Author: Deep Learner
A graphql API build using ariadne python that serves a graphql-endpoint at port 3002 to perform language translation
Language Translation and Identification this machine/deep learning api that will be served as a graphql-api using ariadne, to perform the following tasks. 1. Language Identification Identifying the language which the text belongs to using a simple text classification model. This model will be able to identify 7 different languages: english (en) french (fr) german (de) spanish (es) italian (it) portuguese (pt) swedish (sw) 2. Language Translation Language translation offers a bi-direction english to another language translation for example `english-to-french`. The […]
Read more
An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend
About The Project There are many great streaming services to watch movies online in todays day and age. However, their build in content suggestion system is quite a bit broken and often times distracting, as convenient as it may be. This was the inspiration behind this Project. To iteratively build the best Movie Recommendation System that asks you what type of movie you would like to watch, no tell you what you should be watching in an intrusive way. Why […]
Read more
Log4j rce test environment and poc
log4j rce test environment see: https://www.lunasec.io/docs/blog/log4j-zero-day/ using the included python poc build Either build the jar on your host with mvn clean compile assembly:single Or use docker to build an image with docker build -t log4jpwn . run The server will log 3 things (which are also the triggers). You don’t have to set all 3: The User-Agent header content The request path The pwn query string parameter To use: Run the container with docker run –rm -p8080:8080 log4jpwn (or […]
Read more
Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021
Official Pytorch Implementation for Deep Contextual Video Compression, NeurIPS 2021 Python 3.8 and conda, get Conda CUDA 11.0 Environment conda create -n $YOUR_PY38_ENV_NAME python=3.8 conda activate $YOUR_PY38_ENV_NAME pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html python -m pip install -r requirements.txt Currenlty the spatial resolution of video needs to be cropped into the integral times of 64. The dataset format can be seen in dataset_config_example.json. For example, one video of HEVC Class B can be prepared as: Crop the original YUV […]
Read more
Component for deep integration LedFx from Home Assistant
Component for deep integration LedFx from Home Assistant. FAQ Q. What versions were tested on? A. So far only 0.10.7 Q. Does it support audio settings? A. Yes, it supports Q. Can I change the effect settings? A. You can, for this, enable the appropriate mode from the [PRO] section. The required objects will only be available when supported by the effect. Install Installed through the custom repository HACS – dmamontov/hass-ledfx Or by copying the ledfx folder from the latest […]
Read more
Check broken access control exists in the Java web application
Check broken access control exists in the Java web application. 检查 Java Web 应用程序中是否存在访问控制绕过问题。 python3 javaEeAccessControlCheck.py “http://127.0.0.1/admin/index?id=1” python3 javaEeAccessControlCheck.py “http://127.0.0.1/admin/index” -data id=1 python3 javaEeAccessControlCheck.py “http://127.0.0.1/admin/index” -data-json ‘{“id”:1}’ python3 javaEeAccessControlCheck.py “http://127.0.0.1/admin/index?id=1” -all python3 javaEeAccessControlCheck.py “http://127.0.0.1/admin/index” -data-json ‘{“id”:1}’ -all [GET]自动判断/Automatic judgment [GET]所有Payload长度/All Response Length [POST]自动判断/Automatic judgment [POST]所有Payload长度/All Response Length
Read more
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural Network. Requirements The framework is implemented using python3 with dependencies specified in requirements.txt. git clone https://github.com/Wentao-Xu/SHGNN.git cd SHGNN conda create -n shgnn python=3.8 conda activate shgnn pip install -r requirements.txt Dataset preparation source prepare_data.sh tar -zxvf data.tar.gz mkdir checkpoint Running the code
Read more