524 skills found · Page 9 of 18
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BaohaoLiao / Frac CotAn efficient sampling method for long-CoT LLM with fractured CoT.
7-yaya / SyncPoolA provably secure disambiguating steganography method based on grouping ambiguous pools and synchronous sampling.
JinJunRen / GB SMOTEThis method is a new oversampling algorithm and can circumvent the deficiency of WK-SMOTE (and SMOTE as well as its variants) caused by randomly selecting some minority class samples.
GameDevBox / Advanced Triplanar ShaderIn this section, we're gonna learn about triplanar and how to use it in a Unity shader graph. So first, what is Triplanar? It's a method of generating UVs and sampling a texture by projecting it into the world space. This way, we're not sampling the texture based on the game object itself.
Vu5e / JobFailurePredictionGoogleTraces2019By learning and using prediction for failures, it is one of the important steps to improve the reliability of the cloud computing system. Furthermore, gave the ability to avoid incidents of failure and costs overhead of the system. It created a wonderful opportunity with the breakthroughs of machine learning and cloud storage that utilize generated huge data that provide pathways to predict when the system or hardware malfunction or fails. It can be used to improve the reliability of the system with the help of insights of using statistical analysis on the workload data from the cloud providers. This research will discuss regarding job usage data of tasks on the large “Google Cluster Workload Traces 2019” dataset, using multiple resampling techniques such as “Random Under Sampling, Random Oversampling and Synthetic Minority Oversampling Technique” to handle the imbalanced dataset. Furthermore, using multiple machine learning algorithm which is for traditional machine learning algorithm are “Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier and Extreme Gradient Boosting Classifier” while deep learning algorithm using “Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)” for job failure prediction between imbalanced and balanced dataset. Then, to have a comparison of imbalanced and balanced in terms of model accuracy, error rate, sensitivity, f – measure, and precision. The results are Extreme Gradient Boosting Classifier and Gradient Boosting Classifier is the most performing algorithm with and without imbalanced handling techniques. It showcases that SMOTE is the best method to choose from for handling imbalanced data. The deep learning model of LSTM and Gated Recurrent Unit may be not the best for the in terms of accuracy, based on the ROC Curve its better than the XGBoost Classifier and Gradient Boosting Classifier.
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.
OpenGeoHub / Spatial Sampling MlRmarkdown tutorial on how to generate spatial sampling and how to use resampling methods for ML
galombardi / Method HtSSM ADDMMethod to fit the a hierarchical time-varying sequential sampling and an attentional drift diffusion model.
Infini-AI-Lab / JackpotA rejection-sampling based distribution alignment method for extreme actor-policy mismatch RL Training
Yadhavaramanan / Policy GradientPolicy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. In the off-policy algorithm, actions are sample using behaviour policy and separate target policy is used to optimise for.
pierreroudier / ClhsA R implementation of the conditioned Latin Hypercube Sampling method
iRNA-COSI / APAevalCommunity effort to evaluate computational methods for the detection and quantification of poly(A) sites and estimating their differential usage across RNA-seq samples
Gaochenyin / Monte Carlo Expectation MaximizationEM algorithm: Gibbs sampling incorporated with Metropolis-Hastings step and computed the posterior mode through Louis' method
filiph / Extension Methods PlaygroundA few samples use cases of Dart extension methods.
obliviateandsurrender / Quantum Boltzmann MachineImplementation of an algorithm for training Quantum Boltzmann Machine neural networks using variational methods. Based on https://arxiv.org/abs/1712.05304 and their sample code.
fpgdubost / BstrapBootstrap hypothesis testing Python Package. Bootstrapping is a simple method to compute statistics over your custom metrics, using only one run of the method for each sample in your evaluation set. It has the advantage of being very versatile, and can be used with any metric really.
stepheUp / VideoScanZXingWinRTBarcode and QRCode scanner for Windows Phone 8.1 (WinRT) and Windows 10 using ZXing. Just add the projet to your solution, call 1 method and you're done. You can take the Sample App project provided as an example.
spacocha / Distribution Based ClusteringExtremely accurate algorithm used to group DNA sequences from microbial communities into operational taxonomic units (proxy for species) for ecological or biomedical research. This algorithm uses the information contained in the distribution of DNA sequences across samples along with sequence similarity to cluster sequences more accurately than other methods that are currently available. Developed for Illumina next-generation sequencing libraries, but applicable to any sequencing platform with sufficient number of counts per sample to infer distribution patterns.
mitulmanish / Java AssignmentsYou are required to implement a basic Java program using Java (SE 5.0 or later). This assignment is designed to help you: 1. Practise your knowledge of class design in Java; 2. Practise the implementation of different kinds of OO constructs in Java; 3. Practise the use of polymorphism; 4. Practise error handling in Java; 5. Develop a reasonable sized application in Java. General Implementation Details All input data should be read from the standard input and all output data should be printed to the standard output. Do not use files at all. If the input is formatted incorrectly, that input should be ignored and an appropriate error message should be displayed. Marks will be allocated to your class design. You are required to modularise classes properly---i.e., to use multiple methods as appropriate. No method should be longer than 50 lines. Marks will be allocated to proper documentation and coding layout and style. Your coding style should be consistent with standard coding conventions . Overall Specification You will build out the system from Assignment 1 to manage multiple users purchasing different types of items, including discounts for multiple items. Items to be Purchased The TechStore has been extended to sell Software as well as Books. Like Books, Software can be sold as a (physical) CD or as an online item (i.e., download). As in Assignment 1, a Book can also be sold as a physical copy or as an ebook. You need to keep track of the physical copies of Books and CDs, and whether or not a title is available as an online item. Books have a title and an author; Software items have a title and a publisher. Each item is individually priced---i.e., the price depends on the title and whether it is a physical copy or ebook/software-download. Purchasing Items A User can buy any number of items (books, software, or a mix), adding one item at a time to their Shopping Cart. However, a User can only purchase up to a total of $100, unless they are a Member—if a non-Member User tries to add an item to their Shopping Cart that takes the total over their maximum then this is blocked. A Member has no limit. Items can be added and removed from a Shopping Cart until Checkout. When an Item is added to the Shopping Cart, the system checks that there are enough copies of it available; if an Item is added or removed from the Shopping Cart, the number of copies available must be updated. Checkout clears the Shopping Cart. Users Users can add Items to their Cart, up to their allowed limit (i.e., their Shopping Cart cannot store a total greater than the limit). A User has an id (must be unique) and password (you do NOT need to make these encrypted or secure), as well as a name and email address. A Member is a special kind of user: a Member has no limit on value they can store in their Cart. Once a User has spent a total of 10% more than their limit in total (this obviously must be over multiple Checkouts), then they are offered to become a Member—this offer is made straight after they Checkout with the items that takes them to 10% over their limit. An Administrator is a User that can perform special functions: add to the number of copies of a (physical) Book or Software CD; change the price of an item; print sales statistics: i.e., number of sales (physical and electronic) of each Item; add a new user—the system must checked that the new id is unique. Other Users do not have these options on their menu. A user must keep track of their previous purchases, grouped by Transaction—a Transaction is the set of items purchased at Checkout time. Users can log in and out—they do not need to Checkout before logging out. However, only one user can be logged in at a time—the system must allow something like “change user”. If a User logs back in, their Shopping Cart holds the same contents as before they logged out. Recommended Items and Discounts Each item can store a list of “if you liked this” recommendations. If a User adds an Item to their Shopping Cart, then the system suggests other titles they may like. Only similar types of things are recommended—i.e., when a Book is added, other Books (not Software) are suggested. At the time when a list of Recommended titles is given, the user has the option to add one of the recommended titles to their Shopping Cart. If a user adds the title, then they receive a discount of 15% off that second title (the first one is still full price); the User can add multiple recommended titles for 15% off each of them. If a Member adds the recommended title, then they get 25% discount off all the recommendations added. Note: when a recommended title is added, its recommendations are also shown, and are discounted if purchased at that time. You are NOT required to handle the special case of updating discounts when a User removes recommendations from their Cart. However, there is a Bonus Mark for this. Sample menus The menu for a standard User (i.e., a Shopper) should include the following options: 1. Add item to shopping cart 2. View shopping cart 3. Remove item from shopping cart 4. Checkout 5. List all items 6. Print previous purchases 7. Logout (change user) 0. Quit The menu for an Administrator should include the following options: 1. List all items (this option can include purchase statistics for each title) 2. Add copies to item 3. Change price of item 4. Add new user 5. Logout (change user) 0. Quit * SAMPLE RUNS and TEST DATA will be posted to Blackboard * Program Development When implementing large programs, especially using object-oriented style, it is highly recommended that you build your program incrementally. This assignment proposes a specific incremental implementation process: this is designed to both help you think about building large programs, and to help ensure good progress! You are not strictly required to follow the structure below, but it will help you manage complexity. Part A (2 marks): Extend Assignment 1 Start by extending your Assignment 1 solution (a sample solution will be made available): 1. Rename your main class to TechStore if necessary; 2. Extend your Book class (if necessary) to contain all data and operations it needs for Assignment 2, and appropriate classes for other types of Items to be sold; 3. Define Exceptions to handle problems/errors; in particular, you must handle invalid menu options or inputs. Part B (1 marks): Class Design Define all the classes and any interfaces needed for the described system. In particular, you should try to encapsulate all the appropriate data and operations that a class needs. This may mean some classes refer to each other (e.g., the way Account refers to Customer). At this point, you may just want to think about the data and operations and just write the definitions, not all the code. Part C (3 marks): Main Program Your main program should be in the TechStore class. (Of course, any class can contain a main(); this is useful for testing that class.) The main program will contain a menu that offers all the required options (these can be different for different Users!). The system will allow a User to login by typing their id and password and will check that these match: if it does not then the menu prints an error; if they do match, then the system prints a welcome message with the user’s name and shows them the appropriate menu. The system must keep a list of all its Users: this list must be efficient to look-up by User id. Week 7 Demo (2 marks): You will be required to demonstrate your main program and design (with only bare functionality) by Week 7 at the latest. You must also submit to the associated WebLearn project by the Week 7 lecture. Part D (4 marks): Implement Core Functionality Implement the core functionality of the TechStore system described above, except for the recommendations, members, and discounts. You should be able to implement the rest of the TechStore functionality described above, and run and test your system. Part E (4 marks): Implement Recommendations , Members, Discounts Implement the functionality of providing recommendations, users becoming and being members, and discounts. Other (4 marks) As always, marks will be awarded for coding style, documentation/comments, code layout and clarity, meaningful error and other messages, proper error handling, choice of data structures and other design decisions. You are encouraged to discuss such issues with your tutors and lab assistants, or with the coding mentors. Bonus (2 marks) Note: There will be no hints or help offered on Bonus tasks. 1 bonus mark for early demonstration of Parts A,B,C in Week 6 1 bonus mark for correctly handling removal of recommended books from Cart—e.g., if a Member removes the first item then the 15/25% should be added back to the price of the recommended title, unless there are multiple recommendations linked to that title. Submission Instructions Full assignment submission will be via Weblearn, by 9AM, Tues April 28, 2015. You can submit your assignment as many times as you want before the due date. Each submission will overwrite any previous submissions. 1. You need to submit a class diagram (in pdf, gif or jpeg format). 2. You are required to submit your .java files weekly via Weblearn. Your progress will be taken into consideration if you need an extension. 3. There will be a separate WebLearn submission for Part A,B,C—you must submit to this before the Week 7 lecture to qualify for the 2 marks for Week 7 demo. 4. You must include a README file. This should describe how to run your program, what extra functionality you implemented, any standard functionality you know does not work, and any problems or assumptions. If the tutors have any problem running your program and the README does not help then you will lose marks. 5. For the code submission, you must include only the source files in your submission (do not submit any *.class files!). As always, your code must run on CSIT machines. 6. You must submit a single ZIP file—use zip/WinZIP to zip your files before submitting---do NOT submit rar or zipx files!! 7. If you use packages, it is your responsibility that these unpack properly into the correct folders and that your program compiles correctly.
