ddos

A DDoS ATTACK SCRIPT WITH PYTHON

#Cybersecurity #Cybersec #Securitytalk #DDOSAttack #Breakinternet DDoS attacks occur when a cyber-criminal floods a targeted organization’s network with access requests; this initially disrupts service by denying legitimate requests from actual customers, and eventually overloads the network until it crashes. Criminals execute their DDoS attacks by sending out malicious code to hundreds or even thousands of computers, instructing each one to send requests to a single organization. This is usually accomplished through a botnet; a network of private computers infected with malicious […]

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Function

Python humanize functions

This modest package contains various common humanization utilities, like turninga number into a fuzzy human-readable duration (“3 minutes ago”) or into ahuman-readable size or throughput. It is localized to: Bengali Brazilian Portuguese Catalan Danish Dutch European Portuguese Finnish French German Indonesian Italian Japanese Korean Persian Polish Russian Simplified Chinese Slovak Slovenian Spanish Swedish Turkish Ukrainian Vietnamese API reference https://python-humanize.readthedocs.io Usage Integer humanization

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Machine Learning

Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information

Dynamic MLP, which is parameterized by the learned embeddings of variable locations and dates to help fine-grained image classification. Requirements Experiment Environment python 3.6 pytorch 1.7.1+cu101 torchvision 0.8.2 Get pretrained models for SK-Res2Net following here.Get datasets following here. Train the model 1. Train image-only model Specify –image_only for training image-only models. ResNet-50 (67.924% Top-1 acc) CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py –name res50_image_only –

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Machine Learning

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

All Codes in the paper will be coming soon. Here is the Pytorch Code of BatchFormer def BatchFormer(x, y, encoder, is_training): # x: input features with the shape [N, C] # encoder: TransformerEncoderLayer(C,4,C,0.5) if not is_training: return x, y pre_x = x x = encoder(x.unsqueeze(1)).squeeze(1) x = torch.cat([pre_x, x], dim=0) y = torch.cat([y, y], dim=0) return x, y If you find this repository helpful, please consider cite:

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State

Dialogue Summaries as Dialogue States (DS2)

Installing the directory as pip will resolve all path issues pip install -e . pip install -r requirements.txt # requires python 3.8 Get MWOZ data (for 2.0 change all 2.1 to 2.0) For 2.0 python scripts/create_data_mwoz.py –mwz_ver=2.0 –main_dir=data_mwoz_2.0 –target_path=data_mwoz_2.0/mwz For 2.1 python scripts/create_data_mwoz.py –mwz_ver=2.1 –main_dir=data_mwoz_2.1 –target_path=data_mwoz_2.1/mwz Training and Inference – Cross-domainPre-trainingExample using T5 on Cross-domain pre-training. Note that this code will not work yet because we did not release our pretrained model checkpoints yet due to anonymity issues. We will […]

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Tool

Proxmox based VDI client

This project’s focus is to create a simple VDI client intended for mass deployment. This VDI client connects directly to Proxmox VE and allows users to connect (via Spice) to any VMs they have permission to access. Windows Installation You MUST install virt-viewer prior to using PVE VDI client, you may download it from the official Virtual Machine Manager site. Please visit the releases section to download a prebuilt MSI package If you need to customize the installation, such as […]

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ffmpeg

Performant Pythonic FFmpeg Decoder Library with easy to adapt flexible API

  DeFFcode is a Performant and Robust FFmpeg Pythonic Library that aimed at decoding any stream that you throw at it. Requiring minimal efforts, it aims at providing an easy-to-adapt flexible API for reading frames from a wide range of streams, and can ingest video using any specified decoder(even hardware ones) into any pixel format FFmpeg supports. It also enables accurate seeking for extracting only a specific part of your input as desired. It is cross-platform, runs on Python 3.7+, […]

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Robust

CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild (CVPR2022)

Yang You, Ruoxi Shi, Weiming Wang, Cewu Lu CVPR 2022 CPPF is a pure sim-to-real method that achieves 9D pose estimation in the wild. Our model is trained solely on ShapeNet synthetic models (without any real-world background pasting), and could be directly applied to real-world scenarios (i.e., NOCS REAL275, SUN RGB-D, etc.). CPPF achieves the goal by using only local $SE3$-invariant geometric features, and leverages a bottom-up voting scheme, which is quite different from previous end-to-end learning methods. Our model […]

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GUI

Modern GUI PyDracula PySide6 or PyQt6

🎁 //// DONATE //// This interface is free for any use, but if you are going to use it commercially, consider helping to maintain this project and others with a donation by Gumroado at the link above. This helps to keep this and other projects active. Warning: this project was created using PySide6 and Python 3.9, using previous versions can cause compatibility problems. Presentation and tutorial video with the main functions of the user interface. 🔗 https://youtu.be/9DnaHg4M_AM Qt Widgets is […]

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