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  • composed

    composed.sh

    Simplest “GitOps” for Docker Compose. This script automates the process of updating and deploying services using Docker Compose. It pulls the latest changes from a Git repository, checks for updates in docker-compose.yml or docker-compose.yaml files, and restarts the affected services. Additionally, it sends notifications to a Discord channel if configured.

    Prerequisites

    • Docker and Docker Compose installed.
    • Git installed.
    • Optional: Discord webhook URL for notifications.

    Configuration

    Edit the script and configure the following variables:

    • DEPLOYMENT_ENV: Description of the deployment environment (used in Discord messages).
    • DEPLOYMENTS_DIR: Directory containing Docker Compose files.
    • DISCORD_WEBHOOK_URL: Discord Webhook URL (leave empty if you don’t want to use it).

    Directory Structure

    Ensure the DEPLOYMENTS_DIR is set up with the proper file structure. Each service should have its own directory containing the docker-compose.yml or docker-compose.yaml file.

    Example structure:

    /deployments
  ├── composed.sh
  └── services
      ├── service1
      │   └── docker-compose.yml
      ├── service2
      │   └── docker-compose.yaml
      └── service3
          └── docker-compose.yml

    Git Repository Setup

    The /deployments directory should be a Git repository. This repository contains the Docker Compose files for each service. Each service needs its own folder for the script to work properly (as shown in the example above). To work with the script properly, initialize a Git repository in this directory and commit your changes.

    Example workflow:

    1. Initialize the Git repository: 
      mkdir /deployments
cd /deployments
git init
    2. Add your remote repository: 
      git remote add origin <repository-url>
    3. Place the script in the /deployments directory (scripts/composed.sh): 
      cp /path/to/composed.sh /deployments/
    4. Make the script executable: 
      chmod +x /deployments/composed.sh
    5. Commit these changes and push them to your repository: 
      git add .
git commit -m "Initial commit with deployment script"
git push -u origin main
    6. Set up crontab for execution (instructions below).
    7. When you set up your first service in your remote repository (e.g., on another computer or GitHub), commit and push your changes. You will see the results in the logs on your server and possibly on your Discord.
    8. Enjoy!

    Example Discord message - notification

    Setting Up Crontab

    To automate the execution of the script every 15 seconds and redirect the output to log files:

    1. Open the crontab editor:

      crontab -e

    2. Add the following lines to schedule the script every 15 seconds (ugly but works):

      * * * * * /deployments/composed.sh >> /var/log/composed.log 2>&1
* * * * * sleep 15; /deployments/composed.sh >> /var/log/composed.log 2>&1
* * * * * sleep 30; /deployments/composed.sh >> /var/log/composed.log 2>&1
* * * * * sleep 45; /deployments/composed.sh >> /var/log/composed.log 2>&1

      This configuration ensures the script runs every 15 seconds.

    3. Save and exit the editor.

    Logging

    The script logs its activity with timestamps. Logs can be found in the specified log file (e.g., /var/log/composed.log).

    Disable Service Rule

    To disable the deployment of a specific service, add #disabled as the first line in its docker-compose.yml or docker-compose.yaml file. The script will check for this comment and skip the deployment of services marked as disabled, ensuring they remain stopped.

    Example:

    #disabled
version: '3'
services:
  web:
    image: nginx
    ports:
      - "80:80"

    When the script encounters this comment, it will log that the service is disabled, pull its images, stop the service, and not attempt to restart it. To enable the service again, simply remove the #disabled line from the docker-compose.yml or docker-compose.yaml file.

    Discord Notifications

    To enable Discord notifications, set the DISCORD_WEBHOOK_URL variable in the script with your Discord webhook URL. Notifications will include the deployment environment and the status of updated services.

    License

    This script is provided “as is” without any warranty. Use at your own risk.

    Feel free to modify and extend this script according to your needs. Contributions and improvements are welcome!

    Visit original content creator repository https://github.com/Pelski/composed

  • AntiBio-Advisor

    AntiBio-Advisor

    An intelligent interface project targeted towards educating people of different ages about when and why to use antibiotic medication.

    This will be done by educating users through the use of a simple to use chat-bot which will answer varied questions relating to antibiotics and its use.

    Antibio.Advisor.mp4

    Table of Contents

    Motivation

    Antibiotic overuse breeds treatment resistance. This is a global problem that is rapidly increasing and threatens our ability to treat common infectious diseases.

    According to the World Health Organisation: “Antimicrobials – including antibiotics, antivirals, antifungals and antiparasitics – are medicines used to prevent and treat infections in humans, animals and plants.” Excessive antibiotic use leads to heightened drug resistance, impacting treatment efficacy and complicating infection management, posing severe health risks.

    Antimicrobial Resistance (AMR) generates drug-resistant organisms globally, limiting treatment options, inflating healthcare costs, and elevating risks during medical procedures, necessitating urgent prevention and treatment strategies.

    Proposed Solution

    Antibio Advisor, a web app tailored for individuals 18 or older but available for all. Our web app offers an array of vital features: an intelligent chatbot for medical guidance, an interactive map to locate pharmacies and general practitioners, and a comprehensive term guide.

    Prioritising user awareness, our platform provides essential information in the form of a FAQs page, an informative Quiz and a prescription info page.

    The web app features mentioned above needed to be implemented in such a manner that they are easily accessible and usable. This was facilitated through a variety of different features such as:

    • Web Page Demo which guides the user on proper usage of applicable web pages.
    • Dynamic page size applicable for use with computers, mobile devices and tablets.
    • Colourful and simple interface to ensure easy navigation.
    • Elderly individuals were also considered through the use of a large font to ensure ease of use.

    Chatbot

    Chatbot

    As outlined above the chatbot has a myriad of different features ranging from:

    • Set of initial prompts aimed at providing the user with a possible conversation starter.
    • Clear button allowing the user to clear the current conversation and start afresh.
    • Speech to text button allowing to use voice to communicate with the chatbot directly.
    • Text to Speech button and volume slider allowing users to have messages read out loud.

    These features and the general chatbot layout was selected whilst keeping the possible users in mind to ensure a pleasant user experience. Additionally the UIs adaptability to different screen sizes maintains the ease of use of the chatbot and its features.

    FAQ

    The FAQ page is composed of a variety of relevant questions sorted according to relevant age groups these being General, Adult, Child, Elderly. This is done as certain knowledge is more applicable to certain age demographics. Additionally the FAQ can be sorted in accordance with the symptoms these being General, Cough, Rash, Skin Condition.

    FAQ

    Further adding to the intractability of the webpage each question only displays the answer when selected by the user. This can be seen in the image showcased in this slide.

    Prescription Info

    The main components of the Prescription Info page showcased here consist of three dropdowns relating to the antibiotic name, abbreviations and notations used by doctors when prescribing medicine.

    These dropdowns when appropriately selected provide the user with explanations regarding their antibiotics and the relevant dosages as denoted by a medical professionals prescription notation. The dropdowns in addition to a sample explanation can be seen below.

    Prescription Info

    Map

    The map was designed to showcase the users nearest pharmacies and general practitioners, this was carried out to provide the user with all the required information they might require to purchase, enquire or return unused antibiotics. In the map the blue markers represent general practitioners and the red markers represent pharmacies.

    Map

    Learn More

    The Learn More page similar to the FAQ page provides the user with a further degree of general information on the topic of AMR in contrast to the specific information presented in the FAQ page.

    Learn More

    This page contains an informative video alongside an interactive carousel composed of several infographics and informative paragraphs.

    Quiz

    The webapp also includes a quiz web page which compiles four questions from a possible set. This provides users a fun and engaging manner by which they can test their knowledge on AMR. Upon submitting the quiz users are provided with the correct answer where applicable in addition to a score showcased below. Furthermore the quiz can be regenerated multiple times and additional question can easily be added through the editing of a simple .json file.

    Quiz

    Ethical Considerations

    The website is not intended to replace medical advice, and the chatbot is not a substitute for a doctor. The chatbot is intended to provide general information and guidance on antibiotics and their use. Additionally, a disclaimer is provided on the chatbot page to ensure that users are aware of the chatbot’s limitations.

    Moreover, no user data is stored or kept, ensuring that no chatbot history or personal information is stored on the platform or any cloud storage system.

    Ethical Considerations

    Mobile Functionality

    The web app is designed to be used on a variety of different devices ranging from mobile phones to tablets and computers. This is achieved through the use of a dynamic page size which adapts to the screen size of the device being used. This ensures that the web app is usable on a variety of different devices and maintains the ease of use of the web app.

    Mobile Design

    Interactable Design

    The web app offers feedback to the user in the form of different bot expressions. This is achieved through the use of different images which are displayed depending on the current state of the demo bot.

    Interactable Design Interactable Design

    A Good Design takes a lot of Effort, Feedback and Time

    The design of the web app was a long and arduous process which required a lot of effort and feedback. The design process was broken down into 3 main stages:

    1. Paper Prototyping – This stage involved the creation of a paper prototype which was used to gather feedback from potential users. This feedback was then used to improve the design of the web app.
    2. Computer Prototyping – This stage involved the creation of a computer prototype which was used to gather feedback in the form of recommendations and improvements. This feedback was then used to improve the design of the web app. Additionally, in this stage improvements from the previous stage were implemented into the design.
    3. Final Design – This stage involved the creation of the final design which utilised the feedback gathered from the previous stages to create a final design which was then implemented into the web app.

    Design

    Libraries used

    The following libraries were used in the development of the web app:

    License

    Copyright © Antibio Advisor 2023
Created by: Matthias Bartolo, Jerome Agius and Isaac Muscat
This project was developed under supervision of Dr Vanessa Camilleri and Mr Gavin Schranz at the University of Malta.

    University of Malta Logo

    Visit original content creator repository https://github.com/mbar0075/AntiBio-Advisor

  • Graph-Diffusion-Models-A-Comprehensive-Survey-of-Methods-and-Applications

    Graph Diffusion Models: A Comprehensive Survey of Methods and Applications

    This is the summation of all the methods, datasets, and other survey mentioned in our survey ‘Graph Diffusion Models: A Comprehensive Survey of Methods and Applications’ 🔥. Any problems, please contact shouyuntao@stu.xjtu.edu.cn. Any other interesting papers or codes are welcome. If you find this repository useful to your research or work, it is really appreciated to star this repository ❤️.

    GitHub stars GitHub forks

    Contents

    Molecule generation

    De novo molecule design

    Methods Paper Code Methods Paper Code
    DiGress (ICLR-23) [paper] [code] MiDi (ICLR-23) [paper] [code]
    CDGS (NeurIPS-22) [paper] [code] GCDM (ICLR-23) [paper] [code]
    EDM (ICML-22) [paper] [code] Wu et al. (NeurIPS-22) [paper] –
    MDM (AAAI-23) [paper] [code] DiffLinker [paper] [code]
    JODO [paper] [code] SILVR [paper] –
    HierDiff (ICML-23) [paper] [code] – – –
    BIMODAL [paper] [code] RationaleRL (ICML20) [paper] [code]
    GEOLDM (ICML-23) [paper] [code] MGM [paper] –
    LFM AISTATS-20 [paper] [code] RetMol [paper] [code]
    MolGPT [paper] – Bridge (NeurIPS-2022) [paper] –
    Bresson et al. [paper] – FLAG (ICLR-23) [paper] [code]
    LIMO [paper] [code] D3FG NeurIPS-24 [paper] [code]

    Conformation design

    Methods Paper Code Methods Paper Code
    ConfGF (ICML-21) [paper] [code] DGSM (NeurIPS-21) [paper] –
    GeoDiff (ICLR-22) [paper] [code] ColfNet (ICML-22) [paper] –
    Torsion Diffusion (NeurIPS-22) [paper] [code] DiffMD (AAAI-23) [paper] –
    RINGER [paper] – – – –

    De novo ligand design

    Methods Paper Code Methods Paper Code
    DiffBP [paper] – DiffSBDD [paper] –
    TargetDiff (ICLR-23) [paper] – PMDM [paper] –
    D3FG [paper] – – – –

    Ligand docking

    Methods Paper Code
    DIFFDOCK (NeurIPS-22) [paper] –
    EDM-Dock (JCIM) [paper] [code]
    DPL [paper] [code]
    NeuralPLexer [paper] –
    E3BIND (ICLR-23) [paper] –

    Protein design

    Methods Paper Code Methods Paper Code
    DiffAb (NeurIPS-22) [paper] – Anand [paper] –
    PROTSEED (ICLR-23) [paper] – ProteinSGM [paper] [code]
    SMCDiff (ICLR-23) [paper] [code] GraDe-IF [paper] –
    EigenFold [paper] [code] – – –
    CGM (NeurIPS-19) [paper] [code] SeqDesign [paper] [code]
    Fold2Seq (ICML-21) [paper] [code] EvoDiff [paper] –
    GVP （ICLR-21） [paper] [code] ESM (NeurIPS-21) [paper] –

    Motion generation

    Motion synthesis

    Methods Paper Code Homepage Methods Paper Code Homepage
    MotionDiffuse [paper] [code] [homepage] Modiff [paper] – –
    Ren et al. (ICASSP-23) [paper] – – FLAME (AAAI-23) [paper] – –
    MoFusion (CVPR-23) [paper] – [homepage] MDM (ICLR-23) [paper] [code] [homepage]
    MLD (CVPR-23) [paper] [code] [homepage] PriorMDM [paper] [code] [homepage]
    Alexanderson et al. (ACM Trans. Graph.) [paper] – – EDGE (CVPR-23) [paper] [code] [homepage]
    SceneDiffuser [paper] [code] [homepage] MoDi (CVPR-23) [paper] [code] [homepage]
    BiGraphDiff [paper] – – DiffuPose [paper] – –

    Motion prediction

    Methods Paper Code Homepage
    Ahn et al. (ICRA-23) [paper] [code] [homepage]
    HumanMAC [paper] [code] [homepage]
    TCD (ICRA-23) [paper] [code] –
    DiffMotion [paper] – –

    Others

    Methods Paper Code Homepage Methods Paper Code Homepage
    EDP-GNN [paper] – – GSDM [paper] – –
    NVDiff [paper] – – DPM-GSP [paper] – –
    DiffSTG [paper] – – DIFUSCO [paper] – –
    GraphGDP [paper] [code] – HouseDiffusion (CVPR-23) [paper] [code] [homepage]
    NAP [paper] – [homepage] EDGE [paper] – –
    DruM [paper] – – DDM [Paper] – –
    DiffusionNAG [paper] – – TSDiff [paper] – –
    GraphArm [paper] – – HGDM [paper] [code] –
    Lee et al. [paper] – – SaGess [paper] – –
    SLD [paepr] – – Diff-POI [paper] – –
    Brain Diffuser [paper] – – Lu et al. [paper] – –
    GBD [paper] [code] – DiffGraph [paper] [code] –
    ProGDM [paper] [code] – R-ode [paper] – –

    Datasets

    Dataset Dimensionality Category No.of Graphs (G) No. of Nodes (N)
    Community-small 2D Social 100 11 < N < 20
    Ego-small 2D Social 200 3 < N < 18
    Grid 2D Grid 100 N <= 400
    QM9 3D Bioinformatics/Molecular 130,831 3 < N < 29
    ZINC250K 3D Bioinformatics/Molecular 249,456 6 < N < 38
    Enzymes 3D Bioinformatics/Protein 600 9 < N < 125
    SBM-27 2D Social 200 24 < N < 27
    Planar-60 2D Social 200 N = 60
    AIDS 2D Bioinformatics/Molecular 2000 –
    Synthie 2D Social 300 N = 100
    Proteins 3D Bioinformatics/Protein 1113 N = 39.1

    Molecule generation

    Methods Paper Source Methods Paper Source
    Zinc [paper] [source] GEOM-QM9 [paper] [source]
    GEOM-Drugs [paper] [source] CrossDocked2020 [paper] [source]
    BioLiP [paper] [source] PDBBind [paper] [source]
    SAbDab [paper] [source] – – –

    Motion generation

    Methods Paper Source Methods Paper Source
    Human3.6M [paper] [source] HumanEva-I [paper] [source]
    HumanAct12 [paper] [source] HumanML3D [paper] [source]
    KIT [paper] [source] BABEL [paper] [source]
    UESTC [paper] [source] 3DPW [paper] [source]
    NTU RGB+D [paper] [source] AIST++ [paper] [source]
    TSG [paper] [source] ZeroEGGS [paper] [source]

    Other surveys

    Paper Url Source
    Diffusion-based Graph Generative Methods [paper] [source]
    Diffusion Models: A Comprehensive Survey of Methods and Applications [paper] [source]
    A Survey on Generative Diffusion Model [paper] [source]
    Generative Diffusion Models on Graphs: Methods and Applications [paper] –
    A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material [paper] –
    Graph-based Molecular Representation Learning [paper] –
    Generative Models as an Emerging Paradigm in the Chemical Sciences [paper] –
    A Survey on Deep Graph Generation: Methods and Applications (LoG-22) [paper] –
    A Survey on Temporal Graph Representation Learning and Generative Modeling [paper] –
    Human motion modeling with deep learning: A survey [paper] –
    MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design [paper] –

    Acknowledgement ❤️

    Thanks to Diffusion-based-Graph-Generative-Methods.

    Star History

    Star History Chart

    Visit original content creator repository https://github.com/yuntaoshou/Graph-Diffusion-Models-A-Comprehensive-Survey-of-Methods-and-Applications

  • kmusic-recos

    kmusic-recos

    When I first began working on this project I wanted to see if there were any commonalities between my favorite artists and songs within k-pop. The scope slowly grew bigger and bigger over the months. Now, not only has data been scraped and a recommender system built, but a micro framework using flask has also been constructed for users to interact with the recommender engine. Future goals for this project include hosting everything in the cloud and scheduling regular scraping of Spotify to ensure data contained within the recommender engine is up to date.

    How to Use This Repo

    In order to run the “website” on your end, see the following steps:

    1. First clone the repo to a desired location: git clone https://github.com/ericwan1/kmusic-recos.git
    2. Open terminal or command prompt and navigate to the repository.
    3. Run export FLASK_APP=app or set FLASK_APP=app if you are using the latter.
    4. Then run flask run.
    5. Following the output from step 4, you will notice that the framework is most likely running on the default port (5000). Follow the url provided http://127.0.0.1:5000 and you can begin playing around with the site!

    Some Details

    • The model uses nearest neighbors. Scraping was done using the Spotipy API, and a basic analysis notebook is also included.
    • Flask was selected due to ease of set up, features, and easy integration with the code I already wrote.

    Data Dictionary

    • 273_kArtists.csv contains 273 artists and their Spotify IDs
      • Artist (Artist Name)
      • Spotify_Id (Artists’ Unique Spotify ID)
    • album_to_track_df.csv houses artist, album, and track information
      • Artist (Artist Name)
      • Artist_Id (Artists’ Unique Spotify ID)
      • Album_Name (Album Name)
      • Album_Id (Album’s Unique Spotify ID)
      • Track_Title (Title of the Track)
      • Track_Id (A Track’s Unique ID)
      • misc. song information – see bottom of dictionary
    • artist_appears_on_df.csv contains information on other albums the artist appears on
      • Artist (Artist Name)
      • Artist_Id (Artists’ Unique Spotify ID)
      • Appears_On_Name (“Albums” the Artist has Appeared On)
      • Appears_On_Id (The ID of the “Album” the Artist Appeared On)
    • compilation_track.csv includes artist information and compilation information, as well as track information for tracks contained within the artist compilation
      • Artist (Artist Name)
      • Artist_Id (Artists’ Unique Spotify ID)
      • Compilation_Name (Compilation Name)
      • Compilation_Id (The Compilation’s Unique Spotify ID)
      • Track_Title (Title of the Track)
      • Track_Id (A Track’s Unique ID)
      • misc. song information – see bottom of dictionary
    • single_album_track_data.csv contains artist information, any “single albums” and tracks contained within said albums and track information
      • Artist (Artist Name)
      • Artist_Id (Artists’ Unique Spotify ID)
      • Single_Album_Name (Name of the Single Album)
      • Single_Album_Id (Artists’ Unique Spotify ID)
      • Track_Title (Title of the Track)
      • Track_Id (A Track’s Unique ID)
      • misc. song information – see bottom of dictionary
    • misc. song information
      • danceability (Track Suitability to Dancing, 0.0 to 1.0)
      • energy (Measurement of Intensity and Activity, 0.0. to 1.0)
      • key (The Key of the Track – see pitch class table Guide Here)
      • loudness (Overall Loudness in Decibels, Typically -60 to 0 db)
      • mode (Major or Minor, 1 or 0)
      • speechiness (Presence of Words – the higher, the more wordy. 0.0 to 1.0)
      • acousticness (Confidence Measurement of a Track Being Acoustic. 0.0 to 1.0)
      • instrumentalness (Predicts if a Track Contains no Vocals, 0.0 to 1.0)
      • liveness (Predicts if a Live Audience in the Track, 0.0 to 1.0)
      • valence (Musical Positiveness, the Higher, the ‘Happier’. 0.0 to 1.0)
      • tempo (Track’s Estimated BPM)
      • duration_ms (Track Duration in Milliseconds)
      • time_signature (Time Signature, 3 to 7 for 3/4 to 7/4)

    Future To Do’s:

    • Host everything on a server and make the website interactable to the world
    • Automate the scraping notebook and orchestrate runs; potentially store and run everything in the cloud

    Visit original content creator repository
    https://github.com/ericwan1/kmusic-recos

  • squash

    squash

    squash is the web frontend to embed the bokeh apps and navigate through them. You can learn more about SQuaSH at SQR-009.

    Build Status

    Requirements

    The squash web frontend requires the squash-restful-api and squash-bokeh microservices, and the TLS certificats that are installed by the squash-deployment.

    Kubernetes deployment

    You can provision a Kubernetes cluster in GKE, clone this repo and deploy the squash microservice using:

    cd squash
TAG=latest make service deployment

    The variables SQUASH_MONITOR_APP and SQUASH_BOKEH_APPS can be used to control the bokeh apps allowed in this deployment, for example:

    export SQUASH_MONITOR_APP=code_changes
export SQUASH_BOKEH_APPS="code_changes AMx PAx"

    their default values correspond to the production deployment.

    Debugging

    Use the kubectl logs command to view the logs of the nginx and dash containers:

    kubectl logs deployment/squash-api nginx
kubectl logs deployment/squash-api dash

    Use the kubectl exec to run an interactive shell inside a container. Use tab completion or kubectl get pods command to find the pod’s name:

    kubectl exec -it <squash pod> -c dash /bin/bash

    Rolling out updates

    Check the update history with:

    kubectl rollout history deployment squash

    Modify the squash image and then apply the new configuration for the kubernetes deployment:

    # we need to setup the env for django to collect static files
virtualenv env -p python3
source env/bin/activate
pip install -r requirements.txt
TAG=latest make build push update

    Check the deployment changes:

    kubectl describe deployments squash

    Scaling up the squash microservice

    Use the kubectl get replicasets command to view the current set of replicas, and then the kubectl scale command to scale up the squash deployment:

    kubectl scale deployments squash --replicas=3

    or change the kubernetes/deployment.yaml configuration file and apply the new configuration:

    kubectl apply -f kubernetes/deployment.yaml

    Check the deployment changes:

    kubectl describe deployments squash
kubectl get pods
kubectl get replicasets

    Development workflow

    You can install the dependencies for developing

    1. Install the software dependencies
    git clone  https://github.com/lsst-sqre/squash.git

cd squash

virtualenv env -p python3
source env/bin/activate
pip install -r requirements.txt
    1. Run squash
    export SQUASH_DASH_DEBUG=True
export SQUASH_BOKEH_URL=<suqash-bokeh url> # e.g. the one from squash-bokeh deployment
export SQUASH_API_URL=<squash-restful-api url> # e.g. the one from squash-restful-api deployment

    The variables SQUASH_MONITOR_APP and SQUASH_BOKEH_APPS can be used to control the bokeh apps allowed in this deployment, for example:

    export SQUASH_MONITOR_APP=code_changes
export SQUASH_BOKEH_APPS="code_changes AMx PAx"

python manage.py runserver

    The squash will run at http://localhost:8000.

    Visit original content creator repository https://github.com/lsst-sqre/squash

  • tails

    Logo resembling fox
    Preview

    An experimental functional systems programming language,
    written in Rust, and powered by LLVM as a backend.


    🎯 Goal: The intent is to create a programming language that balances simplicity and performance, focusing on functional programming paradigms and compile-time safety mechanisms. The language aims to facilitate explicit tracking of side effects and embrace a predictable coding environment. See the principles section for more information.

    Features

    Principles

    • ✨ Simplicity: The language should be easy to learn but powerful enough for complex tasks. A simple syntax helps lower the barrier to entry.
    • 📐 Consistency & orthogonality: Stick to one way of doing things. The language should use a few basic constructs that can be combined in many ways. This makes it easier to learn and use.
    • 🔮 Predictable, explicit, and deterministic semantics: The type system should be straightforward and avoid surprises. No guessing games—you should always know what the code is doing.
    • ❕ Opinionated: Make the language so there’s a ‘right way’ to do things. This ensures everyone writes code that looks and works similarly, making it easier to understand and collaborate.
    • 🙅🏻‍♂️ No meta-programming or reflection: Avoid features like meta-programming and reflection. They’re often misused and make the language harder to learn and less consistent.
    • ⚡ Compile-time emphasis: Do most heavy lifting, like error checking and memory management, when the code is compiled. This makes the executables faster and more reliable.
    • ⛓️ Functional paradigm: Use functional programming to simplify code and reduce errors. It minimizes side effects and makes the code easier to reason about.

    Hello world example

    Syntax highlighting of a code snippet showing an application that prints 'hello world' to the console

    The foreign keyword lets you include functions and variables from other programming languages. You can put multiple such items in a foreign block to keep your code neat.

    The unsafe keyword is used for actions that have potential risks, like calling foreign functions or using pointers. This is much like Rust’s unsafe keyword and helps you be explicit about the risky parts of your code.

    nat is a type that stands for unsigned integers, which are 32-bit by default. You can also use variations like nat8, nat16, and nat64 for different bit sizes.

    The compiler doesn’t automatically coerce types, so you can use 0::nat to specify a literal’s type. This is handy when the type you want doesn’t match the compiler’s default 32-bit numeric type.

    Building and running

    Prerequisites

    Building

    cargo build

    Running tests

    cargo test
    Visit original content creator repository https://github.com/yurixander/tails

  • Whitepaper

    Warning - this repo is synced with the online version here: https://whitepaper.treecoin.online

    Tree Coin Whitepaper

    The Tree Coin® is an alternative solidarity model linked to an electronic symbol, the Tree Coin Logo, as academic Non-Financial, Non-Monetary, Non-Currency solidarity experiment.
    It’s a non-exchangeable electronic instrument, deemed to facilitate ethical approvisionnement of basic goods trough ethical barter.
    It’s a solidarity symbol, based on CREDIT, on sustainability, on mutual trust, respect for others and Mother Nature.

    • The Tree Coin is not a money
    • The Tree Coin is a symbol to facilitate barter
    • The Tree Coin is an ecological concept

    With the Tree Coin® you can generate your [ethical-artistic-note] by yourself and share it with your circles as alternative collaborative instrument.

    The Tree Coin® follow lunar phases and regenerates the minimum amount 1000 TREE every lunar cycle (28 days), as the plants regenerates itself in Mother Nature.
    You cannot never own less than zero, and it’s specifically designed for emerging realities such as eco-villages and rural areas.

    The Tree Coin® project allow anyone attaining the goal of protecting land and trees, as well as to establish eco-villages, relying on the concept of Sustainable Development and Climate Change reduction and better life conditions, in attainment of the United Nations Sustainable Development Goals (SDGs), promulgated by the United Nations General Assembly on 25 September 2015.

    Participation to the UN Global goals

    The Tree Coin® project works in attainment of the United Nations Sustainable Development Goals (SDGs) on three main following UN Global Goals:

    • Reforestation, and Life on Earth (SDG 15)
    • Reduction of CO₂ and Climate Change (SDG 13)
    • Post Pandemic Recovery and General well-being (SDG 3)

    → Discover more on the official website

    Visit original content creator repository https://github.com/Tree-Coin/Whitepaper

  • OtakuDB

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    OtakuDB – JSON-based simple database for otaku.
    Copyright (C) 2024-2025. DUB1401.

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

  If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:

    OtakuDB. Copyright (C) 2024-2025. DUB1401.
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License.  Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".

  You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.

  The GNU General Public License does not permit incorporating your program
into proprietary programs.  If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library.  If this is what you want to do, use the GNU Lesser General
Public License instead of this License.  But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

    Visit original content creator repository
    https://github.com/OtakuDB/OtakuDB

  • jakarta-demorepo-cargotracker

    Eclipse Cargo Tracker – Applied Domain-Driven Design Blueprints for Jakarta EE

    Overview

    The project demonstrates how you can develop applications with Jakarta EE using widely adopted architectural best practices like Domain-Driven Design (DDD). The project is directly based on the well known original Java DDD sample application developed by DDD pioneer Eric Evans’ company Domain Language and the Swedish software consulting company Citerus. The cargo example actually comes from Eric Evans’ seminal book on DDD. The original application is written in Spring, Hibernate and Jetty whereas the application is built on Jakarta EE.

    The application is an end-to-end system for keeping track of shipping cargo. It has several interfaces described in the following sections.

    For further details on the project, please visit: https://eclipse-ee4j.github.io/cargotracker/.

    Cargo Tracker cover

    Getting Started

    The project website has detailed information on how to get started.

    The simplest steps are the following (no IDE required):

    • Get the project source code.
    • Ensure you are running Java SE 8 or Java SE 11.
    • Make sure JAVA_HOME is set.
    • As long as you have Maven set up properly, navigate to the project source root and type: mvn clean package cargo:run
    • Go to http://localhost:8080/cargo-tracker

    To set up in Eclipse, follow these steps:

    • Set up Java SE 8 or Java SE 11, Eclipse for Enterprise Java Developers and Payara 5. You will also need to set up Payara Tools in Eclipse.
    • Import this code in Eclipse as a Maven project, Eclipse will do the rest for you. Proceed with clean/building the application.
    • After the project is built (which will take a while the very first time as Maven downloads dependencies), simply run it via Payara 5.

    Exploring the Application

    After the application runs, it will be available at: http://localhost:8080/cargo-tracker/. Under the hood, the application uses a number of Jakarta EE features including Faces, CDI, Enterprise Beans, Persistence, REST, Batch, JSON Binding, JSON Processing, Bean Validation and Messaging.

    There are several web interfaces, REST interfaces and a file system scanning interface. It’s probably best to start exploring the interfaces in the rough order below.

    The tracking interface let’s you track the status of cargo and is intended for the general public. Try entering a tracking ID like ABC123 (the application is pre-populated with some sample data).

    The administrative interface is intended for the shipping company that manages cargo. The landing page of the interface is a dashboard providing an overall view of registered cargo. You can book cargo using the booking interface. One cargo is booked, you can route it. When you initiate a routing request, the system will determine routes that might work for the cargo. Once you select a route, the cargo will be ready to process handling events at the port. You can also change the destination for cargo if needed or track cargo.

    The Handling Event Logging interface is intended for port personnel registering what happened to cargo. The interface is primarily intended for mobile devices, but you can use it via a desktop browser. The interface is accessible at this URL: http://localhost:8080/cargo-tracker/event-logger/index.xhtml. For convenience, you could use a mobile emulator instead of an actual mobile device. Generally speaking cargo goes through these events:

    • It’s received at the origin location.
    • It’s loaded and unloaded onto voyages on it’s itinerary.
    • It’s claimed at it’s destination location.
    • It may go through customs at arbitrary points.

    While filling out the event registration form, it’s best to have the itinerary handy. You can access the itinerary for registered cargo via the admin interface. The cargo handling is done via Messaging for scalability. While using the event logger, note that only the load and unload events require as associated voyage.

    You should also explore the file system based bulk event registration interface. It reads files under /tmp/uploads. The files are just CSV files. A sample CSV file is available under src/test/resources/handling_events.csv. The sample is already set up to match the remaining itinerary events for cargo ABC123. Just make sure to update the times in the first column of the sample CSV file to match the itinerary as well.

    Sucessfully processed entries are archived under /tmp/archive. Any failed records are archived under /tmp/failed.

    Don’t worry about making mistakes. The application is intended to be fairly error tolerant. If you do come across issues, you should report them.

    All data entered is wiped upon application restart, so you can start from a blank slate easily if needed.

    You can also use the soapUI scripts included in the source code to explore the REST interfaces as well as the numerous unit tests covering the code base generally.

    Exploring the Code

    As mentioned earlier, the real point of the application is demonstrating how to create well architected, effective Jakarta EE applications. To that end, once you have gotten some familiarity with the application functionality the next thing to do is to dig right into the code.

    DDD is a key aspect of the architecture, so it’s important to get at least a working understanding of DDD. As the name implies, Domain-Driven Design is an approach to software design and development that focuses on the core domain and domain logic.

    For the most part, it’s fine if you are new to Jakarta EE. As long as you have a basic understanding of server-side applications, the code should be good enough to get started. For learning Jakarta EE further, we have recommended a few links in the resources section of the project site. Of course, the ideal user of the project is someone who has a basic working understanding both Jakarta EE and DDD. Though it’s not our goal to become a kitchen sink example for demonstrating the vast amount of APIs and features in Jakarta EE, we do use a very representative set. You’ll find that you’ll learn a fair amount by simply digging into the code to see how things are implemented.

    Exploring the Tests

    Cargo Tracker’s testing is done using JUnit and Arquillian. The Arquillian configuration uses a remote container (Payara 5). Therefore, to perform a test you will need to make sure to have a container running.

    Testing Locally with Payara

    For testing locally you will first need to run a Payara 5 server.

    You can do that with the following script:

    wget https://repo1.maven.org/maven2/fish/payara/distributions/payara/5.2021.1/payara-5.2021.1.zip
unzip payara-5.2021.1.zip && cd payara5/bin
./asadmin start-domain

    Now for running the tests:

    mvn clean verify -DskipTests=false

    Contributing

    This project complies with the Google Java Style Guide. You can use the google-java-format tool to help you comply with the Google Java Style Guide. You can use the tool with most major IDEs such as Eclipse and IntelliJ.

    In addition, for all XML, XHTML and HTML files we use a column/line width of 100 and we use 4 spaces for indentation. Please adjust the formatting settings of your IDE accordingly.

    Known Issues

    • You may get a log message stating that Payara SSL certificates have expired. This won’t get in the way of functionality, but it will stop log messages from being printed to the IDE console. You can solve this issue by manually removing the expired certificates from the Payara domain, as explained here.
    • If you restart the application a few times, you will run into a bug causing a spurious deployment failure. While the problem can be annoying, it’s harmless. Just re-run the application (make sure to completely shut down Payara first).
    • Sometimes when the server is not shut down correctly or there is a locking/permissions issue, the H2 database that the application uses get’s corrupted, resulting in strange database errors. If this occurs, you will need to stop the application and clean the database. You can do this by simply removing ./cargo-tracker-database from the file system and restarting the application. This directory will typically be under $your-payara-installation/glassfish/domains/domain1/config.
    Visit original content creator repository https://github.com/silviotorre/jakarta-demorepo-cargotracker

  • react-is-in-viewport

    React Is In Viewport

    fit-overlap

    The component allows to track the other React components whether or not it is in the Viewport.

    version codecov Build Status Greenkeeper badge License: MIT PRs Welcome quality

    Package Quality

    Installation

    To install, you can use npm or yarn:

    $ npm install react-is-in-viewport
$ yarn add react-is-in-viewport

    Props

    Name Type Default value Description
    children React Node or string React component or string that display in UI
    delay number 100 Delay time to execute scrolling event callback
    type ‘fit’ or ‘overlap’ fit Mode to track component fits in the viewport or overlaps with viewport
    id string Identifier of Viewport
    className string Custom CSS class
    autoTrack boolean false It will count how many seconds the user spending on the component
    waitToStartAutoTrack number After waiting how many seconds, then starting the auto track on the component

    The autoTrack only works with type = fit

    Here is how type = 'fit' and type = 'overlap' look like:

    fit

    fit

    overlap

    overlap

    API

    Name Type Parameter Description
    onLoad void When components first appear and fit in the viewport
    onEnter void enterCount When scrolling to a component, the enterCount increase 1
    onLeave void leaveCount When scrolling away from a component, the leaveCount increase 1
    onFocusOut void focusCount When component is not in the viewport, then onFocusOut called with seconds user spent on the component

    Example

    With overlap & fit mode

    https://codesandbox.io/s/react-is-in-viewport-fit-overlap-mode-zx897

    import ReactDOM from 'react-dom';
import React from 'react';
import { Viewport } from 'react-is-in-viewport';

class App extends React.Component {
  render() {
    return (
      <div>
        <div style={{ marginTop: '50%' }}>
          <Viewport
            type="fit"
            onLoad={this.onLoadRed}
            onEnter={this.onEnterRed}
            onLeave={this.onLeaveRed}
          >
            <div style={{ height: 100, background: 'red' }}></div>
          </Viewport>
        </div>

        <div style={{ marginTop: '200%' }}>
          <Viewport type="overlap" onEnter={this.onEnterBlue} onLeave={this.onLeaveBlue}>
            <div style={{ height: 100, background: 'blue' }}></div>
          </Viewport>
        </div>
      </div>
    );
  }

  onLoadRed = () => {
    console.log('component RED loaded')
  };

  onEnterRed = (enterTimes) => {
    console.log('enter red', enterTimes);
  };

  onLeaveRed = (leaveTimes) => {
    console.log('leave red', leaveTimes);
  };

  onEnterBlue = (enterTimes) => {
    console.log('enter blue', enterTimes);
  };

  onLeaveBlue = (leaveTimes) => {
    console.log('leave blue', leaveTimes);
  };
}

ReactDOM.render(<App />, document.getElementById('root'));

    With autoTrack & waitToStartAutoTrack mode

    https://codesandbox.io/s/react-is-in-viewport-autotrack-mode-9f9vz

    import ReactDOM from 'react-dom';
import React from 'react';
import { Viewport } from 'react-is-in-viewport';

class App extends React.Component {
  render() {
    return (
      <div>
        <div style={{ marginTop: '50%' }}>
          <Viewport
            autoTrack
            waitToStartAutoTrack={2}
            type="fit"
            onLoad={this.onLoadRed}
            onEnter={this.onEnterRed}
            onLeave={this.onLeaveRed}
            onFocusOut={this.onFocusOut}
          >
            <div style={{ height: 100, background: 'red' }}></div>
          </Viewport>
        </div>

        <div style={{ marginTop: '200%' }}>
          <Viewport type="overlap" onEnter={this.onEnterBlue} onLeave={this.onLeaveBlue}>
            <div style={{ height: 100, background: 'blue' }}></div>
          </Viewport>
        </div>
      </div>
    );
  }

  onLoadRed = () => {
    console.log('component RED loaded')
  };

  onEnterRed = (enterTimes) => {
    console.log('enter red', enterTimes);
  };

  onLeaveRed = (leaveTimes) => {
    console.log('leave red', leaveTimes);
  };

  onFocusOut = (focusTimes) => {
    console.log('out focus red', focusTimes);
  };

  onEnterBlue = (enterTimes) => {
    console.log('enter blue', enterTimes);
  };

  onLeaveBlue = (leaveTimes) => {
    console.log('leave blue', leaveTimes);
  };
}

ReactDOM.render(<App />, document.getElementById('root'));

    License

    This project is licensed under the MIT License – see the LICENSE file for details

    Visit original content creator repository https://github.com/davidnguyen11/react-is-in-viewport