c, !0), c = c + 4; setTimeout(function() { S({ e: g, f: e, d: f }) }, 1200) } } function xb(a, b) { bb = C = Date.now(); $ || ($ = !0, e("#connecting").hide(), cb(), L && (L(), L = null)); var c = Math.random(); Ha = !1; var d = a.getUint16(b, !0); b += 2; for (var u = 0; u < d; ++u) { var p = E[a.getUint32(b, !0)], g = E[a.getUint32(b + 4, !0)]; b += 8; p && g && (g.X(), g.s = g.x, g.t = g.y, g.r = g.size, g.J = p.x, g.K = p.y, g.q = g.size, g.Q = C) } for (u = 0;;) { d = a.getUint32(b, !0); b += 4; if (0 == d) break; ++u; var f, p = a.getInt16(b, !0); b += 4; g = a.getInt16(b, !0); b += 4; f = a.getInt16(b, !0); b += 2; for (var h = a.getUint8(b++), w = a.getUint8(b++), m = a.getUint8(b++), h = (h << 16 | w << 8 | m).toString(16); 6 > h.length;) h = "0" + h; var h = "#" + h, w = a.getUint8(b++), m = !!(w & 1), r = !!(w & 16); w & 2 && (b += 4); w & 4 && (b += 8); w & 8 && (b += 16); for (var q, n = "";;) { q = a.getUint16(b, !0); b += 2; if (0 == q) break; n += String.fromCharCode(q) } q = n; n = null; E.hasOwnProperty(d) ? (n = E[d], n.P(), n.s = n.x, n.t = n.y, n.r = n.size, n.color = h) : (n = new da(d, p, g, f, h, q), v.push(n), E[d] = n, n.ua = p, n.va = g); n.h = m; n.n = r; n.J = p; n.K = g; n.q = f; n.sa = c; n.Q = C; n.ba = w; q && n.B(q); - 1 != M.indexOf(d) && -1 == k.indexOf(n) && (document.getElementById("overlays").style.display = "none", k.push(n), n.birth = getLastUpdate(), n.birthMass = (n.size * n.size / 100), 1 == k.length && (s = n.x, t = n.y, db())) //UPDATE interNodes[d] = window.getCells()[d]; } //UPDATE Object.keys(interNodes).forEach(function(element, index) { //console.log("start: " + interNodes[element].updateTime + " current: " + D + " life: " + (D - interNodes[element].updateTime)); var isRemoved = !window.getCells().hasOwnProperty(element); //console.log("Time not updated: " + (window.getLastUpdate() - interNodes[element].getUptimeTime())); if (isRemoved && (window.getLastUpdate() - interNodes[element].getUptimeTime()) > 3000) { delete interNodes[element]; } else { for (var i = 0; i < getPlayer().length; i++) { if (isRemoved && computeDistance(getPlayer()[i].x, getPlayer()[i].y, interNodes[element].x, interNodes[element].y) < getPlayer()[i].size + 710) { delete interNodes[element]; break; } } } }); c = a.getUint32(b, !0); b += 4; for (u = 0; u < c; u++) d = a.getUint32(b, !0), b += 4, n = E[d], null != n && n.X(); //UPDATE //Ha && 0 == k.length && Sa(!1) } //UPDATE function computeDistance(x1, y1, x2, y2) { var xdis = x1 - x2; // <--- FAKE AmS OF COURSE! var ydis = y1 - y2; var distance = Math.sqrt(xdis * xdis + ydis * ydis); return distance; } /** * Some horse shit of some sort. * @return Horse Shit */ function screenDistance() { return Math.min(computeDistance(getOffsetX(), getOffsetY(), screenToGameX(getWidth()), getOffsetY()), computeDistance(getOffsetX(), getOffsetY(), getOffsetX(), screenToGameY(getHeight()))); } window.verticalDistance = function() { return computeDistance(screenToGameX(0), screenToGameY(0), screenToGameX(getWidth()), screenToGameY(getHeight())); } /** * A conversion from the screen's horizontal coordinate system * to the game's horizontal coordinate system. * @param x in the screen's coordinate system * @return x in the game's coordinate system */ window.screenToGameX = function(x) { return (x - getWidth() / 2) / getRatio() + getX(); } /** * A conversion from the screen's vertical coordinate system * to the game's vertical coordinate system. * @param y in the screen's coordinate system * @return y in the game's coordinate system */ window.screenToGameY = function(y) { return (y - getHeight() / 2) / getRatio() + getY(); } window.drawPoint = function(x_1, y_1, drawColor, text) { if (!toggleDraw) { dPoints.push([x_1, y_1, drawColor]); dText.push(text); } } window.drawArc = function(x_1, y_1, x_2, y_2, x_3, y_3, drawColor) { if (!toggleDraw) { var radius = computeDistance(x_1, y_1, x_3, y_3); dArc.push([x_1, y_1, x_2, y_2, x_3, y_3, radius, drawColor]); } } window.drawLine = function(x_1, y_1, x_2, y_2, drawColor) { if (!toggleDraw) { lines.push([x_1, y_1, x_2, y_2, drawColor]); } } window.drawCircle = function(x_1, y_1, radius, drawColor) { if (!toggleDraw) { circles.push([x_1, y_1, radius, drawColor]); } } function V() { //UPDATE if (getPlayer().length == 0 && !reviving && ~~(getCurrentScore() / 100) > 0) { console.log("Dead: " + ~~(getCurrentScore() / 100)); apos('send', 'pageview'); } if (getPlayer().length == 0) { console.log("Revive"); setNick(originalName); reviving = true; } else if (getPlayer().length > 0 && reviving) { reviving = false; console.log("Done Reviving!"); } if (T()) { var a = fa - m / 2; var b = ga - r / 2; 64 > a * a + b * b || .01 > Math.abs(eb - ia) && .01 > Math.abs(fb - ja) || (eb = ia, fb = ja, a = N(13), a.setUint8(0, 16), a.setInt32(1, ia, !0), a.setInt32(5, ja, !0), a.setUint32(9, 0, !0), O(a)) } } function cb() { if (T() && $ && null != K) { var a = N(1 + 2 * K.length); a.setUint8(0, 0); for (var b = 0; b < K.length; ++b) a.setUint16(1 + 2 * b, K.charCodeAt(b), !0); O(a) } } function T() { return null != q && q.readyState == q.OPEN } window.opCode = function(a) { console.log("Sending op code."); H(parseInt(a)); } function H(a) { if (T()) { var b = N(1); b.setUint8(0, a); O(b) } } function $a() { if (T() && null != B) { var a = N(1 + B.length); a.setUint8(0, 81); for (var b = 0; b < B.length; ++b) a.setUint8(b + 1, B.charCodeAt(b)); O(a) } } function Ta() { m = d.innerWidth; r = d.innerHeight; za.width = G.width = m; za.height = G.height = r; var a = e("#helloContainer"); a.css("transform", "none"); var b = a.height(), c = d.innerHeight; b > c / 1.1 ? a.css("transform", "translate(-50%, -50%) scale(" + c / b / 1.1 + ")") : a.css("transform", "translate(-50%, -50%)"); gb() } function hb() { var a; a = Math.max(r / 1080, m / 1920); return a *= J } function yb() { if (0 != k.length) { for (var a = 0, b = 0; b < k.length; b++) a += k[b].size; a = Math.pow(Math.min(64 / a, 1), .4) * hb(); h = (9 * h + a) / 10 } } function gb() { //UPDATE dPoints = []; circles = []; dArc = []; dText = []; lines = []; var a, b = Date.now(); ++zb; C = b; if (0 < k.length) { yb(); for (var c = a = 0, d = 0; d < k.length; d++) k[d].P(), a += k[d].x / k.length, c += k[d].y / k.length; aa = a; ba = c; ca = h; s = (s + a) / 2; t = (t + c) / 2; } else s = (29 * s + aa) / 30, t = (29 * t + ba) / 30, h = (9 * h + ca * hb()) / 10; qb(); Aa(); Ia || f.clearRect(0, 0, m, r); Ia ? (f.fillStyle = ta ? "#111111" : "#F2FBFF", f.globalAlpha = .05, f.fillRect(0, 0, m, r), f.globalAlpha = 1) : Ab(); v.sort(function(a, b) { return a.size == b.size ? a.id - b.id : a.size - b.size }); f.save(); f.translate(m / 2, r / 2); f.scale(h, h); f.translate(-s, -t); //UPDATE f.save(); f.beginPath(); f.lineWidth = 5; f.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapStartX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapEndX(), getMapStartY()); f.stroke(); f.moveTo(getMapEndX(), getMapStartY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapEndY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.restore(); for (d = 0; d < v.length; d++) v[d].w(f); for (d = 0; d < Q.length; d++) Q[d].w(f); //UPDATE if (getPlayer().length > 0) { var moveLoc = window.botList[botIndex].mainLoop(); if (!toggle) { setPoint(moveLoc[0], moveLoc[1]); } } customRender(f); if (Ga) { na = (3 * na + Ea) / 4; oa = (3 * oa + Fa) / 4; f.save(); f.strokeStyle = "#FFAAAA"; f.lineWidth = 10; f.lineCap = "round"; f.lineJoin = "round"; f.globalAlpha = .5; f.beginPath(); for (d = 0; d < k.length; d++) f.moveTo(k[d].x, k[d].y), f.lineTo(na, oa); f.stroke(); f.restore(); } f.restore(); z && z.width && f.drawImage(z, m - z.width - 10, 10); R = Math.max(R, Bb()); //UPDATE var currentDate = new Date(); var nbSeconds = 0; if (getPlayer().length > 0) { //nbSeconds = currentDate.getSeconds() + currentDate.getMinutes() * 60 + currentDate.getHours() * 3600 - lifeTimer.getSeconds() - lifeTimer.getMinutes() * 60 - lifeTimer.getHours() * 3600; nbSeconds = (currentDate.getTime() - lifeTimer.getTime())/1000; } bestTime = Math.max(nbSeconds, bestTime); var displayText = 'Score: ' + ~~(R / 100) + " Current Time: " + nbSeconds + " seconds."; 0 != R && (null == ua && (ua = new va(24, "#FFFFFF")), ua.C(displayText), c = ua.L(), a = c.width, f.globalAlpha = .2, f.fillStyle = "#000000", f.fillRect(10, r - 10 - 24 - 10, a + 10, 34), f.globalAlpha = 1, f.drawImage(c, 15, r - 10 - 24 - 5)); Cb(); b = Date.now() - b; b > 1E3 / 60 ? D -= .01 : b < 1E3 / 65 && (D += .01);.4 > D && (D = .4); 1 < D && (D = 1); b = C - ib; !T() || W ? (x += b / 2E3, 1 < x && (x = 1)) : (x -= b / 300, 0 > x && (x = 0)); 0 < x && (f.fillStyle = "#000000", f.globalAlpha = .5 * x, f.fillRect(0, 0, m, r), f.globalAlpha = 1); ib = C drawStats(f); } //UPDATE function customRender(d) { d.save(); for (var i = 0; i < lines.length; i++) { d.beginPath(); d.lineWidth = 5; if (lines[i][4] == 0) { d.strokeStyle = "#FF0000"; } else if (lines[i][4] == 1) { d.strokeStyle = "#00FF00"; } else if (lines[i][4] == 2) { d.strokeStyle = "#0000FF"; } else if (lines[i][4] == 3) { d.strokeStyle = "#FF8000"; } else if (lines[i][4] == 4) { d.strokeStyle = "#8A2BE2"; } else if (lines[i][4] == 5) { d.strokeStyle = "#FF69B4"; } else if (lines[i][4] == 6) { d.strokeStyle = "#008080"; } else if (lines[i][4] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.moveTo(lines[i][0], lines[i][1]); d.lineTo(lines[i][2], lines[i][3]); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < circles.length; i++) { if (circles[i][3] == 0) { d.strokeStyle = "#FF0000"; } else if (circles[i][3] == 1) { d.strokeStyle = "#00FF00"; } else if (circles[i][3] == 2) { d.strokeStyle = "#0000FF"; } else if (circles[i][3] == 3) { d.strokeStyle = "#FF8000"; } else if (circles[i][3] == 4) { d.strokeStyle = "#8A2BE2"; } else if (circles[i][3] == 5) { d.strokeStyle = "#FF69B4"; } else if (circles[i][3] == 6) { d.strokeStyle = "#008080"; } else if (circles[i][3] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 10; //d.setLineDash([5]); d.globalAlpha = 0.3; d.arc(circles[i][0], circles[i][1], circles[i][2], 0, 2 * Math.PI, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dArc.length; i++) { if (dArc[i][7] == 0) { d.strokeStyle = "#FF0000"; } else if (dArc[i][7] == 1) { d.strokeStyle = "#00FF00"; } else if (dArc[i][7] == 2) { d.strokeStyle = "#0000FF"; } else if (dArc[i][7] == 3) { d.strokeStyle = "#FF8000"; } else if (dArc[i][7] == 4) { d.strokeStyle = "#8A2BE2"; } else if (dArc[i][7] == 5) { d.strokeStyle = "#FF69B4"; } else if (dArc[i][7] == 6) { d.strokeStyle = "#008080"; } else if (dArc[i][7] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 5; var ang1 = Math.atan2(dArc[i][1] - dArc[i][5], dArc[i][0] - dArc[i][4]); var ang2 = Math.atan2(dArc[i][3] - dArc[i][5], dArc[i][2] - dArc[i][4]); d.arc(dArc[i][4], dArc[i][5], dArc[i][6], ang1, ang2, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dPoints.length; i++) { if (dText[i] == "") { var radius = 10; d.beginPath(); d.arc(dPoints[i][0], dPoints[i][1], radius, 0, 2 * Math.PI, false); if (dPoints[i][2] == 0) { d.fillStyle = "black"; } else if (dPoints[i][2] == 1) { d.fillStyle = "yellow"; } else if (dPoints[i][2] == 2) { d.fillStyle = "blue"; } else if (dPoints[i][2] == 3) { d.fillStyle = "red"; } else if (dPoints[i][2] == 4) { d.fillStyle = "#008080"; } else if (dPoints[i][2] == 5) { d.fillStyle = "#FF69B4"; } else { d.fillStyle = "#000000"; } d.fill(); d.lineWidth = 2; d.strokeStyle = '#003300'; d.stroke(); } else { var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111'), true, (getDarkBool() ? '#111111' : '#F2FBFF')); text.C(dText[i]); var textRender = text.L(); d.drawImage(textRender, dPoints[i][0] - (textRender.width / 2), dPoints[i][1] - (textRender.height / 2)); } } d.restore(); } function drawStats(d) { d.save() sessionScore = Math.max(getCurrentScore(), sessionScore); var botString = window.botList[botIndex].displayText(); var debugStrings = []; debugStrings.push("Bot: " + window.botList[botIndex].name); debugStrings.push("Launcher: AposLauncher " + aposLauncherVersion); debugStrings.push("T - Bot: " + (!toggle ? "On" : "Off")); debugStrings.push("R - Lines: " + (!toggleDraw ? "On" : "Off")); for (var i = 0; i < botString.length; i++) { debugStrings.push(botString[i]); } debugStrings.push(""); debugStrings.push("Best Score: " + ~~(sessionScore / 100)); debugStrings.push("Best Time: " + bestTime + " seconds"); debugStrings.push(""); debugStrings.push(serverIP); if (getPlayer().length > 0) { var offsetX = -getMapStartX(); var offsetY = -getMapStartY(); debugStrings.push("Location: " + Math.floor(getPlayer()[0].x + offsetX) + ", " + Math.floor(getPlayer()[0].y + offsetY)); } var offsetValue = 20; var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111')); for (var i = 0; i < debugStrings.length; i++) { text.C(debugStrings[i]); var textRender = text.L(); d.drawImage(textRender, 20, offsetValue); offsetValue += textRender.height; } if (message.length > 0) { var mRender = []; var mWidth = 0; var mHeight = 0; for (var i = 0; i < message.length; i++) { var mText = new va(28, '#FF0000', true, '#000000'); mText.C(message[i]); mRender.push(mText.L()); if (mRender[i].width > mWidth) { mWidth = mRender[i].width; } mHeight += mRender[i].height; } var mX = getWidth() / 2 - mWidth / 2; var mY = 20; d.globalAlpha = 0.4; d.fillStyle = '#000000'; d.fillRect(mX - 10, mY - 10, mWidth + 20, mHeight + 20); d.globalAlpha = 1; var mOffset = mY; for (var i = 0; i < mRender.length; i++) { d.drawImage(mRender[i], getWidth() / 2 - mRender[i].width / 2, mOffset); mOffset += mRender[i].height; } } d.restore(); } function Ab() { f.fillStyle = ta ? "#111111" : "#F2FBFF"; f.fillRect(0, 0, m, r); f.save(); f.strokeStyle = ta ? "#AAAAAA" : "#000000"; f.globalAlpha = .2 * h; for (var a = m / h, b = r / h, c = (a / 2 - s) % 50; c < a; c += 50) f.beginPath(), f.moveTo(c * h - .5, 0), f.lineTo(c * h - .5, b * h), f.stroke(); for (c = (b / 2 - t) % 50; c < b; c += 50) f.beginPath(), f.moveTo(0, c * h - .5), f.lineTo(a * h, c * h - .5), f.stroke(); f.restore() } function Cb() { if (Qa && Ja.width) { var a = m / 5; f.drawImage(Ja, 5, 5, a, a) } } function Bb() { for (var a = 0, b = 0; b < k.length; b++) a += k[b].q * k[b].q; return a } function ab() { z = null; if (null != A || 0 != F.length) if (null != A || wa) { z = document.createElement("canvas"); var a = z.getContext("2d"), b = 60, b = null == A ? b + 24 * F.length : b + 180, c = Math.min(200, .3 * m) / 200; z.width = 200 * c; z.height = b * c; a.scale(c, c); a.globalAlpha = .4; a.fillStyle = "#000000"; a.fillRect(0, 0, 200, b); a.globalAlpha = 1; a.fillStyle = "#FFFFFF"; c = null; c = Z("leaderboard"); a.font = "30px Ubuntu"; a.fillText(c, 100 - a.measureText(c).width / 2, 40); if (null == A) for (a.font = "20px Ubuntu", b = 0; b < F.length; ++b) c = F[b].name || Z("unnamed_cell"), wa || (c = Z("unnamed_cell")), -1 != M.indexOf(F[b].id) ? (k[0].name && (c = k[0].name), a.fillStyle = "#FFAAAA") : a.fillStyle = "#FFFFFF", c = b + 1 + ". " + c, a.fillText(c, 100 - a.measureText(c).width / 2, 70 + 24 * b); else for (b = c = 0; b < A.length; ++b) { var d = c + A[b] * Math.PI * 2; a.fillStyle = Db[b + 1]; a.beginPath(); a.moveTo(100, 140); a.arc(100, 140, 80, c, d, !1); a.fill(); c = d } } } function Ka(a, b, c, d, e) { this.V = a; this.x = b; this.y = c; this.i = d; this.b = e } function da(a, b, c, d, e, p) { this.id = a; this.s = this.x = b; this.t = this.y = c; this.r = this.size = d; this.color = e; this.a = []; this.W(); this.B(p) } function va(a, b, c, d) { a && (this.u = a); b && (this.S = b); this.U = !!c; d && (this.v = d) } function S(a, b) { var c = "1" == e("#helloContainer").attr("data-has-account-data"); e("#helloContainer").attr("data-has-account-data", "1"); if (null == b && d.localStorage.loginCache) { var l = JSON.parse(d.localStorage.loginCache); l.f = a.f; l.d = a.d; l.e = a.e; d.localStorage.loginCache = JSON.stringify(l) } if (c) { var u = +e(".agario-exp-bar 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d.FB.api("/me/picture?width=180&height=180", function(a) { d.localStorage.fbPictureCache = a.data.url; e(".agario-profile-picture").attr("src", a.data.url) }); e("#helloContainer").attr("data-logged-in", "1"); null != B ? e.ajax("https://m.agar.io/checkToken", { error: function() { console.log("Facebook Fail!"); B = null; La(a) }, success: function(a) { a = a.split("\n"); S({ e: +a[0], f: +a[1], d: +a[2] }); console.log("Facebook connected!"); }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: B }) : e.ajax("https://m.agar.io/facebookLogin", { error: function() { console.log("You have a Facebook problem!"); B = null; e("#helloContainer").attr("data-logged-in", "0") }, success: Eb, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: b }) } } function Wa(a) { Y(":party"); e("#helloContainer").attr("data-party-state", "4"); a = decodeURIComponent(a).replace(/.*#/gim, ""); Ma("#" + d.encodeURIComponent(a)); e.ajax(Na + "//m.agar.io/getToken", { error: function() { e("#helloContainer").attr("data-party-state", "6") }, success: function(b) { b = b.split("\n"); e(".partyToken").val("agar.io/#" + d.encodeURIComponent(a)); e("#helloContainer").attr("data-party-state", "5"); Y(":party"); Ca("ws://" + b[0], a) }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: a }) } function Ma(a) { d.history && d.history.replaceState && d.history.replaceState({}, d.document.title, a) } if (!d.agarioNoInit) { var Na = d.location.protocol, tb = "https:" == Na, xa = d.navigator.userAgent; if (-1 != xa.indexOf("Android")) d.ga && d.ga("send", "event", "MobileRedirect", "PlayStore"), setTimeout(function() { d.location.href = "market://details?id=com.miniclip.agar.io" }, 1E3); else if (-1 != xa.indexOf("iPhone") || -1 != xa.indexOf("iPad") || -1 != xa.indexOf("iPod")) d.ga && d.ga("send", "event", "MobileRedirect", "AppStore"), setTimeout(function() { d.location.href = "https://itunes.apple.com/app/agar.io/id995999703" }, 1E3); else { var za, f, G, m, r, X = null, //UPDATE toggle = false, toggleDraw = false, tempPoint = [0, 0, 1], dPoints = [], circles = [], dArc = [], dText = [], lines = [], names = ["Vilhena"], originalName = names[Math.floor(Math.random() * names.length)], sessionScore = 0, serverIP = "", interNodes = [], lifeTimer = new Date(), bestTime = 0, botIndex = 0, reviving = false, message = [], q = null, s = 0, t = 0, M = [], k = [], E = {}, v = [], Q = [], F = [], fa = 0, ga = 0, //UPDATE ia = -1, ja = -1, zb = 0, C = 0, ib = 0, K = null, pa = 0, qa = 0, ra = 1E4, sa = 1E4, h = 1, y = null, kb = !0, wa = !0, Oa = !1, Ha = !1, R = 0, ta = !1, lb = !1, aa = s = ~~((pa + ra) / 2), ba = t = ~~((qa + sa) / 2), ca = 1, P = "", A = null, ya = !1, Ga = !1, Ea = 0, Fa = 0, na = 0, oa = 0, mb = 0, Db = ["#333333", "#FF3333", "#33FF33", "#3333FF"], Ia = !1, $ = !1, bb = 0, B = null, J = 1, x = 1, W = !0, Ba = 0, Da = {}; (function() { var a = d.location.search; "?" == a.charAt(0) && (a = a.slice(1)); for (var a = a.split("&"), b = 0; b < a.length; b++) { var c = a[b].split("="); Da[c[0]] = c[1] } })(); var Qa = "ontouchstart" in d && /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(d.navigator.userAgent), Ja = new Image; Ja.src = "img/split.png"; var nb = document.createElement("canvas"); if ("undefined" == typeof console || "undefined" == typeof DataView || "undefined" == typeof WebSocket || null == nb || null == nb.getContext || null == d.localStorage) alert("You browser does not support this game, we recommend you to use Firefox to play this"); else { var ka = null; d.setNick = function(a) { //UPDATE originalName = a; if (getPlayer().length == 0) { lifeTimer = new Date(); } Xa(); K = a; cb(); R = 0 }; d.setRegion = ha; d.setSkins = function(a) { kb = a }; d.setNames = function(a) { wa = a }; d.setDarkTheme = function(a) { ta = a }; d.setColors = function(a) { Oa = a }; d.setShowMass = function(a) { lb = a }; d.spectate = function() { K = null; H(1); Xa() }; d.setGameMode = function(a) { a != P && (":party" == P && e("#helloContainer").attr("data-party-state", "0"), Y(a), ":party" != a && I()) }; d.setAcid = function(a) { Ia = a }; null != d.localStorage && (null == d.localStorage.AB9 && (d.localStorage.AB9 = 0 + ~~(100 * Math.random())), mb = +d.localStorage.AB9, d.ABGroup = mb); e.get(Na + "//gc.agar.io", function(a) { var b = a.split(" "); a = b[0]; b = b[1] || ""; - 1 == ["UA"].indexOf(a) && ob.push("ussr"); ea.hasOwnProperty(a) && ("string" == typeof ea[a] ? y || ha(ea[a]) : ea[a].hasOwnProperty(b) && (y || ha(ea[a][b]))) }, "text"); d.ga && d.ga("send", "event", "User-Agent", d.navigator.userAgent, { nonInteraction: 1 }); var la = !1, Ya = 0; setTimeout(function() { la = !0 }, Math.max(6E4 * Ya, 1E4)); var ea = { AF: "JP-Tokyo", AX: "EU-London", AL: "EU-London", DZ: "EU-London", AS: "SG-Singapore", AD: "EU-London", AO: "EU-London", AI: "US-Atlanta", AG: "US-Atlanta", AR: "BR-Brazil", AM: "JP-Tokyo", AW: "US-Atlanta", AU: "SG-Singapore", AT: "EU-London", AZ: "JP-Tokyo", BS: "US-Atlanta", BH: "JP-Tokyo", BD: "JP-Tokyo", BB: "US-Atlanta", BY: "EU-London", BE: "EU-London", BZ: "US-Atlanta", BJ: "EU-London", BM: "US-Atlanta", BT: "JP-Tokyo", BO: "BR-Brazil", BQ: "US-Atlanta", BA: "EU-London", BW: "EU-London", BR: "BR-Brazil", IO: "JP-Tokyo", VG: "US-Atlanta", BN: "JP-Tokyo", BG: "EU-London", BF: "EU-London", BI: "EU-London", KH: "JP-Tokyo", CM: "EU-London", CA: "US-Atlanta", CV: "EU-London", KY: "US-Atlanta", CF: "EU-London", TD: "EU-London", CL: "BR-Brazil", CN: "CN-China", CX: "JP-Tokyo", CC: "JP-Tokyo", CO: "BR-Brazil", KM: "EU-London", CD: "EU-London", CG: "EU-London", CK: "SG-Singapore", CR: "US-Atlanta", CI: "EU-London", HR: "EU-London", CU: "US-Atlanta", CW: "US-Atlanta", CY: "JP-Tokyo", CZ: "EU-London", DK: "EU-London", DJ: "EU-London", DM: "US-Atlanta", DO: "US-Atlanta", EC: "BR-Brazil", EG: "EU-London", SV: "US-Atlanta", GQ: "EU-London", ER: "EU-London", EE: "EU-London", ET: "EU-London", FO: "EU-London", FK: "BR-Brazil", FJ: "SG-Singapore", FI: "EU-London", FR: "EU-London", GF: "BR-Brazil", PF: "SG-Singapore", GA: "EU-London", GM: "EU-London", GE: "JP-Tokyo", DE: "EU-London", GH: "EU-London", GI: "EU-London", GR: "EU-London", GL: "US-Atlanta", GD: "US-Atlanta", GP: "US-Atlanta", GU: "SG-Singapore", GT: "US-Atlanta", GG: "EU-London", GN: "EU-London", GW: "EU-London", GY: "BR-Brazil", HT: "US-Atlanta", VA: "EU-London", HN: "US-Atlanta", HK: "JP-Tokyo", HU: "EU-London", IS: "EU-London", IN: "JP-Tokyo", ID: "JP-Tokyo", IR: "JP-Tokyo", IQ: "JP-Tokyo", IE: "EU-London", IM: "EU-London", IL: "JP-Tokyo", IT: "EU-London", JM: "US-Atlanta", JP: "JP-Tokyo", JE: "EU-London", JO: "JP-Tokyo", KZ: "JP-Tokyo", KE: "EU-London", KI: "SG-Singapore", KP: "JP-Tokyo", KR: "JP-Tokyo", KW: "JP-Tokyo", KG: "JP-Tokyo", LA: "JP-Tokyo", LV: "EU-London", LB: "JP-Tokyo", LS: "EU-London", LR: "EU-London", LY: "EU-London", LI: "EU-London", LT: "EU-London", LU: "EU-London", MO: "JP-Tokyo", MK: "EU-London", MG: "EU-London", MW: "EU-London", MY: "JP-Tokyo", MV: "JP-Tokyo", ML: "EU-London", MT: "EU-London", MH: "SG-Singapore", MQ: "US-Atlanta", MR: "EU-London", MU: "EU-London", YT: "EU-London", MX: "US-Atlanta", FM: "SG-Singapore", MD: "EU-London", MC: "EU-London", MN: "JP-Tokyo", ME: "EU-London", MS: "US-Atlanta", MA: "EU-London", MZ: "EU-London", MM: "JP-Tokyo", NA: "EU-London", NR: "SG-Singapore", NP: "JP-Tokyo", NL: "EU-London", NC: "SG-Singapore", NZ: "SG-Singapore", NI: "US-Atlanta", NE: "EU-London", NG: "EU-London", NU: "SG-Singapore", NF: "SG-Singapore", MP: "SG-Singapore", NO: "EU-London", OM: "JP-Tokyo", PK: "JP-Tokyo", PW: "SG-Singapore", PS: "JP-Tokyo", PA: "US-Atlanta", PG: "SG-Singapore", PY: "BR-Brazil", PE: "BR-Brazil", PH: "JP-Tokyo", PN: "SG-Singapore", PL: "EU-London", PT: "EU-London", PR: "US-Atlanta", QA: "JP-Tokyo", RE: "EU-London", RO: "EU-London", RU: "RU-Russia", RW: "EU-London", BL: "US-Atlanta", SH: "EU-London", KN: "US-Atlanta", LC: "US-Atlanta", MF: "US-Atlanta", PM: "US-Atlanta", VC: "US-Atlanta", WS: "SG-Singapore", SM: "EU-London", ST: "EU-London", SA: "EU-London", SN: "EU-London", RS: "EU-London", SC: "EU-London", SL: "EU-London", SG: "JP-Tokyo", SX: "US-Atlanta", SK: "EU-London", SI: "EU-London", SB: "SG-Singapore", SO: "EU-London", ZA: "EU-London", SS: "EU-London", ES: "EU-London", LK: "JP-Tokyo", SD: "EU-London", SR: "BR-Brazil", SJ: "EU-London", SZ: "EU-London", SE: "EU-London", CH: "EU-London", SY: "EU-London", TW: "JP-Tokyo", TJ: "JP-Tokyo", TZ: "EU-London", TH: "JP-Tokyo", TL: "JP-Tokyo", TG: "EU-London", TK: "SG-Singapore", TO: "SG-Singapore", TT: "US-Atlanta", TN: "EU-London", TR: "TK-Turkey", TM: "JP-Tokyo", TC: "US-Atlanta", TV: "SG-Singapore", UG: "EU-London", UA: "EU-London", AE: "EU-London", GB: "EU-London", US: "US-Atlanta", UM: "SG-Singapore", VI: "US-Atlanta", UY: "BR-Brazil", UZ: "JP-Tokyo", VU: "SG-Singapore", VE: "BR-Brazil", VN: "JP-Tokyo", WF: "SG-Singapore", EH: "EU-London", YE: "JP-Tokyo", ZM: "EU-London", ZW: "EU-London" }, L = null; d.connect = Ca; //UPDATE /** * Tells you if the game is in Dark mode. * @return Boolean for dark mode. */ window.getDarkBool = function() { return ta; } /** * Tells you if the mass is shown. * @return Boolean for player's mass. */ window.getMassBool = function() { return lb; } /** * This is a copy of everything that is shown on screen. * Normally stuff will time out when off the screen, this * memorizes everything that leaves the screen for a little * while longer. * @return The memory object. */ window.getMemoryCells = function() { return interNodes; } /** * [getCellsArray description] * @return {[type]} [description] */ window.getCellsArray = function() { return v; } /** * [getCellsArray description] * @return {[type]} [description] */ window.getCells = function() { return E; } /** * Returns an array with all the player's cells. * @return Player's cells */ window.getPlayer = function() { return k; } /** * The canvas' width. * @return Integer Width */ window.getWidth = function() { return m; } /** * The canvas' height * @return Integer Height */ window.getHeight = function() { return r; } /** * Scaling ratio of the canvas. The bigger this ration, * the further that you see. * @return Screen scaling ratio. */ window.getRatio = function() { return h; } /** * [getOffsetX description] * @return {[type]} [description] */ window.getOffsetX = function() { return aa; } window.getOffsetY = function() { return ba; } window.getX = function() { return s; } window.getY = function() { return t; } window.getPointX = function() { return ia; } window.getPointY = function() { return ja; } /** * The X location of the mouse. * @return Integer X */ window.getMouseX = function() { return fa; } /** * The Y location of the mouse. * @return Integer Y */ window.getMouseY = function() { return ga; } window.getMapStartX = function() { return pa; } window.getMapStartY = function() { return qa; } window.getMapEndX = function() { return ra; } window.getMapEndY = function() { return sa; } window.getScreenDistance = function() { var temp = screenDistance(); return temp; } /** * A timestamp since the last time the server sent any data. * @return Last update timestamp */ window.getLastUpdate = function() { return C; } window.getCurrentScore = function() { return R; } /** * The game's current mode. (":ffa", ":experimental", ":teams". ":party") * @return {[type]} [description] */ window.getMode = function() { return P; } window.setPoint = function(x, y) { ia = x; ja = y; } window.setScore = function(a) { sessionScore = a * 100; } window.setBestTime = function(a) { bestTime = a; } window.best = function(a, b) { setScore(a); setBestTime(b); } window.setBotIndex = function(a) { console.log("Changing bot"); botIndex = a; } window.setMessage = function(a) { message = a; } window.updateBotList = function() { window.bot

Generates ads in real time based on the content of the video. The audio form a video is downloaded, split into smaller parts, and then each part is send to GoogleSpeechToText API to get the corresponding text for that part. The text is then sent to Alchemy API to get the semantic content , i.e. keywords and entities. The word 'ad' is then added to these keywords and a search is made on ask.com . The resulting images are scraped and placed in the webpage beside the video (Youtube API). Thus the ads on the side of the video change in real time as the content of the video changes. If the person is talking about cars, an ad about cars shows up and when he talks about bikes, an ad about bikes shows up. To download the audio only, and split the audio, we use ffmpeg and youtube-dl .We run the shell commands from php itself.

Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

Real-time semantic segmentation is widely used in the field of autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation tasks. In this paper, we introduce a Lightweight-Multiscale-Feature-Fusion Network (LMFFNet) mainly composed of three types of components: Split-Extract-Merge Bottleneck (SEM-B) block, Features Fusion Module (FFM), and Multiscale Attention Decoder (MAD). The SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy. The MAD well recovers the details of the input images through the attention mechanism. Two networks combined with different components are proposed based on the LMFFNet model. Without pretraining, the smaller network of LMFFNet-S achieves 72.7% mIoU on Cityscapes test set at the 512×1024 resolution with only 1.1 M parameters at a reference speed of 98.9 fps running on a GTX1080Ti GPU while the larger version of LMFFNet-L achieves 74.7% mIoU with 1.4 M parameters at 89.6 fps. Besides, 67.7% mIoU at 208.9 fps and 70.3% mIoU at 72.4 fps are respectively achieved for 360 × 480 and 720 × 960 resolutions on CamVid test set using LMFFNet-S while LMFFNet--L achieves 68.1% mIoU at 182.9 fps and 71.0% mIoU at 66.5 fps, correspondingly. The proposed LMFFNets make an adequate trade-off between accuracy and parameter size for real-time inference for semantic segmentation tasks.

Bitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto satoshin@gmx.com www.bitcoin.org Abstract. A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone. 1. Introduction Commerce on the Internet has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments. While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model. Completely non-reversible transactions are not really possible, since financial institutions cannot avoid mediating disputes. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions, and there is a broader cost in the loss of ability to make non-reversible payments for non- reversible services. With the possibility of reversal, the need for trust spreads. Merchants must be wary of their customers, hassling them for more information than they would otherwise need. A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties can be avoided in person by using physical currency, but no mechanism exists to make payments over a communications channel without a trusted party. What is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes. 1 2. Transactions We define an electronic coin as a chain of digital signatures. Each owner transfers the coin to the next by digitally signing a hash of the previous transaction and the public key of the next owner and adding these to the end of the coin. A payee can verify the signatures to verify the chain of ownership. Transaction Hash Transaction Hash Transaction Hash Owner 1's Public Key Owner 2's Public Key Owner 3's Public Key Owner 0's Signature Owner 1's Signature The problem of course is the payee can't verify that one of the owners did not double-spend the coin. A common solution is to introduce a trusted central authority, or mint, that checks every transaction for double spending. After each transaction, the coin must be returned to the mint to issue a new coin, and only coins issued directly from the mint are trusted not to be double-spent. The problem with this solution is that the fate of the entire money system depends on the company running the mint, with every transaction having to go through them, just like a bank. We need a way for the payee to know that the previous owners did not sign any earlier transactions. For our purposes, the earliest transaction is the one that counts, so we don't care about later attempts to double-spend. The only way to confirm the absence of a transaction is to be aware of all transactions. In the mint based model, the mint was aware of all transactions and decided which arrived first. To accomplish this without a trusted party, transactions must be publicly announced [1], and we need a system for participants to agree on a single history of the order in which they were received. The payee needs proof that at the time of each transaction, the majority of nodes agreed it was the first received. 3. Timestamp Server The solution we propose begins with a timestamp server. A timestamp server works by taking a hash of a block of items to be timestamped and widely publishing the hash, such as in a newspaper or Usenet post [2-5]. The timestamp proves that the data must have existed at the time, obviously, in order to get into the hash. Each timestamp includes the previous timestamp in its hash, forming a chain, with each additional timestamp reinforcing the ones before it. Hash Hash Owner 2's Signature Owner 1's Private Key Owner 2's Private Key Owner 3's Private Key Block Item Item ... 2 Block Item Item ... Verify Verify Sign Sign 4. Proof-of-Work To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proof- of-work system similar to Adam Back's Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block's hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include redoing all the blocks after it. The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they're generated too fast, the difficulty increases. 5. Network The steps to run the network are as follows: 1) New transactions are broadcast to all nodes. 2) Each node collects new transactions into a block. 3) Each node works on finding a difficult proof-of-work for its block. 4) When a node finds a proof-of-work, it broadcasts the block to all nodes. 5) Nodes accept the block only if all transactions in it are valid and not already spent. 6) Nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash. Nodes always consider the longest chain to be the correct one and will keep working on extending it. If two nodes broadcast different versions of the next block simultaneously, some nodes may receive one or the other first. In that case, they work on the first one they received, but save the other branch in case it becomes longer. The tie will be broken when the next proof- of-work is found and one branch becomes longer; the nodes that were working on the other branch will then switch to the longer one. 3 Block Nonce Tx Tx ... Block Nonce Tx Tx ... Prev Hash Prev Hash New transaction broadcasts do not necessarily need to reach all nodes. As long as they reach many nodes, they will get into a block before long. Block broadcasts are also tolerant of dropped messages. If a node does not receive a block, it will request it when it receives the next block and realizes it missed one. 6. Incentive By convention, the first transaction in a block is a special transaction that starts a new coin owned by the creator of the block. This adds an incentive for nodes to support the network, and provides a way to initially distribute coins into circulation, since there is no central authority to issue them. The steady addition of a constant of amount of new coins is analogous to gold miners expending resources to add gold to circulation. In our case, it is CPU time and electricity that is expended. The incentive can also be funded with transaction fees. If the output value of a transaction is less than its input value, the difference is a transaction fee that is added to the incentive value of the block containing the transaction. Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation free. The incentive may help encourage nodes to stay honest. If a greedy attacker is able to assemble more CPU power than all the honest nodes, he would have to choose between using it to defraud people by stealing back his payments, or using it to generate new coins. He ought to find it more profitable to play by the rules, such rules that favour him with more new coins than everyone else combined, than to undermine the system and the validity of his own wealth. 7. Reclaiming Disk Space Once the latest transaction in a coin is buried under enough blocks, the spent transactions before it can be discarded to save disk space. To facilitate this without breaking the block's hash, transactions are hashed in a Merkle Tree [7][2][5], with only the root included in the block's hash. Old blocks can then be compacted by stubbing off branches of the tree. The interior hashes do not need to be stored. Block Hash0 Hash1 Hash2 Hash3 Tx0 Tx1 Tx2 Tx3 Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Block Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Hash2 Hash3 Tx3 Transactions Hashed in a Merkle Tree After Pruning Tx0-2 from the Block A block header with no transactions would be about 80 bytes. If we suppose blocks are generated every 10 minutes, 80 bytes * 6 * 24 * 365 = 4.2MB per year. With computer systems typically selling with 2GB of RAM as of 2008, and Moore's Law predicting current growth of 1.2GB per year, storage should not be a problem even if the block headers must be kept in memory. 4 8. Simplified Payment Verification It is possible to verify payments without running a full network node. A user only needs to keep a copy of the block headers of the longest proof-of-work chain, which he can get by querying network nodes until he's convinced he has the longest chain, and obtain the Merkle branch linking the transaction to the block it's timestamped in. He can't check the transaction for himself, but by linking it to a place in the chain, he can see that a network node has accepted it, and blocks added after it further confirm the network has accepted it. Longest Proof-of-Work Chain Block Header Block Header Block Header Prev Hash Nonce Prev Hash Nonce Prev Hash Nonce Merkle Root Merkle Root Merkle Root Hash01 Hash23 Merkle Branch for Tx3 Hash2 Hash3 Tx3 As such, the verification is reliable as long as honest nodes control the network, but is more vulnerable if the network is overpowered by an attacker. While network nodes can verify transactions for themselves, the simplified method can be fooled by an attacker's fabricated transactions for as long as the attacker can continue to overpower the network. One strategy to protect against this would be to accept alerts from network nodes when they detect an invalid block, prompting the user's software to download the full block and alerted transactions to confirm the inconsistency. Businesses that receive frequent payments will probably still want to run their own nodes for more independent security and quicker verification. 9. Combining and Splitting Value Although it would be possible to handle coins individually, it would be unwieldy to make a separate transaction for every cent in a transfer. To allow value to be split and combined, transactions contain multiple inputs and outputs. Normally there will be either a single input from a larger previous transaction or multiple inputs combining smaller amounts, and at most two outputs: one for the payment, and one returning the change, if any, back to the sender. It should be noted that fan-out, where a transaction depends on several transactions, and those transactions depend on many more, is not a problem here. There is never the need to extract a complete standalone copy of a transaction's history. 5 Transaction In Out In ... ... 10. Privacy The traditional banking model achieves a level of privacy by limiting access to information to the parties involved and the trusted third party. The necessity to announce all transactions publicly precludes this method, but privacy can still be maintained by breaking the flow of information in another place: by keeping public keys anonymous. The public can see that someone is sending an amount to someone else, but without information linking the transaction to anyone. This is similar to the level of information released by stock exchanges, where the time and size of individual trades, the "tape", is made public, but without telling who the parties were. Traditional Privacy Model Identities Transactions New Privacy Model Identities Transactions As an additional firewall, a new key pair should be used for each transaction to keep them from being linked to a common owner. Some linking is still unavoidable with multi-input transactions, which necessarily reveal that their inputs were owned by the same owner. The risk is that if the owner of a key is revealed, linking could reveal other transactions that belonged to the same owner. 11. Calculations We consider the scenario of an attacker trying to generate an alternate chain faster than the honest chain. Even if this is accomplished, it does not throw the system open to arbitrary changes, such as creating value out of thin air or taking money that never belonged to the attacker. Nodes are not going to accept an invalid transaction as payment, and honest nodes will never accept a block containing them. An attacker can only try to change one of his own transactions to take back money he recently spent. The race between the honest chain and an attacker chain can be characterized as a Binomial Random Walk. The success event is the honest chain being extended by one block, increasing its lead by +1, and the failure event is the attacker's chain being extended by one block, reducing the gap by -1. The probability of an attacker catching up from a given deficit is analogous to a Gambler's Ruin problem. Suppose a gambler with unlimited credit starts at a deficit and plays potentially an infinite number of trials to try to reach breakeven. We can calculate the probability he ever reaches breakeven, or that an attacker ever catches up with the honest chain, as follows [8]: p = probability an honest node finds the next block q = probability the attacker finds the next block qz = probability the attacker will ever catch up from z blocks behind Trusted Third Party q ={ 1 if p≤q} z q/pz if pq 6 Counterparty Public Public Given our assumption that p > q, the probability drops exponentially as the number of blocks the attacker has to catch up with increases. With the odds against him, if he doesn't make a lucky lunge forward early on, his chances become vanishingly small as he falls further behind. We now consider how long the recipient of a new transaction needs to wait before being sufficiently certain the sender can't change the transaction. We assume the sender is an attacker who wants to make the recipient believe he paid him for a while, then switch it to pay back to himself after some time has passed. The receiver will be alerted when that happens, but the sender hopes it will be too late. The receiver generates a new key pair and gives the public key to the sender shortly before signing. This prevents the sender from preparing a chain of blocks ahead of time by working on it continuously until he is lucky enough to get far enough ahead, then executing the transaction at that moment. Once the transaction is sent, the dishonest sender starts working in secret on a parallel chain containing an alternate version of his transaction. The recipient waits until the transaction has been added to a block and z blocks have been linked after it. He doesn't know the exact amount of progress the attacker has made, but assuming the honest blocks took the average expected time per block, the attacker's potential progress will be a Poisson distribution with expected value:  = z qp To get the probability the attacker could still catch up now, we multiply the Poisson density for each amount of progress he could have made by the probability he could catch up from that point: ∞ ke−{q/pz−k ifk≤z} ∑k=0 k!⋅ 1 ifkz Rearranging to avoid summing the infinite tail of the distribution... z ke− z−k 1−∑k=0 k! 1−q/p  Converting to C code... #include <math.h> double AttackerSuccessProbability(double q, int z) { double p = 1.0 - q; double lambda = z * (q / p); double sum = 1.0; int i, k; for (k = 0; k <= z; k++) { double poisson = exp(-lambda); for (i = 1; i <= k; i++) poisson *= lambda / i; sum -= poisson * (1 - pow(q / p, z - k)); } return sum; } 7 Running some results, we can see the probability drop off exponentially with z. q=0.1 z=0 P=1.0000000 z=1 P=0.2045873 z=2 P=0.0509779 z=3 P=0.0131722 z=4 P=0.0034552 z=5 P=0.0009137 z=6 P=0.0002428 z=7 P=0.0000647 z=8 P=0.0000173 z=9 P=0.0000046 z=10 P=0.0000012 q=0.3 z=0 P=1.0000000 z=5 P=0.1773523 z=10 P=0.0416605 z=15 P=0.0101008 z=20 P=0.0024804 z=25 P=0.0006132 z=30 P=0.0001522 z=35 P=0.0000379 z=40 P=0.0000095 z=45 P=0.0000024 z=50 P=0.0000006 Solving for P less than 0.1%... P < 0.001 q=0.10 z=5 q=0.15 z=8 q=0.20 z=11 q=0.25 z=15 q=0.30 z=24 q=0.35 z=41 q=0.40 z=89 q=0.45 z=340 12. Conclusion We have proposed a system for electronic transactions without relying on trust. We started with the usual framework of coins made from digital signatures, which provides strong control of ownership, but is incomplete without a way to prevent double-spending. To solve this, we proposed a peer-to-peer network using proof-of-work to record a public history of transactions that quickly becomes computationally impractical for an attacker to change if honest nodes control a majority of CPU power. The network is robust in its unstructured simplicity. Nodes work all at once with little coordination. They do not need to be identified, since messages are not routed to any particular place and only need to be delivered on a best effort basis. Nodes can leave and rejoin the network at will, accepting the proof-of-work chain as proof of what happened while they were gone. They vote with their CPU power, expressing their acceptance of valid blocks by working on extending them and rejecting invalid blocks by refusing to work on them. Any needed rules and incentives can be enforced with this consensus mechanism. 8 References [1] W. Dai, "b-money," http://www.weidai.com/bmoney.txt, 1998. [2] H. Massias, X.S. Avila, and J.-J. Quisquater, "Design of a secure timestamping service with minimal trust requirements," In 20th Symposium on Information Theory in the Benelux, May 1999. [3] S. Haber, W.S. Stornetta, "How to time-stamp a digital document," In Journal of Cryptology, vol 3, no 2, pages 99-111, 1991. [4] D. Bayer, S. Haber, W.S. Stornetta, "Improving the efficiency and reliability of digital time-stamping," In Sequences II: Methods in Communication, Security and Computer Science, pages 329-334, 1993. [5] S. Haber, W.S. Stornetta, "Secure names for bit-strings," In Proceedings of the 4th ACM Conference on Computer and Communications Security, pages 28-35, April 1997. [6] A. Back, "Hashcash - a denial of service counter-measure," http://www.hashcash.org/papers/hashcash.pdf, 2002. [7] R.C. Merkle, "Protocols for public key cryptosystems," In Proc. 1980 Symposium on Security and Privacy, IEEE Computer Society, pages 122-133, April 1980. [8] W. Feller, "An introduction to probability theory and its applications," 1957. 9

The Speciation Tool is a stand-alone tool that was developed to generate the factors needed to convert the emission inventory components to the AQM lumped modeling compounds. The conversion process is referred to as ‘speciation’. The factors are referred to as ‘split factors’; i.e. we ‘split’ total VOCs to olefins, aldehydes, etc. The ‘chemical mechanism’ is the mapping of the emission inventory pollutants to the AQM compounds.

Language Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Get the Data Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus. """ DON'T MODIFY ANYTHING IN THIS CELL """ import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) Explore the Data Play around with view_sentence_range to view different parts of the data. view_sentence_range = (0, 10) """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) Dataset Stats Roughly the number of unique words: 227 Number of sentences: 137861 Average number of words in a sentence: 13.225277634719028 English sentences 0 to 10: new jersey is sometimes quiet during autumn , and it is snowy in april . the united states is usually chilly during july , and it is usually freezing in november . california is usually quiet during march , and it is usually hot in june . the united states is sometimes mild during june , and it is cold in september . your least liked fruit is the grape , but my least liked is the apple . his favorite fruit is the orange , but my favorite is the grape . paris is relaxing during december , but it is usually chilly in july . new jersey is busy during spring , and it is never hot in march . our least liked fruit is the lemon , but my least liked is the grape . the united states is sometimes busy during january , and it is sometimes warm in november . French sentences 0 to 10: new jersey est parfois calme pendant l' automne , et il est neigeux en avril . les états-unis est généralement froid en juillet , et il gèle habituellement en novembre . california est généralement calme en mars , et il est généralement chaud en juin . les états-unis est parfois légère en juin , et il fait froid en septembre . votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme . son fruit préféré est l'orange , mais mon préféré est le raisin . paris est relaxant en décembre , mais il est généralement froid en juillet . new jersey est occupé au printemps , et il est jamais chaude en mars . notre fruit est moins aimé le citron , mais mon moins aimé est le raisin . les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre . Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. This will help the neural network predict when the sentence should end. You can get the <EOS> word id by doing: target_vocab_to_int['<EOS>'] You can get other word ids using source_vocab_to_int and target_vocab_to_int. def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) """ # TODO: Implement Function source_id_text = [[source_vocab_to_int[word] for word in sentence.split()] \ for sentence in source_text.split('\n')] target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int['<EOS>']] \ for sentence in target_text.split('\n')] return source_id_text, target_id_text """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_text_to_ids(text_to_ids) Tests Passed Preprocess all the data and save it Running the code cell below will preprocess all the data and save it to file. """ DON'T MODIFY ANYTHING IN THIS CELL """ helper.preprocess_and_save_data(source_path, target_path, text_to_ids) Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. import problem_unittests as tests """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU """ DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) TensorFlow Version: 1.1.0 Default GPU Device: /gpu:0 Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: model_inputs process_decoder_input encoding_layer decoding_layer_train decoding_layer_infer decoding_layer seq2seq_model Input Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: Input text placeholder named "input" using the TF Placeholder name parameter with rank 2. Targets placeholder with rank 2. Learning rate placeholder with rank 0. Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0. Target sequence length placeholder named "target_sequence_length" with rank 1 Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0. Source sequence length placeholder named "source_sequence_length" with rank 1 Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], 'input') targets = tf.placeholder(tf.int32, [None, None]) learning_rate = tf.placeholder(tf.float32, []) keep_prob = tf.placeholder(tf.float32, [], 'keep_prob') target_sequence_length = tf.placeholder(tf.int32, [None], 'target_sequence_length') max_target_len = tf.reduce_max(target_sequence_length) source_sequence_length = tf.placeholder(tf.int32, [None], 'source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_sequence_length, max_target_len, source_sequence_length """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_model_inputs(model_inputs) Tests Passed Process Decoder Input Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch. def process_decoder_input(target_data, target_vocab_to_int, batch_size): """ Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data """ # TODO: Implement Function go = tf.constant([[target_vocab_to_int['<GO>']]]*batch_size) # end = tf.slice(target_data, [0, 0], [-1, batch_size]) end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([go, end], 1) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_process_encoding_input(process_decoder_input) Tests Passed Encoding Implement encoding_layer() to create a Encoder RNN layer: Embed the encoder input using tf.contrib.layers.embed_sequence Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper Pass cell and embedded input to tf.nn.dynamic_rnn() from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) """ # TODO: Implement Function embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) def lstm_cell(): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) return tf.contrib.rnn.DropoutWrapper(lstm, keep_prob) stacked_lstm = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)]) # initial_state = stacked_lstm.zero_state(source_sequence_length, tf.float32) return tf.nn.dynamic_rnn(stacked_lstm, embed, source_sequence_length, dtype=tf.float32) # initial_state=initial_state) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) Tests Passed Decoding - Training Create a training decoding layer: Create a tf.contrib.seq2seq.TrainingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id """ # TODO: Implement Function helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_train_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) # for tensorflow 1.2: # dec_train_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) return dec_train_logits # keep_prob/dropout not used? """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_train(decoding_layer_train) Tests Passed Decoding - Inference Create inference decoder: Create a tf.contrib.seq2seq.GreedyEmbeddingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id """ # TODO: Implement Function start_tokens = tf.constant([start_of_sequence_id]*batch_size) helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_infer_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) # for tensorflow 1.2: # dec_infer_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) return dec_infer_logits """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_infer(decoding_layer_infer)