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ANTFARM (Advanced Network Toolkit for Assessments and Remote Mapping) is a passive network mapping application that utilizes output from existing network examination tools to populate its OSI-modeled database. This data can then be used to form a ‘picture’ of the network being analyzed. ANTFARM is a data fusion tool that does not directly interact with the network. The analyst can use a variety of passive or active data gathering techniques, the outputs of which are loaded into ANTFARM and incorporated into the network map. Data gathering can be limited to completely passive techniques when minimizing the risk of disrupting the operational network is a concern. Code development takes place from GitHub.
A Python toolkit for evaluating the quality of classification models for tasks of Zero-Shot Learning (ZSL) including the models construction with different insides (e.g. miscellaneous NN architectures), procedure for training and saving models for further evaluation.
A toolkit for training neural networks to perform line-level Handwritten Text Recognition (HTR)
The toolkit is built on top of TensorFlow/Keras. It is shipped with a ready-to-train CNN-1DRNN-CTC  model and all the surrounding code enabling training, performance evaluation, and prediction. In a nutshell, you only have to tell the toolkit how to obtain the raw handwriting examples of a form line image -> text. The rest will be taken care of automatically including things like data preprocessing, normalization, generating batches of training data, training, etc. You can train the model on the IAM Handwriting dataset as well as your own. Also, the code should work for arbitrary written language, not just English (at least in theory).
Txt2Vec is a toolkit to represent text by vector. It's based on Google's word2vec project, but with some new features, such incremental training, model vector quantization and so on. For a specified term, phrase or sentence, Txt2vec is able to generate correpsonding vector according its semantics in text. And each dimension of the vector represents a feature. Txt2Vec is based on neural network for model encoding and cosine distance for terms similarity. Furthermore, Txt2Vec has fixed some issues of word2vec when encoding model in multiple-threading environment. The following is the introduction about how to use console tool to train and use model. For API parts, I will update it later.
Txt2VecConsole tool supports four modes. Run the tool without any options, it will shows usage about modes. Txt2VecConsole.exe
Txt2VecConsole for Text Distributed Representation
Specify the running mode:
: train model to build vectors for words
: calculating the similarity between two words
: multi-words semantic analogy
: shrink down the size of model
: dump model to text format
: build vector quantization model in text format
With train mode, you can train a word-vector model from given corpus. Note that, before you train the model, the words in training corpus should be word broken. The following are parameters for training mode
Txt2VecConsole.exe -mode train
Parameters for training:
-trainfile : Use text data from to train the model
-modelfile : Use to save the resulting word vectors / word clusters
-vector-size : Set size of word vectors; default is 200
-window : Set max skip length between words; default is 5
-sample : Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5
-threads : the number of threads (default 1)
-min-count : This will discard words that appear less than times; default is 5
-alpha : Set the starting learning rate; default is 0.025
-debug : Set the debug mode (default = 2 = more info during training)
-cbow : Use the continuous bag of words model; default is 0 (skip-gram model)
-vocabfile : Save vocabulary into file
-save-step : Save model after every words processed. it supports K, M and G for larger number
-iter : Run more training iterations (default 5)
-negative : Number of negative examples; default is 5, common value are 3 - 15
-pre-trained-modelfile : Use which is pre-trained-model file
-only-update-corpus-word : Use 1 to only update corpus words, 0 to update all words
Txt2VecConsole.exe -mode train -trainfile corpus.txt -modelfile vector.bin -vocabfile vocab.txt -debug 1 -vector-size 200 -window 5 -min-count 5 -sample 1e-4 -cbow 1 -threads 1 -save-step 100M -negative 15 -iter 5
After the training is finished. The tool will generate three files. vector.bin contains words and vector in binary format, vocab.txt contains all words with their frequency in given training corpus, and vector.bin.syn which is used for incremental model training in future.
Incremental Model Training
After we collected some new corpus and new words, to get these new words' vector or update existing words' vector by new corpus, we need to re-train existing model in incremental model. Here is an example:
Txt2VecConsole.exe -mode train -trainfile corpus_new.txt -modelfile vector_new.bin -vocabfile vocab_new.txt -debug 1 -window 10 -min-count 1 -sample 1e-4 -threads 4 -save-step 100M -alpha 0.1 -cbow 1 -iter 10 -pre-trained-modelfile vector_trained.bin -only-update-corpus-word 1
We have already trained a model "vector_trained.bin" before, currently, we have collected some new corpus named "corpus_new.txt" and new words saved into "vocab_new.txt". The above command line will re-train existing model incrementally, and generate a new model file named "vector_new.bin". To get better result, the "alpha" value should be usually bigger than that in full corpus and vocabulary size training.
Incremental model training is very useful for incremental corpus and new word. In this mode, we are able to generate new words vector aligned with existing words efficiently.
Calculating word similarity
With distance mode, you are able to calculate the similarity between two words. Here are parameters for this mode Txt2VecConsole.exe -mode distance
Parameters for calculating word similarity
-modelfile : encoded model needs to be loaded
-maxword : the maximum word number in result. Default is 40
After the model is loaded, you can input a word from console and then the tool will return the Top-N most similar words.
LinuxKit, a toolkit for building custom minimal, immutable Linux distributions. LinuxKit currently supports the x86_64, arm64, and s390x architectures on a variety of platforms, both as virtual machines and baremetal (see below for details).
Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity. Lark can parse all context-free languages. To put it simply, it means that it is capable of parsing almost any programming language out there, and to some degree most natural languages too. Who is it for? What can it do? And many more features. Read ahead and find out! Most importantly, Lark will save you time and prevent you from getting parsing headaches.
Why use Gluegun?
You might want to use Gluegun if:
You need to build a CLI app
You want to have powerful tools at your fingertips
And you don't want to give up flexibility at the same time
Firmware Analysis Toolkit
FAT is a toolkit built in order to help security researchers analyze and identify vulnerabilities in IoT and embedded device firmware. This is built in order to use for the "Offensive IoT Exploitation" training conducted by Attify.
As of now, it is simply a script to automate Firmadyne which is a tool used for firmware emulation. In case of any issues with the actual emulation, please post your issues in the firmadyne issues.
In case you are facing issues, you can try AttifyOS which has Firmware analysis toolkit and other tools pre-installed and ready to use. Firmware Analysis Toolkit (FAT henceforth) is based on Firmadyne with some changes. Firmadyne uses a PostgreSQL database to store information about the emulated images. However just for the core functionality i.e. emulating firmware, PostgreSQL is not really needed. Hence FAT doesn't use it.
MDK-SE (Malware's Development Kit for SE) is a toolkit to help with in-game script (programmable block) development for Keen Software House's space sandbox Space Engineers. It helps you create a ready-to-code project for writing ingame scripts, and provides an analyzer which warns you if you're trying to use something that is not allowed in Space Engineers. Because there hasn't been any need to. It's for all intents and purposes "done". If and when something breaks it, either a Visual Studio update or an SE update, I will do my best to fix it. Or, obviously, if I come up with a feature I want... but for now, there's nothing to do. "But there's bugs", I hear you say. Yeah, there's some minor issues. But they're small enough that I can't manage to find the time to fix them. I have limited time for this and not much help...
Can I use this in VSCode?
No. Visual Studio Code and Visual Studio has nothing in common outside of the name.
Helps you create a fully connected script project in Visual Studio, with all references in place
Requires that you have the game installed, but does not require you to have it running
Class templates for normal utility classes and extension classes
Tells you if you're using code that's not allowed in Space Engineers (whitelist checker)
Deploys multiple classes into a single PB script, which then is placed in the local Workshop space for easy access in-game - no copy/paste needed
Supports optional code minifying: Fit more code within the limits of the programmable block
Allows real reusable code libraries through the use of Visual Studio's Shared Project
Out-of-game script blueprint manager allows you to rename and delete script blueprints without starting the game
MDK/SE Wiki page
Quick Introduction to Space Engineers Ingame Scripts
(You don't have to use the extension to get something out of this guide)
Contributing to MDK Space Engineers is trademarked to Keen Software House. This toolkit is fan-made, and its developer has no relation to Keen Software House.
ClusterKit is an elegant and efficiant clustering controller for maps. Its flexible architecture make it very customizable, you can use your own algorithm and even your own map provider.
PIMPPA is a toolkit to automatically retrieve, skip, sort, process and backup binary files (pictures, music, animations, etc.) from the Internet. Primary file source is newsgroups, more loosely FTP and IRC.
Opal is a toolkit for wrapping scientific applications as Web services on Cluster, Grid and Cloud resources with ease. Users may access these software as a service using simple Web service APIs from their custom application and workflow environment.
A Backend Server Software Development Kit and Creation Suite
AJAX based tool-kit to generate HTML form from any XSD file. It can add/delete nodes per XSD. Utilizes DOM, Transforms XSD to meta XSD and meta XSD to GUI DOM and to HTML. Populates valid XML file after rendering the XSD based HTML form on browser.
Ryzom Core is a toolkit for the development of massively online universes. It provides the base technologies and a set of development methodologies for the development of both client and server code.
Jena is Java toolkit for developing semantic web applications based on W3C recommendations for RDF and OWL. It provides an RDF API; ARP, an RDF parser; SPARQL, the W3C RDF query language; an OWL API; and rule-based inference for RDFS and OWL.
Archive your personal history
ResCarta Toolkit offers an open source solution to creating, storing, viewing, and searching digital collections. Applications in the toolkit let users create and edit metadata, convert data to open standard ResCarta format, index and host collections.
Toolkit for working with and mapping geospatial data
GeoTools is an open source (LGPL) Java code library which provides standards compliant methods for the manipulation of geospatial data. GeoTools is an Open Source Geospatial Foundation project. The GeoTools library data structures are based on Open Geospatial Consortium (OGC) specifications.
A toolkit for building high-level compound widgets in Python using the Tkinter module. It contains a set of flexible and extensible megawidgets, including notebooks, comboboxes, selection widgets, paned widgets, scrolled widgets, and dialog windows. Python megawidgets is Python 3 compatible through the Pmw 2 download. Pmw 1 is destined for Python 2. Both are now accessible through the new pypi compatible package.
The 3D Toolkit provides algorithms and methods to process 3D point clouds. In includes automatic precise registration (6D simultaneous localization and mapping, 6D SLAM) and other tools, e.g., a fast 3D viewer, plane extraction software, etc.
Library and tools for dealing with the RLE raster image format
Utah Raster Toolkit is a collection of programs and C routines for dealing with raster images commonly encountered in computer graphics. Called the RLE format, it uses run-length encoding to reduce storage space for most images.
MMDAgent is the toolkit for building voice interaction systems. Users can design users own dialog scenario, 3D agents, and voices. This software is released under the Modified BSD license.
TubeKit is a toolkit for creating YouTube crawlers. It allows one to build one's own crawler that can crawl YouTube based on a set of seed queries and collect up to 17 different attributes.
Toolkit for Automatic Control and Dynamic Optimization
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization. ACADO Toolkit is implemented as self-contained C++ code and comes along with user-friendly MATLAB interface. The object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines.
The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
This is a widget set to quickly develop cross-platform GUI software using the Free Pascal Compiler. It doesn't rely on large third party libraries - which makes fpGUI applications easy to deploy. Also included: help viewer, visual form designer.
MitM pentesting opensource toolkit
Operative Systems Suported are: Linux-ubuntu, kali-linux, backtack-linux (un-continued), freeBSD, Mac osx (un-continued) Netool its a toolkit written using 'bash, python, ruby' that allows you to automate frameworks like Nmap, Driftnet, Sslstrip, Metasploit and Ettercap MitM attacks. this toolkit makes it easy tasks such as SNIFFING tcp/udp traffic, Man-In-The-Middle attacks, SSL-sniff, DNS-spoofing, D0S attacks in wan/lan networks, TCP/UDP packet manipulation using etter-filters, and gives you the ability to capture pictures of target webbrowser surfing (driftnet), also uses macchanger to decoy scans changing the mac address. Rootsector module allows you to automate some attacks over DNS_SPOOF + MitM (phishing - social engineering) using metasploit, apache2 and ettercap frameworks. Like the generation of payloads, shellcode, backdoors delivered using dns_spoof and MitM method to redirect a target to your phishing webpage. recent as introducted the scanner inurlbr (by cleiton)
Inguma is a free penetration testing and vulnerability discovery toolkit entirely written in python. Framework includes modules to discover hosts, gather information about, fuzz targets, brute force usernames and passwords, exploits, and a disassemble.
A lightweight OpenSource tool for Asset Management, Software Deployment, Remote Control and Network Monitoring, on Windows and Linux systems. Similar to Tivoli, SMS or Unicenter, having an advantage in performance, convenience, cost and requirements.
In the field of Toolkit learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Toolkit is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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