Harvesting Vector - Moozonian

GitHub Repo https://github.com/GentlemanNASA/Website-Cloning-SMB-Enumeration

GentlemanNASA/Website-Cloning-SMB-Enumeration

To demonstrate how attackers clone legitimate websites to harvest credentials using the Social-Engineer Toolkit (SET) and how a simple HTML redirect can be used as a phishing vector.

GitHub Repo https://github.com/sfomuseum/go-embeddings-harvest

sfomuseum/go-embeddings-harvest

Go package for harvesting data from a variety of providers, deriving vector embeddings for those data and writing everything to Parquet files.

GitHub Repo https://github.com/OCSF-Logrrr/vector

OCSF-Logrrr/vector

Kubernetes-ready Vector setup that harvests container logs and streams them to Kafka topics.

GitHub Repo https://github.com/PrachiBhati/Ensemble-models-for-Rainfall-Prediction--Austin-data-set

PrachiBhati/Ensemble-models-for-Rainfall-Prediction--Austin-data-set

Rainfall prediction is most important part of the agriculture and helped farmers in harvesting the crops at right time. Predicting the rainfall with correct accuracy will save the lives of people in coastal areas where flood and heavy rainfall is common. Various research works have been done on rainfall predication using statistical and Artificial intelligence approaches. In this proposed work Multi linear Regression and Support Vector Regression are implemented. Then Ensemble learning Models –Decision tree Regressor, Random forest Regressor and tried with Austin dataset and comparison results are shown in the graph.

GitHub Repo https://github.com/marcorathjens-ux/Project-GASC-Annular-Shear-Cascade

marcorathjens-ux/Project-GASC-Annular-Shear-Cascade

Description: Gravitational Annular Shear Cascade (GASC) – A modular, high-velocity kinetic energy harvesting system based on boundary layer momentum shearing (Vector 2030).

GitHub Repo https://github.com/fekihatem11/espace-ets-mcp

fekihatem11/espace-ets-mcp

MCP server that exposes ÉTS research publications as AI-accessible tools — hybrid search (BM25 + vector) over 20k+ papers harvested via OAI-PMH

GitHub Repo https://github.com/shellkraft/LinPeek

shellkraft/LinPeek

LinPeek is a comprehensive post-exploitation toolkit for Linux systems, designed for security professionals to assess system security, identify privilege escalation vectors, harvest credentials, and establish persistence.

GitHub Repo https://github.com/DevDizzle/sci-paper-hub

DevDizzle/sci-paper-hub

PaperRec: arXiv-powered paper recommender. Harvests cs.AI/cs.LG/cs.CL/cs.RO/cs.CV, embeds abstracts (Gemini), indexes in Vertex AI Vector Search, and serves a /search API on Cloud Run.

GitHub Repo https://github.com/kzxl/LLMSematicBrain

kzxl/LLMSematicBrain

Local RAG knowledge base for Agents & LLMs with Vector Search and Auto-Harvest Proxy

GitHub Repo https://github.com/hc2116/MMPPsampler

hc2116/MMPPsampler

Efficient implementation of the Gibbs sampler by Fearnheard and Sherlock (2006) for the Markov modulated Poisson process that uses 'C++' via the 'Rcpp' interface. Fearnheard and Sherlock (2006) proposed an exact Gibbs-sampler for performing Bayesian inference on Markov Modulated Poisson processes. This package is an efficient implementation of their proposal for binned data. Furthermore, the package contains an efficient implementation of the hierarchical MMPP framework, proposed by Clausen, Adams, and Briers (2018), that is tailored towards inference on network flow arrival data and extends Fearnheard and Sherlock's Gibbs sampler. Both frameworks harvests greatly from routines that are optimised for this specific problem in order to remain scalable and efficient for large amounts of input data. These optimised routines include matrix exponentiation and multiplication, and endpoint-conditioned Markov process sampling. Both implementations require an input vector that contains the binned observations, the length of a binning interval, the number of states of the hidden Markov process, and lose prior hyperparameters. As a return, the user receives the desired number of sample trajectories of the hidden Markov process as well as the likelihood of each trajectory.