Isaac Villalobos

Recently, we have published a new research paper in the Logic Journal of the IGPL, published by Oxford University Press: Title: "Source code obfuscation with genetic algorithms using LLVM code optimizations" Authors: Juan Carlos de la Torre, Javier Jareño, José Miguel Aragón-Jurado, Sébastien Varrette, Bernabé Dorronsoro Our paper introduces a novel method for source code obfuscation, enhancing protection against reverse-engineering by leveraging LLVM optimization sequences and genetic algorithms. This approach effectively balances obfuscation strength and program performance, making code more resistant to reverse-engineering while maintaining efficiency. Check out the full paper here! https://lnkd.in/eBpWJKXS #Research #Cybersecurity #Obfuscation #GeneticAlgorithms #LLVM #CodeTransformations

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Trail of Bits summer intern Jason An has significantly enhanced Pwndbg, a popular GDB plugin for reverse engineering. An's contributions bring two major improvements to stripped binary analysis: 🥷 Binary Ninja Integration: Pwndbg now seamlessly integrates with Binary Ninja, providing rich debugging information and syncing decompilation, breakpoints, symbols, and function signatures. 🟢 Go Structure Dumping: A new 'go-dump' command allows for improved debugging of Go binaries by enabling the dumping of Go values. These enhancements bring an IDE-like experience to GDB with Pwndbg, particularly beneficial for analyzing stripped binaries. The new features are available in the latest Pwndbg release, marking a significant step forward in reverse engineering capabilities.

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Trail of Bits summer intern Jason An has significantly enhanced Pwndbg, a popular GDB plugin for reverse engineering. An's contributions bring two major improvements to stripped binary analysis: 🥷 Binary Ninja Integration: Pwndbg now seamlessly integrates with Binary Ninja, providing rich debugging information and syncing decompilation, breakpoints, symbols, and function signatures. 🟢 Go Structure Dumping: A new 'go-dump' command allows for improved debugging of Go binaries by enabling the dumping of Go values. These enhancements bring an IDE-like experience to GDB with Pwndbg, particularly beneficial for analyzing stripped binaries. The new features are available in the latest Pwndbg release, marking a significant step forward in reverse engineering capabilities.

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I am happy to share our recently announced manuscript. I proudly directed the bachelor thesis project of Adán Castillo Guerrero that brought this manuscript to life. I believe the project is quite interesting, we explored a framework to construct dynamics of a coarse grained (or "macroscopic") description of a microscopic quantum system that undergoes unitary dynamics (the system is closed). Among the highlights of this project we have: 1) Nonlinear dynamics have emerged in most scenarios. 2) The Maximum Entropy Principle is used to connect both scales (together with a coarse graining CPTP map previously defined). 3) The framework is quite suitable to study many-body systems. 4) The induced non-linear dynamics turns out to be completely positive and trace preserving. I believe this is a signature of the health of our framework, given that the path connecting this to classical stochastic maps is mathematically consistent in this regard. https://lnkd.in/eF2MzZsG

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Very useful advise, tips and tricks for writing a robotics paper

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Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks Read the complete article written by Hernández-Flores and others (2022) here:  ➡️ https://lnkd.in/eT9NyVNb #TTTdiagrams #ArtificialNeuralNetworks #ThermalTreatment #AdditiveManufacturing The heat treatment of a metal involves a series of heating and cooling cycles to alter its microstructure and, consequently, its properties. Temperature–time–transformation (TTT) diagrams are crucial for interpreting the microstructures resulting from these heat treatments. This study introduces a novel approach to predict TTT diagrams of the γ' phase for the Ni-Al alloy using artificial neural networks (ANNs). The methodology consists of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) analysis of the proposed models. Two approaches were taken: one to predict only the nose point of the TTT diagrams and another to predict the entire curve. Ultimately, the best models from each approach were combined to create a more accurate hybrid model. The results indicate that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The predictions of the nose point and the complete curve showed accuracies of 98.07% and 86.41%, respectively. The final hybrid model achieved an accuracy of 96.59%.

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Happy to share that our work with Borja Aizpurua Altuna and Román Orús has been published in #PhysicalReviewResearch! In "Improving gradient methods via coordinate transformations: Applications to quantum machine learning" we present some ideas to boost gradient methods via coordinate transformations, showcasing our tool using various well-known examples in the literature 📚 !! Check it out at: https://lnkd.in/dFrpFqYU #QuantumMachineLearning #QuantumAlgorithms #Optimization #ReferenceFrame

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I’m pleased to share a novel paper, with the collaboration of Prof. Leonardo Tomazeli Duarte, published in IEEE Transactions on Fuzzy Systems. Here is the link for this paper. https://lnkd.in/dsUbyDrN In this paper, entitled "A Novel Unsupervised Capacity Identification Approach to Deal With Redundant Criteria in Multicriteria Decision Making Problems,", we focus on a novel unsupervised approach to reduce bias in ranking problems.

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🎉 Excited to share that our paper, 'An AI-Driven User-Centric Framework Reinforced by Autonomic Computing: A Case Study in the Aluminium Sector', has been accepted for presentation at the IHIET 2024 conference! As part of the research team at AIMEN Centro Tecnológico. I'm thrilled to have contributed to this research alongside my colleagues Andrea Fernández Martínez and Andrea Gregores Coto. Our work focuses on the impact of industrial automation and AI frameworks in the aluminium sector, as part of the s-X-AIPI project. The conference will take place on August 27th, 2024 📅 at the IUAV University of Venice 🏛️ and online 🌐. Our research explores innovative solutions at the intersection of AI and autonomic computing. Learn more about our research and how it's revolutionizing the aluminium industry. #IHIET2024 #AI #AutonomicComputing #AluminiumIndustry #IndustrialAutomation #AimenCentroTecnologico"

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We are happy to share that our paper, "Orchestration for Quantum Services: The Power of Load Balancing Across Multiple Service Providers", has been published in the journal Science of Computer Programming!!! JAIME ALVARADO VALIENTE Enrique Moguel Jose Garcia-Alonso Juan Manuel Murillo Rodríguez In this study, we propose a quantum orchestrator that is designed to facilitate the orchestration and execution of quantum circuits across multiple quantum service providers. The proposed solution aims to simplify the process for developers and facilitate the execution of quantum tasks using resources offered by different providers. The proposal is validated with the implementation of the proposed orchestrator for Amazon Braket and IBM Quantum. It can support both quantum and classical developers in defining, configuring, and executing circuits independently of the selected provider. Link to the publication: https://lnkd.in/d9-Rr_P9

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I’m happy to announce that our paper "AccProf: Increasing the Accuracy of Embedded Application Profiling using FPGAs" has been published in the proceedings of the  International Conference on Architecture of Computing Systems (ARCS) 2024: https://lnkd.in/eudtNKgB A big thank you to my co-authors Andrew Attwood John Goodacre Mikel Lujan and everyone who supported us throughout this process! You can check out the full paper and the proceedings through the following link: https://lnkd.in/eBvSSeCw Or the manuscript here: https://lnkd.in/eFjrkYFr Abstract Accurate software profiling is an essential step in the development of embedded systems. The accuracy of profiling data collected is critically important for embedded systems that operate under fixed timing constraints, which if not met, could lead to system failure. Existing profiling solutions targeting embedded systems introduce an overhead to the running application that distorts the collected profiling data. This paper proposes AccProf for System on Chips with integrated FPGAs. AccProf is a FPGA-assisted profiling framework combining compiler extensions and bespoke hardware. AccProf is composed of (1) an LLVM pass which inserts lightweight instrumentation into the application binary running on general purpose processors , and (2) FPGA-based hardware capable of performing offloaded profiling. Offloading part of the profiling task, and supporting data-structures to the FPGA, reduces pollution of the collected profiling data leading to higher accuracy. This paper addresses on control graph profiling and evaluates AccProf on a range of benchmarks ported to SeL4 microkernel running on the AMD Zynq MpSoC. We measure performance metrics of these benchmarks across a range of processor statistics including cycles, instruction and data cache misses. We show that the impact for certain metrics is reduced for up to 5 times when compared against an equivalent software-based framework.

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Today I want to share my two new published papers in the field of Quantum Computing with Miguel Angel Martin-Delgado. In the first one, entitled 'A Semiclassical Approach to a Quantum Search Engine' we propose a modification of the quantum searchrank algorithm based on semiclassical walks. With this we obtain an algorithm that is able of ranking nodes on a network at the same time that finds marked nodes with a high probability. In order to perform the simulations on big networks we have needed to develop a novel simulation algorithm for Szegedy's quantum walk, saving memory and time resources. We have developed a python library for a simulator using our algorithm, and published it in 'SQUWALS: A Szegedy QUantum WALks Simulator'. https://lnkd.in/ebQiruQW https://lnkd.in/etE5qSqb

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🔎 Embedded programming with Rust 🦀 is perfectly practical today. We wrote a research article that investigates the most popular embedded operating systems and frameworks for Rust, that showcases their strengths.

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The Quantum Fourier Transform (QFT) is the fundamental building block of many quantum algorithms 👷 🛠️ 💻 Lucky for you, Guillermo Alonso-Linaje has brought you the holy grail of QFT explainers. Learn exactly how it's constructed and how you can use it 👇 https://lnkd.in/gdFmTCYa

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How are LLMs built? (question 3) Most high profile LLMs go through 3 stages of training: pre-training of a base neural network, fine-tuning for a specific task, and reinforcement learning from human feedback (RLHF). In that first stage, the neural network is just being trained to predict the next word in an extremely large dataset - large enough that a human would take thousands (maybe millions) of years nonstop to read through. The neural network used for LLMs can be very different from each other, although the most common architecture currently used is the transformers architecture. The important thing about that architecture is that not only the network learns to predict the next word but also what previous words in the sentence are useful to make that prediction. The following step is the fine-tuning. In that step, the network goes through a similar training process, but instead of being trained on the largest dataset possible, it is trained on a specific task, such as being a chatbot. After this step, the answers will not feel like just continuing a previous statement, but rather feel like an answer to the previous statement, even when that statement is not really a question. This behavior is solidified further in the final stage, RLHF, in which humans have to rate answers in terms of how aligned the answers are to what the researchers are expecting. For example: is the answer creating new information? Is it disseminating violent or harmful content? All of these steps have been thoroughly discussed and illustrated before, and newer architectures are being developed, but the core of the biggest models to which we have information are, for now, still as outlined here. In the coming posts, I will be discussing how these networks relate to human cognition, if at all: do LLMs learn like we do? Do LLMs speak like we do? Looking forward to sharing more!

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Very happy to share our last article:  Cloud continuum testbeds and next-generation ICTs: Trends, challenges, and perspectives, in Computer Science Review (Elsevier, IF 13.3). A huge effort with Peio Lopez-Iturri and Constantinos Patsakis.  The article explores the full spectrum of the cloud continuum testbeds and their features, providing a taxonomy that can be practically used as an entry point to identify each testbed’s scope. Moreover, we extract the challenges found in the literature to deliver a profound discussion, correlating the analysed testbeds and their features. Our findings highlight the main gaps and potential roadmaps to provide effective testbeds considering the next generation of ICTs. You can read it using the following link: https://lnkd.in/dseShrwU 

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Do you want to know how to write papers people love reading? Learn from Prof. Cyrill Stachniss how to write good papers and increase the probability of getting more emails starting with "I am pleased to inform you ... " This video is part one of the IEEE RAS Young Professionals event in IROS 2024 https://lnkd.in/g8_GXQ6b

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The #specialissue "Nature-Inspired Algorithms in Machine Learning (2nd Edition)" has published the fifth paper. We thank the Guest Editors Dr. Szymon Łukasik, Dr. Piotr A. Kowalski, and Dr. Rohit Salgotra for their efforts in this special issue! We invite you to read and share the following: https://lnkd.in/g9zMmabs via MDPI #natureinspiredalgorithms #callforpaper

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