Month: October 2021

Domain

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization

This is an official implementation of “Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization” (ACMMM 2021 Oral) Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization Seogkyu Jeon, Kibeom Hong, Pilhyeon Lee, Jewook Lee, Hyeran Byun (Yonsei Univ.) Paper : https://arxiv.org/abs/2108.08596 Abstract: Domain generalization aims to enhance the model robustness against domain shift without accessing the target domain. Since the available source domains for training are limited, recent approaches focus on generating samples of novel domains. Nevertheless, they either […]

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Django

A Django project skeleton that is modern and cutting edge

{% comment “This comment section will be deleted in the generated project” %} A Fantastic Django project starter. Features Ready Bootstrap-themed pages User Registration/Sign up Better Security with 12-Factor recommendations Logging/Debugging Helpers Works on Python 3 and Django 2 Formatted with Black More information at: http://django-edge.readthedocs.org/ Contribute using: Edge Dev Tools✨ ? ✨ Quick start: Before creating a new project from this template, you need to create a fresh virtual environment and install Django: $ python -m venv ./myenv $ […]

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End-to-End

End-to-end Python framework for building natural language search interfaces to data

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface’s Transformers, Elasticsearch, or Milvus. What to […]

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Analysis

A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media

VADER-Sentiment-Analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. It is fully open-sourced under the [MIT License] (we sincerely appreciate all attributions and readily accept most contributions, but please don’t hold us liable). Features and Updates Many thanks to George Berry, Ewan Klein, Pierpaolo Pantone for key contributions to make VADER better. The new updates includes capabilities regarding: Refactoring for Python 3 compatibility, […]

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Matching

A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution

dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data. dedupe will help you: remove duplicate entries from a spreadsheet of names and addresses link a list with customer information to another with order history, even without unique customer IDs take a database of campaign contributions and figure out which ones were made by the same person, even if the names were entered slightly differently for each record dedupe […]

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Text Processing

A Python (2 and 3) library for processing textual data

Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. from textblob import TextBlob text = ”’ The titular threat of The Blob has always struck me as the ultimate movie monster: an insatiably hungry, amoeba-like mass able to penetrate virtually any safeguard, capable of–as a doomed doctor chillingly […]

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Numpy

A numpy compatible python binary package that offers a number of APIs for fast tSNE calculation

nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation and HSNE modelling. For more detail see the documentation for the current release – 1.1.0 Currently python 3.6, 3.7, and 3.8 are supported on Windows, Mac and Linux using cibuildwheel Demo software using nptsne Can be downloaded from Building The requirements.txt and the pyproject.toml contain the list of build requirements. Add the biovault conan remote (for prebuilt packages): conan remote add conan-biovault […]

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Natural Language Processing

A Python modules, data sets, and tutorials supporting research and development in Natural Language Processing

NLTK — the Natural Language Toolkit — is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.5, 3.6, 3.7, or 3.8. For documentation, please visit nltk.org. Contributing Do you want to contribute to NLTK development? Great! Please read CONTRIBUTING.md for more details. See also how to contribute to NLTK. Donate Have you found the toolkit helpful? Please support NLTK development by donating to the project […]

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Optimization

A Lightweight Hyperparameter Optimization Tool

The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machine Learning Experiment (MLE) pipeline. It supports real, integer & categorical search variables and single- or multi-objective optimization. Core features include the following: API Simplicity: strategy.ask(), strategy.tell() interface & space definition. Strategy Diversity: Grid, random, coordinate search, SMBO & wrapping around FAIR’s nevergrad. Search Space Refinement based on the top performing configs via strategy.refine(top_k=10). Export of configurations to execute via e.g. python train.py –config_fname config.yaml. Storage […]

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Automation

An IDA Pro Python plugin that helps automate some tedious Windows Kernel Drivers reverse engineering tasks

Installation Copy DriverBuddyReloaded folder and DriverBuddyReloaded.py file into the IDA plugins folder (e.g. C:Program Files (x86)IDA 7plugins) or wherever you have installed IDA. Usage To use the auto-analysis feature: Start IDA and load a Windows kernel driver. Go to Edit -> Plugins -> Driver Buddy Reloaded or press CTRL+ALT+A to start the auto-analysis. Check the “Output” window for the analysis results. To decode an IOCTLs: Place the mouse cursor on the line containing a suspected IOCTL code. Right-click and select […]

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