SonicWall SMA-100 Unauth RCE Exploit (CVE-2021-20038)
Bad Blood is an exploit for CVE-2021-20038, a stack-based buffer overflow in the httpd binary of SMA-100 series systems using firmware versions 10.2.1.x. I’ve written a lot of the technical details here: The exploit, as written, will open up a telnet bind shell on port 1270. An attacker that connects to the shell will achieve execution as nobody. Example Output
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Aprendendo Python na ByLearn
PYTHON Identação Escopo Pai Escopo filho Escopo neto Variaveis int ==> Inteiro (São números inteiros) str ==> String (São textos, sepre dentro de aspas) float ==> Decimais (Números com “virgula”/usar o ponto) bool ==> Booleano (True ou False) Output print(“Hello World”) print(f”Meu IMC é: {imc}”) Input input(“Digite o dado: “) seuDado = float(input(“Digite o dado: “)) GitHub View Github
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Transimage Rest API For Python
0. Flask Rest API – in SunnyWeb : 이미지 변환 웹의 Flask Rest API 1. 구성 flaskRestAPI01 : 이미지 변환 기능 중 유명 화가풍으로 변환, 안경과 동물들의 특징을 이미지에 추가하는 기능을 담당하는 flask Rest API a) imgs : 안경, 돼지(코), 쥐(코, 귀) 이미지 b) models b-1) instance_norm b-2) eccv16 c) api_test01.py : json으로 request를 주었을 때를 테스트하는 모듈 d) app01.py : flask로 웹 서버를 올려 json request를 json response를 보내주는 flask Rest API e) img_download.py : s3에서 이미지를 다운 받아서 […]
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MUGE Text To Image Generation Baseline
Requirements and Installation More details see fairseq. Briefly, python == 3.6.4 pytorch == 1.7.1 Installing fairseq and other requirements git clone https://github.com/MUGE-2021/image-caption-baseline cd muge_baseline/ pip install -r requirements.txt cd fairseq/ pip install –editable . Downloading data and place to dataset/ directory,file structure is text2image-baseline – dataset – ECommerce-T2I – T2I_train.img.tsv – T2I_train.text.tsv – … Getting Started The model is a BART-like model with vqgan as a image tokenizer, please see models/t2i_baseline.py for detailed model structure. Training
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Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)
This is the official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training (ISBI 2022). We evaluate our methods on three datasets, DRIVE, CHASE_DB1 and STARE. You can download the three datasets from Google drive.Of course, you can download the dataset from DRIVE, CHASE_DB1 and STARE respectively. Requirement Refer to Pytorch to install Pytorch >= 1.1. pip install -r requirements.txt Config file
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Coding Club prediction ig
Coding Club prediction ig Basically:Create an application that displays trendsShould implement a graph (either bar or line)Should take input parameters into accountList of parameters:Has it been tweeted aboutHow many followers does the tweeter haveAre they verifiedIs their tweet positive or negative (this will be hard to algorithmically tell)Prices of other coins/predicted future prices of other coinsStock market prosperityPast trendsAmount in circulationUser should be able to mouse over graph and see specific data pointsShould be able to calculate profits/lossesShould take user […]
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Predicts the likelihood of Polycystic Ovary Syndrome based on patient attributes and symptoms
Predicts the likelihood of Polycystic Ovary Syndrome based on patient attributes and symptoms using Logistic Regression. Clone the Repository git clone https://github.com/smv5467/pcos-prediction Add Dependencies with Poetry If you don’t have poetry install with: curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python – poetry install Download Data Retrieve data from Kaggle: https://www.kaggle.com/prasoonkottarathil/polycystic-ovary-syndrome-pcosDownload PCOS_data_without_infertility.xlsxOpen excel file and save as a CSV file under the same name Run program poetry run python pcos_predictor.py GitHub View Github
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