16 skills found
zixun / GodEyeAutomaticly display Log,Crash,Network,ANR,Leak,CPU,RAM,FPS,NetFlow,Folder and etc with one line of code based on Swift. Just like God opened his eyes
azlux / Log2ramramlog like for systemd (Put log into a ram folder)
01101010110 / MODWINA tool to customize your Windows Installation ISOs to make them as tiny or as large as you like. Able to add new apps, packages, drivers, files, folders, and registry entries. Able to auto install if selected and has an optional TPM bypass (req min 1gb ram and 2 core cpu). Works on all Windows 10 and 11 ISOs, UUPDUMP supported too.
bobafetthotmail / Folder2rammount those folders to ram without losing access to their counterpart on disk!
FutureProofRetail / Ember Cli RamdiskARCHIVED: Replaces your broccoli tmp/ folder w ram disk for faster builds (?) / minimal SSD thrashing
BlockchainLabs / PebblecoinPebblecoin UPDATE 2015/12/31: Version 0.4.4.1 is now out. The major change is optimizing the daemon to use less RAM. It no longer keeps all the blocks, which are rarely needed, in RAM, and so RAM usage has decreased from around 2 gigabytes, to under 200 megabytes. Mac binaries are also now available. The new wallet is compatible with the old wallet - simply turn off the old wallet, and start the new wallet, and the blockchain will update automatically to use less RAM. Code: Release Notes 0.4.4.1 - (All) Fix blockchain RAM usage, from almost 2 GB to less than 200 MB - Seamless blockchain conversion on first run with new binaries - (Qt) Fix high CPU usage - (Qt) Fix sync indicator (# of total blocks) - (Mac) Mac binaries - Technical Notes: - (All) Blockchain disk-backed storage with sqlite3 and stxxl - (Mac) Fix mac compilation - (All) Update build files & instructions for linux, mac, windows - (All) Remove unused protobuf and OpenSSL dependencies for Qt wallet - (Tests) Fix valgrind errors - (Tests) Use local directory for blockchain instead of default directory - (Tests) Run tests on Windows if using new enough MSVC LINKS: Windows 64-bit: https://www.dropbox.com/s/b4kubwwnb4t7o4w/pebblecoin-all-win32-x64-v0.4.4.1.zip?dl=0 Mac 64-bit: https://www.dropbox.com/s/uoy9z1oxu4x53cv/pebblecoin-all-mac-x64-v0.4.4.1.tar.gz?dl=0 Linux 64-bit: https://www.dropbox.com/s/jq3h3bc29jmndks/pebblecoin-all-linux-x64-v0.4.4.1.tar.gz?dl=0 Exchange: https://poloniex.com/exchange#btc_xpb . Source: https://github.com/xpbcreator/pebblecoin/ CONTACT: xpbcreator@torguard.tg IRC: irc.freenode.net, #pebblecoin UPDATE 2015/06/08: Version 0.4.3.1 is now out. This is a minor, mostly bug-fix release. Work continues on the next major release which will bring us user-created currencies and user-graded contracts. Release notes: Code: Release Notes 0.4.3.1 - RPC calls for DPOS: - getdelegateinfos RPC call - get kimageseqs RPC call - block header contains signing_delegate_id - fix checkpoint rollback bug - fix inability to send coins if voting history was lost UPDATE 2015/05/04: Version 0.4.2.2 is now out. This is a bug-fix/cosmetic release. Release notes: Payment ID support Windows installer Logos updated Improved DPOS tab Sync issues fully fixed Fix rare crash bug Fix min out 0 bug Fix debit display Fix GUI not updating Updated hard-coded seed nodes UPDATE 2015/04/24: The switch-over to DPOS has succeeded without a hitch! DPOS blocks are being signed as we speak, at the far faster pace of 15 seconds per block. This marks the start of a new era for Pebblecoin. UPDATE 2015/04/21: Congratulations to the first registered delegate! This indicates the start of the forking change so everybody please update your daemons if you haven't already. To promote the coin and encourage people to become delegates, we've come up with an incentive scheme. First, we'll send a free 100 XPB to anybody who PMs me their public address, for people to play around with and to start using the coin. Second, once DPOS starts, for the first month of DPOS I'll send an extra 0.5 XPB to the signing delegate for every block they process. This is on top of the usual transaction fees they will receive. This is to encourage more people to become delegates at this important phase of the coin. UPDATE 2015/04/19: All went well on the testnet release, so after a few further minor modifications, we are releasing version 0.4.1.2 to the public. This is a forking change, so please update your clients and servers (links below). At block 83120, sometime on April 21st, registration for DPOS delegates will begin. At block 85300, sometime on April 24th, the network will switch over to DPOS. As with the testnet, to become a delegate and receive block fees for securing the network, just turn on your wallet, register to be a delegate (5 XPB fee), and then leave your wallet on. It will sign the blocks when it is your turn. While Roman works on the next phase of the release - introducing subcurrencies - I will be fixing up some loose ends on the wallet, adding payment ID support, etc. This is truly an exciting time for Pebblecoin. RELEASE NOTES: All clients adjust internal clocks using ntp (client list in src/common/ntp_time.cpp) Added testnet support DPOS registration starts Block 83120 (~April 21st) DPOS phase starts Block 85300 (~April 24th) Default fee bumped to 0.10 XPB Low-free transactions no longer get relayed by default Significantly improved wallet sync Checkpoint at Block 79000 TOTAL CURRENT COINS: Available at this link. BLOCK TARGET TIME: 2 minutes EXPECTED EMISSION: At Block 3600 (End of Day 5): ~78 XPBs At Block 6480 (End of Day 9): ~758 XPBs At Block 9360 (End of Day 13): 6,771.0 XPBs At Block 12240 (End of Day 17): ~61,000 XPBs At Block 15120 (End of Day 21): ~550,000 XPBs, start of regular 300/block emission At Block 21900 (End of Month 1): ~2,600,000 XPBs, 300/block At Block 43800 (End of Month 2): ~9,150,000 XPBs, 300/block At Block 85300 (End of POW phase): ~21,500,300 XPBs. UPDATE: The Pebblecoin Pool is now live! Instructions: Download the linux miner and run it: ./minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x UPDATE: The Pebblecoin wallet is now live! There have been thousands of attempts at alternative currencies in the community. Many are 100% copies of existing blockchains with a different name. Some are very slight variations with no significant differences. From recent history it is apparent the only realistic chance for viability of a new currency is one that is innovation and continued support and development. The bitcoin community for good reason has shown interest in currencies that provide privacy of transactions, several currencies such as darkcoin, have become popular based on this desire. The best technology for privacy is cryptonote although for a variety of reasons there hasnt been much development for ease of use, and as a result there has not been significant adoption. Pebblecoin (XPB) is a cryptonote based coin with improvements and changes in some areas, and the promise of development in others. I invite developers to work on this technology with me. There is no premine, any tips or support of any developer including myself will be completely voluntary. These are the following areas which I have determined needs changes/updates: I welcome suggestions, and am interested what else I can try to improve. 1) New Mining algorithm (active) A mining algorithm is either susceptible to ASIC development or to being botnetted, meaning it is either more efficient to have a centralized mining entity (as is the case with bitcoin) or to have an algorithm that requires a real CPU, in which case botnets become very attractive. To my knowledge there does not exist a blockchain that attempts to solve both problems, by having an algorithm that only works on a general purpose computer and is difficult to botnet. Cryptonote coins currently are primarily mined with botnets. Boulderhash is a new mining algorithm requiring 13 GB RAM, nearly eliminating all possible zombie (botnet controlled) computers from mining. Most infected computers in the world do not have 13 GB available, so an algorithm that requires that much RAM severely limits the productivity of a botnet. 13 GB also makes ASICs cost prohibitive, and the current GPUs do not have that much RAM. What's left is general purpose computers as was the original intent of bitcoin's mining process. 2) Distribution of coins (active) It is very common in the launch of a new cryptocurrency the distribution algorithm heavily is weighted towards the very early adopters. Such distribution is designed to give a massive advantage to people who are fully prepared to mine at launch, with a very large difference shortly after sometimes a few days later. If the point of mining is to both secure the network and fairly distribute coins a gradual build up of rewards makes more sense, with no drop off in mining rewards. At a standard block reward of 300, at launch each block will reward 0.3 coins leading up to 3, 30, and finally the standard reward of 300 which will be the standard unchanging reward from that point. It will take approximately 3 weeks for the block reward of 300 to be reached. 3) GUI Software (active) There are no current cryptonote coins that have a downloadable GUI, which makes the user experience much worse than that of bitcoin. It is hard to achieve signficant adoption with a command line interface. The very first update had the exact GUI written for bitcoin fully working with Pebblecoin. The GUI was released on Jan 19, before the full 300 XPB reward was awarded for winning the block. 4) IRC Chat support embedded in Client GUI (active) For user support, and to talk to core developers message boards such as Bitcointalk and reddit are primarily used. I have embedded an IRC client in the GUI and be available at set hours for any kind of support. 5) Address aliasing (to be worked on) Just as a user visiting google does not need to know the ip address, similarly an address should have the ability to have an associated userid. If I ask a friend to send me pebblecoins it would be easier to tell him send it to @myuserid rather than a very long address or scanning a QR code. There should be a way of registering a userid on the blockchain that will permanently translate to a pebblecoin addresss. QT INSTRUCTIONS: Download the package for your respective platform Run the Qt executable. The software will generate a new wallet for you and use a default folder: ~/.pebblecoin on Linux and %appdata%\pebblecoin on Windows. To use an existing wallet, copy the wallet.keys file into the default folder. To use a different data directory and/or wallet file, run the software like so: ./pebblecoin-qt --data-dir <DataDir> --wallet-file <FileName>. To enable mining, run the start_mining_NEEDS_13GB_RAM.bat batch file. Or run the qt wallet with the --enable-boulderhash command line option, or put enable-boulderhash=1 into the config file. It will start mining to the wallet address. To change the number of mining threads (13GB required per thread), do --mining-threads <NumThreads> or edit the batch file. DAEMON + SIMPLEWALLET INSTRUCTIONS: Download the package, run: ./pebblecoind --data-dir pebblecoin_data Once the daemon finished syncing, run the simplewallet: ./simplewallet POOL INSTRUCTIONS: Download the miner binary for your platform. Run the miner using a wallet address gotten from simplewallet or the Qt Wallet: Code: minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x [/li] DEV WALLET (for donations): PByFqCfuDRUPVsNrzrUXnuUdF7LpXsTTZXeq5cdHpJDogbJ8EBXopciN7DmQiGhLEo5ArA7dFqGga2A AhbRaZ2gL8jjp9VmYgk
leichenNUSJ / AAMandDCMThis project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2].  ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by chenleinj@njust.edu.cn ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .
ajaybhatiya1234 / DEEP FACE Dectection01 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
manicman1999 / ImageData GeneratorConverts folders of images to chunks which can easily be saved/loaded into RAM (numpy).
srhoe / MorevirusSimple C program simulating RAM installation and storage upgrade so when you select RAM it crashes your computer and when you select storage in add 100k folders which also crashes the computer. Educational tool showcasing user input validation, loops, process creation, and directory handling.
codemonauts / S3 EcouploaderSync a folder to an S3 bucket and try to use not much ram, bandwith and storage space
rimdrira / ABC GAWe present in this project the evaluation of our work. This evaluation is based on several simulations. Our simulation consists of two parts. We set, firstly, the optimal parameters configuration of our ABC-GA algorithm that provides the optimal solution. This part is implemented in the folder validation_test. Second, we evaluate the performance of the ABC-GA algorithm based on several performance metrics. This part is implemented in the folder evaluation_test. The data structure folder present the data structure used in our algorithm to design a composition plan. We implement genetic operation (cross-over and mutation) in the folder genetic_operations. All the simulations carried out in our work are executed on a machine whose characteristics are: •Processor: 2.9 GHz Intel Core i5 dual core. •RAM capacity: 8GB. •Operating system: Mac OS. The development language used in our work is Python. The QoS attributes considered in our simulations are cost,response time, availability and reliability. The simulations performed are based on a variation of the values of theseattributes in order to generate several solutions. We define the laws of variation ofthese attributes as follows: The cost follows the Uniform law between[0.2,0.95]. Response time follows the Uniform law [20,1500]. Availability follows the Uniform law between[0.9,0.99]. Reliability follows the Uniform Law between[0.7,0.95] We assign the weight = 0.25to each quality of service attribute.
ivanhao / Pve Folder2rampve-folder2ram is optimazed use usb storage as OS partition.
flipcoder / RampageQuickly mount/unmount existing folders to RAM
tchenu / HackintoshEFI folder allowing to realize a hackintosh on the following configuration: Ryzen 7 2700x, Nvidia 1070Ti and 32GB of RAM. 🍎
shwetakumawat / Voice Assistant Intelligent ApplicationFeatures: It can do a lot of cool things, some of them being: - Greet user - Tell current time and date - Launch applications/softwares - Open any website - Tells about weather of any city - Open location of any place plus tells the distance between your place and queried place - Tells your current system status (RAM Usage, battery health, CPU usage) - Tells about your upcoming events (Google Calendar) - Tells about any person (via Wikipedia) - Can search anything on Google - Can play any song on YouTube - Tells top headlines (via Times of India) - Plays music - Send email (with subject and content) - Calculate any mathematical expression (example: Jarvis, calculate x + 135 - 234 = 345) - Answer any generic question (via Wolframalpha) - Take important note in notepad - Tells a random joke - Tells your IP address - Can switch the window - Can take screenshot and save it with custom filename - Can hide all files in a folder and also make them visible again - Has a cool Graphical User Interface ## API Keys To run this program you will require a bunch of API keys. Register your API key by clicking the following links - [OpenWeatherMap API](https://openweathermap.org/api) - [Wolframalpha](https://www.wolframalpha.com/) - [Google Calendar API](https://developers.google.com/calendar/auth) ## Installation - First clone the repo - Make a config.py file and include the following in it: ```weather_api_key = "<your_api_key>" email = "<your_email>" email_password = "<your_email_password>" wolframalpha_id = "<your_wolframalpha_id>" - Copy the config.py file in Jarvis>config folder - Make a new python environment If you are using anaconda just type ```conda create -n jarvis python==3.8.5 ``` in anaconda prompt - To activate the environment ``` conda activate jarvis ``` - Navigate to the directory of your project - Install all the requirements by just hitting ``` pip install -r requirements.txt ``` - Install PyAudio from wheel file by following instructions given [here](https://stackoverflow.com/a/55630212) - Run the program by ``` python main.py ``` - Enjoy !!!! ## Code Structure ├── driver ├── Jarvis # Main folder for features │ ├── config # Contains all secret API Keys │ ├── features # All functionalities of JARVIS │ └── utils # GUI images ├── __init__.py # Definition of feature's functions ├── gui.ui # GUI file (in .ui format) ├── main.py # main driver program of Jarvis ├── requirements.txt # all dependencies of the program - The code structure if pretty simple. The code is completely modularized and is highly customizable - To add a new feature: - Make a new file in features folder, write the feature's function you want to include - Add the function's definition to __init__.py - Add the voice commands through which you want to invoke the function ## Contribute Please read [CONTRIBUTING.md](https://github.com/Gladiator07/JARVIS/blob/master/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests. ## License This project is licensed under [MIT License](https://github.com/Gladiator07/JARVIS/blob/master/LICENSE) 2021 Atharva Ingle ## Future Improvements - Generalized conversations can be made possible by incorporating Natural Language Processing - GUI can be made more nicer to look at and functional - More functionalities can be added
