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Nowadays, 5G architecture is characterized by the use of monolithic hardware, where the configuration of its elements is completely proprietary for each manufacturer. In recent years, as an alternative to this centralized architecture, a new model has emerged: the Open Radio Access Network (Open RAN). One of its main features has been the split of the Base Band Unit (BBU) into new simpler hardware with more specific functions approaching to a more modular model. As a consequence of this split, new interfaces appeared to connect these components that need to be protected. With the developments in the field of quantum computing, traditional protection mechanisms for this kind of interfaces may be deprecated in the near future. This security issue motivates this paper, which aims to study how to integrate post-quantum cryptography (PQC) mechanisms to current security standards, such as IPsec and MACsec. In addition, the proposal is also put into practice to compare the performance of traditional mechanisms with PQC implementations. This research shows that the new implementation does not reduce the performance of the aforementioned standards, while the security is reinforced against quantum attacks.

Mutation testing may be used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy., Comment: Accept for publication in the Journal of System and Software - JSS. This work is partially supported by Brazilian Funding Agencies FAPESP (Grant n. 2019/23160-0 and 2023/00001-9), CAPES, and CNPq

In recent years, we have witnessed a marked development and growth in Artificial Intelligence. The growth of the data volume generated by sensors and machines, combined with the information flow resulting from the user actions on the Internet, with high investments of the governments and the companies in this area, provided the practice and developed the algorithms of the Artificial Intelligence However, the people, in general, started to feel a particular fear regarding the security and privacy of their data and the theme of the Artificial Intelligence Ethics began to be discussed more regularly. The investigation aim of this work is to understand the possibility of adopting Artificial Intelligence nowadays in our society, having, as a mandatory assumption, Ethics and respect towards data and people's privacy. With that purpose in mind, a model has been created, mainly supported by the theories that were used to create the model. The suggested model has been tested and validated through Structural equation modeling based on data taken back from the respondents' answers to the questionnaire online: 237 answers, mainly from the Investigation Technologies area. The results obtained enabled the validation of seven of the nine investigation hypotheses of the proposed model. It was impossible to confirm any association between the Social Influence construct and the variables of Behavioral Intention and the Use of Artificial Intelligence. The aim of this work was accomplished once the investigation theme was validated and proved that it is possible to adopt Artificial Intelligence in our society, using the Attitude Towards Ethical Behavioral construct as the mainstay of the model., Comment: 9 pages, V tables

Recently Qi S. Zhang provides examples of solutions to the Navier-Stokes equations which, under suitable hypothesis, blow up in finite time. He considers axially symmetric solutions in a cylinder $D\,$ under appropriate boundary conditions and under the effect of super critical external forces $f\,.$ The loss of boundedness for the velocity field, as $t\rightarrow T\,,$ is the basic case of blow up. However a more general situation is considered below, as explained in the preamble.\par In his main result Zhang exhibits, for each $q<\infty\,,$ a blow up solution with an external force $f\in L^q(0,T;L^1(D))\,.$ Following Zhang, we construct blow up solutions with forcing terms in $L^q(0,T;L^p(D))\,,$ for suitable pairs $(q,p)\,.$ In particular our results contain Zhang's result. A significant particular case is the existence of external forces $f \in L^1(0,T;L^p(D))\,,$ for every $p<2$, for which the velocity blows up in a finite time. The significant case $p=2$ remains open., Comment: 16 pages

In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server. A fully decentralized deployment of prototype-based learning, without a central agregartor of prototypes, is more robust upon network failures and reacts faster to changes in the statistical distribution of the data, suggesting potential advantages and quick adaptation in dynamic learning tasks, e.g., when the data sources are IoT devices or when data is non-iid. In this paper, we consider the problem of designing a communication-efficient decentralized learning system based on prototypes. We address the challenge of prototype redundancy by leveraging on a twofold data compression technique, i.e., sending only update messages if the prototypes are informationtheoretically useful (via the Jensen-Shannon distance), and using clustering on the prototypes to compress the update messages used in the gossip protocol. We also use parallel instead of sequential gossiping, and present an analysis of its age-of-information (AoI). Our experimental results show that, with these improvements, the communications load can be substantially reduced without decreasing the convergence rate of the learning algorithm., Comment: 15 pages, 2 tables, 7 figures, 6 algorithms

We study the problem of stock replenishment and transshipment in the retail industry. We develop a model that can accommodate different policies, including centralized redistribution (replenishment) and decentralized redistribution (lateral transshipments), allowing for direct comparisons between them. We present a numeric analysis in which the benchmark instances stem from the collaboration with a high-end clothing retail company. The underlying model, as usually in the field, is a large-scale mixed-integer linear programming problem. We develop a specific algorithmic procedure to solve this MILP problem and compare its performance with the direct solution via state-of-the-art solvers.

In computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervised problems are signals defined on meshes, and a natural question is the extension of general scalar output regression models to such complex outputs. Changes between input geometries in terms of both size and adjacency structure in particular make this transition non-trivial. In this work, we propose an innovative strategy for Gaussian process regression where inputs are large and sparse graphs with continuous node attributes and outputs are signals defined on the nodes of the associated inputs. The methodology relies on the combination of regularized optimal transport, dimension reduction techniques, and the use of Gaussian processes indexed by graphs. In addition to enabling signal prediction, the main point of our proposal is to come with confidence intervals on node values, which is crucial for uncertainty quantification and active learning. Numerical experiments highlight the efficiency of the method to solve real problems in fluid dynamics and solid mechanics.

We carry out a stability and convergence analysis for the fully discrete scheme obtained by combining a finite or virtual element spatial discretization with the upwind-discontinuous Galerkin time-stepping applied to the time-dependent advection-diffusion equation. A space-time streamline-upwind Petrov-Galerkin term is used to stabilize the method. More precisely, we show that the method is inf-sup stable with constant independent of the diffusion coefficient, which ensures the robustness of the method in the convection- and diffusion-dominated regimes. Moreover, we prove optimal convergence rates in both regimes for the error in the energy norm. An important feature of the presented analysis is the control in the full $L^2(0,T;L^2(\Omega))$ norm without the need of introducing an artificial reaction term in the model. We finally present some numerical experiments in $(3 + 1)$-dimensions that validate our theoretical results.

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

The impacts of climate change are intensifying existing vulnerabilities and disparities within urban communities around the globe, as extreme weather events, including floods and heatwaves, are becoming more frequent and severe, disproportionately affecting low-income and underrepresented groups. Tackling these increasing challenges requires novel approaches that integrate expertise across multiple domains, including computer science, engineering, climate science, and public health. Urban computing can play a pivotal role in these efforts by integrating data from multiple sources to support decision-making and provide actionable insights into weather patterns, infrastructure weaknesses, and population vulnerabilities. However, the capacity to leverage technological advancements varies significantly between the Global South and Global North. In this paper, we present two multiyear, multidisciplinary projects situated in Chicago, USA and Niter\'oi, Brazil, highlighting the opportunities and limitations of urban computing in these diverse contexts. Reflecting on our experiences, we then discuss the essential requirements, as well as existing gaps, for visual analytics tools that facilitate the understanding and mitigation of climate-related risks in urban environments., Comment: Accepted at the Viz4Climate + Sustainability: IEEE VIS 2024 Workshop on Visualization for Climate Action and Sustainability (https://svs.gsfc.nasa.gov/events/2024/Viz4ClimateAndSustainability/)

The electron spin polarization on half-metallic double-perovskites is usually conditioned to the ordered rock-salt arrangement of the transition-metal ions along the lattice. In this work, we investigate a polycrystalline sample of the Ca1.5La0.5MnRuO6 compound by employing x-ray powder diffraction, high resolution transmission electron microscopy, x-ray absorption and magnetic circular dichroism at the Mn-L2,3 and Ru-M2,3 edges, magnetometry, electrical transport and first principles calculations in order to show that this is a fully disordered material exhibiting near room temperature ferrimagnetism and half-metallic conductivity, with significant intergrain tunneling magnetoresistance. These unprecedented results are compared to those of archetypical ordered double-perovskites, and discussed in terms of the Mn and Ru valences and their orbital hybridization., Comment: 13 pages, 6 figures

Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most Byzantine-robust algorithms proposed in the literature are designed for centralized scenarios. In this paper, we present a novel Byzantine-robust aggregation algorithm to enhance the security of Decentralized Federated Learning environments, coined WFAgg. This proposal handles the adverse conditions and strength robustness of dynamic decentralized topologies at the same time by employing multiple filters to identify and mitigate Byzantine attacks. Experimental results demonstrate the effectiveness of the proposed algorithm in maintaining model accuracy and convergence in the presence of various Byzantine attack scenarios, outperforming state-of-the-art centralized Byzantine-robust aggregation schemes (such as Multi-Krum or Clustering). These algorithms are evaluated on an IID image classification problem in both centralized and decentralized scenarios., Comment: 18 pages, 7 figures, 1 table

In this work we design a novel $C^1$-conforming virtual element method of arbitrary order $k \geq 2$, to solve the biharmonic problem on a domain with curved boundary and internal curved interfaces in two dimensions. By introducing a suitable stabilizing form, we develop a rigorous interpolation, stability, and convergence analysis obtaining optimal error estimates in the energy norm. Finally, we validate the theoretical findings through numerical experiments., Comment: 24 pages, 6 figures

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke applications that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at https://urbantk.org/curio., Comment: Accepted at IEEE VIS 2024. Source code available at https://urbantk.org/curio

This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.

Radial basis functions (RBFs) play an important role in function interpolation, in particular in an arbitrary set of interpolation nodes. The accuracy of the interpolation depends on a parameter called the shape parameter. There are many approaches in literature on how to appropriately choose it as to increase the accuracy of interpolation while avoiding instability issues. However, finding the optimal shape parameter value in general remains a challenge. In this work, we present a novel approach to determine the shape parameter in RBFs. First, we construct an optimisation problem to obtain a shape parameter that leads to an interpolation matrix with bounded condition number, then, we introduce a data-driven method that controls the condition of the interpolation matrix to avoid numerically unstable interpolations, while keeping a very good accuracy. In addition, a fall-back procedure is proposed to enforce a strict upper bound on the condition number, as well as a learning strategy to improve the performance of the data-driven method by learning from previously run simulations. We present numerical test cases to assess the performance of the proposed methods in interpolation tasks and in a RBF based finite difference (RBF-FD) method, in one and two-space dimensions., Comment: 39 pages

We study the convergence analysis of a finite element method for the approximation of solutions to a seven-fields formulation of a magnetohydrodynamics model, which preserves the energy of the system, and the magnetic and cross helicities on the discrete level.

The effect of thermal surface cleaning on the Gilbert damping, $\alpha$, of yttrium iron garnet (YIG), before capping with a metallic layer, has been investigated. Our results show that $\alpha$ is strongly effected by relatively mild annealing conditions (T = 300 C) when performed in a vacuum. This increase needs to be taken into account when obtaining the spin mixing conductance from spin pumping measurements. We measure an increase in $\alpha$ by a factor of 8 when the YIG is vacuum annealed at 300 C. No such changes in $\alpha$ are observed when annealed at the same temperature in 0.1 mbar of oxygen., Comment: 5 page, 4 figures

We explain the relation between the $r=1$ logistic map $x_{i+1}=rx_i(1-x_i)$, $x_i\in\mathbb R$, $i=0,1,\ldots$, $r>0$ and $x_0\geq0$, and the RG flow in the multiscale analysis of zero fixed point, asymptotic free QFT models as e.g. the ultraviolet (1+1)-dimensional Gross-Neveu model and QCD, and the infrared $\phi^4_4$. We obtain the asymptotics of the mapping, showing an inverse power decay to the fixed point $x^*=0$ Gaussian fixed point, with logarithmic-like corrections. This asymptotics is independent of the initial condition $x_0\in(0,1)$ (so, there is no constraint for $x_0$ to be small, as usual in QFT models), and only depends on the lowest orders in a polynomial perturbation. In asymptotic free QFT, this means that knowing the RG $\beta$ function expansion up to higher orders in the coupling does not improve our knowledge of the flow asymptotics. A comparison with an ODE with continuous time is made by analyzing stability of the solution with higher order monomial perturbations. We also obtain the asymptotics for $0

Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. Although this flexibility makes MLS ideal for implementations in distributed environments, a number of issues need to be overcome. Particularly, the use of digital certificates for authentication in a group goes against the group members' privacy. In this work we provide an alternative method of authentication in which the solicitors, instead of revealing their identity, only need to prove possession of certain attributes, dynamically defined by the group, to become a member. Instead of digital certificates, we employ Attribute-Based Credentials accompanied with Selective Disclosure in order to reveal the minimum required amount of information and to prevent attackers from linking the activity of a user through multiple groups. We formally define a CGKA variant named Attribute-Authenticated Continuous Group Key Agreement (AA-CGKA) and provide security proofs for its properties of Requirement Integrity, Unforgeability and Unlinkability. We also provide guidelines for an integration of our construction in MLS., Comment: Submitted to IEEE Transactions on Dependable and Secure Computing This work has been submitted to the IEEE for possible publication

We propose and analyze two convection quasi-robust and pressure robust finite element methods for a fully nonlinear time-dependent magnetohydrodynamics problem. The schemes make use of suitable upwind and CIP stabilizations to handle the fluid and magnetic convective terms. The developed error estimates are uniform in both diffusion parameters and optimal with respect to the diffusive norm; furthermore, for the second (more complex) method we are able to show a quicker error reduction rate in convection dominated regimes. A set of numerical tests support our theoretical findings.

Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even so, FL has some privacy vulnerabilities that have been tried to be overcome by applying some techniques like Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). However, these techniques have some important drawbacks that might narrow their range of application: problems to work with non-linear functions and to operate large matrix multiplications and high communication and computational costs to manage semi-honest nodes. In this context, we propose a solution to guarantee privacy in FL schemes that simultaneously solves the previously mentioned problems. Our proposal is based on the Berrut Approximated Coded Computing, a technique from the Coded Distributed Computing paradigm, adapted to a Secret Sharing configuration, to provide input privacy to FL in a scalable way. It can be applied for computing non-linear functions and treats the special case of distributed matrix multiplication, a key primitive at the core of many automated learning tasks. Because of these characteristics, it could be applied in a wide range of FL scenarios, since it is independent of the machine learning models or aggregation algorithms used in the FL scheme. We provide analysis of the achieved privacy and complexity of our solution and, due to the extensive numerical results performed, a good trade-off between privacy and precision can be observed.