arXiv subjects

Rodrigo Ramele

Publications and source records attributed to Rodrigo Ramele.

6 recordsLinked to original sources

arXiv2026

Renting the Cracking Machine with a Cost-and-Time Analysis of Exhaustive DES-56 Key Search in the Cloud

The Data Encryption Standard (DES), with its 56-bit key, has been considered cryptographically broken since 1998. However, a concrete, reproducible measurement of the cost and time required to perform an exhaustive key search using today's commodity cloud infrastructure has not been widely reported in recent literature. In this paper we present a distributed brute-force system built on AWS EC2 that partitions the $2^{56}$ keyspace across 37 \texttt{c6i.2xlarge} instances running a C/OpenMP worker, achieving a measured throughput of $2.91$\,M\,keys/s per instance (${\sim}108 \times 10^6$ keys/s aggregate). We conduct 15 independent trials covering keyspace offsets from $10^6$ to $1.5 \times 10^{10}$ keys, measuring wall-clock time and monetary cost per trial. For small offsets ($\leq 10^8$), total time is dominated by AWS instance boot latency (${\approx}\;90$\,s), yielding a mean of $116.8\pm20.1$\,s at \$0.41 per attack. For larger offsets the search time dominates and grows linearly: a key at offset $1.5{\times}10^{10}$ requires ~${\approx} \; 87$ minutes and \$18. At the measured aggregate throughput of 108\,M\,keys/s, exhausting the full $2^{56}$ keyspace with these 37 instances would take ${\approx} \; 21$ years; however, because the workload is embarrassingly parallel and cloud capacity is elastic, the same search can be traded for money almost linearly. Extrapolating our measured cost, a complete exhaustive search would cost ${\approx} \; \$1.2$M and, with a sufficiently large fleet, could be completed in about one day. The system is thus practical for bounded-subspace attacks at negligible cost, and full DES exhaustion, while expensive, is firmly within reach of a well-funded attacker using only commodity cloud resources.

cs.CR↗

arXiv2026

KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective

Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. In the context of Variational Autoencoders (VAEs), it serves as a central regularization term, imposing structure on the latent space and thereby enabling the model to exhibit generative capabilities. In this work, we present a detailed derivation of the closed-form expression for the KL divergence between Gaussian distributions, a case of particular importance in practical VAE implementations. Starting from the general definition for continuous random variables, we derive the expression for the univariate case and extend it to the multivariate setting under the assumption of diagonal covariance. Finally, we discuss the interpretation of each term in the resulting expression and its impact on the training dynamics of the model.

cs.LG↗

arXiv2024

Black Box Meta-Learning Intrinsic Rewards

The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environments. Meta-learning has emerged as a promising approach to address these issues by optimizing components of the learning algorithm to meet desired characteristics. Additionally, a different line of work has extensively studied the use of intrinsic rewards to enhance the exploration capabilities of algorithms. This work investigates how meta-learning can improve the training signal received by RL agents. We introduce a method to learn intrinsic rewards within a reinforcement learning framework that bypasses the typical computation of meta-gradients through an optimization process by treating policy updates as black boxes. We validate our approach against training with extrinsic rewards, demonstrating its effectiveness, and additionally compare it to the use of a meta-learned advantage function. Experiments are carried out on distributions of continuous control tasks with both parametric and non-parametric variations. Furthermore, only sparse rewards are used during evaluation. Code is available at: https: //github.com/Octavio-Pappalardo/Meta-learning-rewards

cs.LG↗

arXiv2024

A Strong Inductive Bias: Gzip for binary image classification

Deep learning networks have become the de-facto standard in Computer Vision for industry and research. However, recent developments in their cousin, Natural Language Processing (NLP), have shown that there are areas where parameter-less models with strong inductive biases can serve as computationally cheaper and simpler alternatives. We propose such a model for binary image classification: a nearest neighbor classifier combined with a general purpose compressor like Gzip. We test and compare it against popular deep learning networks like Resnet, EfficientNet and Mobilenet and show that it achieves better accuracy and utilizes significantly less space, more than two order of magnitude, within a few-shot setting. As a result, we believe that this underlines the untapped potential of models with stronger inductive biases in few-shot scenarios.

cs.CV↗

arXiv2023

Equilivest: A Robotic Vest to aid in Post-Stroke Dynamic Balance Rehabilitation

Stroke is a medical condition that can affect motor function, particularly dynamic balance. Biofeedback can aid in rehabilitation procedures which help patients to regain lost motor activity and recover functionality. In this work, we are presenting a robotic smart-vest device that can analyze Inertial Measurement Unit (IMU) data and assist in rehabilitation procedures by providing timed feedback in the form of vibrotactile stimulation. Information provided by principal caregivers and patients in the form of surveys and interviews, is used to hypothesize potential clinical causes and to derive alternative three alternative clinical modalities: Artificial Vestibular Feedback, Gait Pacemaker and Risk-Predictor.

cs.RO↗

arXiv2022

EPOC Emotiv EEG Basics

This document provides some basic guidance to start working with the EPOC Emotiv neuroheadset device and describes how to use it to perform basic Brain-Computer Interface (BCI) research. A brief tutorial on how to set up the device, from its electrophysiological point of view, as well as a description and practical code to perform some basic analysis, is explained. A basic experiment is introduced to detect one of the oldest and, indeed, quite still valuable electrophysiological correlate, visual occipital alpha waves, or Berger Rhythm. An additional experiment is expounded where the power spectrum of alpha waves is reduced when a subject is affected by background cognitive disturbances. This document also briefs about the extraction of information by using the EPOC Emotiv library and also with python Emokit package. This report presents a basic guide on how to use EEGLAB and MATLAB, as well as python stack to perform the neurophysiological analysis. Finally, a basic analysis on different feature extraction and classification methods is provided.

eess.SP↗