A distributed system made up of ESP32 devices that sniffs probe request packets sent by smartphones searching for Wi-Fi and send to a server that generates device positions. The real-time smartphones location, the frequency with which each smartphone appears, and many other informations are accessible through a UI.
This demo shows how the ESP32 firmware works.
Make your customized wordlist for penetration testing practice (brute force attack, dictionary attack, etc.). Wordlists can be generated through a variety of methods, pick one and enjoy your wordlist.
Educational resource to understand how ransomware works under the hood by demonstrating the cryptographic principles and workflows commonly employed by this malware.
The repository provides documentation and resources for understanding the basic concepts behind Large Language Models (LLMs) and the process of augments LLMs prompt with Retrieval Augmented Generation (RAG) by integrating external custom data from a variety of sources (e.g. text files, web pages, PDFs, etc.) allowing you to ask questions about such documents.
State-of-the-art research to study and design data models for Network Function Virtualization (NFV) and Software Defined Networking (SDN) architectures - associated with PoliTO Netgroup (Computer Networks Group at Politecnico di Torino).
League of Legends fight tool to download champion data (i.e. skills, damage, hp, etc.), have two champions fight and get the best round of spells for which the first champion can kill the second one as quickly as possible.
A thread safe in-memory key-value cache suitable for single instance microservices. Any object can be stored with a given duration or no expiration time at all. Since the cache is thread safe, it can be safely used by multiple goroutines.
Android food delivery APP made by three different apps:
1. It is showed what happens if different Principal Components (PC) are chosen as basis for images representation and classification. Then, the Naive Bayes Classifier has been chosen and applied in order to classify the image.
2. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.
Check out report with data and results: PCA & Naive Bayes Classifier and SVM.
Implementation of a Neural Network (NN) and a Convolutional Neural Network (CNN) from scratch with data loading, training and evaluation (testing) phase on CIFAR 100 dataset using pytorch.
Check out report with data and results.
Python Telegram BOT ables to store and retrieve data from database (create, delete and read items from your personal list) and set alarms. There are also some easter eggs which I created to have fun my with my friends. Try to find them!
Try it out on Telegram: @J4NN0_Bot
Python Web Scraper for LinkedIn that simulates human activity by controlling the web browser to retrieve company data (e.g. name, description, size, industry, etc.) from LinkedIn web pages and save it to an .xls file.
The aim of the problem is to assign time-slots and resources to the meetings so as to satisfy the constraints as much as possible. The solution contains different implementation of meta-heuristic algorithm like Graph Coloring, Tabù Search and Simulated Annealing.
Check out this short description of the project and the related mathematical model.
Prepare your wedding at best by seating your guests in the best possible way based on each guest's interaction with the others, by calculating the average mood of the room and assigning each guest a specific seat.
- WeStudyBot: developed in 24h during the "Hello Bot!" hackathon organized by HKN PoliTO in Turin the 11-12th November 2017.
- Hungry Student App: developed in 24h during the "Like@Home" hackathon in Turin on 30-31th March 2019. It's an home assistant that use natural language processor and it is able to connect to the Google Assistant or Cloud Speech-to-Text service.