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dimitrinicolas / LeptoAutomated image Editing, Optimization and Analysis via CLI and a web interface. You give to lepto your input and output directories, the plugins you want to use and their options. Then lepto does his job, you keep your original files and the structure of the input directory. Some plugins can even collect data (like primary colors) from your images and save them in a JSON file.
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.
IndEcol / PymrioMulti-Regional Input-Output Analysis in Python.
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
jspw / VS Code ConfigVS Code Setting (Live input output) to make competitive programming easy and program analysis !
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
it-is-me-mario / MARIOMultifunctional Analysis of Regions through Input-Output
quasiblob / ComfyUI EsesImageCompareInteractive A/B image comparison node with a draggable slider to reveal one image over another. Includes difference and other blend modes for more detailed analysis, allowing one to spot changes in similar images. Node also outputs a passthrough image of input A, and a grayscale difference mask.
Komal01 / Phishing URL DetectionPhishing website detection system provides strong security mechanism to detect and prevent phishing domains from reaching user. This project presents a simple and portable approach to detect spoofed webpages and solve security vulnerabilities using Machine Learning. It can be easily operated by anyone since all the major tasks are happening in the backend. The user is required to provide URL as input to the GUI and click on submit button. The output is shown as “YES” for phishing URL and “NO” for not phished URL. PYTHON DEPENDENCIES: • NumPy, Pandas, Scikit-learn: For Data cleaning, Data analysis and Data modelling. • Pickle: For exporting the model to local machine • Tkinter, Pyqt, QtDesigner: For building up the Graphical User Interface (GUI) of the software. To avoid the pain of installing independent packages and libraries of python, install Anaconda from www.anaconda.com. It is a Python data science platform which has all the ML libraries, Data analysis libraries, Jupyter Notebooks, Spyder etc. built in it which makes it easy to use and efficient. Steps to be followed for running the code of the software: • Install anaconda in the system. • gui.py : It contains the code for the GUI and is linked to other modules of the software. • Feature_extractor.py: It contains the code of Data analysis and data modelling. • Rf_model.py: It contains the trained machine learning model. • Only gui.py is to be run to execute the whole software.
RfastOfficial / Rfast2A collection of Rfast2 functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.
mcuntz / Jams FortranA collection of general Fortran modules in the categories Computational, Date and Time, Input / Output, Math / Numerics, Screening, Sensitivity Analysis and Optimising / Fitting, and Miscellaneous.
adamlaho / AMLPAMLP integrates dataset creation, input/output handling, and analysis for machine learning interatomic potentials. It supports Gaussian, VASP, and CP2K, with LLM agents for code selection and ASE-based AMLP-Analysis for molecular simulations and validation.
beyondepic / PyspaA python package for conducting structural path analysis on square technological matrices of process or input-output data, using environmental, social and/or financial satellites
CIRAIG / OpenIO CanadaModule to create symmetric Environmentally Extended Input-Output tables for Canada.
CMLPlatform / PycirkA python package to model Circular Economy policy and technological interventions in Environmentally Extended Input-Output Analysis starting from mrSUTs (EXIOBASE V3.3)
hpnog / JavaDependenceGraphThis tool is a Program Dependence Graph generator for a given input file in the programming language Java that can be outputed as a dot file. It's wrapped around an easy to use GUI for a better analysis of the code provided to the application through the intermediate representation of a PDG.
ultranet1 / APACHE AIRFLOW DATA PIPELINESProject Description: A music streaming company wants to introduce more automation and monitoring to their data warehouse ETL pipelines and they have come to the conclusion that the best tool to achieve this is Apache Airflow. As their Data Engineer, I was tasked to create a reusable production-grade data pipeline that incorporates data quality checks and allows for easy backfills. Several analysts and Data Scientists rely on the output generated by this pipeline and it is expected that the pipeline runs daily on a schedule by pulling new data from the source and store the results to the destination. Data Description: The source data resides in S3 and needs to be processed in a data warehouse in Amazon Redshift. The source datasets consist of JSON logs that tell about user activity in the application and JSON metadata about the songs the users listen to. Data Pipeline design: At a high-level the pipeline does the following tasks. Extract data from multiple S3 locations. Load the data into Redshift cluster. Transform the data into a star schema. Perform data validation and data quality checks. Calculate the most played songs for the specified time interval. Load the result back into S3. dag Structure of the Airflow DAG Design Goals: Based on the requirements of our data consumers, our pipeline is required to adhere to the following guidelines: The DAG should not have any dependencies on past runs. On failure, the task is retried for 3 times. Retries happen every 5 minutes. Catchup is turned off. Do not email on retry. Pipeline Implementation: Apache Airflow is a Python framework for programmatically creating workflows in DAGs, e.g. ETL processes, generating reports, and retraining models on a daily basis. The Airflow UI automatically parses our DAG and creates a natural representation for the movement and transformation of data. A DAG simply is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG describes how you want to carry out your workflow, and Operators determine what actually gets done. By default, airflow comes with some simple built-in operators like PythonOperator, BashOperator, DummyOperator etc., however, airflow lets you extend the features of a BaseOperator and create custom operators. For this project, I developed several custom operators. operators The description of each of these operators follows: StageToRedshiftOperator: Stages data to a specific redshift cluster from a specified S3 location. Operator uses templated fields to handle partitioned S3 locations. LoadFactOperator: Loads data to the given fact table by running the provided sql statement. Supports delete-insert and append style loads. LoadDimensionOperator: Loads data to the given dimension table by running the provided sql statement. Supports delete-insert and append style loads. SubDagOperator: Two or more operators can be grouped into one task using the SubDagOperator. Here, I am grouping the tasks of checking if the given table has rows and then run a series of data quality sql commands. HasRowsOperator: Data quality check to ensure that the specified table has rows. DataQualityOperator: Performs data quality checks by running sql statements to validate the data. SongPopularityOperator: Calculates the top ten most popular songs for a given interval. The interval is dictated by the DAG schedule. UnloadToS3Operator: Stores the analysis result back to the given S3 location. Code for each of these operators is located in the plugins/operators directory. Pipeline Schedule and Data Partitioning: The events data residing on S3 is partitioned by year (2018) and month (11). Our task is to incrementally load the event json files, and run it through the entire pipeline to calculate song popularity and store the result back into S3. In this manner, we can obtain the top songs per day in an automated fashion using the pipeline. Please note, this is a trivial analyis, but you can imagine other complex queries that follow similar structure. S3 Input events data: s3://<bucket>/log_data/2018/11/ 2018-11-01-events.json 2018-11-02-events.json 2018-11-03-events.json .. 2018-11-28-events.json 2018-11-29-events.json 2018-11-30-events.json S3 Output song popularity data: s3://skuchkula-topsongs/ songpopularity_2018-11-01 songpopularity_2018-11-02 songpopularity_2018-11-03 ... songpopularity_2018-11-28 songpopularity_2018-11-29 songpopularity_2018-11-30 The DAG can be configured by giving it some default_args which specify the start_date, end_date and other design choices which I have mentioned above. default_args = { 'owner': 'shravan', 'start_date': datetime(2018, 11, 1), 'end_date': datetime(2018, 11, 30), 'depends_on_past': False, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=5), 'catchup_by_default': False, 'provide_context': True, } How to run this project? Step 1: Create AWS Redshift Cluster using either the console or through the notebook provided in create-redshift-cluster Run the notebook to create AWS Redshift Cluster. Make a note of: DWN_ENDPOINT :: dwhcluster.c4m4dhrmsdov.us-west-2.redshift.amazonaws.com DWH_ROLE_ARN :: arn:aws:iam::506140549518:role/dwhRole Step 2: Start Apache Airflow Run docker-compose up from the directory containing docker-compose.yml. Ensure that you have mapped the volume to point to the location where you have your DAGs. NOTE: You can find details of how to manage Apache Airflow on mac here: https://gist.github.com/shravan-kuchkula/a3f357ff34cf5e3b862f3132fb599cf3 start_airflow Step 3: Configure Apache Airflow Hooks On the left is the S3 connection. The Login and password are the IAM user's access key and secret key that you created. Basically, by using these credentials, we are able to read data from S3. On the right is the redshift connection. These values can be easily gathered from your Redshift cluster connections Step 4: Execute the create-tables-dag This dag will create the staging, fact and dimension tables. The reason we need to trigger this manually is because, we want to keep this out of main dag. Normally, creation of tables can be handled by just triggering a script. But for the sake of illustration, I created a DAG for this and had Airflow trigger the DAG. You can turn off the DAG once it is completed. After running this DAG, you should see all the tables created in the AWS Redshift. Step 5: Turn on the load_and_transform_data_in_redshift dag As the execution start date is 2018-11-1 with a schedule interval @daily and the execution end date is 2018-11-30, Airflow will automatically trigger and schedule the dag runs once per day for 30 times. Shown below are the 30 DAG runs ranging from start_date till end_date, that are trigged by airflow once per day. schedule
denman2328 / Help------------------ System Information ------------------ Time of this report: 8/10/2013, 08:36:20 Machine name: BRYCE-PC Operating System: Windows 8 Pro 64-bit (6.2, Build 9200) (9200.win8_rtm.120725-1247) Language: English (Regional Setting: English) System Manufacturer: To Be Filled By O.E.M. System Model: To Be Filled By O.E.M. BIOS: BIOS Date: 04/13/12 20:22:30 Ver: 04.06.05 Processor: Intel(R) Core(TM) i5-3570K CPU @ 3.40GHz (4 CPUs), ~3.4GHz Memory: 8192MB RAM Available OS Memory: 8086MB RAM Page File: 4736MB used, 11541MB available Windows Dir: C:\WINDOWS DirectX Version: DirectX 11 DX Setup Parameters: Not found User DPI Setting: Using System DPI System DPI Setting: 96 DPI (100 percent) DWM DPI Scaling: Disabled DxDiag Version: 6.02.9200.16384 64bit Unicode ------------ DxDiag Notes ------------ Display Tab 1: No problems found. Display Tab 2: No problems found. Sound Tab 1: No problems found. Sound Tab 2: No problems found. Sound Tab 3: No problems found. 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Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YUY2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(UYVY,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(YV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(NV12,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC1,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC2,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC3,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend {BF752EF6-8CC4-457A-BE1B-08BD1CAEEE9F}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,1) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_EdgeFiltering {335AA36E-7884-43A4-9C91-7F87FAF3E37E}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend DeinterlaceTech_BOBVerticalStretch {5A54A0C9-C7EC-4BD9-8EDE-F3C75DC4393B}: Format(In/Out)=(IMC4,YUY2) Frames(Prev/Fwd/Back)=(0,0,0) Caps=VideoProcess_YUV2RGB VideoProcess_StretchX VideoProcess_StretchY VideoProcess_AlphaBlend D3D9 Overlay: Supported DXVA-HD: Supported DDraw Status: Enabled D3D Status: Enabled AGP Status: Enabled ------------- Sound Devices ------------- Description: Speakers (Plantronics GameCom 780) Default Sound Playback: Yes Default Voice Playback: Yes Hardware ID: USB\VID_047F&PID_C010&REV_0100&MI_00 Manufacturer ID: 65535 Product ID: 65535 Type: WDM Driver Name: USBAUDIO.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/26/2012 03:26:27, 121856 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No Description: Speakers (High Definition Audio Device) Default Sound Playback: No Default Voice Playback: No Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0899&SUBSYS_18491898&REV_1000 Manufacturer ID: 1 Product ID: 65535 Type: WDM Driver Name: HdAudio.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/26/2012 03:26:51, 339968 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No Description: Digital Audio (S/PDIF) (High Definition Audio Device) Default Sound Playback: No Default Voice Playback: No Hardware ID: HDAUDIO\FUNC_01&VEN_10EC&DEV_0899&SUBSYS_18491898&REV_1000 Manufacturer ID: 1 Product ID: 65535 Type: WDM Driver Name: HdAudio.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail WHQL Logo'd: Yes Date and Size: 7/26/2012 03:26:51, 339968 bytes Other Files: Driver Provider: Microsoft HW Accel Level: Basic Cap Flags: 0xF1F Min/Max Sample Rate: 100, 200000 Static/Strm HW Mix Bufs: 1, 0 Static/Strm HW 3D Bufs: 0, 0 HW Memory: 0 Voice Management: No EAX(tm) 2.0 Listen/Src: No, No I3DL2(tm) Listen/Src: No, No Sensaura(tm) ZoomFX(tm): No --------------------- Sound Capture Devices --------------------- Description: Microphone (Plantronics GameCom 780) Default Sound Capture: Yes Default Voice Capture: Yes Driver Name: USBAUDIO.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail Date and Size: 7/26/2012 03:26:27, 121856 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: SPDIF Interface (Plantronics GameCom 780) Default Sound Capture: No Default Voice Capture: No Driver Name: USBAUDIO.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail Date and Size: 7/26/2012 03:26:27, 121856 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF Description: Line (Plantronics GameCom 780) Default Sound Capture: No Default Voice Capture: No Driver Name: USBAUDIO.sys Driver Version: 6.02.9200.16384 (English) Driver Attributes: Final Retail Date and Size: 7/26/2012 03:26:27, 121856 bytes Cap Flags: 0x1 Format Flags: 0xFFFFF ------------------- DirectInput Devices ------------------- Device Name: Mouse Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Keyboard Attached: 1 Controller ID: n/a Vendor/Product ID: n/a FF Driver: n/a Device Name: Plantronics GameCom 780 Attached: 1 Controller ID: 0x0 Vendor/Product ID: 0x047F, 0xC010 FF Driver: n/a Poll w/ Interrupt: No ----------- USB Devices ----------- + USB Root Hub | Vendor/Product ID: 0x8086, 0x1E2D | Matching Device ID: USB\ROOT_HUB20 | Service: usbhub | Driver: usbhub.sys, 7/26/2012 06:00:58, 496368 bytes | Driver: usbd.sys, 7/26/2012 06:00:58, 21744 bytes | +-+ Generic USB Hub | | Vendor/Product ID: 0x8087, 0x0024 | | Location: Port_#0001.Hub_#0001 | | Matching Device ID: USB\Class_09 | | Service: usbhub | | Driver: usbhub.sys, 7/26/2012 06:00:58, 496368 bytes | | Driver: usbd.sys, 7/26/2012 06:00:58, 21744 bytes ---------------- Gameport Devices ---------------- ------------ PS/2 Devices ------------ + Standard PS/2 Keyboard | Matching Device ID: *PNP0303 | Service: i8042prt | Driver: i8042prt.sys, 7/26/2012 03:28:51, 112640 bytes | Driver: kbdclass.sys, 7/26/2012 06:00:52, 48368 bytes | + HID Keyboard Device | Vendor/Product ID: 0x1532, 0x0015 | Matching Device ID: HID_DEVICE_SYSTEM_KEYBOARD | Service: kbdhid | Driver: kbdhid.sys, 7/26/2012 03:28:49, 29184 bytes | Driver: kbdclass.sys, 7/26/2012 06:00:52, 48368 bytes | + HID-compliant mouse | Vendor/Product ID: 0x1532, 0x0015 | Matching Device ID: HID_DEVICE_SYSTEM_MOUSE | Service: mouhid | Driver: mouhid.sys, 7/26/2012 03:28:47, 26112 bytes | Driver: mouclass.sys, 7/26/2012 06:00:55, 45808 bytes ------------------------ Disk & DVD/CD-ROM Drives ------------------------ Drive: C: Free Space: 1843.8 GB Total Space: 1874.6 GB File System: NTFS Model: ST2000DM001-1CH164 Drive: D: Free Space: 273.0 GB Total Space: 715.4 GB File System: NTFS Model: WDC WD7500AACS-00D6B0 Drive: E: Model: PIONEER DVD-RW DVR-220L Driver: c:\windows\system32\drivers\cdrom.sys, 6.02.9200.16384 (English), 7/26/2012 03:26:36, 174080 bytes -------------- System Devices -------------- Name: Intel(R) 7 Series/C216 Chipset Family USB Enhanced Host Controller - 1E2D Device ID: PCI\VEN_8086&DEV_1E2D&SUBSYS_1E2D1849&REV_04\3&11583659&0&D0 Driver: C:\WINDOWS\system32\drivers\usbehci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 78576 bytes Driver: C:\WINDOWS\system32\drivers\usbport.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 487664 bytes Driver: C:\WINDOWS\system32\drivers\usbhub.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 496368 bytes Name: Intel(R) 7 Series/C216 Chipset Family USB Enhanced Host Controller - 1E26 Device ID: PCI\VEN_8086&DEV_1E26&SUBSYS_1E261849&REV_04\3&11583659&0&E8 Driver: C:\WINDOWS\system32\drivers\usbehci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 78576 bytes Driver: C:\WINDOWS\system32\drivers\usbport.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 487664 bytes Driver: C:\WINDOWS\system32\drivers\usbhub.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 496368 bytes Name: High Definition Audio Controller Device ID: PCI\VEN_8086&DEV_1E20&SUBSYS_18981849&REV_04\3&11583659&0&D8 Driver: C:\WINDOWS\system32\DRIVERS\hdaudbus.sys, 6.02.9200.16384 (English), 7/26/2012 03:27:36, 71168 bytes Name: Intel(R) 7 Series/C216 Chipset Family SMBus Host Controller - 1E22 Device ID: PCI\VEN_8086&DEV_1E22&SUBSYS_1E221849&REV_04\3&11583659&0&FB Driver: n/a Name: Intel(R) 7 Series/C216 Chipset Family PCI Express Root Port 5 - 1E18 Device ID: PCI\VEN_8086&DEV_1E18&SUBSYS_1E181849&REV_C4\3&11583659&0&E4 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Intel(R) 7 Series/C216 Chipset Family PCI Express Root Port 4 - 1E16 Device ID: PCI\VEN_8086&DEV_1E16&SUBSYS_1E161849&REV_C4\3&11583659&0&E3 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: High Definition Audio Controller Device ID: PCI\VEN_10DE&DEV_0E0A&SUBSYS_355A1458&REV_A1\4&15001D53&0&0108 Driver: C:\WINDOWS\system32\DRIVERS\hdaudbus.sys, 6.02.9200.16384 (English), 7/26/2012 03:27:36, 71168 bytes Name: Intel(R) 7 Series/C216 Chipset Family PCI Express Root Port 1 - 1E10 Device ID: PCI\VEN_8086&DEV_1E10&SUBSYS_1E101849&REV_C4\3&11583659&0&E0 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Intel(R) HD Graphics 4000 Device ID: PCI\VEN_8086&DEV_0162&SUBSYS_01621849&REV_09\3&11583659&0&10 Driver: C:\WINDOWS\system32\DRIVERS\igdkmd64.sys, 9.17.0010.2932 (English), 12/14/2012 02:42:22, 5353888 bytes Driver: C:\WINDOWS\system32\igdumd64.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:34, 12615680 bytes Driver: C:\WINDOWS\system32\igd10umd64.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:26, 12858368 bytes Driver: C:\WINDOWS\system32\igfxcmrt64.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:20, 518656 bytes Driver: C:\WINDOWS\system32\igfx11cmrt64.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:26, 483840 bytes Driver: C:\WINDOWS\system32\igfxcmjit64.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:12, 3511296 bytes Driver: C:\WINDOWS\system32\IccLibDll_x64.dll, 12/14/2012 02:42:12, 94208 bytes Driver: C:\WINDOWS\system32\igcodeckrng700.bin, 12/14/2012 02:42:24, 754652 bytes Driver: C:\WINDOWS\system32\igvpkrng700.bin, 12/14/2012 02:42:24, 598384 bytes Driver: C:\WINDOWS\SysWow64\igcodeckrng700.bin, 12/14/2012 02:42:24, 754652 bytes Driver: C:\WINDOWS\SysWow64\igvpkrng700.bin, 12/14/2012 02:42:24, 598384 bytes Driver: C:\WINDOWS\system32\igdde64.dll, 12/14/2012 02:42:24, 80384 bytes Driver: C:\WINDOWS\SysWow64\igdde32.dll, 12/14/2012 02:42:30, 64512 bytes Driver: C:\WINDOWS\system32\iglhxs64.vp, 12/14/2012 02:42:20, 17102 bytes Driver: C:\WINDOWS\system32\iglhxo64.vp, 6/2/2012 15:32:34, 59425 bytes Driver: C:\WINDOWS\system32\iglhxc64.vp, 6/2/2012 15:32:34, 59230 bytes Driver: C:\WINDOWS\system32\iglhxg64.vp, 6/2/2012 15:32:34, 59398 bytes Driver: C:\WINDOWS\system32\iglhxo64_dev.vp, 6/2/2012 15:32:34, 58109 bytes Driver: C:\WINDOWS\system32\iglhxc64_dev.vp, 6/2/2012 15:32:34, 59104 bytes Driver: C:\WINDOWS\system32\iglhxg64_dev.vp, 6/2/2012 15:32:34, 58796 bytes Driver: C:\WINDOWS\system32\iglhxa64.vp, 6/2/2012 15:32:34, 1074 bytes Driver: C:\WINDOWS\system32\iglhxa64.cpa, 6/2/2012 15:32:34, 1981696 bytes Driver: C:\WINDOWS\system32\iglhcp64.dll, 3.00.0001.0016 (English), 12/14/2012 02:42:10, 216064 bytes Driver: C:\WINDOWS\system32\iglhsip64.dll, 3.00.0000.0012 (English), 12/14/2012 02:42:24, 524800 bytes Driver: C:\WINDOWS\SysWow64\igdumd32.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:24, 11049472 bytes Driver: C:\WINDOWS\SysWow64\igfxdv32.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 330752 bytes Driver: C:\WINDOWS\SysWow64\igd10umd32.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:30, 11174912 bytes Driver: C:\WINDOWS\SysWow64\iglhcp32.dll, 3.00.0001.0015 (English), 12/14/2012 02:42:30, 180224 bytes Driver: C:\WINDOWS\SysWow64\iglhsip32.dll, 3.00.0000.0012 (English), 12/14/2012 02:42:24, 519680 bytes Driver: C:\WINDOWS\SysWow64\IntelCpHeciSvc.exe, 1.00.0001.0014 (English), 12/14/2012 02:42:10, 277616 bytes Driver: C:\WINDOWS\SysWow64\igfxcmrt32.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:28, 640512 bytes Driver: C:\WINDOWS\SysWow64\igfx11cmrt32.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:24, 459264 bytes Driver: C:\WINDOWS\SysWow64\igfxcmjit32.dll, 2.04.0000.1019 (English), 12/14/2012 02:42:28, 3121152 bytes Driver: C:\WINDOWS\system32\difx64.exe, 1.04.0002.0000 (English), 12/14/2012 02:42:22, 185968 bytes Driver: C:\WINDOWS\system32\hccutils.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 110592 bytes Driver: C:\WINDOWS\system32\igfxsrvc.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 64000 bytes Driver: C:\WINDOWS\system32\igfxsrvc.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:28, 512112 bytes Driver: C:\WINDOWS\system32\igfxpph.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:34, 384512 bytes Driver: C:\WINDOWS\system32\igfxcpl.cpl, 8.15.0010.2932 (English), 12/14/2012 02:42:16, 126976 bytes Driver: C:\WINDOWS\system32\igfxdev.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:16, 442880 bytes Driver: C:\WINDOWS\system32\igfxdo.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:24, 142336 bytes Driver: C:\WINDOWS\system32\igfxtray.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:14, 172144 bytes Driver: C:\WINDOWS\system32\hkcmd.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:10, 399984 bytes Driver: C:\WINDOWS\system32\igfxress.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:26, 9007616 bytes Driver: C:\WINDOWS\system32\igfxpers.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:14, 441968 bytes Driver: C:\WINDOWS\system32\igfxTMM.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:14, 410112 bytes Driver: C:\WINDOWS\system32\gfxSrvc.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:12, 175104 bytes Driver: C:\WINDOWS\system32\GfxUI.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:12, 5906032 bytes Driver: C:\WINDOWS\system32\GfxUI.exe.config, 12/14/2012 02:42:28, 268 bytes Driver: C:\WINDOWS\system32\IGFXDEVLib.dll, 1.00.0000.0000 (Invariant Language), 12/14/2012 02:42:36, 9728 bytes Driver: C:\WINDOWS\system32\igfxext.exe, 8.15.0010.2932 (English), 12/14/2012 02:42:28, 255088 bytes Driver: C:\WINDOWS\system32\igfxexps.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 28672 bytes Driver: C:\WINDOWS\SysWow64\igfxexps32.dll, 8.15.0010.2932 (English), 12/14/2012 02:42:22, 25088 bytes Driver: C:\WINDOWS\system32\igfxrara.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 435712 bytes Driver: C:\WINDOWS\system32\igfxrchs.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:26, 428544 bytes Driver: C:\WINDOWS\system32\igfxrcht.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 429056 bytes Driver: C:\WINDOWS\system32\igfxrdan.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:16, 437248 bytes Driver: C:\WINDOWS\system32\igfxrdeu.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:28, 438784 bytes Driver: C:\WINDOWS\system32\igfxrenu.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:24, 286208 bytes Driver: C:\WINDOWS\system32\igfxresn.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:26, 439808 bytes Driver: C:\WINDOWS\system32\igfxrfin.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:20, 438272 bytes Driver: C:\WINDOWS\system32\igfxrfra.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:14, 439808 bytes Driver: C:\WINDOWS\system32\igfxrheb.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 435712 bytes Driver: C:\WINDOWS\system32\igfxrhrv.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:12, 438784 bytes Driver: C:\WINDOWS\system32\igfxrita.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:16, 438784 bytes Driver: C:\WINDOWS\system32\igfxrjpn.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:20, 432128 bytes Driver: C:\WINDOWS\system32\igfxrkor.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 431104 bytes Driver: C:\WINDOWS\system32\igfxrnld.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:28, 438784 bytes Driver: C:\WINDOWS\system32\igfxrnor.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:36, 437760 bytes Driver: C:\WINDOWS\system32\igfxrplk.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:12, 438784 bytes Driver: C:\WINDOWS\system32\igfxrptb.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 437760 bytes Driver: C:\WINDOWS\system32\igfxrptg.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:24, 438784 bytes Driver: C:\WINDOWS\system32\igfxrrom.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:22, 439296 bytes Driver: C:\WINDOWS\system32\igfxrrus.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:24, 439296 bytes Driver: C:\WINDOWS\system32\igfxrsky.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:12, 438784 bytes Driver: C:\WINDOWS\system32\igfxrslv.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:10, 437760 bytes Driver: C:\WINDOWS\system32\igfxrsve.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:20, 437760 bytes Driver: C:\WINDOWS\system32\igfxrtha.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 437248 bytes Driver: C:\WINDOWS\system32\igfxrcsy.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:22, 438272 bytes Driver: C:\WINDOWS\system32\igfxrell.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:30, 440320 bytes Driver: C:\WINDOWS\system32\igfxrhun.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:16, 438272 bytes Driver: C:\WINDOWS\system32\igfxrtrk.lrc, 8.15.0010.2932 (English), 12/14/2012 02:42:26, 437760 bytes Driver: C:\WINDOWS\system32\Gfxres.ar-SA.resources, 12/14/2012 02:42:30, 166124 bytes Driver: C:\WINDOWS\system32\Gfxres.cs-CZ.resources, 12/14/2012 02:42:12, 142267 bytes Driver: C:\WINDOWS\system32\Gfxres.da-DK.resources, 12/14/2012 02:42:16, 137132 bytes Driver: C:\WINDOWS\system32\Gfxres.de-DE.resources, 12/14/2012 02:42:28, 147360 bytes Driver: C:\WINDOWS\system32\Gfxres.el-GR.resources, 12/14/2012 02:42:30, 209986 bytes Driver: C:\WINDOWS\system32\Gfxres.es-ES.resources, 12/14/2012 02:42:22, 147269 bytes Driver: C:\WINDOWS\system32\Gfxres.en-US.resources, 12/14/2012 02:42:30, 132623 bytes Driver: C:\WINDOWS\system32\Gfxres.fi-FI.resources, 12/14/2012 02:42:22, 141998 bytes Driver: C:\WINDOWS\system32\Gfxres.fr-FR.resources, 12/14/2012 02:42:36, 145470 bytes Driver: C:\WINDOWS\system32\Gfxres.he-IL.resources, 12/14/2012 02:42:10, 158986 bytes Driver: C:\WINDOWS\system32\Gfxres.hr-HR.resources, 12/14/2012 02:42:30, 141038 bytes Driver: C:\WINDOWS\system32\Gfxres.hu-HU.resources, 12/14/2012 02:42:30, 143916 bytes Driver: C:\WINDOWS\system32\Gfxres.it-IT.resources, 12/14/2012 02:42:10, 149649 bytes Driver: C:\WINDOWS\system32\Gfxres.ja-JP.resources, 12/14/2012 02:42:30, 163379 bytes Driver: C:\WINDOWS\system32\Gfxres.ko-KR.resources, 12/14/2012 02:42:24, 148018 bytes Driver: C:\WINDOWS\system32\Gfxres.nb-NO.resources, 12/14/2012 02:42:24, 137793 bytes Driver: C:\WINDOWS\system32\Gfxres.nl-NL.resources, 12/14/2012 02:42:12, 143989 bytes Driver: C:\WINDOWS\system32\Gfxres.pl-PL.resources, 12/14/2012 02:42:26, 142682 bytes Driver: C:\WINDOWS\system32\Gfxres.pt-BR.resources, 12/14/2012 02:42:36, 144235 bytes Driver: C:\WINDOWS\system32\Gfxres.pt-PT.resources, 12/14/2012 02:42:24, 143249 bytes Driver: C:\WINDOWS\system32\Gfxres.ro-RO.resources, 12/14/2012 02:42:22, 145974 bytes Driver: C:\WINDOWS\system32\Gfxres.ru-RU.resources, 12/14/2012 02:42:28, 194121 bytes Driver: C:\WINDOWS\system32\Gfxres.sk-SK.resources, 12/14/2012 02:42:24, 141833 bytes Driver: C:\WINDOWS\system32\Gfxres.sl-SI.resources, 12/14/2012 02:42:22, 137880 bytes Driver: C:\WINDOWS\system32\Gfxres.sv-SE.resources, 12/14/2012 02:42:24, 142876 bytes Driver: C:\WINDOWS\system32\Gfxres.th-TH.resources, 12/14/2012 02:42:12, 223492 bytes Driver: C:\WINDOWS\system32\Gfxres.tr-TR.resources, 12/14/2012 02:42:30, 144637 bytes Driver: C:\WINDOWS\system32\Gfxres.zh-CN.resources, 12/14/2012 02:42:30, 124662 bytes Driver: C:\WINDOWS\system32\Gfxres.zh-TW.resources, 12/14/2012 02:42:12, 126294 bytes Driver: C:\WINDOWS\system32\ig7icd64.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:22, 11633152 bytes Driver: C:\WINDOWS\SysWow64\ig7icd32.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:20, 8621056 bytes Driver: C:\WINDOWS\system32\Intel_OpenCL_ICD64.dll, 1.02.0001.0000 (English), 12/14/2012 02:42:22, 56832 bytes Driver: C:\WINDOWS\system32\IntelOpenCL64.dll, 1.01.0000.1003 (English), 12/14/2012 02:42:26, 241664 bytes Driver: C:\WINDOWS\system32\igdbcl64.dll, 9.17.0010.2884 (English), 12/14/2012 02:42:14, 3581440 bytes Driver: C:\WINDOWS\system32\igdrcl64.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:12, 27664896 bytes Driver: C:\WINDOWS\system32\igdfcl64.dll, 8.01.0000.2932 (English), 12/14/2012 02:42:20, 27457536 bytes Driver: C:\WINDOWS\SysWow64\Intel_OpenCL_ICD32.dll, 1.02.0001.0000 (English), 12/14/2012 02:42:12, 56320 bytes Driver: C:\WINDOWS\SysWow64\IntelOpenCL32.dll, 1.01.0000.1003 (English), 12/14/2012 02:42:36, 196096 bytes Driver: C:\WINDOWS\SysWow64\igdbcl32.dll, 9.17.0010.2884 (English), 12/14/2012 02:42:12, 2898944 bytes Driver: C:\WINDOWS\SysWow64\igdrcl32.dll, 9.17.0010.2932 (English), 12/14/2012 02:42:16, 27643904 bytes Driver: C:\WINDOWS\SysWow64\igdfcl32.dll, 8.01.0000.2932 (English), 12/14/2012 02:42:36, 21850112 bytes Driver: C:\WINDOWS\system32\igfxCoIn_v2932.dll, 1.02.0030.0000 (English), 12/14/2012 02:42:20, 116224 bytes Name: NVIDIA GeForce GTX 670 Device ID: PCI\VEN_10DE&DEV_1189&SUBSYS_355A1458&REV_A1\4&15001D53&0&0008 Driver: C:\Program Files\NVIDIA Corporation\Drs\dbInstaller.exe, 8.17.0013.1106 (English), 2/26/2013 00:32:28, 233760 bytes Driver: C:\Program Files\NVIDIA Corporation\Drs\nvdrsdb.bin, 2/26/2013 00:32:36, 1102808 bytes Driver: C:\WINDOWS\System32\DriverStore\FileRepository\nv_dispi.inf_amd64_67d640ab45cc6b34\NvCplSetupInt.exe, 1.00.0001.0000 (English), 2/26/2013 00:32:22, 73372616 bytes Driver: C:\Program Files (x86)\NVIDIA Corporation\coprocmanager\Nvd3d9wrap.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:42, 286536 bytes Driver: C:\Program Files (x86)\NVIDIA Corporation\coprocmanager\detoured.dll, 2/26/2013 00:32:42, 4096 bytes Driver: C:\Program Files (x86)\NVIDIA Corporation\coprocmanager\nvdxgiwrap.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:40, 193336 bytes Driver: C:\Program Files\NVIDIA Corporation\coprocmanager\Nvd3d9wrapx.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:28, 327248 bytes Driver: C:\Program Files\NVIDIA Corporation\coprocmanager\detoured.dll, 2/26/2013 00:32:36, 4096 bytes Driver: C:\Program Files\NVIDIA Corporation\coprocmanager\nvdxgiwrapx.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:04, 228880 bytes Driver: C:\Program Files\NVIDIA Corporation\license.txt, 2/26/2013 00:32:08, 21898 bytes Driver: C:\Program Files\NVIDIA Corporation\NVSMI\MCU.exe, 1.00.4647.21994 (English), 2/26/2013 00:32:08, 1562400 bytes Driver: C:\Program Files\NVIDIA Corporation\NVSMI\nvdebugdump.exe, 2/26/2013 00:32:44, 223008 bytes Driver: C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.1.pdf, 2/26/2013 00:32:40, 40574 bytes Driver: C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe, 8.17.0013.1106 (English), 2/26/2013 00:32:42, 241440 bytes Driver: C:\Program Files\NVIDIA Corporation\NVSMI\nvml.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:42, 428320 bytes Driver: C:\Program Files\NVIDIA Corporation\OpenCL\OpenCL.dll, 1.00.0000.0000 (English), 2/26/2013 00:32:06, 53024 bytes Driver: C:\Program Files\NVIDIA Corporation\OpenCL\OpenCL64.dll, 1.00.0000.0000 (English), 2/26/2013 00:32:40, 61216 bytes Driver: C:\WINDOWS\system32\DRIVERS\nvlddmkm.sys, 9.18.0013.1106 (English), 2/26/2013 00:32:32, 11036448 bytes Driver: C:\WINDOWS\system32\nvEncodeAPI64.dll, 6.14.0013.1106 (English), 2/26/2013 00:32:36, 420128 bytes Driver: C:\WINDOWS\system32\nvapi64.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:40, 2826040 bytes Driver: C:\WINDOWS\system32\nvcompiler.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:44, 25256224 bytes Driver: C:\WINDOWS\system32\nvcuda.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:06, 9390760 bytes Driver: C:\WINDOWS\system32\nvcuvenc.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:34, 2346784 bytes Driver: C:\WINDOWS\system32\nvcuvid.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:28, 2904352 bytes Driver: C:\WINDOWS\system32\nvd3dumx.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:38, 18055184 bytes Driver: C:\WINDOWS\system32\nvinfo.pb, 2/26/2013 00:32:08, 17266 bytes Driver: C:\WINDOWS\system32\nvinitx.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:32, 245872 bytes Driver: C:\WINDOWS\system32\nvoglv64.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:36, 26929440 bytes Driver: C:\WINDOWS\system32\nvopencl.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:08, 7564040 bytes Driver: C:\WINDOWS\system32\nvumdshimx.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:38, 1107440 bytes Driver: C:\WINDOWS\system32\nvwgf2umx.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:26, 15053264 bytes Driver: C:\WINDOWS\SysWow64\nvEncodeAPI.dll, 6.14.0013.1106 (English), 2/26/2013 00:32:28, 364832 bytes Driver: C:\WINDOWS\SysWow64\nvapi.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:44, 2505144 bytes Driver: C:\WINDOWS\SysWow64\nvcompiler.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:24, 17560352 bytes Driver: C:\WINDOWS\SysWow64\nvcuda.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:34, 7932256 bytes Driver: C:\WINDOWS\SysWow64\nvcuvenc.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:08, 1985824 bytes Driver: C:\WINDOWS\SysWow64\nvcuvid.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:36, 2720544 bytes Driver: C:\WINDOWS\SysWow64\nvd3dum.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:42, 15129960 bytes Driver: C:\WINDOWS\SysWow64\nvinit.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:04, 201576 bytes Driver: C:\WINDOWS\SysWow64\nvoglv32.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:26, 20449056 bytes Driver: C:\WINDOWS\SysWow64\nvopencl.dll, 8.17.0013.1106 (English), 2/26/2013 00:32:40, 6262608 bytes Driver: C:\WINDOWS\SysWow64\nvumdshim.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:36, 958120 bytes Driver: C:\WINDOWS\SysWow64\nvwgf2um.dll, 9.18.0013.1106 (English), 2/26/2013 00:32:08, 12641992 bytes Driver: C:\WINDOWS\system32\nvdispco64.dll, 2.00.0029.0004 (English), 2/26/2013 00:32:38, 1814304 bytes Driver: C:\WINDOWS\system32\nvdispgenco64.dll, 2.00.0016.0002 (English), 2/26/2013 00:32:32, 1510176 bytes Name: Xeon(R) processor E3-1200 v2/3rd Gen Core processor PCI Express Root Port - 0151 Device ID: PCI\VEN_8086&DEV_0151&SUBSYS_01511849&REV_09\3&11583659&0&08 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Intel(R) 7 Series/C216 Chipset Family SATA AHCI Controller Device ID: PCI\VEN_8086&DEV_1E02&SUBSYS_1E021849&REV_04\3&11583659&0&FA Driver: C:\WINDOWS\system32\DRIVERS\iaStorA.sys, 11.07.0000.1013 (English), 11/19/2012 12:10:38, 652344 bytes Name: PCI standard PCI-to-PCI bridge Device ID: PCI\VEN_1B21&DEV_1080&SUBSYS_10801849&REV_03\4&C7A4F95&0&00E5 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Intel(R) 7 Series/C216 Chipset Family PCI Express Root Port 8 - 1E1E Device ID: PCI\VEN_8086&DEV_1E1E&SUBSYS_1E1E1849&REV_C4\3&11583659&0&E7 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Broadcom NetLink (TM) Gigabit Ethernet Device ID: PCI\VEN_14E4&DEV_16B1&SUBSYS_96B11849&REV_10\4&2B8260C3&0&00E4 Driver: C:\WINDOWS\system32\DRIVERS\k57nd60a.sys, 15.04.0000.0009 (English), 8/25/2012 22:11:34, 433976 bytes Name: Intel(R) 7 Series/C216 Chipset Family PCI Express Root Port 6 - 1E1A Device ID: PCI\VEN_8086&DEV_1E1A&SUBSYS_1E1A1849&REV_C4\3&11583659&0&E5 Driver: C:\WINDOWS\system32\DRIVERS\pci.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 234224 bytes Name: Intel(R) Z77 Express Chipset LPC Controller - 1E44 Device ID: PCI\VEN_8086&DEV_1E44&SUBSYS_1E441849&REV_04\3&11583659&0&F8 Driver: C:\WINDOWS\system32\DRIVERS\msisadrv.sys, 6.02.9200.16384 (English), 7/26/2012 06:00:55, 17136 bytes Name: ASMedia XHCI Controller Device ID: PCI\VEN_1B21&DEV_1042&SUBSYS_10421849&REV_00\4&37A73C8A&0&00E7 Driver: C:\WINDOWS\system32\DRIVERS\asmtxhci.sys, 1.16.0002.0000 (English), 8/20/2012 10:38:12, 416072 bytes Name: Asmedia 106x SATA Controller Device ID: PCI\VEN_1B21&DEV_0612&SUBSYS_06121849&REV_01\4&33B94F4C&0&00E3 Driver: C:\WINDOWS\system32\DRIVERS\asahci64.sys, 1.03.0008.0000 (English), 7/18/2012 11:29:46, 49048 bytes Driver: C:\WINDOWS\system32\ahcipp64.dll, 1.00.0000.0001 (English), 7/8/2011 21:29:04, 48736 bytes Name: Intel(R) Management Engine Interface Device ID: PCI\VEN_8086&DEV_1E3A&SUBSYS_1E3A1849&REV_04\3&11583659&0&B0 Driver: C:\WINDOWS\system32\DRIVERS\HECIx64.sys, 9.00.0000.1287 (English), 1/11/2013 19:02:34, 64624 bytes Name: Intel(R) USB 3.0 eXtensible Host Controller - 0100 (Microsoft) Device ID: PCI\VEN_8086&DEV_1E31&SUBSYS_1E311849&REV_04\3&11583659&0&A0 Driver: C:\WINDOWS\system32\DRIVERS\UCX01000.SYS, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 212208 bytes Driver: C:\WINDOWS\system32\DRIVERS\USBXHCI.SYS, 6.02.9200.16384 (English), 7/26/2012 06:00:58, 337136 bytes Name: Xeon(R) processor E3-1200 v2/3rd Gen Core processor DRAM Controller - 0150 Device ID: PCI\VEN_8086&DEV_0150&SUBSYS_01501849&REV_09\3&11583659&0&00 Driver: n/a ------------------ DirectShow Filters ------------------ DirectShow Filters: WMAudio Decoder DMO,0x00800800,1,1,WMADMOD.DLL,6.02.9200.16384 WMAPro over S/PDIF DMO,0x00600800,1,1,WMADMOD.DLL,6.02.9200.16384 WMSpeech Decoder DMO,0x00600800,1,1,WMSPDMOD.DLL,6.02.9200.16384 MP3 Decoder DMO,0x00600800,1,1,mp3dmod.dll,6.02.9200.16384 Mpeg4s Decoder DMO,0x00800001,1,1,mp4sdecd.dll,6.02.9200.16384 WMV Screen decoder DMO,0x00600800,1,1,wmvsdecd.dll,6.02.9200.16384 WMVideo Decoder DMO,0x00800001,1,1,wmvdecod.dll,6.02.9200.16384 Mpeg43 Decoder DMO,0x00800001,1,1,mp43decd.dll,6.02.9200.16384 Mpeg4 Decoder DMO,0x00800001,1,1,mpg4decd.dll,6.02.9200.16384 DV Muxer,0x00400000,0,0,qdv.dll,6.06.9200.16384 Color Space Converter,0x00400001,1,1,quartz.dll,6.06.9200.16384 WM ASF Reader,0x00400000,0,0,qasf.dll,12.00.9200.16384 AVI Splitter,0x00600000,1,1,quartz.dll,6.06.9200.16384 VGA 16 Color Ditherer,0x00400000,1,1,quartz.dll,6.06.9200.16384 SBE2MediaTypeProfile,0x00200000,0,0,sbe.dll,6.06.9200.16384 Microsoft DTV-DVD Video Decoder,0x005fffff,2,4,msmpeg2vdec.dll,12.00.8500.0000 AC3 Parser Filter,0x00600000,1,1,mpg2splt.ax,6.06.9200.16384 StreamBufferSink,0x00200000,0,0,sbe.dll,6.06.9200.16384 MJPEG Decompressor,0x00600000,1,1,quartz.dll,6.06.9200.16384 MPEG-I Stream Splitter,0x00600000,1,2,quartz.dll,6.06.9200.16384 SAMI (CC) Parser,0x00400000,1,1,quartz.dll,6.06.9200.16384 VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.9200.16384 MPEG-2 Splitter,0x005fffff,1,0,mpg2splt.ax,6.06.9200.16384 Closed Captions Analysis Filter,0x00200000,2,5,cca.dll,6.06.9200.16384 SBE2FileScan,0x00200000,0,0,sbe.dll,6.06.9200.16384 Microsoft MPEG-2 Video Encoder,0x00200000,1,1,msmpeg2enc.dll,12.00.9200.16384 Internal Script Command Renderer,0x00800001,1,0,quartz.dll,6.06.9200.16384 MPEG Audio Decoder,0x03680001,1,1,quartz.dll,6.06.9200.16384 DV Splitter,0x00600000,1,2,qdv.dll,6.06.9200.16384 Video Mixing Renderer 9,0x00200000,1,0,quartz.dll,6.06.9200.16384 Microsoft MPEG-2 Encoder,0x00200000,2,1,msmpeg2enc.dll,12.00.9200.16384 ACM Wrapper,0x00600000,1,1,quartz.dll,6.06.9200.16384 Video Renderer,0x00800001,1,0,quartz.dll,6.06.9200.16384 MPEG-2 Video Stream Analyzer,0x00200000,0,0,sbe.dll,6.06.9200.16384 Line 21 Decoder,0x00600000,1,1,, Video Port Manager,0x00600000,2,1,quartz.dll,6.06.9200.16384 Video Renderer,0x00400000,1,0,quartz.dll,6.06.9200.16384 VPS Decoder,0x00200000,0,0,WSTPager.ax,6.06.9200.16384 WM ASF Writer,0x00400000,0,0,qasf.dll,12.00.9200.16384 VBI Surface Allocator,0x00600000,1,1,vbisurf.ax,6.02.9200.16384 File writer,0x00200000,1,0,qcap.dll,6.06.9200.16384 DVD Navigator,0x00200000,0,3,qdvd.dll,6.06.9200.16384 Overlay Mixer2,0x00200000,1,1,, Microsoft MPEG-2 Audio Encoder,0x00200000,1,1,msmpeg2enc.dll,12.00.9200.16384 WST Pager,0x00200000,1,1,WSTPager.ax,6.06.9200.16384 MPEG-2 Demultiplexer,0x00600000,1,1,mpg2splt.ax,6.06.9200.16384 DV Video Decoder,0x00800000,1,1,qdv.dll,6.06.9200.16384 SampleGrabber,0x00200000,1,1,qedit.dll,6.06.9200.16384 Null Renderer,0x00200000,1,0,qedit.dll,6.06.9200.16384 MPEG-2 Sections and Tables,0x005fffff,1,0,Mpeg2Data.ax,6.06.9200.16384 Microsoft AC3 Encoder,0x00200000,1,1,msac3enc.dll,6.02.9200.16384 StreamBufferSource,0x00200000,0,0,sbe.dll,6.06.9200.16384 Smart Tee,0x00200000,1,2,qcap.dll,6.06.9200.16384 Overlay Mixer,0x00200000,0,0,, AVI Decompressor,0x00600000,1,1,quartz.dll,6.06.9200.16384 AVI/WAV File Source,0x00400000,0,2,quartz.dll,6.06.9200.16384 Wave Parser,0x00400000,1,1,quartz.dll,6.06.9200.16384 MIDI Parser,0x00400000,1,1,quartz.dll,6.06.9200.16384 Multi-file Parser,0x00400000,1,1,quartz.dll,6.06.9200.16384 File stream renderer,0x00400000,1,1,quartz.dll,6.06.9200.16384 Microsoft DTV-DVD Audio Decoder,0x005fffff,1,1,msmpeg2adec.dll,12.00.8506.0000 StreamBufferSink2,0x00200000,0,0,sbe.dll,6.06.9200.16384 AVI Mux,0x00200000,1,0,qcap.dll,6.06.9200.16384 Line 21 Decoder 2,0x00600002,1,1,quartz.dll,6.06.9200.16384 File Source (Async.),0x00400000,0,1,quartz.dll,6.06.9200.16384 File Source (URL),0x00400000,0,1,quartz.dll,6.06.9200.16384 AudioRecorder WAV Dest,0x00200000,0,0,WavDest.dll, AudioRecorder Wave Form,0x00200000,0,0,WavDest.dll, SoundRecorder Null Renderer,0x00200000,0,0,WavDest.dll, Infinite Pin Tee Filter,0x00200000,1,1,qcap.dll,6.06.9200.16384 Enhanced Video Renderer,0x00200000,1,0,evr.dll,6.02.9200.16384 BDA MPEG2 Transport Information Filter,0x00200000,2,0,psisrndr.ax,6.06.9200.16384 MPEG Video Decoder,0x40000001,1,1,quartz.dll,6.06.9200.16384 WDM Streaming Tee/Splitter Devices: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.02.9200.16384 Video Compressors: WMVideo8 Encoder DMO,0x00600800,1,1,wmvxencd.dll,6.02.9200.16384 WMVideo9 Encoder DMO,0x00600800,1,1,wmvencod.dll,6.02.9200.16384 MSScreen 9 encoder DMO,0x00600800,1,1,wmvsencd.dll,6.02.9200.16384 DV Video Encoder,0x00200000,0,0,qdv.dll,6.06.9200.16384 MJPEG Compressor,0x00200000,0,0,quartz.dll,6.06.9200.16384 Audio Compressors: WM Speech Encoder DMO,0x00600800,1,1,WMSPDMOE.DLL,6.02.9200.16384 WMAudio Encoder DMO,0x00600800,1,1,WMADMOE.DLL,6.02.9200.16384 IMA ADPCM,0x00200000,1,1,quartz.dll,6.06.9200.16384 PCM,0x00200000,1,1,quartz.dll,6.06.9200.16384 Microsoft ADPCM,0x00200000,1,1,quartz.dll,6.06.9200.16384 GSM 6.10,0x00200000,1,1,quartz.dll,6.06.9200.16384 CCITT A-Law,0x00200000,1,1,quartz.dll,6.06.9200.16384 CCITT u-Law,0x00200000,1,1,quartz.dll,6.06.9200.16384 MPEG Layer-3,0x00200000,1,1,quartz.dll,6.06.9200.16384 Audio Capture Sources: Microphone (Plantronics GameCom 780),0x00200000,0,0,qcap.dll,6.06.9200.16384 SPDIF Interface (Plantronics GameCom 780),0x00200000,0,0,qcap.dll,6.06.9200.16384 Line (Plantronics GameCom 780),0x00200000,0,0,qcap.dll,6.06.9200.16384 PBDA CP Filters: PBDA DTFilter,0x00600000,1,1,CPFilters.dll,6.06.9200.16384 PBDA ETFilter,0x00200000,0,0,CPFilters.dll,6.06.9200.16384 PBDA PTFilter,0x00200000,0,0,CPFilters.dll,6.06.9200.16384 Midi Renderers: Default MidiOut Device,0x00800000,1,0,quartz.dll,6.06.9200.16384 Microsoft GS Wavetable Synth,0x00200000,1,0,quartz.dll,6.06.9200.16384 WDM Streaming Capture Devices: Plantronics GameCom 780,0x00200000,4,2,ksproxy.ax,6.02.9200.16384 WDM Streaming Rendering Devices: HD Audio SPDIF out,0x00200000,1,1,ksproxy.ax,6.02.9200.16384 HD Audio Speaker,0x00200000,1,1,ksproxy.ax,6.02.9200.16384 Plantronics GameCom 780,0x00200000,4,2,ksproxy.ax,6.02.9200.16384 BDA Network Providers: Microsoft ATSC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.9200.16384 Microsoft DVBC Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.9200.16384 Microsoft DVBS Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.9200.16384 Microsoft DVBT Network Provider,0x00200000,0,1,MSDvbNP.ax,6.06.9200.16384 Microsoft Network Provider,0x00200000,0,1,MSNP.ax,6.06.9200.16384 Multi-Instance Capable VBI Codecs: VBI Codec,0x00600000,1,4,VBICodec.ax,6.06.9200.16384 BDA Transport Information Renderers: BDA MPEG2 Transport Information Filter,0x00600000,2,0,psisrndr.ax,6.06.9200.16384 MPEG-2 Sections and Tables,0x00600000,1,0,Mpeg2Data.ax,6.06.9200.16384 BDA CP/CA Filters: Decrypt/Tag,0x00600000,1,1,EncDec.dll,6.06.9200.16384 Encrypt/Tag,0x00200000,0,0,EncDec.dll,6.06.9200.16384 PTFilter,0x00200000,0,0,EncDec.dll,6.06.9200.16384 XDS Codec,0x00200000,0,0,EncDec.dll,6.06.9200.16384 WDM Streaming Communication Transforms: Tee/Sink-to-Sink Converter,0x00200000,1,1,ksproxy.ax,6.02.9200.16384 Audio Renderers: Speakers (Plantronics GameCom 780),0x00200000,1,0,quartz.dll,6.06.9200.16384 Default DirectSound Device,0x00800000,1,0,quartz.dll,6.06.9200.16384 Default WaveOut Device,0x00200000,1,0,quartz.dll,6.06.9200.16384 DirectSound: Speakers (Plantronics GameCom 780),0x00200000,1,0,quartz.dll,6.06.9200.16384 DirectSound: Speakers (High Definition Audio Device),0x00200000,1,0,quartz.dll,6.06.9200.16384 DirectSound: Digital Audio (S/PDIF) (High Definition Audio Device),0x00200000,1,0,quartz.dll,6.06.9200.16384 Speakers (High Definition Audio Device),0x00200000,1,0,quartz.dll,6.06.9200.16384 Digital Audio (S/PDIF) (High Definition Audio Device),0x00200000,1,0,quartz.dll,6.06.9200.16384 ---------------------------- Preferred DirectShow Filters ---------------------------- [HKEY_LOCAL_MACHINE\Software\Microsoft\DirectShow\Preferred] <media subtype GUID>, [<filter friendly name>, ]<filter CLSID> MEDIASUBTYPE_WMAUDIO_LOSSLESS, WMAudio Decoder DMO, CLSID_CWMADecMediaObject MEDIASUBTYPE_MPG4, Mpeg4 Decoder DMO, CLSID_CMpeg4DecMediaObject WMMEDIASUBTYPE_WMSP2, WMSpeech Decoder DMO, CLSID_CWMSPDecMediaObject MEDIASUBTYPE_WVC1, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject {64687664-0000-0010-8000-00AA00389B71}, DV Video Decoder, CLSID_DVVideoCodec MEDIASUBTYPE_h264, Microsoft DTV-DVD Video Decoder, CLSID_CMPEG2VidDecoderDS MEDIASUBTYPE_MPEG1AudioPayload, MPEG Audio Decoder, CLSID_CMpegAudioCodec {78766964-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_WMAUDIO3, WMAudio Decoder DMO, CLSID_CWMADecMediaObject MEDIASUBTYPE_WMV2, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_MPEG2_AUDIO, Microsoft DTV-DVD Audio Decoder, CLSID_CMPEG2AudDecoderDS {64697678-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject WMMEDIASUBTYPE_MP3, MP3 Decoder DMO, CLSID_CMP3DecMediaObject MEDIASUBTYPE_mp42, Mpeg4 Decoder DMO, CLSID_CMpeg4DecMediaObject MEDIASUBTYPE_MSS1, WMV Screen decoder DMO, CLSID_CMSSCDecMediaObject MEDIASUBTYPE_WVP2, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_WMV1, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_WMVP, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_WMV3, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_WMVR, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_MJPG, MJPEG Decompressor, CLSID_MjpegDec MEDIASUBTYPE_mp43, Mpeg43 Decoder DMO, CLSID_CMpeg43DecMediaObject MEDIASUBTYPE_MSS2, WMV Screen decoder DMO, CLSID_CMSSCDecMediaObject {64737664-0000-0010-8000-00AA00389B71}, DV Video Decoder, CLSID_DVVideoCodec WMMEDIASUBTYPE_WMAudioV8, WMAudio Decoder DMO, CLSID_CWMADecMediaObject {44495658-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject WMMEDIASUBTYPE_WMSP1, WMSpeech Decoder DMO, CLSID_CWMSPDecMediaObject MEDIASUBTYPE_RAW_AAC1, Microsoft DTV-DVD Audio Decoder, CLSID_CMPEG2AudDecoderDS {6C737664-0000-0010-8000-00AA00389B71}, DV Video Decoder, CLSID_DVVideoCodec MEDIASUBTYPE_MP43, Mpeg43 Decoder DMO, CLSID_CMpeg43DecMediaObject MEDIASUBTYPE_MPEG1Payload, MPEG Video Decoder, CLSID_CMpegVideoCodec MEDIASUBTYPE_AVC1, Microsoft DTV-DVD Video Decoder, CLSID_CMPEG2VidDecoderDS {20637664-0000-0010-8000-00AA00389B71}, DV Video Decoder, CLSID_DVVideoCodec {58564944-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_MP42, Mpeg4 Decoder DMO, CLSID_CMpeg4DecMediaObject MEDIASUBTYPE_MPEG_ADTS_AAC, Microsoft DTV-DVD Audio Decoder, CLSID_CMPEG2AudDecoderDS MEDIASUBTYPE_mpg4, Mpeg4 Decoder DMO, CLSID_CMpeg4DecMediaObject MEDIASUBTYPE_M4S2, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_m4s2, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_MP4S, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_mp4s, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_MPEG1Packet, MPEG Video Decoder, CLSID_CMpegVideoCodec {5634504D-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject {7634706D-0000-0010-8000-00AA00389B71}, Mpeg4s Decoder DMO, CLSID_CMpeg4sDecMediaObject MEDIASUBTYPE_H264, Microsoft DTV-DVD Video Decoder, CLSID_CMPEG2VidDecoderDS MEDIASUBTYPE_MPEG2_VIDEO, Microsoft DTV-DVD Video Decoder, CLSID_CMPEG2VidDecoderDS MEDIASUBTYPE_WMVA, WMVideo Decoder DMO, CLSID_CWMVDecMediaObject MEDIASUBTYPE_MSAUDIO1, WMAudio Decoder DMO, CLSID_CWMADecMediaObject MEDIASUBTYPE_DVD_LPCM_AUDIO, Microsoft DTV-DVD Audio Decoder, CLSID_CMPEG2AudDecoderDS MEDIASUBTYPE_MPEG_LOAS, Microsoft DTV-DVD Audio Decoder, CLSID_CMPEG2AudDecoderDS --------------------------- Media Foundation Transforms --------------------------- [HKEY_LOCAL_MACHINE\Software\Classes\MediaFoundation\Transforms] <category>: <transform friendly name>, <transform CLSID>, <flags>, [<merit>, ]<file name>, <file version> Video Decoders: Microsoft MPEG Video Decoder MFT, {2D709E52-123F-49B5-9CBC-9AF5CDE28FB9}, 0x1, msmpeg2vdec.dll, 12.00.8500.0000 DV Decoder MFT, {404A6DE5-D4D6-4260-9BC7-5A6CBD882432}, 0x1, mfdvdec.dll, 6.02.9200.16384 Mpeg4s Decoder MFT, CLSID_CMpeg4sDecMFT, 0x1, mp4sdecd.dll, 6.02.9200.16384 Microsoft H264 Video Decoder MFT, CLSID_CMSH264DecoderMFT, 0x1, msmpeg2vdec.dll, 12.00.8500.0000 WMV Screen decoder MFT, CLSID_CMSSCDecMediaObject, 0x1, wmvsdecd.dll, 6.02.9200.16384 WMVideo Decoder MFT, CLSID_CWMVDecMediaObject, 0x1, wmvdecod.dll, 6.02.9200.16384 MJPEG Decoder MFT, {CB17E772-E1CC-4633-8450-5617AF577905}, 0x1, mfmjpegdec.dll, 6.02.9200.16384 Mpeg43 Decoder MFT, CLSID_CMpeg43DecMediaObject, 0x1, mp43decd.dll, 6.02.9200.16384 Mpeg4 Decoder MFT, CLSID_CMpeg4DecMediaObject, 0x1, mpg4decd.dll, 6.02.9200.16384 Video Encoders: Intel® Quick Sync Video H.264 Encoder MFT, {4BE8D3C0-0515-4A37-AD55-E4BAE19AF471}, 0x4, 7, mfx_mft_h264ve_64.dll, 3.12.0010.0031 H264 Encoder MFT, {6CA50344-051A-4DED-9779-A43305165E35}, 0x1, mfh264enc.dll, 6.02.9200.16384 WMVideo8 Encoder MFT, CLSID_CWMVXEncMediaObject, 0x1, wmvxencd.dll, 6.02.9200.16384 WMVideo9 Encoder MFT, CLSID_CWMV9EncMediaObject, 0x1, wmvencod.dll, 6.02.9200.16384 Microsoft MPEG-2 Video Encoder MFT, {E6335F02-80B7-4DC4-ADFA-DFE7210D20D5}, 0x2, msmpeg2enc.dll, 12.00.9200.16384 Video Effects: Frame Rate Converter, CLSID_CFrameRateConvertDmo, 0x1, mfvdsp.dll, 6.02.9200.16384 Resizer MFT, CLSID_CResizerDMO, 0x1, vidreszr.dll, 6.02.9200.16384 VideoStabilization MFT, {51571744-7FE4-4FF2-A498-2DC34FF74F1B}, 0x1, MSVideoDSP.dll, 6.02.9200.16384 Color Control, CLSID_CColorControlDmo, 0x1, mfvdsp.dll, 6.02.9200.16384 Color Converter MFT, CLSID_CColorConvertDMO, 0x1, colorcnv.dll, 6.02.9200.16384 Video Processor: Microsoft Video Processor MFT, {88753B26-5B24-49BD-B2E7-0C445C78C982}, 0x1, msvproc.dll, 12.00.9200.16384 Audio Decoders: Microsoft Dolby Digital Plus Decoder MFT, {177C0AFE-900B-48D4-9E4C-57ADD250B3D4}, 0x1, MSAudDecMFT.dll, 6.02.9200.16384 WMAudio Decoder MFT, CLSID_CWMADecMediaObject, 0x1, WMADMOD.DLL, 6.02.9200.16384 Microsoft AAC Audio Decoder MFT, CLSID_CMSAACDecMFT, 0x1, MSAudDecMFT.dll, 6.02.9200.16384 GSM ACM Wrapper MFT, {4A76B469-7B66-4DD4-BA2D-DDF244C766DC}, 0x1, mfcore.dll, 12.00.9200.16384 WMAPro over S/PDIF MFT, CLSID_CWMAudioSpdTxDMO, 0x1, WMADMOD.DLL, 6.02.9200.16384 Microsoft MPEG Audio Decoder MFT, {70707B39-B2CA-4015-ABEA-F8447D22D88B}, 0x1, MSAudDecMFT.dll, 6.02.9200.16384 WMSpeech Decoder DMO, CLSID_CWMSPDecMediaObject, 0x1, WMSPDMOD.DLL, 6.02.9200.16384 G711 Wrapper MFT, {92B66080-5E2D-449E-90C4-C41F268E5514}, 0x1, mfcore.dll, 12.00.9200.16384 IMA ADPCM ACM Wrapper MFT, {A16E1BFF-A80D-48AD-AECD-A35C005685FE}, 0x1, mfcore.dll, 12.00.9200.16384 MP3 Decoder MFT, CLSID_CMP3DecMediaObject, 0x1, mp3dmod.dll, 6.02.9200.16384 ADPCM ACM Wrapper MFT, {CA34FE0A-5722-43AD-AF23-05F7650257DD}, 0x1, mfcore.dll, 12.00.9200.16384 Audio Encoders: MP3 Encoder ACM Wrapper MFT, {11103421-354C-4CCA-A7A3-1AFF9A5B6701}, 0x1, mfcore.dll, 12.00.9200.16384 WM Speech Encoder DMO, CLSID_CWMSPEncMediaObject2, 0x1, WMSPDMOE.DLL, 6.02.9200.16384 Microsoft MPEG-2 Audio Encoder MFT, {46A4DD5C-73F8-4304-94DF-308F760974F4}, 0x1, msmpeg2enc.dll, 12.00.9200.16384 WMAudio Encoder MFT, CLSID_CWMAEncMediaObject, 0x1, WMADMOE.DLL, 6.02.9200.16384 Microsoft AAC Audio Encoder MFT, {93AF0C51-2275-45D2-A35B-F2BA21CAED00}, 0x1, mfAACEnc.dll, 6.02.9200.16384 Microsoft Dolby Digital Encoder MFT, {AC3315C9-F481-45D7-826C-0B406C1F64B8}, 0x1, msac3enc.dll, 6.02.9200.16384 Audio Effects: AEC, CLSID_CWMAudioAEC, 0x1, mfwmaaec.dll, 6.02.9200.16384 Resampler MFT, CLSID_CResamplerMediaObject, 0x1, resampledmo.dll, 6.02.9200.16384 Multiplexers: Microsoft MPEG2 Multiplexer MFT, {AB300F71-01AB-46D2-AB6C-64906CB03258}, 0x2, mfmpeg2srcsnk.dll, 12.00.9200.16384 Others: Microsoft H264 Video Remux (MPEG2TSToMP4) MFT, {05A47EBB-8BF0-4CBF-AD2F-3B71D75866F5}, 0x1, msmpeg2vdec.dll, 12.00.8500.0000 -------------------------------------------- Media Foundation Enabled Hardware Categories -------------------------------------------- [HKEY_LOCAL_MACHINE\Software\Microsoft\Windows Media Foundation\HardwareMFT] EnableEncoders = 1 ------------------------------------- Media Foundation Byte Stream Handlers ------------------------------------- [HKEY_LOCAL_MACHINE\Software\Microsoft\Windows Media Foundation\ByteStreamHandlers] [HKEY_LOCAL_MACHINE\Software\Classes\MediaFoundation\MediaSources\Preferred] <file ext. or MIME type>, <handler CLSID>, <brief description>[, Preferred] .3g2, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .3gp, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .3gp2, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .3gpp, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .aac, {926F41F7-003E-4382-9E84-9E953BE10562}, ADTS Byte Stream Handler, Preferred .ac3, {46031BA1-083F-47D9-8369-23C92BDAB2FF}, AC-3 Byte Stream Handler, Preferred .adt, {926F41F7-003E-4382-9E84-9E953BE10562}, ADTS Byte Stream Handler, Preferred .adts, {926F41F7-003E-4382-9E84-9E953BE10562}, ADTS Byte Stream Handler, Preferred .asf, {41457294-644C-4298-A28A-BD69F2C0CF3B}, ASF Byte Stream Handler, Preferred .avi, {7AFA253E-F823-42F6-A5D9-714BDE467412}, AVI Byte Stream Handler, Preferred .dvr-ms, {65964407-A5D8-4060-85B0-1CCD63F768E2}, dvr-ms Byte Stream Handler, Preferred .dvr-ms, {A8721937-E2FB-4D7A-A9EE-4EB08C890B6E}, MF SBE Source ByteStreamHandler .ec3, {46031BA1-083F-47D9-8369-23C92BDAB2FF}, AC-3 Byte Stream Handler, Preferred .m2t, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .m2ts, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .m4a, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .m4v, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .mod, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .mov, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .mp2v, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .mp3, {A82E50BA-8E92-41EB-9DF2-433F50EC2993}, MP3 Byte Stream Handler, Preferred .mp4, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .mp4v, {271C3902-6095-4C45-A22F-20091816EE9E}, MPEG4 Byte Stream Handler, Preferred .mpa, {A82E50BA-8E92-41EB-9DF2-433F50EC2993}, MP3 Byte Stream Handler, Preferred .mpeg, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .mpg, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .mts, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .nsc, {B084785C-DDE0-4D30-8CA8-05A373E185BE}, NSC Byte Stream Handler, Preferred .sami, {7A56C4CB-D678-4188-85A8-BA2EF68FA10D}, SAMI Byte Stream Handler, Preferred .smi, {7A56C4CB-D678-4188-85A8-BA2EF68FA10D}, SAMI Byte Stream Handler, Preferred .tod, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .ts, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .tts, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .vob, {40871C59-AB40-471F-8DC3-1F259D862479}, MPEG2 Byte Stream Handler, Preferred .wav, {42C9B9F5-16FC-47EF-AF22-DA05F7C842E3}, WAV Byte Stream
ioos / Cloud SandboxIOOS' Coastal Modeling Cloud Sandbox provides a framework for developing, modifying and running models in the cloud. It provides repeatable configurations, model code and required libraries, input data and analysis of model outputs. The Sandbox supports not only the development of services and models, but also Cloud HPC to run and validate models.
