3,016 research outputs found
Two-scale homogenization of a stationary mean-field game
In this paper, we characterize the asymptotic behavior of a first-order
stationary mean-field game (MFG) with a logarithm coupling, a quadratic
Hamiltonian, and a periodically oscillating potential. This study falls into
the realm of the homogenization theory, and our main tool is the two-scale
convergence. Using this convergence, we rigorously derive the two-scale
homogenized and the homogenized MFG problems, which encode the so-called
macroscopic or effective behavior of the original oscillating MFG. Moreover, we
prove existence and uniqueness of the solution to these limit problems.Comment: 36 page
Activation thresholds in epidemic spreading with motile infectious agents on scale-free networks
We investigate a fermionic susceptible-infected-susceptible model with
mobility of infected individuals on uncorrelated scale-free networks with
power-law degree distributions of exponents
. Two diffusive processes with diffusion rate of an infected
vertex are considered. In the \textit{standard diffusion}, one of the
nearest-neighbors is chosen with equal chance while in the \textit{biased
diffusion} this choice happens with probability proportional to the neighbor's
degree. A non-monotonic dependence of the epidemic threshold on with an
optimum diffusion rate , for which the epidemic spreading is more
efficient, is found for standard diffusion while monotonic decays are observed
in the biased case. The epidemic thresholds go to zero as the network size is
increased and the form that this happens depends on the diffusion rule and
degree exponent. We analytically investigated the dynamics using quenched and
heterogeneous mean-field theories. The former presents, in general, a better
performance for standard and the latter for biased diffusion models, indicating
different activation mechanisms of the epidemic phases that are rationalized in
terms of hubs or max -core subgraphs.Comment: 9 pages, 4 figure
Disruption Prediction in Fusion Devices through Feature Extraction and Logistic Regression
This document describes an approach used in the Multi-Machine Disruption
Prediction Challenge for Fusion Energy by ITU, a data science competition which
ran from September to November 2023, on the online platform Zindi. The
competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT
- with most of the training data coming from the last two, and the test data
coming from the first one. Each device has multiple diagnostics and signals,
and it turns out that a critical issue in this competition was to identify
which signals, and especially which features from those signals, were most
relevant to achieve accurate predictions. The approach described here is based
on extracting features from signals, and then applying logistic regression on
top of those features. Each signal is treated as a separate predictor and, in
the end, a combination of such predictors achieved the first place on the
leaderboard
Framework for IoT Service Oriented Systems
The forth industrial revolution is here, and with it Industry 4.0, which translates in many changes to the industry. With the introduction of paradigms like Internet of Things, Cyber Physical Systems or Cloud Computing, the so called Smart Factories are becoming a main part of today’s manufacturing systems. The vf-OS Project, where this thesis falls, intends to be an Open Operating System for Virtual Factories where the overall network of a collaborative manufacturing and logistics environment can be managed and thus enabling humans, applications and devices to communicate and interoperate in an interconnected operative environment.
This thesis intends to contribute to the vision that any kind of sensor or actuator plugged to the virtual factory network, becomes promptly accessible in the operative environment and the services that it provides can be accessed and used by any API composing the system. Finally, it also aims to prove that an IoT Service Oriented Sys-tem constituted of open software components can be of great assistance and provide numerous contributions to the emerging Industry 4.0 and consequently to the Factories of the Future.
With that aim, this thesis will focus on the development of two out of five inter-connected applications that answer not only to different use case scenarios presented in the vf-OS but also provide solutions to answer a practical agriculture scenario, which uses mainly IoT devices and other cutting-edge technologies like cloud compu-ting and FIWARE
Automatic binary patching for flaws repairing using static rewriting and reverse dataflow analysis
Tese de Mestrado, Segurança Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasThe C programming language is widely used in embedded systems, kernel and hardware programming, making it one of the most commonly used programming languages. However, C lacks of boundary verification of variables, making it one of the most vulnerable languages. Because of this and associated with its high usability, it is also the language with most reported vulnerabilities in the past ten years, being the memory corruption the most common type of vulnerabilities, specifically buffer overflows. These vulnerabilities when exploited can produce critical consequences, being thus extremely important not only to correctly identify these vulnerabilities but also to properly fix them. This work aims to study buffer overflow vulnerabilities in C binary programs by identifying possible malicious inputs that can trigger such vulnerabilities and finding their root
cause in order to mitigate the vulnerabilities by rewriting the binary assembly code and thus generating a new binary without the original flaw. The main focus of this thesis is the use of binary patching to automatically fix stack overflow vulnerabilities and validate its effectiveness while ensuring that these do not add new
vulnerabilities. Working with the binary code of applications and without accessing their source code is a challenge because any required change to its binary code (i.e, assembly) needs to take into consideration that new instructions must be allocated, and this typically means that existing instructions will need to be moved to create room for new ones and recover the control flow information, otherwise the application would be compromised. The approach we propose to address this problem was successfully implemented in a tool
and evaluated with a set of test cases and real applications. The evaluation results showed that the tool was effective in finding vulnerabilities, as well as in patching them
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