7 skills found
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.'
ManojKumarPatnaik / Major Project ListA list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.
skalenetwork / LibBLSSolidity-compatible BLS signatures, threshold encryption, distributed key generation library in modern C++. Actively maintained and used by SKALE for consensus, distributed random number gen, inter-chain communication and protection of transactions.
BlockchainLabs / PebblecoinPebblecoin UPDATE 2015/12/31: Version 0.4.4.1 is now out. The major change is optimizing the daemon to use less RAM. It no longer keeps all the blocks, which are rarely needed, in RAM, and so RAM usage has decreased from around 2 gigabytes, to under 200 megabytes. Mac binaries are also now available. The new wallet is compatible with the old wallet - simply turn off the old wallet, and start the new wallet, and the blockchain will update automatically to use less RAM. Code: Release Notes 0.4.4.1 - (All) Fix blockchain RAM usage, from almost 2 GB to less than 200 MB - Seamless blockchain conversion on first run with new binaries - (Qt) Fix high CPU usage - (Qt) Fix sync indicator (# of total blocks) - (Mac) Mac binaries - Technical Notes: - (All) Blockchain disk-backed storage with sqlite3 and stxxl - (Mac) Fix mac compilation - (All) Update build files & instructions for linux, mac, windows - (All) Remove unused protobuf and OpenSSL dependencies for Qt wallet - (Tests) Fix valgrind errors - (Tests) Use local directory for blockchain instead of default directory - (Tests) Run tests on Windows if using new enough MSVC LINKS: Windows 64-bit: https://www.dropbox.com/s/b4kubwwnb4t7o4w/pebblecoin-all-win32-x64-v0.4.4.1.zip?dl=0 Mac 64-bit: https://www.dropbox.com/s/uoy9z1oxu4x53cv/pebblecoin-all-mac-x64-v0.4.4.1.tar.gz?dl=0 Linux 64-bit: https://www.dropbox.com/s/jq3h3bc29jmndks/pebblecoin-all-linux-x64-v0.4.4.1.tar.gz?dl=0 Exchange: https://poloniex.com/exchange#btc_xpb . Source: https://github.com/xpbcreator/pebblecoin/ CONTACT: xpbcreator@torguard.tg IRC: irc.freenode.net, #pebblecoin UPDATE 2015/06/08: Version 0.4.3.1 is now out. This is a minor, mostly bug-fix release. Work continues on the next major release which will bring us user-created currencies and user-graded contracts. Release notes: Code: Release Notes 0.4.3.1 - RPC calls for DPOS: - getdelegateinfos RPC call - get kimageseqs RPC call - block header contains signing_delegate_id - fix checkpoint rollback bug - fix inability to send coins if voting history was lost UPDATE 2015/05/04: Version 0.4.2.2 is now out. This is a bug-fix/cosmetic release. Release notes: Payment ID support Windows installer Logos updated Improved DPOS tab Sync issues fully fixed Fix rare crash bug Fix min out 0 bug Fix debit display Fix GUI not updating Updated hard-coded seed nodes UPDATE 2015/04/24: The switch-over to DPOS has succeeded without a hitch! DPOS blocks are being signed as we speak, at the far faster pace of 15 seconds per block. This marks the start of a new era for Pebblecoin. UPDATE 2015/04/21: Congratulations to the first registered delegate! This indicates the start of the forking change so everybody please update your daemons if you haven't already. To promote the coin and encourage people to become delegates, we've come up with an incentive scheme. First, we'll send a free 100 XPB to anybody who PMs me their public address, for people to play around with and to start using the coin. Second, once DPOS starts, for the first month of DPOS I'll send an extra 0.5 XPB to the signing delegate for every block they process. This is on top of the usual transaction fees they will receive. This is to encourage more people to become delegates at this important phase of the coin. UPDATE 2015/04/19: All went well on the testnet release, so after a few further minor modifications, we are releasing version 0.4.1.2 to the public. This is a forking change, so please update your clients and servers (links below). At block 83120, sometime on April 21st, registration for DPOS delegates will begin. At block 85300, sometime on April 24th, the network will switch over to DPOS. As with the testnet, to become a delegate and receive block fees for securing the network, just turn on your wallet, register to be a delegate (5 XPB fee), and then leave your wallet on. It will sign the blocks when it is your turn. While Roman works on the next phase of the release - introducing subcurrencies - I will be fixing up some loose ends on the wallet, adding payment ID support, etc. This is truly an exciting time for Pebblecoin. RELEASE NOTES: All clients adjust internal clocks using ntp (client list in src/common/ntp_time.cpp) Added testnet support DPOS registration starts Block 83120 (~April 21st) DPOS phase starts Block 85300 (~April 24th) Default fee bumped to 0.10 XPB Low-free transactions no longer get relayed by default Significantly improved wallet sync Checkpoint at Block 79000 TOTAL CURRENT COINS: Available at this link. BLOCK TARGET TIME: 2 minutes EXPECTED EMISSION: At Block 3600 (End of Day 5): ~78 XPBs At Block 6480 (End of Day 9): ~758 XPBs At Block 9360 (End of Day 13): 6,771.0 XPBs At Block 12240 (End of Day 17): ~61,000 XPBs At Block 15120 (End of Day 21): ~550,000 XPBs, start of regular 300/block emission At Block 21900 (End of Month 1): ~2,600,000 XPBs, 300/block At Block 43800 (End of Month 2): ~9,150,000 XPBs, 300/block At Block 85300 (End of POW phase): ~21,500,300 XPBs. UPDATE: The Pebblecoin Pool is now live! Instructions: Download the linux miner and run it: ./minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x UPDATE: The Pebblecoin wallet is now live! There have been thousands of attempts at alternative currencies in the community. Many are 100% copies of existing blockchains with a different name. Some are very slight variations with no significant differences. From recent history it is apparent the only realistic chance for viability of a new currency is one that is innovation and continued support and development. The bitcoin community for good reason has shown interest in currencies that provide privacy of transactions, several currencies such as darkcoin, have become popular based on this desire. The best technology for privacy is cryptonote although for a variety of reasons there hasnt been much development for ease of use, and as a result there has not been significant adoption. Pebblecoin (XPB) is a cryptonote based coin with improvements and changes in some areas, and the promise of development in others. I invite developers to work on this technology with me. There is no premine, any tips or support of any developer including myself will be completely voluntary. These are the following areas which I have determined needs changes/updates: I welcome suggestions, and am interested what else I can try to improve. 1) New Mining algorithm (active) A mining algorithm is either susceptible to ASIC development or to being botnetted, meaning it is either more efficient to have a centralized mining entity (as is the case with bitcoin) or to have an algorithm that requires a real CPU, in which case botnets become very attractive. To my knowledge there does not exist a blockchain that attempts to solve both problems, by having an algorithm that only works on a general purpose computer and is difficult to botnet. Cryptonote coins currently are primarily mined with botnets. Boulderhash is a new mining algorithm requiring 13 GB RAM, nearly eliminating all possible zombie (botnet controlled) computers from mining. Most infected computers in the world do not have 13 GB available, so an algorithm that requires that much RAM severely limits the productivity of a botnet. 13 GB also makes ASICs cost prohibitive, and the current GPUs do not have that much RAM. What's left is general purpose computers as was the original intent of bitcoin's mining process. 2) Distribution of coins (active) It is very common in the launch of a new cryptocurrency the distribution algorithm heavily is weighted towards the very early adopters. Such distribution is designed to give a massive advantage to people who are fully prepared to mine at launch, with a very large difference shortly after sometimes a few days later. If the point of mining is to both secure the network and fairly distribute coins a gradual build up of rewards makes more sense, with no drop off in mining rewards. At a standard block reward of 300, at launch each block will reward 0.3 coins leading up to 3, 30, and finally the standard reward of 300 which will be the standard unchanging reward from that point. It will take approximately 3 weeks for the block reward of 300 to be reached. 3) GUI Software (active) There are no current cryptonote coins that have a downloadable GUI, which makes the user experience much worse than that of bitcoin. It is hard to achieve signficant adoption with a command line interface. The very first update had the exact GUI written for bitcoin fully working with Pebblecoin. The GUI was released on Jan 19, before the full 300 XPB reward was awarded for winning the block. 4) IRC Chat support embedded in Client GUI (active) For user support, and to talk to core developers message boards such as Bitcointalk and reddit are primarily used. I have embedded an IRC client in the GUI and be available at set hours for any kind of support. 5) Address aliasing (to be worked on) Just as a user visiting google does not need to know the ip address, similarly an address should have the ability to have an associated userid. If I ask a friend to send me pebblecoins it would be easier to tell him send it to @myuserid rather than a very long address or scanning a QR code. There should be a way of registering a userid on the blockchain that will permanently translate to a pebblecoin addresss. QT INSTRUCTIONS: Download the package for your respective platform Run the Qt executable. The software will generate a new wallet for you and use a default folder: ~/.pebblecoin on Linux and %appdata%\pebblecoin on Windows. To use an existing wallet, copy the wallet.keys file into the default folder. To use a different data directory and/or wallet file, run the software like so: ./pebblecoin-qt --data-dir <DataDir> --wallet-file <FileName>. To enable mining, run the start_mining_NEEDS_13GB_RAM.bat batch file. Or run the qt wallet with the --enable-boulderhash command line option, or put enable-boulderhash=1 into the config file. It will start mining to the wallet address. To change the number of mining threads (13GB required per thread), do --mining-threads <NumThreads> or edit the batch file. DAEMON + SIMPLEWALLET INSTRUCTIONS: Download the package, run: ./pebblecoind --data-dir pebblecoin_data Once the daemon finished syncing, run the simplewallet: ./simplewallet POOL INSTRUCTIONS: Download the miner binary for your platform. Run the miner using a wallet address gotten from simplewallet or the Qt Wallet: Code: minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x [/li] DEV WALLET (for donations): PByFqCfuDRUPVsNrzrUXnuUdF7LpXsTTZXeq5cdHpJDogbJ8EBXopciN7DmQiGhLEo5ArA7dFqGga2A AhbRaZ2gL8jjp9VmYgk
uvhw / Bitcoin FoundationBitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto satoshin@gmx.com www.bitcoin.org Abstract. A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone. 1. Introduction Commerce on the Internet has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments. While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model. Completely non-reversible transactions are not really possible, since financial institutions cannot avoid mediating disputes. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions, and there is a broader cost in the loss of ability to make non-reversible payments for non- reversible services. With the possibility of reversal, the need for trust spreads. Merchants must be wary of their customers, hassling them for more information than they would otherwise need. A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties can be avoided in person by using physical currency, but no mechanism exists to make payments over a communications channel without a trusted party. What is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes. 1 2. Transactions We define an electronic coin as a chain of digital signatures. Each owner transfers the coin to the next by digitally signing a hash of the previous transaction and the public key of the next owner and adding these to the end of the coin. A payee can verify the signatures to verify the chain of ownership. Transaction Hash Transaction Hash Transaction Hash Owner 1's Public Key Owner 2's Public Key Owner 3's Public Key Owner 0's Signature Owner 1's Signature The problem of course is the payee can't verify that one of the owners did not double-spend the coin. A common solution is to introduce a trusted central authority, or mint, that checks every transaction for double spending. After each transaction, the coin must be returned to the mint to issue a new coin, and only coins issued directly from the mint are trusted not to be double-spent. The problem with this solution is that the fate of the entire money system depends on the company running the mint, with every transaction having to go through them, just like a bank. We need a way for the payee to know that the previous owners did not sign any earlier transactions. For our purposes, the earliest transaction is the one that counts, so we don't care about later attempts to double-spend. The only way to confirm the absence of a transaction is to be aware of all transactions. In the mint based model, the mint was aware of all transactions and decided which arrived first. To accomplish this without a trusted party, transactions must be publicly announced [1], and we need a system for participants to agree on a single history of the order in which they were received. The payee needs proof that at the time of each transaction, the majority of nodes agreed it was the first received. 3. Timestamp Server The solution we propose begins with a timestamp server. A timestamp server works by taking a hash of a block of items to be timestamped and widely publishing the hash, such as in a newspaper or Usenet post [2-5]. The timestamp proves that the data must have existed at the time, obviously, in order to get into the hash. Each timestamp includes the previous timestamp in its hash, forming a chain, with each additional timestamp reinforcing the ones before it. Hash Hash Owner 2's Signature Owner 1's Private Key Owner 2's Private Key Owner 3's Private Key Block Item Item ... 2 Block Item Item ... Verify Verify Sign Sign 4. Proof-of-Work To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proof- of-work system similar to Adam Back's Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block's hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include redoing all the blocks after it. The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they're generated too fast, the difficulty increases. 5. Network The steps to run the network are as follows: 1) New transactions are broadcast to all nodes. 2) Each node collects new transactions into a block. 3) Each node works on finding a difficult proof-of-work for its block. 4) When a node finds a proof-of-work, it broadcasts the block to all nodes. 5) Nodes accept the block only if all transactions in it are valid and not already spent. 6) Nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash. Nodes always consider the longest chain to be the correct one and will keep working on extending it. If two nodes broadcast different versions of the next block simultaneously, some nodes may receive one or the other first. In that case, they work on the first one they received, but save the other branch in case it becomes longer. The tie will be broken when the next proof- of-work is found and one branch becomes longer; the nodes that were working on the other branch will then switch to the longer one. 3 Block Nonce Tx Tx ... Block Nonce Tx Tx ... Prev Hash Prev Hash New transaction broadcasts do not necessarily need to reach all nodes. As long as they reach many nodes, they will get into a block before long. Block broadcasts are also tolerant of dropped messages. If a node does not receive a block, it will request it when it receives the next block and realizes it missed one. 6. Incentive By convention, the first transaction in a block is a special transaction that starts a new coin owned by the creator of the block. This adds an incentive for nodes to support the network, and provides a way to initially distribute coins into circulation, since there is no central authority to issue them. The steady addition of a constant of amount of new coins is analogous to gold miners expending resources to add gold to circulation. In our case, it is CPU time and electricity that is expended. The incentive can also be funded with transaction fees. If the output value of a transaction is less than its input value, the difference is a transaction fee that is added to the incentive value of the block containing the transaction. Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation free. The incentive may help encourage nodes to stay honest. If a greedy attacker is able to assemble more CPU power than all the honest nodes, he would have to choose between using it to defraud people by stealing back his payments, or using it to generate new coins. He ought to find it more profitable to play by the rules, such rules that favour him with more new coins than everyone else combined, than to undermine the system and the validity of his own wealth. 7. Reclaiming Disk Space Once the latest transaction in a coin is buried under enough blocks, the spent transactions before it can be discarded to save disk space. To facilitate this without breaking the block's hash, transactions are hashed in a Merkle Tree [7][2][5], with only the root included in the block's hash. Old blocks can then be compacted by stubbing off branches of the tree. The interior hashes do not need to be stored. Block Hash0 Hash1 Hash2 Hash3 Tx0 Tx1 Tx2 Tx3 Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Block Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Hash2 Hash3 Tx3 Transactions Hashed in a Merkle Tree After Pruning Tx0-2 from the Block A block header with no transactions would be about 80 bytes. If we suppose blocks are generated every 10 minutes, 80 bytes * 6 * 24 * 365 = 4.2MB per year. With computer systems typically selling with 2GB of RAM as of 2008, and Moore's Law predicting current growth of 1.2GB per year, storage should not be a problem even if the block headers must be kept in memory. 4 8. Simplified Payment Verification It is possible to verify payments without running a full network node. A user only needs to keep a copy of the block headers of the longest proof-of-work chain, which he can get by querying network nodes until he's convinced he has the longest chain, and obtain the Merkle branch linking the transaction to the block it's timestamped in. He can't check the transaction for himself, but by linking it to a place in the chain, he can see that a network node has accepted it, and blocks added after it further confirm the network has accepted it. Longest Proof-of-Work Chain Block Header Block Header Block Header Prev Hash Nonce Prev Hash Nonce Prev Hash Nonce Merkle Root Merkle Root Merkle Root Hash01 Hash23 Merkle Branch for Tx3 Hash2 Hash3 Tx3 As such, the verification is reliable as long as honest nodes control the network, but is more vulnerable if the network is overpowered by an attacker. While network nodes can verify transactions for themselves, the simplified method can be fooled by an attacker's fabricated transactions for as long as the attacker can continue to overpower the network. One strategy to protect against this would be to accept alerts from network nodes when they detect an invalid block, prompting the user's software to download the full block and alerted transactions to confirm the inconsistency. Businesses that receive frequent payments will probably still want to run their own nodes for more independent security and quicker verification. 9. Combining and Splitting Value Although it would be possible to handle coins individually, it would be unwieldy to make a separate transaction for every cent in a transfer. To allow value to be split and combined, transactions contain multiple inputs and outputs. Normally there will be either a single input from a larger previous transaction or multiple inputs combining smaller amounts, and at most two outputs: one for the payment, and one returning the change, if any, back to the sender. It should be noted that fan-out, where a transaction depends on several transactions, and those transactions depend on many more, is not a problem here. There is never the need to extract a complete standalone copy of a transaction's history. 5 Transaction In Out In ... ... 10. Privacy The traditional banking model achieves a level of privacy by limiting access to information to the parties involved and the trusted third party. The necessity to announce all transactions publicly precludes this method, but privacy can still be maintained by breaking the flow of information in another place: by keeping public keys anonymous. The public can see that someone is sending an amount to someone else, but without information linking the transaction to anyone. This is similar to the level of information released by stock exchanges, where the time and size of individual trades, the "tape", is made public, but without telling who the parties were. Traditional Privacy Model Identities Transactions New Privacy Model Identities Transactions As an additional firewall, a new key pair should be used for each transaction to keep them from being linked to a common owner. Some linking is still unavoidable with multi-input transactions, which necessarily reveal that their inputs were owned by the same owner. The risk is that if the owner of a key is revealed, linking could reveal other transactions that belonged to the same owner. 11. Calculations We consider the scenario of an attacker trying to generate an alternate chain faster than the honest chain. Even if this is accomplished, it does not throw the system open to arbitrary changes, such as creating value out of thin air or taking money that never belonged to the attacker. Nodes are not going to accept an invalid transaction as payment, and honest nodes will never accept a block containing them. An attacker can only try to change one of his own transactions to take back money he recently spent. The race between the honest chain and an attacker chain can be characterized as a Binomial Random Walk. The success event is the honest chain being extended by one block, increasing its lead by +1, and the failure event is the attacker's chain being extended by one block, reducing the gap by -1. The probability of an attacker catching up from a given deficit is analogous to a Gambler's Ruin problem. Suppose a gambler with unlimited credit starts at a deficit and plays potentially an infinite number of trials to try to reach breakeven. We can calculate the probability he ever reaches breakeven, or that an attacker ever catches up with the honest chain, as follows [8]: p = probability an honest node finds the next block q = probability the attacker finds the next block qz = probability the attacker will ever catch up from z blocks behind Trusted Third Party q ={ 1 if p≤q} z q/pz if pq 6 Counterparty Public Public Given our assumption that p > q, the probability drops exponentially as the number of blocks the attacker has to catch up with increases. With the odds against him, if he doesn't make a lucky lunge forward early on, his chances become vanishingly small as he falls further behind. We now consider how long the recipient of a new transaction needs to wait before being sufficiently certain the sender can't change the transaction. We assume the sender is an attacker who wants to make the recipient believe he paid him for a while, then switch it to pay back to himself after some time has passed. The receiver will be alerted when that happens, but the sender hopes it will be too late. The receiver generates a new key pair and gives the public key to the sender shortly before signing. This prevents the sender from preparing a chain of blocks ahead of time by working on it continuously until he is lucky enough to get far enough ahead, then executing the transaction at that moment. Once the transaction is sent, the dishonest sender starts working in secret on a parallel chain containing an alternate version of his transaction. The recipient waits until the transaction has been added to a block and z blocks have been linked after it. He doesn't know the exact amount of progress the attacker has made, but assuming the honest blocks took the average expected time per block, the attacker's potential progress will be a Poisson distribution with expected value: = z qp To get the probability the attacker could still catch up now, we multiply the Poisson density for each amount of progress he could have made by the probability he could catch up from that point: ∞ ke−{q/pz−k ifk≤z} ∑k=0 k!⋅ 1 ifkz Rearranging to avoid summing the infinite tail of the distribution... z ke− z−k 1−∑k=0 k! 1−q/p Converting to C code... #include <math.h> double AttackerSuccessProbability(double q, int z) { double p = 1.0 - q; double lambda = z * (q / p); double sum = 1.0; int i, k; for (k = 0; k <= z; k++) { double poisson = exp(-lambda); for (i = 1; i <= k; i++) poisson *= lambda / i; sum -= poisson * (1 - pow(q / p, z - k)); } return sum; } 7 Running some results, we can see the probability drop off exponentially with z. q=0.1 z=0 P=1.0000000 z=1 P=0.2045873 z=2 P=0.0509779 z=3 P=0.0131722 z=4 P=0.0034552 z=5 P=0.0009137 z=6 P=0.0002428 z=7 P=0.0000647 z=8 P=0.0000173 z=9 P=0.0000046 z=10 P=0.0000012 q=0.3 z=0 P=1.0000000 z=5 P=0.1773523 z=10 P=0.0416605 z=15 P=0.0101008 z=20 P=0.0024804 z=25 P=0.0006132 z=30 P=0.0001522 z=35 P=0.0000379 z=40 P=0.0000095 z=45 P=0.0000024 z=50 P=0.0000006 Solving for P less than 0.1%... P < 0.001 q=0.10 z=5 q=0.15 z=8 q=0.20 z=11 q=0.25 z=15 q=0.30 z=24 q=0.35 z=41 q=0.40 z=89 q=0.45 z=340 12. Conclusion We have proposed a system for electronic transactions without relying on trust. We started with the usual framework of coins made from digital signatures, which provides strong control of ownership, but is incomplete without a way to prevent double-spending. To solve this, we proposed a peer-to-peer network using proof-of-work to record a public history of transactions that quickly becomes computationally impractical for an attacker to change if honest nodes control a majority of CPU power. The network is robust in its unstructured simplicity. Nodes work all at once with little coordination. They do not need to be identified, since messages are not routed to any particular place and only need to be delivered on a best effort basis. Nodes can leave and rejoin the network at will, accepting the proof-of-work chain as proof of what happened while they were gone. They vote with their CPU power, expressing their acceptance of valid blocks by working on extending them and rejecting invalid blocks by refusing to work on them. Any needed rules and incentives can be enforced with this consensus mechanism. 8 References [1] W. Dai, "b-money," http://www.weidai.com/bmoney.txt, 1998. [2] H. Massias, X.S. Avila, and J.-J. Quisquater, "Design of a secure timestamping service with minimal trust requirements," In 20th Symposium on Information Theory in the Benelux, May 1999. [3] S. Haber, W.S. Stornetta, "How to time-stamp a digital document," In Journal of Cryptology, vol 3, no 2, pages 99-111, 1991. [4] D. Bayer, S. Haber, W.S. Stornetta, "Improving the efficiency and reliability of digital time-stamping," In Sequences II: Methods in Communication, Security and Computer Science, pages 329-334, 1993. [5] S. Haber, W.S. Stornetta, "Secure names for bit-strings," In Proceedings of the 4th ACM Conference on Computer and Communications Security, pages 28-35, April 1997. [6] A. Back, "Hashcash - a denial of service counter-measure," http://www.hashcash.org/papers/hashcash.pdf, 2002. [7] R.C. Merkle, "Protocols for public key cryptosystems," In Proc. 1980 Symposium on Security and Privacy, IEEE Computer Society, pages 122-133, April 1980. [8] W. Feller, "An introduction to probability theory and its applications," 1957. 9
choiceshack / The Arcana HackHow to Get Free Coins and Keys in Arcana OCTOBER 12, 2018 LENCIPETA UNCATEGORIZED How to cheat in The Arcana: A Mystic Romance on iOS/Android The Arcana: A Mystic Romance The Arcana: A Mystic Romance About The Arcana: A Mystic Romance The Arcana: A Mystic Romance is a mobile game published by Nix Hydra Games . The Arcana: A Mystic Romance is available for download on Playstore and iOS Appstore for $0.0. How to cheat in The Arcana: A Mystic Romance? To use any of our free The Arcana: A Mystic Romance cheats . Just select a cheat code from the list below and enter it into the cheats console and your are done. It’s that easy. There is no need to download any complicated or dangerous hack tools on your phone or pc. Our The Arcana: A Mystic Romance cheats can be used on any device with internet access. In some cases (during high server load etc) you might need to our captcha test to verify that you are not a spam bot. This helps use keep our service safe from spammers, and keep server cost down, so that we can keep providing you with this service free of cost. If you have any problems using our The Arcana: A Mystic Romance hack or need further help please leave a comment below. Cheats for hacking The Arcana: A Mystic Romance : Coin Pack 1 $1.99 >>> “FREE” Coin Pack 2 $4.99 >>> “FREE” Coin Pack 5 $9.99 >>> “FREE” Key Refill 1 $0.99 >>> “FREE” Coin Pack 7 $39.99 >>> “FREE” The Arcana: A Mystic Romance console Features: * Get ALl in-app Items for free * Get Unlimited lives for free * Skip or unlock any level your want More about The Arcana: A Mystic Romance The Arcana is a Romance/Mystery story game set in a mystical Tarot world. Download the best kept secret in the app store. You are a prodigy of the magical arts, left to your own devices by your wandering mentor. Make choices that shape your story as you fall headfirst into a whirlwind adventure, filled with a colorful cast of characters who all have one thing in common: their interest in you. Delve deep into the heart of intrigue, and uncover a murder mystery that still hangs over the city like a shroud. Be careful what secrets you reveal and what choices you make… yours is not the only fate that hangs in the balance. Magic, romance and mystery await. Who will you choose to love and who will you choose to condemn? How will your story unravel? The Arcana is a luxurious and interactive visual story and otome inspired dating sim. You can choose your own romance, story, and date your choice of characters. The mystery unfurls in 22 books (or episodes) that correspond to the 22 Major Arcana cards in Tarot. Inside The Arcana, a player in search of love, romance, magic and mystery can: – indulge in a romance with a character of your choice (or romance them all!) – make choices through an otome-inspired, interactive murder mystery story – choose your own pronouns and be whoever you want to be – play a dating sim with a twist – fall in love… Want to know more about this romance game? Check out our FAQ page: http://bit.ly/Arcana Join our growing community @thearcanagame on Tumblr, Instagram and Twitter. #thearcanagame love, The Arcana The Arcana: A Mystic Romance cheat codes How to hack The Arcana: A Mystic Romance Cheats for The Arcana: A Mystic Romance Hack The Arcana: A Mystic Romance The Arcana: A Mystic Romance mod apk
dcstechnoweb / The Reason Why Everyone Love Mining ToolsAs a rule, mining alludes to the birth of minerals and land accoutrements from the earth, from a gravestone or seal. moment different factors are mended by mining as similar accoutrements aren't developed, horticulturally handled, or misleadingly made. Superb models are precious essence, coal, precious monuments, and gold. Non-inexhaustible sources like ignitable gas, petrol, and indeed water are also booby-trapped. With the application of applicable mining tackle, the vigorous and worrisome undertaking of mining is achieved. Mining is the system involved in disengaging significant minerals and geographical accoutrements. typically, the minerals got from the mining system incorporate earth, coal, aspect gravestone, rock, gemstone, limestone, essence, potash, gemstone swab, and oil painting shale, and that is just the morning. As is generally said, whatever could not be developed through agricultural ways or made in a factory must be booby-trapped. Ever, indeed the birth of ignitable gas, oil painting, and other on-inexhaustible means are also acquired through mining. Mining is a sedulity that has been around for glories. it's been used on an outsized scale since neolithic times and contains long history. Mining makes use of technology so on urge the duty finished effectiveness and through a timely manner. There are numerous several mining tools that should be got used for the duty to be done duly. From the tricks of the trade to plenty of introductory, DCS Techno can give you a regard at completely different mining tools. for several years, the strategy for earning enough to pay the bills was by finding a replacement line of labour and dealing during a mine. Mining tools are a significant tool employed in creating a good kind of minerals from the planet. From the method of rooting minerals, they're also employed in a spread of consumer products. Mining Tools are utilized in the timber of buses, electronics, and jewellery. they're utilized in nearly every hand of society. Presently assuming that you just want to induce minerals from the planet, you need to have the correct apparatuses. Exercising proper and probative gear can make mining tasks more straightforward and more helpful. These are the foundation of any mining association so you should be careful while copping the needed effects. You can browse different tackle, with changing purposes and purposes, to do the errands hastily. Be open and feel free to this gear in the event that you realize it can help with expanding your tasks' effectiveness. The mining business has five significant sections coal mining, gas, and oil painting separating, essence mining, non-metal mining, and supporting exercises. Besides farther developing the exertion sluice, this tackle ought to likewise expand the degree of the well- being of the sloggers hard. As the owner, it's your obligation to take care of their musts. Pre-Historic Mining Early mortal progress has used the world's means through digging for a multifariousness of purposes. an outsized portion of the minerals and components mined in early times were utilized for the assembly of weapons and different devices. During these times, top-notch stone, which happens in masses of sedimentary rocks, was at that time pursued in pieces of Europe. They were utilized as weapons within that period of time. irrespective of the restricted mining gear, Neanderthal men had the choice to quarry and make ad-libbed instruments. Glancing back at history, specialists would affirm that metal and stone mining has been essential for everyday living since antiquated times. In our times, present-day techniques and hardware are being employed to formwork essentially simpler and increment usefulness. these days, the foremost common way of mining includes fundamental advances, recognizing regions where metal bodies are often gotten, dissecting its conceivable benefit, extricating the materials, and afterward recovering the land after the activity is finished up. Obviously, security could be a limitless need, particularly during the mining system itself. Working in such a setting is extremely hazardous thus security estimates should be painstakingly noticed. In view of their riches and influence, the antiquated Egypt civilization was one of the primaries to effectively mine minerals. They were accustomed mine is malachite and gold. The green stones were used for the foremost a part of stoneware and as trimmings. Later on, involving iron devices as mining hardware, they sought minerals, generally gold from Nubia. The stone containing the mineral is ready against a stone face to warm it and afterward drenched with water. Fire-setting was maybe the foremost well-known strategy for mining it slows ago. Mining predisposition is the stuff utilized in eliminating a wide bunch of minerals from the earth. Booby-trapped minerals are employed in enough important every paperback item from vehicles to attack, to gems and also some.These means are acquired using different feathers of mining instruments. Mining Tools are the outfit used in lodging a wide array of minerals from the earth. They range from the most introductory outfit, analogous as shovels and picks, to complex ministry, like drills and crushers. Booby- trapped minerals are used in nearly every consumer product — from motorcars, to electronics, to jewellery and further. They are employed to make everything from plastics to iPads and mobile phones. The mining business is formed out of assorted players, everyone with a basic job to create the work fruitful and compelling. most significantly, the general public authority handles the assignments of overseeing mineral cases moreover as giving investigation grants. Miners, then again, accomplish crafted by using topographical guides and other significant apparatuses to tell apart minerals. Junior investigation organizations are those accountable for testing stores for any attractive metals. Generally speaking, these organizations additionally own working mines. Then, significant mining organizations employ gifted people to accomplish the real work of mining. also flashed back for the rundown are the specialists. These experts handle sophisticated lab work and similar. Administration’s suppliers are likewise significant and they can come in colorful administrations, for illustration, geologists, copter aviators, instructors, and multitudinous others. Gear providers, development associations, assiduity confederations, and fiscal exchange fiscal backers also play their own corridor to satisfy to finish the advanced perspective. It was the Romans who made extraordinary advancements throughout the entire actuality of mining. They were the original bones to use enormous compass quarrying strategies, for illustration, the application of volumes of water to work introductory outfit, exclude trash, etc. This has come to be known as water-fueled mining or hydraulicking. This is a type of mining that utilizes high-constrained shocks of water to move jewels and other residue and detritus. During the 1300s, the interest in essence brands, coverings, and different munitions expanded drastically. further minerals, for illustration, iron, and tableware were extensively excavated. The interest to deliver coins also expanded with the eventual result of causing a lack of tableware. During this period, iron turned into an abecedarian part of structure developments; outfits and other mining gear came pervasive. from open-hole mining, water shops and dark greasepaint have developed into tractors, snares, exchanges, etc. Other mechanical advancements, for illustration, green light rays employed in mining as aphorism attendants and machine arrangements helped diggers with quarrying lands. Feathers of Mining Tools The mining sedulity has a wide compass of endlessly booby-trapping outfits. The mining business is a mind- boggling frame that requires an outfit that not just meets the particular musts of the mining business, yet likewise resolves the issues of the guests. thus, mining associations need gear like cherries on top, safeguards, sizers, the cherry on top pails, gyratory cherries on top, sway cherries on top, and jaw cherries on top. From-noteworthy bias, a huge outfit is presently used to successfully and incontinently uncover lands. These are also used to separate and exclude jewels, indeed mountains. Exceptionally designed gear presently helps in the birth of different precious minerals and other had relations with accoutrements like gypsum and swab. moment, there are as of now five classes of mining coal, essence, on-metallic mineral mining, oil painting, and gas birth. oil painting and gas birth stay to be maybe the topmost business in this present reality. Any intrigued fiscal backer or business person who needs to probe the mining business should communicate to the concerned neighbourhood government workplaces to get some information about the musts. By reaching out to these means, important data will be gotten and every one of the abecedarian advances will be illustrated. Poring the authority spots of mining associations can likewise be useful in realizing mining. Mining is a sedulity that has been around for prodigies. It has been employed on an outsized scale since neolithic times and contains long history. Mining utilizes invention to encourage the obligation to finish with acceptability and as soon as possible. There are colorful mining accoutrements that must be landed employed for the position to be done meetly. From the subtle strategies to a ton of early on, DCS Techno can offer you admire to completely unique mining instruments. From now onward, indefinitely quite a while, the system for making with the end result of dealing with the bills was by finding one more calling and working in a mine Mining instrument are a necessary outfit employed in making a wide multifariousness of minerals from the earth. From the system involved with establishing minerals, they're also employed in a multifariousness of client particulars. Mining Tools are employed in the timber of transports, tackle, and gems. They're employed in basically every hand of society. By and by awaiting that you need to get minerals from the earth, you should have the right bias. Mining inclination is the stuff employed in barring a wide bunch of minerals from the earth. Booby- caught minerals are employed inadequate important every softcover thing from vehicles to handle, to jewels and likewise some. also, minerals like uranium and coal are critical energy sources that record for half of the US's energy force. These coffers are gained exercising colorful awards of mining instruments. Mining Tools are the outfit employed in establishing a wide cluster of minerals from the earth. They range from the most starting outfit, relative as digging tools and picks, to complex tackle, analogous to drills and cherries on top. Booby- caught minerals are employed in nearly every buyer item from transports to widgets, to doodads and further. Mining Industry Overview Mining falls into two classes Surface mining and underground mining. The kind of minerals and mining ways that a mining trouble is trying to suppose will directly enlighten which instruments tractors use in their work. 1.Surface Mining This involves the mining of minerals located at or near the face of the earth. This are the six step processes and these are • Strip Mining- this involves the stripping of the earth's face by the heavy ministry. This system is generally targeted at rooting coal or sedimentary jewels that lay near the earth's face. • Placer Mining- this involves the birth of sediments in beach or clay. It's a simple, old- fashioned way of mining. This system is generally applicable to gold and precious gems that are carried by the inflow of water. • Mountain Top Mining- this is a new system that involves blasting a mountain top to expose coal deposits that lie underneath the mountain crest. • Hydraulic Mining- this is an obsolete system that involves jetting the side of a mountain or hill with high-pressure water to expose gold and another precious essence. • Dredging- it involves the junking of jewels, beaches, and ground underneath a body of water to expose the minerals. • Open hole- this is the most common mining system. It involves the junking of the top layers of soil in the hunt for gold or buried treasure. The miner digs deeper and deeper until a large, open hole is created. 2. Underground Mining This is the process in which a lair is made into the earth to find the mineral ore. The mining operation is generally performed with the use of underground mining outfits. Underground mining is done through the following styles • pitch Mining- it involves the creation of pitches into the ground in order to reach the ore or mineral deposit. This cycle is for the most part applied in coal mining. • Hard gemstone- this system uses dynamite or giant drills to produce large, deep coverts. The miners support the coverts with pillars to help them from collapsing. This is a large- scale mining process and is generally applied in the birth of large bobby, drum, lead, gold, or tableware deposits. • Drift mining- this system is applicable only when the target mineral is accessible from the side of a mountain. It involves the creation of a lair that is slightly lower than the target mineral. The graveness makes the deposit fall to the lair where miners can collect them. • Shaft system- this involves the creation of a perpendicular hallway that goes deep down underground where the deposit is located. Because of the depth, miners are brought in and out of the hole with elevators. • Borehole system- this involves the use of a large drill and high-pressure water to eject the target mineral. These are the introductory styles used in the birth of common minerals. There are more complex systems, but still, they're grounded on these abecedarian processes. To Know more information about Mining Tools Visit: dcstechno Contact us: Plot No 169, Road No.11, Prashasan Nagar, Jubilee Hills, Hyderabad, Telangana – 500096 +91-9849009875
