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This research focused on utilizing ROS2 and Gazebo for simulating the TurtleBot3 robot, with the aim of exploring autonomous navigation capabilities. While the study did not achieve full autonomous navigation, it successfully established the connection between ROS2 and Gazebo and enabled manual simulation of the robot's movements. The primary objective was to understand how these tools can be integrated to support autonomous functions, providing valuable insights into the development process. The results of this work lay the groundwork for future research into autonomous robotics. The topic is particularly engaging for both teenagers and adults interested in discovering how robots function independently and the underlying technology involved. This research highlights the potential for further advancements in autonomous systems and serves as a stepping stone for more in-depth studies in the field., Comment: in Spanish language

This research examines the impact of robotics groups in higher education, focusing on how these activities influence the development of transversal skills and academic motivation. While robotics goes beyond just technical knowledge, participation in these groups has been observed to significantly improve skills such as teamwork, creativity, and problem-solving. The study, conducted with the UruBots group, shows that students involved in robotics not only reinforce their theoretical knowledge but also increase their interest in research and academic commitment. These results highlight the potential of educational robotics to transform the learning experience by promoting active and collaborative learning. This work lays the groundwork for future research on how robotics can continue to enhance higher education and motivate students in their academic and professional careers, Comment: in Spanish language

This article presents initial configuration work and use of the robotic platform and manipulator in question. The development of the ideal configuration for using this robot serves as a guide for new users and also validates its functionality for use in projects. Husky is a large payload capacity and power systems robotics development platform that accommodates a wide variety of payloads, customized to meet research needs. Together with the Cobot UR5 Manipulator attached to its base, it expands the application area of its capacity in projects. Advances in robots and mobile manipulators have revolutionized industries by automating tasks that previously required human intervention. These innovations alone increase productivity but also reduce operating costs, which makes the company more competitive in an evolving global market. Therefore, this article investigates the functionalities of this robot to validate its execution in robotics projects., Comment: in Portuguese language

This study investigates the interaction between humanoid robots NAO and Pepper, emphasizing their potential applications in educational settings. NAO, widely used in education, and Pepper, designed for social interactions, of er new opportunities for autonomous communication and collaboration. Through a series of programmed interactions, the robots demonstrated their ability to communicate and coordinate actions autonomously, highlighting their potential as tools for enhancing learning environments. The research also explores the integration of emerging technologies, such as artificial intelligence, into these systems, allowing robots to learn from each other and adapt their behavior. The findings suggest that NAO and Pepper can significantly contribute to both technical learning and the development of social and emotional skills in students, of ering innovative pedagogical approaches through the use of humanoid robotics., Comment: in Portuguese language

Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances., Comment: Tentative submission for data mining and knowledge discovery

In the present work, we consider a fractional p-Kirchhoff equation in the entire space R^N featuring doubly nonlinearities, involving a generalized nonlocal Choquard subcritical term together with a local critical Sobolev term; the problem also includes a Hardy-type term; additionaly, all terms have critical singular weights. Our result improve upon previous work in the following ways: we focus our attention on the existence of a nontrivial weak solution for fractional p-Kirchhoff equation in the entire space R^N. The possibility of a slower growth in the nonlinearity makes it more difficult to establish a compactness condition; to do so, we use the Cerami condition. The crucial points in our argument are the uniform boundedness of the convolution part and the lack of compactness of the Sobolev embeddings., Comment: 25 pages

We analyze the behavior of light as it passes through systems composed of polarizing filters at different angular orientations. The analysis is initially conducted in the context of classical optics, using Malus's Law to calculate the transmitted intensity for the cases of two perpendicular filters (0 and 90 degrees) and three filters (0, 45, and 90 degrees). We then apply quantum mechanics to describe the phenomenon using state notation and the probabilistic interpretation of the projection of the polarization states of photons., Comment: 3 pages

Partial least squares (PLS) is a simple factorisation method that works well with high dimensional problems in which the number of observations is limited given the number of independent variables. In this article, we show that PLS can perform better than ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO) and ridge regression in forecasting quarterly gross domestic product (GDP) growth, covering the period from 2000 to 2023. In fact, through dimension reduction, PLS proved to be effective in lowering the out-of-sample forecasting error, specially since 2020. For the period 2000-2019, the four methods produce similar results, suggesting that PLS is a valid regularisation technique like LASSO or ridge.

The adoption of microservice architecture has seen a considerable upswing in recent years, mainly driven by the need to modernize legacy systems and address their limitations. Legacy systems, typically designed as monolithic applications, often struggle with maintenance, scalability, and deployment inefficiencies. This study investigates the motivations, activities, and challenges associated with migrating from monolithic legacy systems to microservices, aiming to shed light on common practices and challenges from a practitioner's point of view. We conducted a comprehensive study with 53 software practitioners who use microservices, expanding upon previous research by incorporating diverse international perspectives. Our mixed-methods approach includes quantitative and qualitative analyses, focusing on four main aspects: (i) the driving forces behind migration, (ii) the activities to conduct the migration, (iii) strategies for managing data consistency, and (iv) the prevalent challenges. Thus, our results reveal diverse practices and challenges practitioners face when migrating to microservices. Companies are interested in technical benefits, enhancing maintenance, scalability, and deployment processes. Testing in microservice environments remains complex, and extensive monitoring is crucial to managing the dynamic nature of microservices. Database management remains challenging. While most participants prefer decentralized databases for autonomy and scalability, challenges persist in ensuring data consistency. Additionally, many companies leverage modern cloud technologies to mitigate network overhead, showcasing the importance of cloud infrastructure in facilitating efficient microservice communication.

Microservices emerged as a popular architectural style over the last decade. Although microservices are designed to be self-contained, they must communicate to realize business capabilities, creating dependencies among their data and functionalities. Developers then resort to asynchronous, event-based communication to fulfill such dependencies while reducing coupling. However, developers are often oblivious to the inherent challenges of the asynchronous and event-based paradigm, leading to frustrations and ultimately making them reconsider the adoption of microservices. To make matters worse, there is a scarcity of literature on the practices and challenges of designing, implementing, testing, monitoring, and troubleshooting event-based microservices. To fill this gap, this paper provides the first comprehensive characterization of event management practices and challenges in microservices based on a repository mining study of over 8000 Stack Overflow questions. Moreover, 628 relevant questions were randomly sampled for an in-depth manual investigation of challenges. We find that developers encounter many problems, including large event payloads, modeling event schemas, auditing event flows, and ordering constraints in processing events. This suggests that developers are not sufficiently served by state-of-the-practice technologies. We provide actionable implications to developers, technology providers, and researchers to advance event management in microservices.

Model-Based Engineering (MBE) has streamlined software development by focusing on abstraction and automation. The adoption of MBE in Maintenance and Evolution (MBM&E), however, is still limited due to poor tool support and a lack of perceived benefits. We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of MBM&E. In this sense, we argue that GenAI, driven by Foundation Models, offers promising potential for enhancing MBM&E tasks. With this possibility in mind, we introduce a research vision that contains a classification scheme for GenAI approaches in MBM&E considering two main aspects: (i) the level of augmentation provided by GenAI and (ii) the experience of the engineers involved. We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems. Furthermore, we outline challenges in this field as a research agenda to drive scientific and practical future solutions. With this proposed vision, we aim to bridge the gap between GenAI and MBM&E, presenting a structured and sophisticated way for advancing MBM&E practices.

Software modernization is an inherent activity of software engineering, as technology advances and systems inevitably become outdated. The term "software modernization" emerged as a research topic in the early 2000s, with a differentiation from traditional software evolution. Studies on this topic became popular due to new programming paradigms, technologies, and architectural styles. Given the pervasive nature of software today, modernizing legacy systems is paramount to provide users with competitive and innovative products and services. Despite the large amount of work available in the literature, there are significant limitations: (i) proposed approaches are strictly specific to one scenario or technology, lacking flexibility; (ii) most of the proposed approaches are not aligned with the current modern software development scenario; and (iii) due to a myriad of proposed modernization approaches, practitioners may be misguided on how to modernize legacies. In this work, our goal is to call attention to the need for advances in research and practices toward a well-defined software modernization domain. The focus is on enabling organizations to preserve the knowledge represented in legacy systems while taking advantages of disruptive and emerging technologies. Based on this goal, we put the different perspectives of software modernization in the context of contemporary software development. We also present a research agenda with 10 challenges to motivate new studies.

Testing Highly Configurable Systems (HCSs) is a challenging task, especially in an evolution scenario where features are added, changed, or removed, which hampers test case selection and prioritization. Existing work is usually based on the variability model, which is not always available or updated. Yet, the few existing approaches rely on links between test cases and changed files (or lines of code), not considering how features are implemented, usually spread over several and unchanged files. To overcome these limitations, we introduce FeaTestSelPrio, a feature-oriented test case selection and prioritization approach for HCSs. The approach links test cases to feature implementations, using HCS pre-processor directives, to select test cases based on features affected by changes in each commit. After, the test cases are prioritized according to the number of features they cover. Our approach selects a greater number of tests and takes longer to execute than a changed-file-oriented approach, used as baseline, but FeaTestSelPrio performs better regarding detected failures. By adding the approach execution time to the execution time of the selected test cases, we reached a reduction of $\approx$50%, in comparison with retest-all. The prioritization step allows reducing the average test budget in 86% of the failed commits.

Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets. In this paper, we propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (NOAH) framework. NOAH is the first general framework for marketing optimization that considers both to-business (2B) and to-consumer (2C) products, as well as both owned and paid channels. We describe key modules of the NOAH framework, including prediction, optimization, and adaptive heuristics, providing examples for bidding and content optimization. We then detail the successful application of NOAH to LinkedIn's email marketing system, showcasing significant wins over the legacy ranking system. Additionally, we share details and insights that are broadly useful, particularly on: (i) addressing delayed feedback with lifetime value, (ii) performing large-scale linear programming with randomization, (iii) improving retrieval with audience expansion, (iv) reducing signal dilution in targeting tests, and (v) handling zero-inflated heavy-tail metrics in statistical testing., Comment: KDD 2024

We study how radiation reaction leads plasmas initially in kinetic equilibrium to develop features in momentum space, such as anisotropies and population inversion, resulting in a ring-shaped momentum distribution that can drive kinetic instabilities. We employ the Landau-Lifshiftz radiation reaction model for a plasma in a strong magnetic field, and we obtain the necessary condition for the development of population inversion, we show that isotropic Maxwellian and Maxwell-J\"uttner plasmas, with thermal temperature $T>m_e c^2/\sqrt{3}$, will develop a ring-like momentum distribution. The timescales and features for forming ring-shaped momentum distributions, the effect of collisions and non-uniform magnetic fields are disscussed, and compared with typical astrophysical and laboratory plasmas parameters. Our results show the pervasiveness of ring-like momentum distribution functions in synchrotron dominated plasma conditions., Comment: 10 pages, 3 Figures, Under consideration for PoP

We investigate disordered-driven transitions between trivial and topological insulator (TI) phases in two-dimensional (2D) systems. Our study primarily focuses on the BHZ model with Anderson disorder, while other standard 2DTI models exhibit equivalent features. The analysis is based on the local Chern marker (LCM), a local quantity that allows for the characterization of topological transitions in finite and disordered systems. Our simulations indicate that disorder-driven trivial to topological insulator transitions are nicely characterized by $\mathcal{C}_0$, the disorder averaged LCM near the central cell of the system. We show that $\mathcal{C}_0$ is characterized by a single-parameter scaling, namely, $\mathcal{C}_0(M, W, L) \equiv \mathcal{C}_0(z)$ with $z = [W^\mu-W_c^\mu(M)]L$, where $M$ is the Dirac mass, $W$ is the disorder strength and $L$ is the system size, while $W_c(M) \propto \sqrt{M}$ and $\mu \approx 2$ stand for the critical disorder strength and critical exponent, respectively. Our numerical results are in agreement with a theoretical prediction based on a first-order Born approximation (1BA) analysis. These observations lead us to speculate that the universal scaling function we have found is rather general for amorphous and disorder-driven topological phase transitions., Comment: 7 pages, 4 figures, and a supplemental material with 7 pages, 4 figures

Traceability allows stakeholders to extract and comprehend the trace links among software artifacts introduced across the software life cycle, to provide significant support for software engineering tasks. Despite its proven benefits, software traceability is challenging to recover and maintain manually. Hence, plenty of approaches for automated traceability have been proposed. Most rely on textual similarities among software artifacts, such as those based on Information Retrieval (IR). However, artifacts in different abstraction levels usually have different textual descriptions, which can greatly hinder the performance of IR-based approaches (e.g., a requirement in natural language may have a small textual similarity to a Java class). In this work, we leverage the consensual biterms and transitive relationships (i.e., inner- and outer-transitive links) based on intermediate artifacts to improve IR-based traceability recovery. We first extract and filter biterms from all source, intermediate, and target artifacts. We then use the consensual biterms from the intermediate artifacts to extend the biterms of both source and target artifacts, and finally deduce outer and inner-transitive links to adjust text similarities between source and target artifacts. We conducted a comprehensive empirical evaluation based on five systems widely used in other literature to show that our approach can outperform four state-of-the-art approaches, and how its performance is affected by different conditions of source, intermediate, and target artifacts. The results indicate that our approach can outperform baseline approaches in AP over 15% and MAP over 10% on average., Comment: Accepted by the 46th International Conference on Software Engineering (ICSE 2024)

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network's edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a DRL approach called TPTO, which leverages Transformer Networks and PPO to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach.

In this paper we consider a fractional $p$-Laplacian equation in the entire space $\mathbb{R}^{N}$ with doubly critical singular nonlinearities involving a local critical Sobolev term together with a nonlocal Choquard critical term; the problem also includes a homogeneous singular Hardy term. More precisely, we deal with the problem \begin{align*} \begin{cases} (-\Delta)^{s}_{p,\theta} u -\gamma \dfrac{|u|^{p-2}u}{|x|^{sp+ \theta}} = \dfrac{|u|^{p^*_s(\beta,\theta)-2}u}% {|x|^{\beta}} + \left[ I_{\mu} \ast F_{\delta,\theta,\mu}(\cdot, u) \right](x)f_{\delta,\theta,\mu}(x,u) u \in \dot{W}^{s,p}_{\theta}(\mathbb{R}^N) \end{cases} \end{align*} where $0 < s < 1$; $0 < \alpha, \,\beta < sp + \theta < N$; $0 < \mu < N$; $2\delta + \mu < N$; $\gamma < \gamma_{H}$ with the best fractional Hardy constant $\gamma_{H}$; the Hardy-Sobolev and Stein-Weiss upper critical fractional exponents are respectively defined by $p^*_s(\beta,\theta) := p(N-\beta)/(N-sp-\theta)$, and $p^\sharp_s(\delta,\theta,\mu) := p(N-\delta-\mu/2)/(N-sp-\theta)$. Moreover, $I_{\mu}(x) =|x|^{-\mu}$ is the Riesz potencial; $f_{\delta,\theta,\mu}(x,t) := |x|^{-\delta} |t|^{p^{\sharp}_{s}(\delta,\theta,\mu)-2}t$ and $F_{\delta,\theta,\mu}(x,t) := |x|^{\delta} |t|^{p^{\sharp}_{s}(\delta,\theta,\mu)}$; and the term with convolution integral is known as Choquard type nonlinearity. To prove the main result we have to show new embeddings involving the weighted Morrey spaces and a version of the Caffarelli-Kohn-Nirenberg inequality. With the help of these new embedding results, we provide sufficient conditions under which a weak nontrivial solution to the problem exists via variational methods., Comment: 40 pages, original research article

Copulas are powerful statistical tools for capturing dependencies across data dimensions. Applying Copulas involves estimating independent marginals, a straightforward task, followed by the much more challenging task of determining a single copulating function, $C$, that links these marginals. For bivariate data, a copula takes the form of a two-increasing function $C: (u,v)\in \mathbb{I}^2 \rightarrow \mathbb{I}$, where $\mathbb{I} = [0, 1]$. This paper proposes 2-Cats, a Neural Network (NN) model that learns two-dimensional Copulas without relying on specific Copula families (e.g., Archimedean). Furthermore, via both theoretical properties of the model and a Lagrangian training approach, we show that 2-Cats meets the desiderata of Copula properties. Moreover, inspired by the literature on Physics-Informed Neural Networks and Sobolev Training, we further extend our training strategy to learn not only the output of a Copula but also its derivatives. Our proposed method exhibits superior performance compared to the state-of-the-art across various datasets while respecting (provably for most and approximately for a single other) properties of C.

One of the developers' biggest challenges in low-code platforms is retrieving data from a database using SQL queries. Here, we propose a pipeline allowing developers to write natural language (NL) to retrieve data. In this study, we collect, label, and validate data covering the SQL queries most often performed by OutSystems users. We use that data to train a NL model that generates SQL. Alongside this, we describe the entire pipeline, which comprises a feedback loop that allows us to quickly collect production data and use it to retrain our SQL generation model. Using crowd-sourcing, we collect 26k NL and SQL pairs and obtain an additional 1k pairs from production data. Finally, we develop a UI that allows developers to input a NL query in a prompt and receive a user-friendly representation of the resulting SQL query. We use A/B testing to compare four different models in production and observe a 240% improvement in terms of adoption of the feature, 220% in terms of engagement rate, and a 90% decrease in failure rate when compared against the first model that we put into production, showcasing the effectiveness of our pipeline in continuously improving our feature.

In model-driven engineering, the bidirectional transformation of models plays a crucial role in facilitating the use of editors that operate at different levels of abstraction. This is particularly important in the context of industrial-grade low-code platforms like OutSystems, which feature a comprehensive ecosystem of tools that complement the standard integrated development environment with domain-specific builders and abstract model viewers. We introduce CHAMELEON, a tool that enables the dynamic definition of a live bidirectional model transformation in a declarative manner by leveraging simple and intuitive component patterns. Through this approach, we can gradually define the view and synthesis paths to an abstract model built on top of a low-code metamodel. We devise a standard parser-generating technique for tree-like models that builds upon extended grammar definitions with constraints and name binders. We allow for a greater overlap of model patterns that can still be disambiguated for a clear lens-like behaviour of the transformation. CHAMELEON is evaluated in the fragment of the OutSystems language targeting the definition of user interfaces. To assess performance we used a large set of real OutSystems applications, with approximately 200K UI widgets, and a database of curated widget patterns. We found a worst-case processing time of 92ms for complete models in our benchmark, which is still suitable for the operation of an interactive model editor.

Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used in natural language processing (NLP) to generate new samples for the minority class. However, in this paper, we challenge the common assumption that data augmentation is always necessary to improve predictions on imbalanced datasets. Instead, we argue that adjusting the classifier cutoffs without data augmentation can produce similar results to oversampling techniques. Our study provides theoretical and empirical evidence to support this claim. Our findings contribute to a better understanding of the strengths and limitations of different approaches to dealing with imbalanced data, and help researchers and practitioners make informed decisions about which methods to use for a given task., Comment: 21 pages, 5 figures

This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functions. To tackle these problems, we propose a generalized version of the conditional gradient method, also known as Frank-Wolfe method. The method is analyzed with three step size strategies, including Armijo-type, adaptive, and diminishing step sizes. We establish asymptotic convergence properties and iteration-complexity bounds, with and without convexity assumptions on the objective functions. Numerical experiments illustrating the practical behavior of the methods are presented.

Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing. Artificial Intelligence lacks the exploratory capabilities of even the most underdeveloped organisms, hindering its autonomy and adaptability. Supported by cognitive psychology, this works links human behavior and artificial agents to endorse self-development. In accordance with reported data, paradigms of epistemic and achievement emotion are embedded to machine-learning methodology contingent on their impact when decision making. A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence. Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts. The ramifications of these findings are pondered for both research into human cognition and betterment of artificial intelligence., Comment: 21 pages, 5 figures, journal

The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field., Comment: Accepted to ApJS

In this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better than the univariate Theta method in terms of out-of-sample mean errors.