The Medicine Recommendation System Project is designed to recommend assist users in finding suitable medicines based on their needs. The system leverages Natural Language Processing (NLP) techniques such as TF-IDF and Cosine Similarity to provide intelligent recommendations. Additionally, it incorporates custom-generated review weightages to prioritize recommendations based on products reviews. The system offers two key recommendation features:

1. Product Uses Similarity-Based Recommendations : Provides medicine recommendations based on product uses similarity.

2. Side Effects and Medicine Similarity-Based Recommendations : Offers recommendations by considering both medicine uses similarity and the similarity between side effects of the medicines.

The system leverages a machine learning model exported using a pickle file and uses Flask to host the webpages and includes a user-friendly interface with three main pages: Home , Recommendation , and Contact .

1. Product Uses Similarity-Based Recommendations:

• This feature recommends medicines based on their similarity to the user’s input. Ideal for users looking for alternatives to a specific medicine.

2. Side Effects and Medicine Similarity-Based Recommendations:

• This advanced feature considers not only the similarity between medicines but also the similarity between their side effects. Ensures that the recommended medicines are not only similar in Uses but also similar in terms of potential side effects.

3. NLP-Based Recommendations with Custom Review Weightages:

• • The system uses TF-IDF to transform textual data into numerical vectors. Cosine Similarity is then applied to compute the similarity between the user’s query and the dataset entries. A custom review weightage method is implemented to prioritize recommendations based on products reviews. Higher-rated medicines are ranked higher in recommendations. This ensures that the recommendations are not only technically accurate but also aligned with real-world user experiences.

The system logs user search inputs and recommended medicines into a CSV file. The CSV file contains the following columns:

• user_input: The input provided by the user.
• suggested_names: The recommended medicines generated by the model.
• results: A dictionary containing detailed information about each recommended medicine.
• timestamp: The date and time of the search.

Flask Web Interface : The system provides a clean and intuitive web interface with the following pages:

• Home Page: It contains all the list of medicines which one can view and understand its different uses.
• Recommendation Page: Allows users to search for medicines and view recommendations.
• Contact Page: Provides contact information.

1. Python
2. Flask
3. Pickle
4. Pandas
5. Matplotlib
6. Scikit-learn
7. numpy
8. csv

This project is licensed under the MIT License.

Please Feel free to contribute to this project by submitting issues or pull requests.

Any enhancements, bug fixes, or optimizations are extremely welcomed!

The Medicine Recommendation System Project is designed to recommend assist users in finding suitable medicines based on their needs. The system leverages Natural Language Processing (NLP) techniques such as TF-IDF and Cosine Similarity to provide intelligent recommendations. Additionally, it incorporates custom-generated review weightages to prioritize recommendations based on products reviews. The system offers two key recommendation features:

1. Product Uses Similarity-Based Recommendations : Provides medicine recommendations based on product uses similarity.

2. Side Effects and Medicine Similarity-Based Recommendations : Offers recommendations by considering both medicine uses similarity and the similarity between side effects of the medicines.

The system leverages a machine learning model exported using a pickle file and uses Flask to host the webpages and includes a user-friendly interface with three main pages: Home , Recommendation , and Contact .

1. Product Uses Similarity-Based Recommendations:

• This feature recommends medicines based on their similarity to the user’s input. Ideal for users looking for alternatives to a specific medicine.

2. Side Effects and Medicine Similarity-Based Recommendations:

• This advanced feature considers not only the similarity between medicines but also the similarity between their side effects. Ensures that the recommended medicines are not only similar in Uses but also similar in terms of potential side effects.

3. NLP-Based Recommendations with Custom Review Weightages:

• • The system uses TF-IDF to transform textual data into numerical vectors. Cosine Similarity is then applied to compute the similarity between the user’s query and the dataset entries. A custom review weightage method is implemented to prioritize recommendations based on products reviews. Higher-rated medicines are ranked higher in recommendations. This ensures that the recommendations are not only technically accurate but also aligned with real-world user experiences.

The system logs user search inputs and recommended medicines into a CSV file. The CSV file contains the following columns:

• user_input: The input provided by the user.
• suggested_names: The recommended medicines generated by the model.
• results: A dictionary containing detailed information about each recommended medicine.
• timestamp: The date and time of the search.

Flask Web Interface : The system provides a clean and intuitive web interface with the following pages:

• Home Page: It contains all the list of medicines which one can view and understand its different uses.
• Recommendation Page: Allows users to search for medicines and view recommendations.
• Contact Page: Provides contact information.

1. Python
2. Flask
3. Pickle
4. Pandas
5. Matplotlib
6. Scikit-learn
7. numpy
8. csv

This project is licensed under the MIT License.

Please Feel free to contribute to this project by submitting issues or pull requests.

Any enhancements, bug fixes, or optimizations are extremely welcomed!

I’ve been struggling with the syntax of derived types, inheritance (extension), and so on with Fortran. The concepts are easy, but Modern Fortran is new to me. Seeking a concrete example to work from, I found a reasonable description with code snippets that I could massage into compilable and executable code at Fortran.Cat — Deferred Overloading in Derived Type.

The bulk of the code is straight from the post by Lincador at Fortran.Cat. I made everything lower case and split it into three files: one module file for each type, and a main program to drive some quick tests.

I found no copyright or license at Fortran.Cat, and I make no claim beyond my usual “anything I added is freely available, anything derived keeps its original licensing and rights”:

Some or all of the code in this project is by Troy Brumley (BlameTroi). Prior work preserves its original licensing and author copyrights. My work is Copyright 2023 by Troy Brumley, all rights reserved, and licensed under the UNLICENSE.

Troy Brumley
blametroi@gmail.com

So let it be written. So let it be done.

whorl was created to teach you how async executors work in Rust. It is not the
fastest executor nor is it’s API perfect, but it will teach you about them and
how they work and where to get started if you wanted to make your own. It’s
written in a literate programming style such that reading it from beginning to
end tells you a story about how it works or you can read parts of it in chunks
depending on what you want to get out of it.

You can read it all online here on GitHub
or you can clone the repo yourself and open up src/lib.rs to read through it
in your favorite text editor or play around with it and change things. All of
the code is licensed under the MIT License so you’re mostly free to do with it
as you wish. If you want to make the next tokio or just make something for fun
you can do that.

If you just want to see it in action an example test program is included as part
of the file. You can see it’s output by just running:

cargo test -- --nocapture

Which should look something like this:

whorl on  main [!⇡] is 📦 v0.1.0 via 🦀 v1.56.0 took 10s
❯ cargo test -- --nocapture
   Compiling whorl v0.1.0 (/home/michael/whorl)
    Finished test [unoptimized + debuginfo] target(s) in 0.47s
     Running unittests (target/debug/deps/whorl-6d670ffb5bb225ca)

running 1 test
Begin Asynchronous Execution
Blocking Function Polled To Completion
Spawned Fn #00: Start 1635276666
Spawned Fn #01: Start 1635276666
Spawned Fn #02: Start 1635276666
Spawned Fn #03: Start 1635276666
Spawned Fn #04: Start 1635276666
Spawned Fn #00: Ended 1635276669
Spawned Fn #02: Ended 1635276669
Spawned Fn #03: Ended 1635276669
Spawned Fn #01: Ended 1635276670
Spawned Fn #00: Inner 1635276671
Spawned Fn #03: Inner 1635276674
Spawned Fn #04: Ended 1635276675
Spawned Fn #02: Inner 1635276675
Spawned Fn #01: Inner 1635276678
Spawned Fn #04: Inner 1635276678
End of Asynchronous Execution
test library_test ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 23.00s

   Doc-tests whorl

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

The Insider Incident Data Exchange Standard (IIDES) is a new standard for collecting and sharing insider threat incidents. IIDES is intended to support a community of practitioners, analysts, and researchers in their efforts to reduce threats from insiders across organizations of all sizes and in all sectors. View the IIDES white paper for a deeper understanding of the goals of IIDES and the guiding principles for its development.

The schema itself is written in JSON. All of the IIDES schema components can be found in the JSON folder. The schema files have also been combined into one complete schema file, iides_full_schema.json, for direct use in IIDES implementations. We also have a Python-based reference implementation of IIDES called PyIIDES in a separate repository.

You can find easy-to-read documentation for each component of the schema in the documentation folder of this repository. The complete entity diagram and the entity relationship diagram may also be useful.

We also have a set of IIDES examples to help provide an understanding of how IIDES can be used.

We encourage feedback from our user community and welcome requests for improvements to IIDES, which can be submitted through GitHub.

This file is a part of the Insider Incident Data Exchange Standard (IIDES) – see https://github.com/cmu-sei/IIDES

Copyright 2024 Carnegie Mellon University.

[DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution.

This work is provided “AS-IS” with “NO WARRANTIES OF ANY KIND – EXPRESS OR IMPLIED” and is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Requests for permission for non-licensed uses should be directed to the Software Engineering Institute at permission@sei.cmu.edu.

CERT® is registered in the U.S. Patent and Trademark Office by Carnegie Mellon University.

DM24-0776

My dotfiles to configure programs I use.

• kitty The fast, feature-rich, GPU based terminal emulator, home page.
• tmux Terminal multiplexer, GitHub page.

• zsh Interactive, extended Bourne shell, home page.
• pwsh Powershell Core, GitHub page.
• starship The minimal, fast, and customizable prompt for any shell, home page.

• Neovim Hyperextensible Vim-based text editor, home page.
• VS Code Streamlined code editor with support for development operations like debugging, task running, and version control, home page.
• Vim Configurable text editor built to make creating and changing any kind of text very efficient, home page.

• ranger VIM-inspired file manager for the console, GitHub page.
• lf Terminal file manager written in Go with a heavy inspiration from ranger file manager, GitHub page.

• mpv Free, open source, and cross-platform media player, home page.

• zathura Minimalistic and space saving interface as well as an easy usage that mainly focuses on VIM-inspired keyboard interaction, home page.

• newsboat RSS/Atom feed reader for the text console, which is an actively maintained fork of Newsbeuter, home page.

• InSync Cloud sync with .gitignore, home page.

Imagine never missing out on a price drop for your desired items on Amazon again. With PriceDropBot, you gain the upper hand in online shopping, effortlessly unlocking unbeatable deals and maximizing your savings.

PriceDropBot is an innovative bot that works tirelessly behind the scenes to notify you via email whenever there’s a drop in the price of an item on Amazon. Bid farewell to the frustration of purchasing a product, only to discover it went on sale shortly after. PriceDropBot ensures you stay in the loop, empowering you to make informed buying decisions and seize incredible savings opportunities.

But the potential of PriceDropBot extends far beyond tracking price drops. Here are some additional use cases that will revolutionize your shopping experience:

Wish List Management: Create personalized wish lists on Amazon, and let PriceDropBot monitor prices for your desired items. Receive timely alerts when the prices plummet, enabling you to strike while the iron is hot.

Deal Hunting: Are you a bargain hunter on the lookout for the best deals? PriceDropBot scours Amazon for you, highlighting irresistible discounts and limited-time offers across various product categories. Embrace the thrill of finding hidden gems and saving big on your purchases.

Gift Shopping: Simplify the gift-giving process by using PriceDropBot. Add gift ideas to your watchlist, and let the bot notify you when their prices drop. Stay ahead of the game and surprise your loved ones with thoughtful gifts without breaking the bank.

PriceDropBot is your ticket to smarter shopping, exceptional savings, and a world of unbeatable deals. Don’t miss out on the chance to supercharge your shopping experience. Discover the power of PriceDropBot and unlock a new realm of savings today!

• BeautifulSoup
• Requests
• UrlLib
• CSV
• Datetime
• SmtpLib

• bs4: pip install bs4
• requests: pip install requests
• An email with “Less Secure Apps” turned on, so that the app can send you an email.
  (I Suggest using a secondary email or maybe even creating a new one)

• Download/Clone this Repository
• Run pricedropalert.py

• Surveys
  • On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020) Li Yang, Abdallah Shami

• Automatic Statistician

• Python

  • Adanet (Homepage, PyPI)
  • AgEBO-Tabular (Homepage, Paper)
  • AlphaD3M (Homepage, Paper) – closed source. Uses RL to tackle pipeline search
  • Auptimizer (Homepage, PyPI)
  • Auto-PyTorch (Homepage, Paper)
  • Auto-Sklearn (Homepage, PyPI) – based on bayesian optimization
  • Auto-Sklearn 2.0 (Homepage, PyPI, Paper) – uses portfolio learning
  • AutoGBT (Homepage)
  • AutoGBT-alt (Homepage)
  • AutoGluon (Homepage, PyPI) – uses multi-Layer stack ensembling with k-fold ensemble bagging at all layers
  • AutoKeras (Homepage, PyPI)
  • AutoML Zero (Homepage, Paper)
  • AutoML-DSGE (Homepage, Paper)
  • automl-gs (Homepage, PyPI
  • AutoRec (Homepage, Video) – Automated Recommender System
  • AutoViML(Homepage)
  • AX (Homepage, PyPI)
  • Axolotl (Homepage)
  • BOHB (Homepage)
  • BoTorch (Homepage, PyPI)
  • Gama (Homepage, Paper)
  • H2O AutoML (Homepage)
  • HpBandSter (Homepage)
  • Hyperopt (Homepage, PyPI, Paper)
  • Hyperparameter Hunter (Homepage, PyPI)
  • Katib (Homepage, Paper)
  • Keras Tuner (Homepage, PyPI)
  • Lale (Homepage, PyPI, Paper)
  • Ludwig (Homepage, PyPI)
  • Mango (Homepage, Paper)
  • Milano (Homepage)
  • MLBox (Homepage, PyPI)
  • nni (Homepage, PyPI)
  • Optuna (Homepage, PyPI)
  • Petridish (Homepage, Paper)
  • PHS (Homepage, Paper)
  • ray (Homepage, PyPI)
  • RECIPE (Homepage)
  • ROBO (Homepage)
  • SMAC3 (Homepage, PyPI)
  • Spearmint (Homepage)
  • Talos (Homepage, PyPI
  • TPOT (Homepage, PyPI)
  • VolcanoML (Paper, Code)

• Scala

  • TransmogrifAI (Homepage)

Aioli is a library for running genomics command-line tools in the browser using WebAssembly. See Who uses biowasm for example use cases.

• Documentation
• List of supported packages

Run npm install to install dependencies.

Then run npm run dev to launch the web server and use src/example.js as a sample web app that uses the dev version of Aioli.

Run npm run test.

• Update version in package.json (append -rc1 for release candidates)
• Build: npm run build
• Create npm package: npm pack
• Publish package: npm publish [tgzfile] --tag next
• To use pre-release version: npm install @biowasm/aioli@next

• Update version in package.json
• Build: npm run build
• Publish package: npm publish --access public
• Create release in the GitHub repo
• Add to biowasm.json
• Deploy biowasm CDN with env=stg, tool=aioli and run biowasm tests to validate that stg tests still pass
• Repeat with env=prd

• Aioli creates a single WebWorker, in which all WebAssembly tools run.
• We use a PROXYFS virtual filesystem so we can share a filesystem across all WebAssembly modules, i.e. the output of one tool can be used as the input of another tool.
• We use a WORKERFS virtual filesystem to mount local files efficiently (i.e. without having to load all their contents into memory). To support use cases where tools need to create files in the same folder as those ready-only files (e.g. samtools index), we automatically create a symlink from each local file’s WORKERFS path to a path in PROXYFS.
• Once the WebWorker initializes, it loads the WebAssembly modules one at a time. To support this, we need to encapsulate each module using Emscripten’s -s MODULARIZE=1, i.e. the .js file will contain a Module function that initializes the module and returns a Promise that resolves when the module is loaded.
• We do WebAssembly feature detection at initialization using wasm-feature-detect. If a tool needs WebAssembly SIMD and the user has a browser that does not support it, we will load the non-SIMD version of that tool.
• We communicate with the WebWorker using the Comlink library.

WebAssembly is a fast, low-level, compiled binary instruction format that runs in all major browsers at near native speeds. One key feature of WebAssembly is code reuse: you can port existing C/C++/Rust/etc tools to WebAssembly so those tools can run in the browser.

WebWorkers allow you to run JavaScript in the browser in a background thread, which keeps the browser responsive.

See the biowasm project.

Manta is the privacy layer for Web 3. Manta’s goal is to protect Web 3 users’ fundamental privacy from the first principle.

Disclaimer: The code currently hasn’t been properly security audited (work in progress), use it at your own risk.

👉 Learn more about Manta Network.
👉 Check out our technical documentation.
👉 Get involved in Manta Community.

This is the mono-repo for Manta/Calamari nodes.

• Manta: Manta’s Polkadot parachain network
• Calamari: Manta’s canary network on Kusama
• Dolphin: Manta’s testnet

1. Setup environment

chmod u+x ./scripts/init.sh
./scripts/init.sh

2. Build node binary in production setting

cargo b --profile production

Tips: The binary will be generated under target/production/manta. For less performance critical build, cargo build --release is recommended for faster build time.

3. Run standalone dev chain, useful for local development

cargo run -- --chain=calamari-localdev --alice --tmp

Tip: The chain only produces blocks when you submit extrinsics

Manta/Calamari’s version number: v<x>.<y>.<z>

where:

• <x> is the major version, i.e. major product release.
• <y> is the middle version, i.e. adding major features.
• <z> is the minor version, i.e. performance improvement and bug fixes.

• please submit your code through PR.
• please run cargo +nightly fmt before pushing your code.

This project’s MSRV is rustc 1.74