Search Results for "data capture framework" - Page 18
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Showing 551 open source projects for "data capture framework"
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
Scalable Agent is the open implementation of IMPALA (Importance Weighted Actor-Learner Architectures), a highly scalable distributed reinforcement learning framework developed by Google DeepMind. IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a... -
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ShadowSocksShare
Python ShadowSocks framework
This project obtains the shared ss(r) account from the ss(r) shared website crawler, redistributes the account and generates a subscription link by parsing and verifying the account connectivity. Since Google plus will be closed on April 2, 2019, almost all the available accounts crawled before come from Google plus. So if you are building your own website, please keep an eye on the updates of this project and redeploy using the latest source code. -
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Xplico
Xplico is a Network Forensic Analysis Tool (NFAT)
Xplico is a Network Forensic Analysis Tool (NFAT). The goal of Xplico is extract from an internet traffic capture the applications data contained. For example, from a pcap file Xplico extracts each email (POP, IMAP, and SMTP protocols), all HTTP contents, each VoIP call (SIP, MGCP, MEGACO, RTP), IRC, WhatsApp... Xplico is able to classify more than 140 (application) protocols. Xplico cam be used as sniffer-decoder if used in "live mode" or in conjunction with netsniff-ng. ... -
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django-dynamic-scraper
Creating Scrapy scrapers via the Django admin interface
Django Dynamic Scraper (DDS) is an app for Django build on top of the scraping framework Scrapy. While preserving many of the features of Scrapy it lets you dynamically create and manage spiders via the Django admin interface. With Django Dynamic Scraper (DDS) you can define your Scrapy scrapers dynamically via the Django admin interface and save your scraped items in the database you defined for your Django project. -
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plot.py
direct data plotting and evaluation
The Plot.py project tries to supply a measurement data visualization and treatment framework being easy to use while keeping the freedom for advanced users to execute additional data treatment algorithms. Plotting is done via gnuplot and the script used to produce the graphs can be exported for later use/changes. Many raw experimental data types (mostly of x-ray and neutron scattering experiments) are supported with more to be added on user request. ... -
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Wally
Distributed Stream Processing
Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. -
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Code Catalog in Python
Algorithms and data structures for review for coding interview
code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying about edge performance. ... -
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WiFi-Pumpkin
WiFi-Pumpkin - Framework for Rogue Wi-Fi Access Point Attack
The WiFi-Pumpkin is a rogue AP framework to easily create these fake networks, all while forwarding legitimate traffic to and from the unsuspecting target. It comes stuffed with features, including rogue Wi-Fi access points, deauth attacks on client APs, a probe request and credentials monitor, transparent proxy, Windows update attack, phishing manager, ARP Poisoning, DNS Spoofing, Pumpkin-Proxy, and image capture on the fly. moreover, the WiFi-Pumpkin is a very complete framework for auditing Wi-Fi security check the list of features is quite broad. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
...The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. The project serves primarily as a research implementation that demonstrates how capsule networks can be built and trained using modern deep learning frameworks. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Catalyst
An Algorithmic Trading Library for Crypto-Assets in Python
Catalyst is an algorithmic trading library for crypto-assets written in Python, originally developed to let quants and developers design, backtest, and deploy trading strategies in a unified environment. It builds on top of Zipline, extending that ecosystem to support crypto exchanges and high-resolution historical data (daily and minute bars). Users can express strategies in Python, run backtests against historical price data, and analyze performance through built-in metrics and analytics to evaluate profitability, risk, and behavior under different market conditions. Beyond backtesting, Catalyst was designed to support live trading on multiple crypto exchanges such as Binance, Bitfinex, Bittrex, and Poloniex, bridging simulation and production within the same framework. -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
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fooltrader
Quant framework for stock
Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. ... -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. ... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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CIF2Cell
Generating cells for electronic structure calculations from CIF files
CIF2Cell is a tool to generate the geometrical setup for various electronic structure codes from a CIF (Crystallographic Information Framework) file. The program currently supports output for a number of popular electronic structure programs, including ABINIT, ASE, CASTEP, CP2K, CPMD, CRYSTAL09, Elk, EMTO, Exciting, Fleur, FHI-aims, Hutsepot, MOPAC, Quantum Espresso, RSPt, Siesta, SPR-KKR, VASP. Also exports some related formats like .coo, .cfg and .xyz-files. The program has been published... -
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Python ADB
Python ADB + Fastboot implementation
python-adb provides a pure-Python implementation of the Android Debug Bridge protocol so you can script Android devices without depending on the platform adb binary. It exposes high-level helpers for device discovery, shell commands, file push/pull, port forwarding, and log collection, making it easy to build automation around phones and emulators. Under the hood it speaks the ADB protocol directly and can connect via USB or over TCP, which is useful for lab setups and headless servers.... -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train... -
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Serenata de Amor
Artificial Intelligence for social control of public administration
Serenata de Amor is an open civic technology project that uses data science and artificial intelligence to promote transparency and accountability in public administration. The project was developed by a community of volunteers associated with Open Knowledge Brasil who believe that open data and technology can help citizens monitor government spending. It focuses on analyzing publicly available datasets related to reimbursements claimed by Brazilian congress members in order to detect... -
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survol
RDF-based framework monitoring business systems activity
A Python agent and a web interface aiming to help the analysis and investigation of a legacy application. A set of machines, processes, databases, programs etc ... all communicating with each other, manipulating your data, and whose software architecture has become, with time, complicated, difficult to understand, and undocumented. Data are aggregated with an RDF inference engine, creating a global vision of the business information processing. -
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SaberNet DCS is a labor data collection system, designed to allow organizations to rapidly capture their labor data in real-time. Optimized for bar code input, DCS is the perfect way to automate and improve the accuracy of your time tracking.
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SSL Logger
Decrypts and logs a process's SSL traffic
ssl_logger is a Python-based tool that decrypts and logs a target process’s SSL/TLS traffic on Linux and macOS. It attaches to a running process by name or PID and hooks SSL_read and SSL_write calls to capture plaintext data flowing through encrypted connections. Output can be streamed to the console with verbose metadata or written to a PCAP file for later analysis in standard tooling. The utility is powered by dynamic instrumentation using the Frida framework, allowing it to intercept function calls without recompiling or modifying the target binary. ... -
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Needle
The iOS Security Testing Framework
Needle is an open-source, modular framework to streamline the process of conducting security assessments of iOS apps. Needle was originally made to work with iOS 9 and iOS 10. Since then, Frida was released and become the defacto tool to use with mobile security assessments. Some common Frida mobile security scripts were later implemented within Needle, as some of these scripts worked better or addressed some issues that were present in Needle's custom tooling. Assessing the security of an... -
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Modellers Colour Matching/Mixing Toolkit
Colour matching/mixing toolkit for modellers
One of the problems faced by modellers is matching their available paint colours to those used on the object (eg aircraft) being modelled. The aim of this toolkit is to help them in this endeavour including assistance in mixing paints to achieve the required match if required. Non Windows Versions 1.00 and later require: - Python 3.43 or later - python3-gobject 3.22.0 or later - python3-cairo 1.10.0 or later Windows Versions 1.00 or later require: - Python 3.4.3 - PyGI... -
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seq2seq
A general-purpose encoder-decoder framework for Tensorflow
seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. Researchers used it as a baseline to reproduce classic results and to prototype new attention variants and training tricks. It also offered scripts for data preprocessing, evaluation, and exporting models for serving. ... -
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bulbea
Deep Learning based Python Library for Stock Market Prediction
bulbea is an open-source Python library designed for financial analysis and stock market prediction using machine learning and deep learning techniques. The library provides tools for retrieving financial time series data, preprocessing market data, and training predictive models that estimate future price movements. bulbea integrates common machine learning frameworks such as TensorFlow and Keras to build neural network models capable of learning patterns in historical financial data. It includes utilities for splitting datasets, normalizing time series, and training models such as recurrent neural networks that can capture temporal dependencies in market behavior. ...
