Network

A Network Shredder IDS for python

Network-Shredder Network-Shredder is a python based NIDS. Installation git clone https://github.com/v0lck3r/Network-Shredder.git cd Network-Shredder pip3 install -r requirements.txt Example usage python3 Network-Shredder.py -h Live mode python3 Network-Shredder.py rules.txt –interface tun0 Start web application python3 Network-Shredder.py rules.txt –interface tun0 –web Console quiet mode python3 Network-Shredder.py rules.txt –interface tun0 –web –quiet Specify log directory python3 Network-Shredder.py rules.txt –logdir /tmp/ –interface tun0 –web Pcap based detection python3 Network-Shredder.py rules.txt –pcap pcap_file.pcap GitHub https://github.com/v0lck3r/Network-Shredder    

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Text

Print text color and text format on Term with Python

term-printer Print ‘text color’ and ‘text format’ on Term with Python ※ It may not work depending on the OS and shell used. PIP $ pip install term-printer import from term_printer import Color, Color256, ColorRGB, StdText, cprint If you want to override bultin print function from term_printer import Color, Color256, ColorRGB, StdText, cprint as print Usage 1. Attrs print Applies to all characters. You can specify Format, Color, Color256, and ColorRGB. Able to specify more than one. source from term_printer […]

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Machine Learning

Dependency Cluster Attention for Aspect-level sentiment classification

DLCF-DCA codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted to 《Neurocomputing》. Requirement Python >= 3.6 PyTorch >= 1.0 pytorch-transformers == 1.2.0 SpaCy >= 2.2 To use our models, you need download en_core_web_sm by python -m spacy download en_core_web_sm Training python train.py –model dlcf_dca Model Architecture Note Some important scripts to note: datasets/semeval14/*.seg: Preprocessed training and testing sentences in SemEval2014. datasets/semeval15/*.seg: Preprocessed training and testing sentences in SemEval2015. datasets/semeval16/*.seg: Preprocessed training and […]

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Neural Network

Neural G2P to portuguese language with python

neural-g2p-portuguese Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. This project was adapted from https://github.com/hajix/G2P. Dependencies The following libraries are used:pytorchtqdmmatplotlib Install dependencies using pip: pip3 install -r requirements.txt Dataset The dataset used here was taken from site http://www.portaldalinguaportuguesa.org/, as well as some insertions made by me so that the dataset would give more coverage […]

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Text-to-Speech

Attention Based Grapheme To Phoneme with python

G2P The G2P algorithm is used to generate the most probable pronunciation for a word not contained in the lexicon dictionary. It could be used as a preprocess of text-to-speech system to generate pronunciation for OOV words. Dependencies The following libraries are used:pytorchtqdmmatplotlib Install dependencies using pip: pip3 install -r requirements.txt Dataset Currently the following languages are supported: EN: English FA: Farsi RU: Russian You could easily provide and use your own language specific pronunciatin doctionary for training G2P. More […]

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Beginner, Data Science, NLP, Python, Text, Word Embeddings

Part 8: Step by Step Guide to Master NLP – Useful Natural Language Processing Tasks

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to part-7 of this series, we completed the most useful concepts in NLP. While going away in this series, let’s first discuss some of the useful tasks of NLP so that you have much clarity about what you can do by learning the NLP. After this part, we will start our discussion on […]

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Advanced, NLP

Part 11: Step by Step Guide to Master NLP – Syntactic Analysis

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article, we discussed an entity extraction technique named i.e, Named Entity Recognition. There is also another entity extraction technique which is also a popular technique named Topic Modeling, which we will discuss in the subsequent articles of our blog series. So, In this article, we will deep dive into Syntactic Analysis, […]

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Advanced, Data Engineering, Docker, Machine Learning, Model Deployment, NLP, Profile Building

Deploying Machine learning Application on AWS Fargate

Amazon Web Services(AWS) offers reliable, scalable, and cost-effective cloud computing services. It provides Infrastructure as a service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS) also a new model known as Function as a Service(FaaS) eg. AWS Lambda which is a serverless entity. Before going further, if you don’t have an AWS account please create one to follow along with the hands-on. Amazon Elastic Container Service(ECS) Amazon ECS is a container orchestration platform developed by Amazon, it is similar to […]

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Facebook NLP Research
NLP

Not All Memories are Created Equal: Learning to Forget by Expiring

Abstract Attention mechanisms have shown promising results in sequence modeling tasks that require long term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories (Rae et al., 2020). However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous […]

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