arXiv subjects

Miguel Correia

Publications and source records attributed to Miguel Correia.

At least 37 records · Page 2Linked to original sources

arXiv2025

XChainDataGen: A Cross-Chain Dataset Generation Framework

The number of blockchain interoperability protocols for transferring data and assets between blockchains has grown significantly. However, no open dataset of cross-chain transactions exists to study interoperability protocols in operation. There is also no tool to generate such datasets and make them available to the community. This paper proposes XChainDataGen, a tool to extract cross-chain data from blockchains and generate datasets of cross-chain transactions (cctxs). Using XChainDataGen, we extracted over 35 GB of data from five cross-chain protocols deployed on 11 blockchains in the last seven months of 2024, identifying 11,285,753 cctxs that moved over 28 billion USD in cross-chain token transfers. Using the data collected, we compare protocols and provide insights into their security, cost, and performance trade-offs. As examples, we highlight differences between protocols that require full finality on the source blockchain and those that only demand soft finality (\textit{security}). We compare user costs, fee models, and the impact of variables such as the Ethereum gas price on protocol fees (\textit{cost}). Finally, we produce the first analysis of the implications of EIP-7683 for cross-chain intents, which are increasingly popular and greatly improve the speed with which cctxs are processed (\textit{performance}), thereby enhancing the user experience. The availability of XChainDataGen and this dataset allows various analyses, including trends in cross-chain activity, security assessments of interoperability protocols, and financial research on decentralized finance (DeFi) protocols.

cs.CR↗

arXiv2024

Records from the S-Matrix Marathon: Gravitational Physics from Scattering Amplitudes

These lecture notes explain how classical gravitational physics emerges from scattering amplitudes. We emphasize the role of different kinematic regimes in probing various aspects of bound and unbound problems, as illustrated by the Hydrogen atom example. Classical predictions of General Relativity, such as the Shapiro time delay and perihelion precession, emerge from these considerations. We also explain a number of recent approaches to probing black hole physics from the perspective of amplitudes, including applications of worldline effective field theory in astrophysics, predictions of gravitational waveforms, and the hierarchical three-body problem. These notes are based on a series of lectures held during the S-Matrix Marathon workshop at the Institute for Advanced Study on 11--22 March 2024.

hep-th↗

arXiv2024

Peregrine: ML-based Malicious Traffic Detection for Terabit Networks

Malicious traffic detectors leveraging machine learning (ML), namely those incorporating deep learning techniques, exhibit impressive detection capabilities across multiple attacks. However, their effectiveness becomes compromised when deployed in networks handling Terabit-speed traffic. In practice, these systems require substantial traffic sampling to reconcile the high data plane packet rates with the comparatively slower processing speeds of ML detection. As sampling significantly reduces traffic observability, it fundamentally undermines their detection capability. We present Peregrine, an ML-based malicious traffic detector for Terabit networks. The key idea is to run the detection process partially in the network data plane. Specifically, we offload the detector's ML feature computation to a commodity switch. The Peregrine switch processes a diversity of features per-packet, at Tbps line rates - three orders of magnitude higher than the fastest detector - to feed the ML-based component in the control plane. Our offloading approach presents a distinct advantage. While, in practice, current systems sample raw traffic, in Peregrine sampling occurs after feature computation. This essential trait enables computing features over all traffic, significantly enhancing detection performance. The Peregrine detector is not only effective for Terabit networks, but it is also energy- and cost-efficient. Further, by shifting a compute-heavy component to the switch, it saves precious CPU cycles and improves detection throughput.

cs.NI↗

arXiv2024

Blockchain-based Rental Documentation Management with Audit Support

Document management in the rental market is a critical process to ensure the accuracy of financial transactions and regulatory compliance in the sector. In Portugal, the challenges include the complexity of legislation, particularly GDPR non-compliance, lack of transparency, and bureaucratic process inefficiency. With this in mind, a solution based on Hyperledger Fabric, a blockchain platform, is presented for the implementation of a document management system for the rental process. This system oversees the rental process, which consists of three phases: the application for a property by the prospective tenant through the upload of necessary documents, acceptance/rejection by the landlord of various received applications, and the creation of a report by the system, which only the auditor can request and view. The system smart contract records metadata associated with the documents (hash, owner) and coordinates requests for file access by landlords to prospective tenants. Thus, the system is responsible for creating immutable and traceable records of the entire process. The underlying platform serves as the foundation for conducting future audits. After the landlord verifies the files and accepts the rental proposal, any authorised auditor can request a report for a property by accessing the records through the final report, which includes all events that occurred during the process.

cs.CR↗

arXiv2024

XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision

Skin cancer detection through dermoscopy image analysis is a critical task. However, existing models used for this purpose often lack interpretability and reliability, raising the concern of physicians due to their black-box nature. In this paper, we propose a novel approach for the diagnosis of melanoma using an interpretable prototypical-part model. We introduce a guided supervision based on non-expert feedback through the incorporation of: 1) binary masks, obtained automatically using a segmentation network; and 2) user-refined prototypes. These two distinct information pathways aim to ensure that the learned prototypes correspond to relevant areas within the skin lesion, excluding confounding factors beyond its boundaries. Experimental results demonstrate that, even without expert supervision, our approach achieves superior performance and generalization compared to non-interpretable models.

cs.CV↗

arXiv2023

Vivisecting the Dissection: On the Role of Trusted Components in BFT Protocols

A recent paper by Gupta et al. (EuroSys'23) challenged the usefulness of trusted component (TC) based Byzantine fault-tolerant (BFT) protocols to lower the replica group size from $3f+1$ to $2f+1$, identifying three limitations of such protocols and proposing that TCs should be used instead to improve the performance of BFT protocols. Here, we point out flaws in both arguments and advocate that the most worthwhile use of TCs in BFT protocols is indeed to make them as resilient as crash fault-tolerant (CFT) protocols, which can tolerate up to $f$ faulty replicas using $2f+1$ replicas.

cs.DC↗

arXiv2023

The O(N) Monolith reloaded: Sum rules and Form Factor Bootstrap

We revisit the space of gapped quantum field theories with a global O(N) symmetry in two spacetime dimensions. Previous works using S-matrix bootstrap revealed a rich space in which integrable theories such as the non-linear sigma model appear at special points on the boundary, along with an abundance of unknown models hinting at a non conventional UV behaviour. We extend the S-matrix set-up by including into the bootstrap form factors and spectral functions for the stress-energy tensor and conserved O(N) currents. Sum rules allow us to put bounds on the central charges of the conformal field theory (CFT) in the UV. We find that a big portion of the boundary can only flow from CFTs with infinite central charges. We track this result down to a particular behaviour of the amplitudes in physical kinematics and discuss its physical implications.

hep-th↗

arXiv2023

Using Range-Revocable Pseudonyms to Provide Backward Unlinkability in the Edge (Extended Version)

In this paper we propose a novel abstraction that we have named Range-Revocable Pseudonyms (RRPs). RRPs are a new class of pseudonyms whose validity can be revoked for any time-range within its original validity period. The key feature of RRPs is that the information provided to revoke a pseudonym for a given timerange cannot be linked with the information provided when using the pseudonym outside the revoked range. We provide an algorithm to implement RRPs using efficient cryptographic primitives where the space complexity of the pseudonym is constant, regardless of the granularity of the revocation range, and the space complexity of the revocation information only grows logarithmically with the granularity; this makes the use of RRPs far more efficient than the use of many short-lived pseudonyms. We have used RRPs to design EDGAR, an access control system for VANET scenarios that offers backward unlinkability. The experimental evaluation of EDGAR shows that, when using RRPs, the revocation can be performed efficiently (even when using time slots as small as 1 second) and that users can authenticate with low latency ($0.5-3.5$ ms).

cs.CR↗

arXiv2022

Injecting the UV into the Bootstrap: Ising Field Theory

We merge together recent developments in the S-matrix bootstrap program to develop a dual setup in 2 space-time dimensions incorporating scattering amplitudes of massive particles and matrix elements of local operators. In particular, the stress energy tensor allows us to input UV constraints on IR observables in terms of the central charge $c_{UV}$ of the UV Conformal Field Theory. We consider two applications: (1) We establish a rigorous lower bound on $c_{UV}$ of a class of $\mathbb{Z}_2$ symmetric scalar theories in the IR (including $ϕ^4$); (2) We target Ising Field Theory by, first, minimizing $c_{UV}$ for different values of the magnetic field and, secondly, by determining the allowed range of cubic coupling and one-particle form-factor for fixed $c_{UV} = 1/2$ and magnetic field.

hep-th↗

arXiv2022

Learning to generate Reliable Broadcast Algorithms

Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.

cs.DC↗

arXiv2022

A covariant formulation of relativistic mechanics

Accretion disks surrounding compact objects, and other environmental factors, deviate satellites from geodetic motion. Unfortunately, setting up the equations of motion for such relativistic trajectories is not as simple as in Newtonian mechanics. The principle of general (or Lorentz) covariance and the mass-shell constraint make it difficult to parametrize physically adequate 4-forces. Here, we propose a solution to this old problem. We apply our framework to several conservative and dissipative forces. In particular, we propose covariant formulations for Hooke's law and the constant force, and compute the drag due to gravitational and hard-sphere collisions in dust, gas and radiation media. We recover and covariantly extend known forces such as Epstein drag, Chandrasekhar's dynamical friction and Poynting-Robertson drag. Variable-mass effects are also considered, namely Hoyle-Lyttleton accretion and the variable-mass rocket. We conclude with two applications: 1. The free-falling spring. We find that Hooke's law corrects the deviation equation by an effective Anti-de Sitter tidal force; 2. Black hole infall with drag. We numerically compute some trajectories on a Schwarzschild background supporting a dust-like accretion disk.

gr-qc↗

arXiv2021

Probing multi-particle unitarity with the Landau equations

We consider the $2\to 2$ scattering amplitude of identical massive particles. We identify the Landau curves in the multi-particle region $16m^2 \leq s, t < 36m^2$. We systematically generate and select the relevant graphs and numerically solve the associated Landau equations for the leading singularity. We find an infinite sequence of Landau curves that accumulates at finite $s$ and $t$ on the physical sheet. We expect that such accumulations are generic for $s,t > 16m^2$. Our analysis sheds new light on the complicated analytic structure of nonperturbative relativistic scattering amplitudes.

hep-th↗

arXiv2021

A Survey on Blockchain Interoperability: Past, Present, and Future Trends

Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to seamlessly achieve interoperability among blockchains. Given the novelty and potential of this new domain, we conduct a literature review on blockchain interoperability, by collecting 262 papers, and 70 grey literature documents, constituting a corpus of 332 documents. From those 332 documents, we systematically analyzed and discussed 80 documents, including both peer-reviewed papers and grey literature. Our review classifies studies in three categories: Cryptocurrency-directed interoperability approaches, Blockchain Engines, and Blockchain Connectors. Each category is further divided into sub-categories based on defined criteria. We discuss not only studies within each category and subcategory but also across categories, providing a holistic overview of blockchain interoperability, paving the way for systematic research in this domain. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies. The present survey leverages an interesting approach: we systematically contacted the authors of grey literature papers and industry solutions to obtain an updated view of their work. Finally, this paper discusses supporting technologies, standards, use cases, open challenges, and provides several future research directions.

cs.DC↗

arXiv2020

A Survey on Business Process View Integration

The complexity of a business environment often causes organizations to produce several inconsistent views on the same business process, leading to fragmentation and inefficiencies. Business process view integration attempts to produce an integrated view from different views of the same model, facilitating the management of models. To study trends around business process view integration, we conduct a systematic literature review to summarize findings since the 1970s, up to its potential novel applications. With a starting corpus of 798 documents, this survey draws up a systematic inventory of solutions used in academia and in the industry. By narrowing it down to 51 articles, we discuss in-depth 15 business process integration techniques papers. After that, we classify existing solutions according to their practicality. Our study shows that most integrated views are constructed by annotation, using formal merging rules. Finally, we explore possible future research directions. We highlight the application of view integration to the blockchain research area, where stakeholders can have different views on the same blockchain. We expect that this study contributes to interdisciplinary research across view integration.

cs.SE↗

arXiv2020

An Analytical Toolkit for the S-matrix Bootstrap

We revisit analytical methods for constraining the nonperturbative $S$-matrix of unitary, relativistic, gapped theories in $d \geq 3$ spacetime dimensions. We assume extended analyticity of the two-to-two scattering amplitude and use it together with elastic unitarity to develop two natural expansions of the amplitude. One is the threshold (non-relativistic) expansion and the other is the large spin expansion. The two are related by the Froissart-Gribov inversion formula. When combined with crossing and a local bound on the discontinuity of the amplitude, this allows us to constrain scattering at finite energy and spin in terms of the low-energy parameters measured in the experiment. Finally, we discuss the modern numerical approach to the $S$-matrix bootstrap and how it can be improved based on the results of our analysis.

hep-th↗

arXiv2019

Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages

Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high impact vulnerabilities. Static analysis tools are important to search for bugs automatically in the program source code, supporting developers on their removal. However, building these tools requires programming the knowledge on how to discover the vulnerabilities. This paper presents an alternative approach in which tools learn to detect flaws automatically by resorting to artificial intelligence concepts, more concretely to natural language processing. The approach employs a sequence model to learn to characterize vulnerabilities based on an annotated corpus. Afterwards, the model is utilized to discover and identify vulnerabilities in the source code. It was implemented in the DEKANT tool and evaluated experimentally with a large set of PHP applications and WordPress plugins. Overall, we found several hundred vulnerabilities belonging to 12 classes of input validation vulnerabilities, where 62 of them were zero-day.

cs.CR↗

arXiv2019

Pando: Personal Volunteer Computing in Browsers

The large penetration and continued growth in ownership of personal electronic devices represents a freely available and largely untapped source of computing power. To leverage those, we present Pando, a new volunteer computing tool based on a declarative concurrent programming model and implemented using JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying number of failure-prone personal devices contributed by volunteers to parallelize the application of a function on a stream of values, by using the devices' browsers. We show that Pando can provide throughput improvements compared to a single personal device, on a variety of compute-bound applications including animation rendering and image processing. We also show the flexibility of our approach by deploying Pando on personal devices connected over a local network, on Grid5000, a French-wide computing grid in a virtual private network, and seven PlanetLab nodes distributed in a wide area network over Europe.

cs.DC↗

arXiv2019

Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC

WebRTC enables browsers to exchange data directly but the number of possible concurrent connections to a single source is limited. We overcome the limitation by organizing participants in a fat-tree overlay: when the maximum number of connections of a tree node is reached, the new participants connect to the node's children. Our design quickly scales when a large number of participants join in a short amount of time, by relying on a novel scheme that only requires local information to route connection messages: the destination is derived from the hash value of the combined identifiers of the message's source and of the node that is holding the message. The scheme provides deterministic routing of a sequence of connection messages from a single source and probabilistic balancing of newer connections among the leaves. We show that this design puts at least 83% of nodes at the same depth as a deterministic algorithm, can connect a thousand browser windows in 21-55 seconds in a local network, and can be deployed for volunteer computing to tap into 320 cores in less than 30 seconds on a local network to increase the total throughput on the Collatz application by two orders of magnitude compared to a single core.

cs.DC↗