SilentPatchGF - SilentPatch for The Godfather: The Game (fixes startup crash on Windows 10)

  •        36

SilentPatch aims to fix this annoyance, so now movie player works flawlessly on any operating syetem. This is not the only fix included, however. Project is supposed to build out of the box with Visual Studio 2017.

https://github.com/CookiePLMonster/SilentPatchGF

Tags
Implementation
License
Platform

   




Related Projects

BCN3D-Moveo - Open Source 3D Printed Robotic Arm for educational purposes

  •    C++

This is the repository that contains the CAD files, the STL files, the user manual (with the assembly manual), the firmware, and the Bill of Materials of the BCN3D Moveo. BCN3D Technologies keeps taking important steps in order to achieve his goal of bringing the digital manufacturing technology to everyone. In this occasion we are presenting the BCN3D Moveo, a robotic arm design from scratch and developed by our engineers in collaboration with the Departament d’Ensenyament from the Generalitat de Catalunya. Its structure is fully printed using additive manufacturing technologies and its electronics are controlled by the software Arduino. Moveo, fully functional nowadays, has been born, as all the BCN3D Technologies products, with an open and educational wish. One of the Departament d’Ensenyament worries is the high price of the materials the grade students must use on their internships. Holding that in mind, an Open Source robotic arm, adaptable by the students and low cost reproducible could take several educational itineraries: mechanical design, automatism, industrial programing, etc. Thus, the BCN3D Moveo should allow the educational centers to enjoy a modifiable and easily accessible for the students, at a price far lower than the usual industrial equipment they used to have to acquire, with enough output for training purposes. As a Fundació CIM area, BCN3D Technologies shares its educational vocation. That is the reason why when the Departament d’Ensenyament contacted us in order to suggest and offer this project a year ago we didn’t hesitate on taking that opportunity. Once we had the robotic arm designed and manufactured we started the last phase of the project, which consisted on an assembling and fine tuning workshop for 15 institutes around Catalonia, which took place in the BCN3D Technologies. These institutes already have the BCN3D Moveo in their classrooms and workshops, and will have to present an internship program that proves their knowledge about the arm during September.

EmojiIntelligence - Neural Network built in Apple Playground using Swift

  •    Swift

I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.

vst3sdk - VST 3 Plug-In SDK

  •    CMake

A VST Plug-in is an audio processing component that is utilized within a host application. This host application provides the audio or/and event streams that are processed by the Plug-in's code. Generally speaking, a VST Plug-in can take a stream of audio data, apply a process to the audio, and return the result to the host application. A VST Plug-in performs its process normally using the processor of the computer. The audio stream is broken down into a series of blocks. The host supplies the blocks in sequence. The host and its current environment control the block-size. The VST Plug-in maintains the status of all its own parameters relating to the running process: The host does not maintain any information about what the Plug-in did with the last block of data it processed. From the host application's point of view, a VST Plug-in is a black box with an arbitrary number of inputs, outputs (Event (MIDI) or Audio), and associated parameters. The host needs no implicit knowledge of the Plug-in's process to be able to use it. The Plug-in process can use whatever parameters it wishes, internally to the process, but depending on the capabilities of the host, it can allow the changes to user parameters to be automated by the host.

ApacheDS - Apache Directory Project

  •    Java

ApacheDS is an embeddable directory server entirely written in Java, which has been certified LDAPv3 compatible by the Open Group. Besides LDAP it supports Kerberos 5 and the Change Password Protocol. It has been designed to introduce triggers, stored procedures, queues and views to the world of LDAP which has lacked these rich constructs.

retina-unet - Retina blood vessel segmentation with a convolutional neural network

  •    Python

This repository contains the implementation of a convolutional neural network used to segment blood vessels in retina fundus images. This is a binary classification task: the neural network predicts if each pixel in the fundus image is either a vessel or not. The neural network structure is derived from the U-Net architecture, described in this paper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Also on the STARE datasets, this method reports one of the best performances. The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels. A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images. Although the patches overlap, i.e. different patches may contain same part of the original images, no further data augmentation is performed. The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).


Pier - Content management system in Smalltalk

  •    Pharo

Pier is a content management system that is light, flexible and free. It aims at allowing users to manage their content from the browser.

textarea-caret-position - xy coordinates of a textarea or input's caret

  •    HTML

Get the top and left coordinates of the caret in a <textarea> or <input type="text">, in pixels. Useful for textarea autocompletes like GitHub or Twitter, or for single-line autocompletes like the name drop-down in Twitter or Facebook's search or the company dropdown on Google Finance. How it's done: a faux <div> is created off-screen and styled exactly like the textarea or input. Then, the text of the element up to the caret is copied into the div and a <span> is inserted right after it. Then, the text content of the span is set to the remainder of the text in the <textarea>, in order to faithfully reproduce the wrapping in the faux div (because wrapping can push the currently typed word onto the next line). The same is done for the input to simplify the code, though it makes no difference. Finally, the span's offset within the textarea or input is returned.

datasharing - The Leek group guide to data sharing

  •    

The goals of this guide are to provide some instruction on the best way to share data to avoid the most common pitfalls and sources of delay in the transition from data collection to data analysis. The Leek group works with a large number of collaborators and the number one source of variation in the speed to results is the status of the data when they arrive at the Leek group. Based on my conversations with other statisticians this is true nearly universally. My strong feeling is that statisticians should be able to handle the data in whatever state they arrive. It is important to see the raw data, understand the steps in the processing pipeline, and be able to incorporate hidden sources of variability in one's data analysis. On the other hand, for many data types, the processing steps are well documented and standardized. So the work of converting the data from raw form to directly analyzable form can be performed before calling on a statistician. This can dramatically speed the turnaround time, since the statistician doesn't have to work through all the pre-processing steps first.

RLSeq2Seq - Deep Reinforcement Learning For Sequence to Sequence Models

  •    Python

NOTE: THE CODE IS UNDER DEVELOPMENT, PLEASE ALWAYS PULL THE LATEST VERSION FROM HERE. In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.

pure - A set of small, responsive CSS modules that you can use in every web project.

  •    HTML

Now, all Pure CSS files should be built into the `pure/build/` directory. Allfiles that are in this build directory are also available on the CDN. The namingconventions of the files in the `build/` directory follow these rules:* `[module]-core.css`: The minimal set of styles, ususally structural, that provide the base on which the rest of the module's styles build.* `[module]-nr.css`: Rollup of `[module]-core.css` + `[module].css` + `[module]-[feature].css` from the `src/[module]/` dir. This i

autonomous-rc-car - Autonomous RC car using Raspberry Pi and ANN

  •    Python

This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We have modified a remote controlled car to remove the dependency on the RF remote controller. A Raspberry Pi controls the DC motors via an L293D Motor Driver IC. You can find a post explaining this project in detail here. Here's a video of the car in action. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor.

morphdom - Fast and lightweight DOM diffing/patching (no virtual DOM needed)

  •    Javascript

This module was created to solve the problem of updating the DOM in response to a UI component or page being rerendered. One way to update the DOM is to simply toss away the existing DOM tree and replace it with a new DOM tree (e.g., myContainer.innerHTML = newHTML). While replacing an existing DOM tree with an entirely new DOM tree will actually be very fast, it comes with a cost. The cost is that all of the internal state associated with the existing DOM nodes (scroll positions, input caret positions, CSS transition states, etc.) will be lost. Instead of replacing the existing DOM tree with a new DOM tree we want to transform the existing DOM tree to match the new DOM tree while minimizing the number of changes to the existing DOM tree. This is exactly what the morphdom module does! Give it an existing DOM node tree and a target DOM node tree and it will efficiently transform the existing DOM node tree to exactly match the target DOM node tree with the minimum amount of changes. morphdom does not rely on any virtual DOM abstractions. Because morphdom is using the real DOM, the DOM that the web browser is maintaining will always be the source of truth. Even if you have code that manually manipulates the DOM things will still work as expected. In addition, morphdom can be used with any templating language that produces an HTML string.

proxymachine - A simple TCP routing proxy built on EventMachine that lets you configure the routing logic in Ruby

  •    Ruby

ProxyMachine is a simple content aware (layer 7) TCP routing proxy built on EventMachine that lets you configure the routing logic in Ruby. The idea here is simple. For each client connection, start receiving data chunks and placing them into a buffer. Each time a new chunk arrives, send the buffer to a user specified block. The block's job is to parse the buffer to determine where the connection should be proxied. If the buffer contains enough data to make a determination, the block returns the address and port of the correct backend server. If not, it can choose to do nothing and wait for more data to arrive, close the connection, or close the connection after sending custom data. Once the block returns an address, a connection to the backend is made, the buffer is replayed to the backend, and the client and backend connections are hooked up to form a transparent proxy. This bidirectional proxy continues to exist until either the client or backend close the connection.

redis_failover - redis_failover is a ZooKeeper-based automatic master/slave failover solution for Ruby

  •    Ruby

redis_failover provides a full automatic master/slave failover solution for Ruby. Redis does not currently provide an automatic failover capability when configured for master/slave replication. When the master node dies, a new master must be manually brought online and assigned as the slave's new master. This manual switch-over is not desirable in high traffic sites where Redis is a critical part of the overall architecture. The existing standard Redis client for Ruby also only supports configuration for a single Redis server. When using master/slave replication, it is desirable to have all writes go to the master, and all reads go to one of the N configured slaves. This gem (built using ZK) attempts to address these failover scenarios. One or more Node Manager daemons run as background processes and monitor all of your configured master/slave nodes. When the daemon starts up, it automatically discovers the current master/slaves. Background watchers are setup for each of the redis nodes. As soon as a node is detected as being offline, it will be moved to an "unavailable" state. If the node that went offline was the master, then one of the slaves will be promoted as the new master. All existing slaves will be automatically reconfigured to point to the new master for replication. All nodes marked as unavailable will be periodically checked to see if they have been brought back online. If so, the newly available nodes will be configured as slaves and brought back into the list of available nodes. Note that detection of a node going down should be nearly instantaneous, since the mechanism used to keep tabs on a node is via a blocking Redis BLPOP call (no polling). This call fails nearly immediately when the node actually goes offline. To avoid false positives (i.e., intermittent flaky network interruption), the Node Manager will only mark a node as unavailable if it fails to communicate with it 3 times (this is configurable via --max-failures, see configuration options below). Note that you can (and should) deploy multiple Node Manager daemons since they each report periodic health reports/snapshots of the redis servers. A "node strategy" is used to determine if a node is actually unavailable. By default a majority strategy is used, but you can also configure "consensus" or "single" as well.

CVS

  •    C

CVS is a version control system, an important component of Source Configuration Management (SCM). Using it, you can record the history of sources files, and documents. CVS is a production quality system in wide use around the world, including many free software projects.

open-event-droidgen - Open Event Android App Generator https://github

  •    Java

The Open Event Android project consists of two components. The App Generator is a web application that is hosted on a server and generates an event Android app from a zip with JSON and binary files (examples here) or through an API. The second component we are developing in the project is a generic Android app - the output of the app generator. The mobile app can be installed on any Android device for browsing information about the event. Updates can be made automatically through API endpoint connections from an online source (e.g. server), which needs to defined in the provided event zip with the JSON files. The Android app has a standard configuration file, that sets the details of the app (e.g. color scheme, the logo of an event, link to JSON app data). A) A standard configuration file, that sets the details of the app (e.g. color scheme, the logo of an event, link to JSON app data). A sample of the JSON format is maintained in the Open Event Repository.

GoBot2 - Second Version of The GoBot Botnet, But more advanced.

  •    Go

After seeing another users Go based botnet i wanted to do more work on my GoBot, But i ended up building something a bit more. There is issues with this but it more of a advanced PoC.... I am not a good coder but i was able to make this buy doing some basic reading online. There was more i wanted to do with this project but i stopped, I am getting out of making Malware and virus's... I am going to move on to more legitimet things. Though i will be posting some of my old projects on my Github, and most of witch are malevolent i am putting them here to make it simpler for the 'good guys' to fight them and there kin. The C&C is a program, You can compile it for Windows, Linux, Mac systems. Its a self-running web-server that handles all connections on the selected port in the settings. it will serve the HTLM C&C to a connector if you allow it and it saves data about account, bots and commands as a SQL database and bots files (screenshots, keylogs, ect) as file under the bots own "Profile" You can control the botnet from the program(more secure) or control it from the HTML C&C. The C&C's program is extremely stable, Go based servers are know for handling millions or requests at once without fail, just make sure you have a good connection. The C&C has a build in hard-coded login (kinda like a Backdoor) you can use if you 'forgot' the account login. the C&C can have any number of accounts. With it being a self-contained program this removes the issue of SQLi attacks on the C&C so its more SECURE. The C&C can also run inside a Tor Hidden service if configured right and the client (bot) can connect to it using a onion.to or onion.cab forwarder if needed. Tor can also be used by the bot via a SOCKS proxy... Simple to do, Google it.

keyshuffling - Keyshuffling Attack for Persistent Early Code Execution in the Nintendo 3DS Secure Bootchain

  •    TeX

We demonstrate an attack on the secure bootchain of the Nintendo 3DS in order to gain early code execution. The attack utilizes the block shuffling vulnerability of the ECB cipher mode to rearrange keys in the Nintendo 3DS's encrypted keystore. Because the shuffled keys will deterministically decrypt the encrypted firmware binary to incorrect plaintext data and execute it, and because the device's memory contents are kept between hard reboots, it is possible to reliably reach a branching instruction to a payload in memory. This payload, due to its execution by a privileged processor and its early execution, is able to extract the hash of hardware secrets necessary to decrypt the device's encrypted keystore and set up a persistent exploit of the system. Information in this article (especially the keyshuffling vulnerability) is original, independent work unless cited otherwise. Note that the keyshuffling vulnerability detailed here is the same one documented publicly by much of this team including "stuckpixel" (also known as "dark_samus") on sites such as 3DBrew. Additionally, note that the persistence vulnerability detailed here is the same one documented publicly as "arm9loaderhax" by "plutoo", "derrek", and "smea" at the 2015 32c3 conference.

viewport-units-buggyfill - Making viewport units (vh|vw|vmin|vmax) work properly in Mobile Safari.

  •    Javascript

This is a buggyfill (fixing bad behavior), not a polyfill (adding missing behavior). That said, it provides hacks for you to get viewport units working in old IE and Android Stock Browser as well. If the browser doesn't know how to deal with the viewport units - vw, vh, vmin and vmax - this library will not improve the situation unless you're using the hacks detailed below. The buggyfill uses the CSSOM to access the defined styles rather than ship its own CSS parser, that'S why the hacks abuse the CSS property content to get the values across. The buggyfill iterates through all defined styles the document knows and extracts those that uses a viewport unit. After resolving the relative units against the viewport's dimensions, CSS is put back together and injected into the document in a <style> element. Listening to the orientationchange event allows the buggyfill to update the calculated dimensions accordingly.