Showing 1–50 of 209 results for author: Santos, C

Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting

Authors: Felipe Nunes Carbone de Carvalho, Joyce de Morais Souza, Alan de Aguiar, Charles Morphy D. Santos, João Paulo Gois

Abstract: Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes. For complex organic subjects, such as insect specimens, extracting and rendering textured meshes is challenging. To address this issue, we propose a meshless DR framework that operates on the parameter space of 3D Ga… ▽ More Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes. For complex organic subjects, such as insect specimens, extracting and rendering textured meshes is challenging. To address this issue, we propose a meshless DR framework that operates on the parameter space of 3D Gaussian Splatting (3DGS). Our method employs two independent perturbation pipelines to synthesize randomized training datasets. First, a Photometric DR pipeline alters the baked illumination and color balance by modulating the Spherical Harmonics (SH) coefficients. Second, a Procedural DR pipeline isolates the subject's geometric shape by replacing its original textures with 3D spatial noise. Finally, these perturbed radiance fields are composited over stochastically varied backgrounds using a rasterization engine. Our parameter manipulation provides a meshless alternative for generating robust datasets for complex geometries. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 10 pages, 4 figures

Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

Authors: Arthur Dantas Mangussi, Joana Cristo Santos, Ricardo Cardoso Pereira, Ana Carolina Lorena, Mário A. T. Figueiredo, Pedro Henriques Abreu

Abstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic reliability and downstream clinical applications. Recently, diffusion models have emerged as state-o… ▽ More Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic reliability and downstream clinical applications. Recently, diffusion models have emerged as state-of-the-art generative approaches for medical image inpainting due to their ability to generate anatomically consistent reconstructions. This survey presents a systematic review of diffusion-based methods for medical image inpainting, covering the main architectures, applications, datasets, and evaluation strategies reported across 60 studies. In addition, we propose a taxonomy for diffusion-based approaches. The analysis reveals a rapid growth of research interest in diffusion-based medical image inpainting, with denoising diffusion probabilistic models and latent diffusion models emerging as the dominant architectures. The reviewed studies mainly focus on artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection, particularly in magnetic resonance imaging and computed tomography imaging. Overall, diffusion models demonstrate strong performance in producing anatomically plausible reconstructions and aiding downstream clinical tasks. However, the review also highlights important challenges, including the lack of standardized benchmarks, limited dataset diversity, and restricted validation procedures across diverse clinical applications and imaging scenarios. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering

Authors: Anderson L. de Paula, Pedro C. dos Santos, Renato A. Krohling

Abstract: The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance. However, institutional heterogeneity among schools and the structural complexity of educational data pose substantial challenges to traditional statistical modeling approaches. This study investigates the existence of school typologies based on structur… ▽ More The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance. However, institutional heterogeneity among schools and the structural complexity of educational data pose substantial challenges to traditional statistical modeling approaches. This study investigates the existence of school typologies based on structural, pedagogical, and demographic characteristics, and examines how these typologies relate to performance in the Brazilian Basic Education Assessment System (Saeb). Using data from the Brazilian School Census and Saeb, data preprocessing and normalization procedures are applied followed by hierarchical clustering to identify groups of schools with similar structural profiles. After the identification of these typologies, causal analysis techniques are employed to investigate potential causal relationships between school characteristics and educational outcomes. The results reveal the presence of distinct school profiles and statistically significant differences in average performance among them. The causal analysis provides insights into the structural and contextual factors that may influence educational performance, contributing to a better understanding of the mechanisms associated with school effectiveness. △ Less

Submitted 18 June, 2026; originally announced July 2026.

Comments: 15 pages

An Empirical Study of Security Calibration in Large Language Models for Code

Authors: Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos

Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true correctness of its outputs. We present the first large-scale empirical study of security calibration in LLM… ▽ More Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true correctness of its outputs. We present the first large-scale empirical study of security calibration in LLM-generated code. We evaluate GPT-4o-mini, Gemini-2.0-Flash, and Qwen3-Coder-Next across multiple temperature settings on two complementary benchmarks: self-contained security tasks and multi-language repository-level contexts. Our results suggest that overconfidence is prevalent across the evaluated LLMs. Functional calibration is consistently worse than security calibration, suggesting that models estimate security outcomes more reliably than functional correctness, potentially because functional correctness depends on complex execution behavior. We also examine whether calibration-guided automated repair can help remediate vulnerabilities in LLM-generated code, finding only limited improvements while frequently introducing functional regressions. Moreover, we study different mitigation strategies for reducing False Trust, where models assign high confidence to vulnerable code. The results show that although architectural gating improves calibration on controlled benchmarks, calibration deteriorates in realistic repository-level settings, increasing the risk of high-confidence vulnerable outputs. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Comments: Accepted at the 42nd International Conference on Software Maintenance and Evolution (ICSME 2026) Research Track

Automated Detection of Configuration-Specific Security Vulnerabilities via Patch Analysis

Authors: Felipe de Sant'Anna Paixão, Joanna C. S. Santos, Paulo Anselmo da Mota Silveira Neto, Daniel Sadoc Menasche, Gustavo Bittencourt Figueiredo, Eduardo Santana de Almeida

Abstract: We study how security patches in highly configurable C/C++ systems map onto the space of compile-time variants. We formalize the Vulnerability Impact Condition (VIC) - a Boolean predicate over configuration options that denotes all variants that contained the original flaw - and introduce PatchLens, a purely static technique that recovers VICs by aligning AST-level patch hunks with source-level pr… ▽ More We study how security patches in highly configurable C/C++ systems map onto the space of compile-time variants. We formalize the Vulnerability Impact Condition (VIC) - a Boolean predicate over configuration options that denotes all variants that contained the original flaw - and introduce PatchLens, a purely static technique that recovers VICs by aligning AST-level patch hunks with source-level presence conditions and resolving file inclusion via lightweight build system analysis. Evaluating PatchLens on 1,192 Linux kernel, 289 FFmpeg, and 100 PHP patches, we compute precise, human-readable VICs without the need to compile any system variant. The resulting predicates are compact (avg. 1.84 variables for Linux, 3.23 for FFmpeg, 1.04 for PHP) and show that only a small fraction of vulnerabilities are system-wide, which carry higher CVSS scores; meanwhile, CVE texts almost never encode the required options ($\approx$ 1% average recall), motivating automated enrichment of CVE descriptions with VICs. PatchLens and the accompanying dataset enable immediate applications in CI (variant-aware triage and test selection), targeted sampling and fuzzing, and feature risk scoring, offering a scalable, explainable path to vulnerability assessment in highly configurable software. △ Less

Submitted 24 June, 2026; originally announced June 2026.

Comments: Accepted at FSE 2026

Harmonious Colorings: bounds, heuristics and integer-linear formulations

Authors: Júlio Araújo, Manoel Campêlo, Beatriz Martins, Marcio C. Santos

Abstract: A proper coloring $c$ of a simple graph $G$ is harmonious if, for every pair of distinct edges $uv,xy\in E(G)$, we have that $\{c(u),c(v)\}\neq \{c(x),c(y)\}$. The harmonious chromatic number of $G$, denoted by $h(G)$, is the least positive integer $k$ such that $G$ has a harmonious coloring with $k$ colors. In this work, we extend an idea presented in [Kolay, et al. Harmonious coloring: Parameter… ▽ More A proper coloring $c$ of a simple graph $G$ is harmonious if, for every pair of distinct edges $uv,xy\in E(G)$, we have that $\{c(u),c(v)\}\neq \{c(x),c(y)\}$. The harmonious chromatic number of $G$, denoted by $h(G)$, is the least positive integer $k$ such that $G$ has a harmonious coloring with $k$ colors. In this work, we extend an idea presented in [Kolay, et al. Harmonious coloring: Parameterized algorithms and upper bounds. Theor. Comp. Sci. 772 (2019), 132-142] to compare the harmonious chromatic numbers of two graphs $G$ and $H$, with $H$ being obtained from $G$ by identifying vertices at distance at least three. Furthermore, by fixing a proof presented in the same work, we manage to improve one of its upper bounds. We also introduce and study the first, to the best of our knowledge, integer-linear programming formulations for this problem in the literature, along with some heuristics. We provide some preliminary tests on random instances and instances from the second DIMACS Implementation Challenge. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Comments: 20 pages, 1 figure, 6 tables

MSC Class: 68R10

OrganicHAR: Towards Activity Discovery in Organic Settings for Privacy Preserving Sensors Using Efficient Video Analysis

Authors: Prasoon Patidar, Riku Arakawa, Ricardo Graça, Rúben Moutinho, Adriano Soares, Ana Vasconcelos, Filippo Talami, Joana Couto da Silva, Inês Silva, Cristina Mendes Santos, Mayank Goel, Yuvraj Agarwal

Abstract: Deploying human activity recognition (HAR) at home is still rare because sensor signals vary wildly across houses, people, and time, essentially requiring in-situ data collection and training. Prior approaches use cameras to generate training labels for privacy-preserving sensors (LiDAR, RADAR, Thermal), but this forces sensors to detect predefined activities that cameras can see yet the sensors t… ▽ More Deploying human activity recognition (HAR) at home is still rare because sensor signals vary wildly across houses, people, and time, essentially requiring in-situ data collection and training. Prior approaches use cameras to generate training labels for privacy-preserving sensors (LiDAR, RADAR, Thermal), but this forces sensors to detect predefined activities that cameras can see yet the sensors themselves cannot reliably distinguish. In this work, we introduce OrganicHAR, an activity discovery framework that inverts this relationship by placing sensor capabilities at the center of activity discovery. Our approach identifies naturally occurring signal patterns using privacy-preserving sensors, leverages Vision Language Models (VLMs) only during these key moments for scene understanding, and discovers discrete activity labels at granularities that these sensors can reliably detect. Our evaluation with 12 participants demonstrates OrganicHAR's effectiveness: it achieves 79% accuracy for coarse (4-5) activities using only basic ambient sensors (radar, lidar, thermal arrays), and 73% accuracy for fine-grained (8-9) activities when a wearable IMU, depth, and pose sensor are added. OrganicHAR maintains 77% accuracy on average across configurations while discovering 4-8 categories per user (15 across all users) tailored to each environment and sensor capabilities. By triggering video processing only at key moments identified by local sensors, we reduce queries to VLM by 90%, enabling practical and privacy-preserving activity recognition in natural settings. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Comments: 23 pages, 14 figures, with a 4-page appendix containing 2 additional figures. To appear in Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), Vol. 9, No. 4, Article 203 (December 2025). DOI: 10.1145/3770674

ACM Class: H.5.2; I.2.10; I.5.4

Journal ref: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 9(4) (2025) 203:1-203:32

COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments

Authors: Ege Gursoy, Maxime Sabbah, Arthur Haffemayer, Joao Cavalcanti Santos, Pietro Noah Crestaz, Vladimir Petrik, Nicolas Mansard, Vincent Bonnet

Abstract: Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has emerged as a suitable framework thanks to its capacity to handle hard constraints, provide strong guarantees and zero-shot adaptability through predictive reasoning. However, Gradient-Based MPC (GB-MPC) solvers have demo… ▽ More Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has emerged as a suitable framework thanks to its capacity to handle hard constraints, provide strong guarantees and zero-shot adaptability through predictive reasoning. However, Gradient-Based MPC (GB-MPC) solvers have demonstrated limited performance for collision avoidance in complex environments. Sampling-based approaches such as Model Predictive Path Integral (MPPI) control offer an alternative via stochastic rollouts, but enforcing safety via additive penalties is inherently fragile, as it provides no formal constraint satisfaction guarantees. We propose a collision avoidance framework called COSMIK-MPPI combining MPPI with the toolbox for human motion estimation RT-COSMIK and the Constraints-as-Terminations transcription, which enforces safety by treating constraint violations as terminal events, without relying on large penalty terms or explicit human motion prediction. The proposed approach is evaluated against state-of-the-art GB-MPC and vanilla MPPI in simulation and on a real manipulator arm. Results show that COSMIK-MPPI achieves a 100% task success rate with a constant computation time (22 ms), largely outperforming GB-MPC. In simulated infeasible scenarios, COSMIK-MPPI consistently generates collision-free trajectories, contrary to vanilla MPPI. These properties enabled safe execution of complex real-world human-robot interaction tasks in shared workspaces using an affordable markerless human motion estimator, demonstrating a robust, compliant, and practical solution for predictive collision avoidance (cf. results showcased at https://exquisite-parfait-ffa925.netlify.app) △ Less

Submitted 11 April, 2026; originally announced April 2026.

Beyond Quantum Advantage: Improved Classical Algorithms for the Binary Paint Shop Problem

Authors: Mark Goh, Lara Caroline Pereira dos Santos, Thorge Müller, Matthias Sperl

Abstract: The binary paint shop problem (BPSP) is an APX-hard optimization problem in which, given $n$ car models that occur twice in a sequence of length $2n$, the objective is to find a colouring sequence such that each car model pair is painted differently while minimizing the number of times the paint is swapped along the sequence. A recent classical heuristic, known as the recursive star greedy (RSG) a… ▽ More The binary paint shop problem (BPSP) is an APX-hard optimization problem in which, given $n$ car models that occur twice in a sequence of length $2n$, the objective is to find a colouring sequence such that each car model pair is painted differently while minimizing the number of times the paint is swapped along the sequence. A recent classical heuristic, known as the recursive star greedy (RSG) algorithm, is conjectured to achieve an expected paint swap ratio of $0.361$, thereby outperforming the Quantum Approximate Optimization Algorithm (QAOA) with circuit depth $p=7$. Since the performance of the QAOA with logarithmic circuit depth is instance independent, the average paint swap-ratio is upper-bounded by the QAOA. We provide an improved upper-bound of the BPSP by extending the QAOA to depth $p=17$, outputting an expected paint swap ratio of $0.334$ via an exact computation while numerical extrapolation suggests a further reduction to a value of $0.295$. To provide hardware-relevant comparisons, we additionally implement the BPSP on a D-Wave Quantum Annealer Advantage 2, obtaining a minimum paint swap ratio of $0.329$. Given that the QAOA with logarithmic circuit depth does not exhibit a quantum advantage for sparse optimization problems such as the BPSP, this implies the existence of a classical algorithm that outperforms both the RSG algorithm and logarithmic depth QAOA. We provide numerical evidence that the Mean-Field Approximate Optimization Algorithm (MF-AOA) is one such algorithm, yielding a paint swap ratio of approximately $0.280$ beating all known classical and quantum algorithms for the BPSP. △ Less

Submitted 19 August, 2026; v1 submitted 1 April, 2026; originally announced April 2026.

Comments: 11 pages, 4 figures. Additional results for V3 including improved exact upper-bound rather than numerical upper-bound

Automatic Identification of Parallelizable Loops Using Transformer-Based Source Code Representations

Authors: Izavan dos S. Correia, Henrique C. T. Santos, Tiago A. E. Ferreira

Abstract: Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In this work, we propose a Transformer-bas… ▽ More Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In this work, we propose a Transformer-based approach to classify the parallelization potential of source code, focusing on distinguishing independent (parallelizable) loops from undefined ones. We adopt DistilBERT to process source code sequences using subword tokenization, enabling the model to capture contextual syntactic and semantic patterns without handcrafted features. The approach is evaluated on a balanced dataset combining synthetically generated loops and manually annotated real-world code, using 10-fold cross-validation and multiple performance metrics. Results show consistently high performance, with mean accuracy above 99\% and low false positive rates, demonstrating robustness and reliability. Compared to prior token-based methods, the proposed approach simplifies preprocessing while improving generalization and maintaining computational efficiency. These findings highlight the potential of lightweight Transformer models for practical identification of parallelization opportunities at the loop level. △ Less

Submitted 31 March, 2026; originally announced March 2026.

Comments: 28 pages, 12 figures

Widespread Gender and Pronoun Bias in Moral Judgments Across LLMs

Authors: Gustavo Lúcius Fernandes, Jeiverson C. V. M. Santos, Pedro O. S. Vaz-de-Melo

Abstract: Large language models (LLMs) are increasingly used to assess moral or ethical statements, yet their judgments may reflect social and linguistic biases. This work presents a controlled, sentence-level study of how grammatical person, number, and gender markers influence LLM moral classifications of fairness. Starting from 550 balanced base sentences from the ETHICS dataset, we generated 26 counterf… ▽ More Large language models (LLMs) are increasingly used to assess moral or ethical statements, yet their judgments may reflect social and linguistic biases. This work presents a controlled, sentence-level study of how grammatical person, number, and gender markers influence LLM moral classifications of fairness. Starting from 550 balanced base sentences from the ETHICS dataset, we generated 26 counterfactual variants per item, systematically varying pronouns and demographic markers to yield 14,850 semantically equivalent sentences. We evaluated six model families (Grok, GPT, LLaMA, Gemma, DeepSeek, and Mistral), and measured fairness judgments and inter-group disparities using Statistical Parity Difference (SPD). Results show statistically significant biases: sentences written in the singular form and third person are more often judged as "fair'', while those in the second person are penalized. Gender markers produce the strongest effects, with non-binary subjects consistently favored and male subjects disfavored. We conjecture that these patterns reflect distributional and alignment biases learned during training, emphasizing the need for targeted fairness interventions in moral LLM applications. △ Less

Submitted 13 March, 2026; originally announced March 2026.

Dynamic Map-based Data-Centric Approach for Tourism and Cultural Heritage Preservation Digital Twins

Authors: João Spínola Falcão, João G. Perrone Hohlenwerger, Daniel C. Santos, Lucas Almeida de Sousa, Nazim Agoulmine, Joberto S. B. Martins

Abstract: Tourism is an essential and growing economic activity worldwide, bringing benefits such as job creation, revenue generation, and tax revenue, and driving economic prosperity. Tourism activities may also have negative impacts on cities, including overtourism and pressure on housing and real estate markets. Cultural heritage is an essential asset of cities and countries that must be preserved. Cultu… ▽ More Tourism is an essential and growing economic activity worldwide, bringing benefits such as job creation, revenue generation, and tax revenue, and driving economic prosperity. Tourism activities may also have negative impacts on cities, including overtourism and pressure on housing and real estate markets. Cultural heritage is an essential asset of cities and countries that must be preserved. Cultural heritage, as an asset, is commonly explored through tourism activities and may have negative impacts, including physical degradation, commodification, and loss of authenticity. A digital twin is a data-driven virtual representation of a physical object, system, or environment. However, although digital twin technology has been widely adopted in manufacturing, Industry 4.0, and urban planning for smart cities, there remains a gap in specialized digital twins for tourism and cultural heritage management. This paper proposes a QGIS-based, data-centric approach to digital twin frameworks that supports the management, development, and deployment of tourism and cultural heritage services and applications in smart cities. The data-centric approach is embedded in a specialized digital twin focusing on the Salvador Historic Center - Pelourinho, a highly important cultural asset and tourism spot for the city of Salvador. Currently, Pelourinho faces a persistent challenge in sustaining tourism flux while safeguarding its cultural and heritage assets. Preliminary results indicate that the data-centric approach adopted by Pelourinho's DT facilitates data visualization, integrates data silos, and adequately supports management, enabling managers to address heritage preservation and conservation issues, control over-tourism, and implement urban resilience and climate adaptation measures. △ Less

Submitted 13 February, 2026; originally announced March 2026.

Comments: 8 pages, 5 figures, ADVANCE 26 - International Workshop on ADVANCEs in ICT Infrastructures and Services - March 25th-27th, 2026

The TCF doesn't really A(A)ID -- Automatic Privacy Analysis and Legal Compliance of TCF-based Android Applications

Authors: Victor Morel, Cristiana Santos, Pontus Carlsson, Joel Ahlinder, Romaric Duvignau

Abstract: The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web co… ▽ More The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile applications. No work has systematically studied the privacy implications of the TCF on Android apps. To address this gap, we investigate the prevalence of the TCF in popular Android apps from the Google Play Store, and assess whether these apps respect users' consent banner choices. By scraping and downloading 4482 of the most popular Google Play Store apps on an emulated Android device, we automatically determine which apps use the TCF, automatically interact with consent banners, and analyze the apps' traffic in two different stages, passive (post choices) and active (during banner interaction and post choices). We found that 576 (12.85%) of the 4482 downloadable apps in our dataset implemented the TCF, and we identified potential privacy violations within this subset. In 15 (2.6%) of these apps, users' choices are stored only when consent is granted. Users who refuse consent are shown the consent banner again each time they launch the app. Network traffic analysis conducted during the passive stage reveals that 66.2% of the analyzed TCF-based apps share personal data, through the Android Advertising ID (AAID), in the absence of a lawful basis for processing. 55.3% of apps analyzed during the active stage share AAID before users interact with the apps' consent banners, violating the prior consent requirement. △ Less

Submitted 27 February, 2026; v1 submitted 23 February, 2026; originally announced February 2026.

Comments: Accepted for publication at PETS'26

Characterizing and Modeling the GitHub Security Advisories Review Pipeline

Authors: Claudio Segal, Paulo Segal, Carlos Eduardo Banjar, Felipe de Sant'Anna Paixão, Hudson Silva Borges, Paulo Silveira, Eduardo Santana de Almeida, Joanna C. S. Santos, Anton Kocheturov, Gaurav Kumar Srivastava, Daniel Sadoc Menasché

Abstract: GitHub Security Advisories (GHSA) have become a central component of open-source vulnerability disclosure and are widely used by developers and security tools. A distinctive feature of GHSA is that only a fraction of advisories are reviewed by GitHub, while the mechanisms associated with this review process remain poorly understood. In this paper, we conduct a large-scale empirical study of the GH… ▽ More GitHub Security Advisories (GHSA) have become a central component of open-source vulnerability disclosure and are widely used by developers and security tools. A distinctive feature of GHSA is that only a fraction of advisories are reviewed by GitHub, while the mechanisms associated with this review process remain poorly understood. In this paper, we conduct a large-scale empirical study of the GHSA review processes, analyzing over 288,000 advisories spanning 2019-2025. We characterize which advisories are more likely to be reviewed, quantify review delays, and identify two distinct review-latency regimes: a fast path dominated by GitHub Repository Advisories (GRAs) and a slow path dominated by NVD-first advisories. We further develop a queueing model that accounts for this dichotomy based on the structure of the advisory processing pipeline. △ Less

Submitted 30 April, 2026; v1 submitted 5 February, 2026; originally announced February 2026.

Comments: Paper accepted at 23rd International Mining Software Repositories Conference (MSR 2026)

CodeGuard: Improving LLM Guardrails in CS Education

Authors: Nishat Raihan, Noah Erdachew, Jayoti Devi, Joanna C. S. Santos, Marcos Zampieri

Abstract: Large language models (LLMs) are increasingly embedded in Computer Science (CS) classrooms to automate code generation, feedback, and assessment. However, their susceptibility to adversarial or ill-intentioned prompts threatens student learning and academic integrity. To cope with this important issue, we evaluate existing off-the-shelf LLMs in handling unsafe and irrelevant prompts within the dom… ▽ More Large language models (LLMs) are increasingly embedded in Computer Science (CS) classrooms to automate code generation, feedback, and assessment. However, their susceptibility to adversarial or ill-intentioned prompts threatens student learning and academic integrity. To cope with this important issue, we evaluate existing off-the-shelf LLMs in handling unsafe and irrelevant prompts within the domain of CS education. We identify important shortcomings in existing LLM guardrails which motivates us to propose CodeGuard, a comprehensive guardrail framework for educational AI systems. CodeGuard includes (i) a first-of-its-kind taxonomy for classifying prompts; (ii) the CodeGuard dataset, a collection of 8,000 prompts spanning the taxonomy; and (iii) PromptShield, a lightweight sentence-encoder model fine-tuned to detect unsafe prompts in real time. Experiments show that PromptShield achieves 0.93 F1 score, surpassing existing guardrail methods. Additionally, further experimentation reveals that CodeGuard reduces potentially harmful or policy-violating code completions by 30-65% without degrading performance on legitimate educational tasks. The code, datasets, and evaluation scripts are made freely available to the community. △ Less

Submitted 22 January, 2026; originally announced February 2026.

An Empirical Study on Remote Code Execution in Machine Learning Model Hosting Ecosystems

Authors: Mohammed Latif Siddiq, Tanzim Hossain Romel, Natalie Sekerak, Beatrice Casey, Joanna C. S. Santos

Abstract: Model-sharing platforms, such as Hugging Face, ModelScope, and OpenCSG, have become central to modern machine learning development, enabling developers to share, load, and fine-tune pre-trained models with minimal effort. However, the flexibility of these ecosystems introduces a critical security concern: the execution of untrusted code during model loading (i.e., via trust_remote_code or trust_re… ▽ More Model-sharing platforms, such as Hugging Face, ModelScope, and OpenCSG, have become central to modern machine learning development, enabling developers to share, load, and fine-tune pre-trained models with minimal effort. However, the flexibility of these ecosystems introduces a critical security concern: the execution of untrusted code during model loading (i.e., via trust_remote_code or trust_repo). In this work, we conduct the first large-scale empirical study of custom model loading practices across five major model-sharing platforms to assess their prevalence, associated risks, and developer perceptions. We first quantify the frequency with which models require custom code to function and identify those that execute arbitrary Python files during loading. We then apply three complementary static analysis tools: Bandit, CodeQL, and Semgrep, to detect security smells and potential vulnerabilities, categorizing our findings by CWE identifiers to provide a standardized risk taxonomy. We also use YARA to identify malicious patterns and payload signatures. In parallel, we systematically analyze the documentation, API design, and safety mechanisms of each platform to understand their mitigation strategies and enforcement levels. Finally, we conduct a qualitative analysis of over 600 developer discussions from GitHub, Hugging Face, and PyTorch Hub forums, as well as Stack Overflow, to capture community concerns and misconceptions regarding security and usability. Our findings reveal widespread reliance on unsafe defaults, uneven security enforcement across platforms, and persistent confusion among developers about the implications of executing remote code. We conclude with actionable recommendations for designing safer model-sharing infrastructures and striking a balance between usability and security in future AI ecosystems. △ Less

Submitted 20 January, 2026; originally announced January 2026.

Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub

Authors: Mohammed Latif Siddiq, Xinye Zhao, Vinicius Carvalho Lopes, Beatrice Casey, Joanna C. S. Santos

Abstract: Autonomous coding agents are increasingly deployed as AI teammates in modern software engineering, independently authoring pull requests (PRs) that modify production code at scale. This study aims to systematically characterize how autonomous coding agents contribute to software security in practice, how these security-related contributions are reviewed and accepted, and which observable signals a… ▽ More Autonomous coding agents are increasingly deployed as AI teammates in modern software engineering, independently authoring pull requests (PRs) that modify production code at scale. This study aims to systematically characterize how autonomous coding agents contribute to software security in practice, how these security-related contributions are reviewed and accepted, and which observable signals are associated with PR rejection. We conduct a large-scale empirical analysis of agent-authored PRs using the AIDev dataset, comprising of over 33,000 curated PRs from popular GitHub repositories. Security-relevant PRs are identified using a keyword filtering strategy, followed by manual validation, resulting in 1,293 confirmed security-related agentic-PRs. We then analyze prevalence, acceptance outcomes, and review latency across autonomous agents, programming ecosystems, and types of code changes. Moreover, we apply qualitative open coding to identify recurring security-related actions and underlying intents, and examine review metadata to identify early signals associated with PR rejection. Security-related Agentic-PRs constitute a meaningful share of agent activity (approximately 4\%). Rather than focusing solely on narrow vulnerability fixes, agents most frequently perform supportive security hardening activities, including testing, documentation, configuration, and improved error handling. Compared to non-security PRs, security-related Agentic-PRs exhibit lower merge rates and longer review latency, reflecting heightened human scrutiny, with variation across agents and programming ecosystems. PR rejection is more strongly associated with PR complexity and verbosity than with explicit security topics. △ Less

Submitted 1 January, 2026; originally announced January 2026.

Comments: Submitted to Information and Software Technology Journal

Assessing the Software Security Comprehension of Large Language Models

Authors: Mohammed Latif Siddiq, Natalie Sekerak, Antonio Karam, Maria Leal, Arvin Islam-Gomes, Joanna C. S. Santos

Abstract: Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the security comprehension of five leading LLMs: GPT-4o-Mini, GPT-5-Mini, Gemini-2.5-Flash, Llama-3.1, and Qwen-2.5, using Blooms Taxonomy as a framework. We assess six cognitive dimensions: remembering, understanding, applyi… ▽ More Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the security comprehension of five leading LLMs: GPT-4o-Mini, GPT-5-Mini, Gemini-2.5-Flash, Llama-3.1, and Qwen-2.5, using Blooms Taxonomy as a framework. We assess six cognitive dimensions: remembering, understanding, applying, analyzing, evaluating, and creating. Our methodology integrates diverse datasets, including curated multiple-choice questions, vulnerable code snippets (SALLM), course assessments from an Introduction to Software Security course, real-world case studies (XBOW), and project-based creation tasks from a Secure Software Engineering course. Results show that while LLMs perform well on lower-level cognitive tasks such as recalling facts and identifying known vulnerabilities, their performance degrades significantly on higher-order tasks that require reasoning, architectural evaluation, and secure system creation. Beyond reporting aggregate accuracy, we introduce a software security knowledge boundary that identifies the highest cognitive level at which a model consistently maintains reliable performance. In addition, we identify 51 recurring misconception patterns exhibited by LLMs across Blooms levels. △ Less

Submitted 24 December, 2025; originally announced December 2025.

Comments: Submitted to Empirical Software Engineering (EMSE) journal

Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America

Authors: Matheus Corrêa Domingos, Valdivino Alexandre de Santiago Júnior, Juliana Aparecida Anochi, Elcio Hideiti Shiguemori, Luísa Mirelle Costa dos Santos, Hércules Carlos dos Santos Pereira, André Estevam Costa Oliveira

Abstract: Forecasting meteorological variables is challenging due to the complexity of their processes, requiring advanced models for accuracy. Accurate precipitation forecasts are vital for society. Reliable predictions help communities mitigate climatic impacts. Based on the current relevance of artificial intelligence (AI), classical machine learning (ML) and deep learning (DL) techniques have been used… ▽ More Forecasting meteorological variables is challenging due to the complexity of their processes, requiring advanced models for accuracy. Accurate precipitation forecasts are vital for society. Reliable predictions help communities mitigate climatic impacts. Based on the current relevance of artificial intelligence (AI), classical machine learning (ML) and deep learning (DL) techniques have been used as an alternative or complement to dynamic modeling. However, there is still a lack of broad investigations into the feasibility of purely data-driven approaches for precipitation forecasting. This study aims at addressing this issue where different classical ML and DL approaches for forecasting precipitation in South America, taking into account all 2019 seasons, are considered in a detailed investigation. The selected classical ML techniques were Random Forests and extreme gradient boosting (XGBoost), while the DL counterparts were a 1D convolutional neural network (CNN 1D), a long short-term memory (LSTM) model, and a gated recurrent unit (GRU) model. Additionally, the Brazilian Global Atmospheric Model (BAM) was used as a representative of the traditional dynamic modeling approach. We also relied on explainable artificial intelligence (XAI) to provide some explanations for the models behaviors. LSTM showed strong predictive performance while BAM, the traditional dynamic model representative, had the worst results. Despite presented the higher latency, LSTM was most accurate for heavy precipitation. If cost is a concern, XGBoost offers lower latency with slightly accuracy loss. The results of this research confirm the viability of DL models for climate forecasting, solidifying a global trend in major meteorological and climate forecasting centers. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Can the GPC standard eliminate consent banners in the EU?

Authors: Sebastian Zimmeck, Harshvardhan J. Pandit, Frederik Zuiderveen Borgesius, Cristiana Teixeira Santos, Konrad Kollnig, Robin Berjon

Abstract: In the EU, the General Data Protection Regulation and the ePrivacy Directive mandate consent for the use of personal data for the purpose of behavioural advertising and tracking technologies. However, the ubiquity of consent banners has led to widespread consent fatigue and questions about the effectiveness of these mechanisms in protecting data subjects' data. To simplify digital laws and make th… ▽ More In the EU, the General Data Protection Regulation and the ePrivacy Directive mandate consent for the use of personal data for the purpose of behavioural advertising and tracking technologies. However, the ubiquity of consent banners has led to widespread consent fatigue and questions about the effectiveness of these mechanisms in protecting data subjects' data. To simplify digital laws and make the EU more competitive, the EU Commission recently proposed the Digital Omnibus, introducing a new Article 88b GDPR to express data subjects' choices in a technical way. While the Digital Omnibus is under legislative negotiation, California residents and residents of other US states can already exercise their rights via Global Privacy Control (GPC), a privacy signal to automatically broadcast a legally binding opt-out request to websites. In light of the Digital Omnibus, we evaluate to which extent GPC can be adapted to the EU legal framework to reduce consent banners, mitigate consent fatigue, and improve data protection for EU users. GPC is based on a technical specification, currently being standardised at the World Wide Web Consortium. By sending a GPC signal, data subjects can express their refusal or withdrawal of consent under the GDPR to the use of their personal data for cross-context ad targeting and, in some cases, to express their objection under the GDPR against the use of their data for such purposes. Our evaluation identifies friction between the GPC specification and current EU data protection law. In the longer term, it would be possible for the EU legislator to amend EU laws, as proposed in the current Digital Omnibus, in such a way that internet users can use automated signals to express choices about personal data use and online tracking. In the shorter term, websites and companies who conduct online tracking can already honour GPC. △ Less

Submitted 6 May, 2026; v1 submitted 9 December, 2025; originally announced December 2025.

Journal ref: Computer Law & Security Review, 61, 106332 (2026)

Large Language Models for Software Engineering: A Reproducibility Crisis

Authors: Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak, Joanna C. S. Santos

Abstract: Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, ma… ▽ More Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, machine learning, and natural language processing venues, extracting structured metadata from publications, repositories, and documentation. Guided by four research questions, we examine (i) the prevalence of reproducibility smells, (ii) how reproducibility has evolved over time, (iii) whether artifact evaluation badges reliably reflect reproducibility quality, and (iv) how publication venues influence transparency practices. Using a taxonomy of seven smell categories: Code and Execution, Data, Documentation, Environment and Tooling, Versioning, Model, and Access and Legal, we manually annotated all papers and associated artifacts. Our analysis reveals persistent gaps in artifact availability, environment specification, versioning rigor, and documentation clarity, despite modest improvements in recent years and increased adoption of artifact evaluation processes at top SE venues. Notably, we find that badges often signal artifact presence but do not consistently guarantee execution fidelity or long-term reproducibility. Motivated by these findings, we provide actionable recommendations to mitigate reproducibility smells and introduce a Reproducibility Maturity Model (RMM) to move beyond binary artifact certification toward multi-dimensional, progressive evaluation of reproducibility rigor. △ Less

Submitted 29 November, 2025; originally announced December 2025.

Comments: Submitted to Empirical Software Engineering (EMSE) journal; 112 pages (81 pages of references)

The Unspoken Crisis of Learning: The Surging Zone of No Development

Authors: Euzeli C. dos Santos Jr., Tracey Birdwell

Abstract: AI has redefined the boundaries of assistance in education, often blurring the line between guided learning and dependency. This paper revisits Vygotsky's Zone of Proximal Development (ZPD) through the lens of the P2P Teaching framework. By contrasting temporary scaffolding with the emerging phenomenon of permanent digital mediation, the study introduces the concept of the Zone of No Development (… ▽ More AI has redefined the boundaries of assistance in education, often blurring the line between guided learning and dependency. This paper revisits Vygotsky's Zone of Proximal Development (ZPD) through the lens of the P2P Teaching framework. By contrasting temporary scaffolding with the emerging phenomenon of permanent digital mediation, the study introduces the concept of the Zone of No Development (ZND), a state in which continuous assistance replaces cognitive struggle and impedes intellectual autonomy. Through theoretical synthesis and framework design, P2P Teaching demonstrates how deliberate disconnection and ethical fading can restore the learner's agency, ensuring that technological tools enhance rather than replace developmental effort. The paper argues that productive struggle, self-regulation, and first-principles reasoning remain essential for durable learning, and that responsible use of AI in education must include explicit mechanisms to end its help when mastery begins. △ Less

Submitted 16 November, 2025; originally announced November 2025.

Comments: 6 pages, 3 figures

"I need to learn better searching tactics for privacy policy laws." Investigating Software Developers' Behavior When Using Sources on Privacy Issues

Authors: Stefan Albert Horstmann, Sandy Hong, Maziar Niazian, Cristiana Santos, Alena Naiakshina

Abstract: Since the introduction of the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), software developers increasingly have to make privacy-related decisions during system design and implementation. However, past research showed that they often lack legal expertise and struggle with privacy-compliant development. To shed light on how effective current inf… ▽ More Since the introduction of the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), software developers increasingly have to make privacy-related decisions during system design and implementation. However, past research showed that they often lack legal expertise and struggle with privacy-compliant development. To shed light on how effective current information sources are in supporting them with privacy-sensitive implementation, we conducted a qualitative study with 30 developers. Participants were presented with a privacy-sensitive scenario and asked to identify privacy issues and suggest measures using their knowledge, online resources, and an AI assistant. We observed developers' decision-making in think-aloud sessions and discussed it in follow-up interviews. We found that participants struggled with all three sources: personal knowledge was insufficient, web content was often too complex, and while AI assistants provided clear and user-tailored responses, they lacked contextual relevance and failed to identify scenario-specific issues. Our study highlights major shortcomings in existing support for privacy-related development tasks. Based on our findings, we discuss the need for more accessible, understandable, and actionable privacy resources for developers. △ Less

Submitted 8 December, 2025; v1 submitted 11 November, 2025; originally announced November 2025.

Journal ref: 2026 IEEE/ACM 48th International Conference on Software Engineering (ICSE26)

HideAndSeg: an AI-based tool with automated prompting for octopus segmentation in natural habitats

Authors: Alan de Aguiar, Michaella Pereira Andrade, Charles Morphy D. Santos, João Paulo Gois

Abstract: Analyzing octopuses in their natural habitats is challenging due to their camouflage capability, rapid changes in skin texture and color, non-rigid body deformations, and frequent occlusions, all of which are compounded by variable underwater lighting and turbidity. Addressing the lack of large-scale annotated datasets, this paper introduces HideAndSeg, a novel, minimally supervised AI-based tool… ▽ More Analyzing octopuses in their natural habitats is challenging due to their camouflage capability, rapid changes in skin texture and color, non-rigid body deformations, and frequent occlusions, all of which are compounded by variable underwater lighting and turbidity. Addressing the lack of large-scale annotated datasets, this paper introduces HideAndSeg, a novel, minimally supervised AI-based tool for segmenting videos of octopuses. It establishes a quantitative baseline for this task. HideAndSeg integrates SAM2 with a custom-trained YOLOv11 object detector. First, the user provides point coordinates to generate the initial segmentation masks with SAM2. These masks serve as training data for the YOLO model. After that, our approach fully automates the pipeline by providing a bounding box prompt to SAM2, eliminating the need for further manual intervention. We introduce two unsupervised metrics - temporal consistency $DICE_t$ and new component count $NC_t$ - to quantitatively evaluate segmentation quality and guide mask refinement in the absence of ground-truth data, i.e., real-world information that serves to train, validate, and test AI models. Results show that HideAndSeg achieves satisfactory performance, reducing segmentation noise compared to the manually prompted approach. Our method can re-identify and segment the octopus even after periods of complete occlusion in natural environments, a scenario in which the manually prompted model fails. By reducing the need for manual analysis in real-world scenarios, this work provides a practical tool that paves the way for more efficient behavioral studies of wild cephalopods. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Investigating Software Aging in LLM-Generated Software Systems

Authors: César Santos, Ermeson Andrade, Roberto Natella

Abstract: Automatically generated software, especially code produced by Large Language Models (LLMs), is increasingly adopted to accelerate development and reduce manual effort. However, little is known about the long-term reliability of such systems under sustained execution. In this paper, we experimentally investigate the phenomenon of software aging in applications generated by LLM-based tools. Using th… ▽ More Automatically generated software, especially code produced by Large Language Models (LLMs), is increasingly adopted to accelerate development and reduce manual effort. However, little is known about the long-term reliability of such systems under sustained execution. In this paper, we experimentally investigate the phenomenon of software aging in applications generated by LLM-based tools. Using the Bolt platform and standardized prompts from Baxbench, we generated four service-oriented applications and subjected them to 50-hour load tests. Resource usage, response time, and throughput were continuously monitored to detect degradation patterns. The results reveal significant evidence of software aging, including progressive memory growth, increased response time, and performance instability across all applications. Statistical analyzes confirm these trends and highlight variability in the severity of aging according to the type of application. Our findings show the need to consider aging in automatically generated software and provide a foundation for future studies on mitigation strategies and long-term reliability evaluation. △ Less

Submitted 28 October, 2025; originally announced October 2025.

Comments: Presented at the 17th International Workshop on Software Aging and Rejuvenation (WoSAR), 2025

Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processes

Authors: Pedro Cortes dos Santos, Matheus Becali Rocha, Renato A Krohling

Abstract: Industrial processes generate complex data that challenge fault detection systems, often yielding opaque or underwhelming results despite advanced machine learning techniques. This study tackles such difficulties using the Tennessee Eastman Process, a well-established benchmark known for its intricate dynamics, to develop an innovative fault detection framework. Initial attempts with standard mode… ▽ More Industrial processes generate complex data that challenge fault detection systems, often yielding opaque or underwhelming results despite advanced machine learning techniques. This study tackles such difficulties using the Tennessee Eastman Process, a well-established benchmark known for its intricate dynamics, to develop an innovative fault detection framework. Initial attempts with standard models revealed limitations in both performance and interpretability, prompting a shift toward a more tractable approach. By employing SHAP (SHapley Additive exPlanations), we transform the problem into a more manageable and transparent form, pinpointing the most critical process features driving fault predictions. This reduction in complexity unlocks the ability to apply causal analysis through Directed Acyclic Graphs, generated by multiple algorithms, to uncover the underlying mechanisms of fault propagation. The resulting causal structures align strikingly with SHAP findings, consistently highlighting key process elements-like cooling and separation systems-as pivotal to fault development. Together, these methods not only enhance detection accuracy but also provide operators with clear, actionable insights into fault origins, a synergy that, to our knowledge, has not been previously explored in this context. This dual approach bridges predictive power with causal understanding, offering a robust tool for monitoring complex manufacturing environments and paving the way for smarter, more interpretable fault detection in industrial systems. △ Less

Submitted 27 October, 2025; originally announced October 2025.

Discovering Software Parallelization Points Using Deep Neural Networks

Authors: Izavan dos S. Correia, Henrique C. T. Santos, Tiago A. E. Ferreira

Abstract: This study proposes a deep learning-based approach for discovering loops in programming code according to their potential for parallelization. Two genetic algorithm-based code generators were developed to produce two distinct types of code: (i) independent loops, which are parallelizable, and (ii) ambiguous loops, whose dependencies are unclear, making them impossible to define if the loop is para… ▽ More This study proposes a deep learning-based approach for discovering loops in programming code according to their potential for parallelization. Two genetic algorithm-based code generators were developed to produce two distinct types of code: (i) independent loops, which are parallelizable, and (ii) ambiguous loops, whose dependencies are unclear, making them impossible to define if the loop is parallelizable or not. The generated code snippets were tokenized and preprocessed to ensure a robust dataset. Two deep learning models - a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) - were implemented to perform the classification. Based on 30 independent runs, a robust statistical analysis was employed to verify the expected performance of both models, DNN and CNN. The CNN showed a slightly higher mean performance, but the two models had a similar variability. Experiments with varying dataset sizes highlighted the importance of data diversity for model performance. These results demonstrate the feasibility of using deep learning to automate the identification of parallelizable structures in code, offering a promising tool for software optimization and performance improvement. △ Less

Submitted 1 October, 2025; v1 submitted 5 September, 2025; originally announced September 2025.

Comments: 17 pages, 10 figures

AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generation

Authors: Enrico Palumbo, Gustavo Penha, Alva Liu, Marcus Eltscheminov, Jefferson Carvalho dos Santos, Alice Wang, Hugues Bouchard, Humberto Jesús Corona Pampin, Michelle Tran Luu

Abstract: Spotify has recently introduced audiobooks as part of its catalog, complementing its music and podcast offering. Search is often the first entry point for users to access new items, and an important goal for Spotify is to support users in the exploration of the audiobook catalog. More specifically, we would like to enable users without a specific item in mind to broadly search by topic, genre, sto… ▽ More Spotify has recently introduced audiobooks as part of its catalog, complementing its music and podcast offering. Search is often the first entry point for users to access new items, and an important goal for Spotify is to support users in the exploration of the audiobook catalog. More specifically, we would like to enable users without a specific item in mind to broadly search by topic, genre, story tropes, decade, and discover audiobooks, authors and publishers they may like. To do this, we need to 1) inspire users to type more exploratory queries for audiobooks and 2) augment our retrieval systems to better deal with exploratory audiobook queries. This is challenging in a cold-start scenario, where we have a retrievabiliy bias due to the little amount of user interactions with audiobooks compared to previously available items such as music and podcast content. To address this, we propose AudioBoost, a system to boost audiobook retrievability in Spotify's Search via synthetic query generation. AudioBoost leverages Large Language Models (LLMs) to generate synthetic queries conditioned on audiobook metadata. The synthetic queries are indexed both in the Query AutoComplete (QAC) and in the Search Retrieval engine to improve query formulation and retrieval at the same time. We show through offline evaluation that synthetic queries increase retrievability and are of high quality. Moreover, results from an online A/B test show that AudioBoost leads to a +0.7% in audiobook impressions, +1.22% in audiobook clicks, and +1.82% in audiobook exploratory query completions. △ Less

Submitted 8 September, 2025; originally announced September 2025.

Comments: EARL Workshop @ RecSys25

An Initial Study of Bird's-Eye View Generation for Autonomous Vehicles using Cross-View Transformers

Authors: Felipe Carlos dos Santos, Eric Aislan Antonelo, Gustavo Claudio Karl Couto

Abstract: Bird's-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV's channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of differe… ▽ More Bird's-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV's channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of different camera layouts, and two loss formulations (focal and L1). Using training data from only a town, a four-camera CVT trained with the L1 loss delivers the most robust test performance, evaluated in a new town. Overall, our results underscore CVT's promise for mapping camera inputs to reasonably accurate BEV maps. △ Less

Submitted 17 August, 2025; originally announced August 2025.

Comments: 12 pages,submitted in ENIAC 2025

Attacking interpretable NLP systems

Authors: Eldor Abdukhamidov, Tamer Abuhmed, Joanna C. S. Santos, Mohammed Abuhamad

Abstract: Studies have shown that machine learning systems are vulnerable to adversarial examples in theory and practice. Where previous attacks have focused mainly on visual models that exploit the difference between human and machine perception, text-based models have also fallen victim to these attacks. However, these attacks often fail to maintain the semantic meaning of the text and similarity. This pa… ▽ More Studies have shown that machine learning systems are vulnerable to adversarial examples in theory and practice. Where previous attacks have focused mainly on visual models that exploit the difference between human and machine perception, text-based models have also fallen victim to these attacks. However, these attacks often fail to maintain the semantic meaning of the text and similarity. This paper introduces AdvChar, a black-box attack on Interpretable Natural Language Processing Systems, designed to mislead the classifier while keeping the interpretation similar to benign inputs, thus exploiting trust in system transparency. AdvChar achieves this by making less noticeable modifications to text input, forcing the deep learning classifier to make incorrect predictions and preserve the original interpretation. We use an interpretation-focused scoring approach to determine the most critical tokens that, when changed, can cause the classifier to misclassify the input. We apply simple character-level modifications to measure the importance of tokens, minimizing the difference between the original and new text while generating adversarial interpretations similar to benign ones. We thoroughly evaluated AdvChar by testing it against seven NLP models and three interpretation models using benchmark datasets for the classification task. Our experiments show that AdvChar can significantly reduce the prediction accuracy of current deep learning models by altering just two characters on average in input samples. △ Less

Submitted 21 July, 2025; originally announced July 2025.

ACM Class: I.2.7; I.2.6; I.2.3; D.4.6

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Authors: Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, Luke Marris, Sam Petulla, Colin Gaffney, Asaf Aharoni, Nathan Lintz, Tiago Cardal Pais, Henrik Jacobsson, Idan Szpektor, Nan-Jiang Jiang, Krishna Haridasan, Ahmed Omran, Nikunj Saunshi, Dara Bahri, Gaurav Mishra, Eric Chu , et al. (3410 additional authors not shown)

Abstract: In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal unde… ▽ More In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving. △ Less

Submitted 19 December, 2025; v1 submitted 7 July, 2025; originally announced July 2025.

Comments: 72 pages, 17 figures

Deep Inertial Pose: A deep learning approach for human pose estimation

Authors: Sara M. Cerqueira, Manuel Palermo, Cristina P. Santos

Abstract: Inertial-based Motion capture system has been attracting growing attention due to its wearability and unsconstrained use. However, accurate human joint estimation demands several complex and expertise demanding steps, which leads to expensive software such as the state-of-the-art MVN Awinda from Xsens Technologies. This work aims to study the use of Neural Networks to abstract the complex biomecha… ▽ More Inertial-based Motion capture system has been attracting growing attention due to its wearability and unsconstrained use. However, accurate human joint estimation demands several complex and expertise demanding steps, which leads to expensive software such as the state-of-the-art MVN Awinda from Xsens Technologies. This work aims to study the use of Neural Networks to abstract the complex biomechanical models and analytical mathematics required for pose estimation. Thus, it presents a comparison of different Neural Network architectures and methodologies to understand how accurately these methods can estimate human pose, using both low cost(MPU9250) and high end (Mtw Awinda) Magnetic, Angular Rate, and Gravity (MARG) sensors. The most efficient method was the Hybrid LSTM-Madgwick detached, which achieved an Quaternion Angle distance error of 7.96, using Mtw Awinda data. Also, an ablation study was conducted to study the impact of data augmentation, output representation, window size, loss function and magnetometer data on the pose estimation error. This work indicates that Neural Networks can be trained to estimate human pose, with results comparable to the state-of-the-art fusion filters. △ Less

Submitted 7 June, 2025; originally announced June 2025.

Turning to Online Forums for Legal Information: A Case Study of GDPR's Legitimate Interests

Authors: Lin Kyi, Cristiana Santos, Sushil Ammanaghatta Shivakumar, Franziska Roesner, Asia Biega

Abstract: Practitioners building online services and tools often turn to online forums such as Reddit, Law Stack Exchange, and Stack Overflow for legal guidance to ensure compliance with the GDPR. The legal information presented in these forums directly impacts present-day industry practitioner's decisions. Online forums can serve as gateways that, depending on the accuracy and quality of the answers provid… ▽ More Practitioners building online services and tools often turn to online forums such as Reddit, Law Stack Exchange, and Stack Overflow for legal guidance to ensure compliance with the GDPR. The legal information presented in these forums directly impacts present-day industry practitioner's decisions. Online forums can serve as gateways that, depending on the accuracy and quality of the answers provided, may either support or undermine the protection of privacy and data protection fundamental rights. However, there is a need for deeper investigation into practitioners' decision-making processes and their understanding of legal compliance when seeking for legal information online. Using GDPR's ``legitimate interests'' legal ground for processing personal data as a case study, we investigate how practitioners use online forums to identify common areas of confusion in applying legitimate interests in practice, and evaluate how legally sound online forum responses are. Our analysis found that applying the legal basis of legitimate interest is complex for practitioners, with important implications for how the GDPR is implemented in practice. The legal analysis showed that crowdsourced legal information tends to be legally sound, though sometimes incomplete. We outline recommendations to improve the quality of online forums by ensuring that responses are more legally sound and comprehensive, enabling practitioners to apply legitimate interests effectively in practice and uphold the GDPR. △ Less

Submitted 23 July, 2025; v1 submitted 2 June, 2025; originally announced June 2025.

Comments: Accepted at Annual Privacy Forum 2025

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics

Authors: Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

Abstract: In this work, we introduce a new class of neural network operators designed to handle problems where memory effects and randomness play a central role. In this work, we introduce a new class of neural network operators designed to handle problems where memory effects and randomness play a central role. These operators merge symmetrized activation functions, Caputo-type fractional derivatives, and… ▽ More In this work, we introduce a new class of neural network operators designed to handle problems where memory effects and randomness play a central role. In this work, we introduce a new class of neural network operators designed to handle problems where memory effects and randomness play a central role. These operators merge symmetrized activation functions, Caputo-type fractional derivatives, and stochastic perturbations introduced via Itô type noise. The result is a powerful framework capable of approximating functions that evolve over time with both long-term memory and uncertain dynamics. We develop the mathematical foundations of these operators, proving three key theorems of Voronovskaya type. These results describe the asymptotic behavior of the operators, their convergence in the mean-square sense, and their consistency under fractional regularity assumptions. All estimates explicitly account for the influence of the memory parameter $α$ and the noise level $σ$. As a practical application, we apply the proposed theory to the fractional Navier-Stokes equations with stochastic forcing, a model often used to describe turbulence in fluid flows with memory. Our approach provides theoretical guarantees for the approximation quality and suggests that these neural operators can serve as effective tools in the analysis and simulation of complex systems. By blending ideas from neural networks, fractional calculus, and stochastic analysis, this research opens new perspectives for modeling turbulent phenomena and other multiscale processes where memory and randomness are fundamental. The results lay the groundwork for hybrid learning-based methods with strong analytical backing. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Comments: 17 pages

"I will never pay for this" Perception of fairness and factors affecting behaviour on 'pay-or-ok' models

Authors: Victor Morel, Farzaneh Karegar, Cristiana Santos

Abstract: The rise of cookie paywalls ('pay-or-ok' models) has prompted growing debates around the right to privacy and data protection, monetisation, and the legitimacy of user consent. Despite their increasing use across sectors, limited research has explored how users perceive these models or what shapes their decisions to either consent to tracking or pay. To address this gap, we conducted four focus gr… ▽ More The rise of cookie paywalls ('pay-or-ok' models) has prompted growing debates around the right to privacy and data protection, monetisation, and the legitimacy of user consent. Despite their increasing use across sectors, limited research has explored how users perceive these models or what shapes their decisions to either consent to tracking or pay. To address this gap, we conducted four focus groups (n= 14) to examine users' perceptions of cookie paywalls, their judgments of fairness, and the conditions under which they might consider paying, alongside a legal analysis within the EU data protection legal framework. Participants primarily viewed cookie paywalls as profit-driven, with fairness perceptions varying depending on factors such as the presence of a third option beyond consent or payment, transparency of data practices, and the authenticity or exclusivity of the paid content. Participants voiced expectations for greater transparency, meaningful control over data collection, and less coercive alternatives, such as contextual advertising or "reject all" buttons. Although some conditions, including trusted providers, exclusive content, and reasonable pricing, could make participants consider paying, most expressed reluctance or unwillingness to do so. Crucially, our findings raise concerns about economic exclusion, where privacy and data protection might end up becoming a privilege rather than fundamental rights. Consent given under financial pressure may not meet the standard of being freely given, as required by the GDPR. To address these concerns, we recommend user-centred approaches that enhance transparency, reduce coercion, ensure the value of paid content, and explore inclusive alternatives. These measures are essential for supporting fairness, meaningful choice, and user autonomy in consent-driven digital environments. △ Less

Submitted 31 July, 2025; v1 submitted 19 May, 2025; originally announced May 2025.

Comments: Accepted for publication at APF2025

Multi-level Cellular Automata for FLIM networks

Authors: Felipe Crispim Salvagnini, Jancarlo F. Gomes, Cid A. N. Santos, Silvio Jamil F. Guimarães, Alexandre X. Falcão

Abstract: The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern and classical techniques offers a path to maintaini… ▽ More The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern and classical techniques offers a path to maintaining competitive performance while enabling practical applications. Feature Learning from Image Markers (FLIM) methodology empowers experts to design convolutional encoders through user-drawn markers, with filters learned directly from these annotations. Recent findings demonstrate that coupling a FLIM encoder with an adaptive decoder creates a flyweight network suitable for SOD, requiring significantly fewer parameters than lightweight models and eliminating the need for backpropagation. Cellular Automata (CA) methods have proven successful in data-scarce scenarios but require proper initialization -- typically through user input, priors, or randomness. We propose a practical intersection of these approaches: using FLIM networks to initialize CA states with expert knowledge without requiring user interaction for each image. By decoding features from each level of a FLIM network, we can initialize multiple CAs simultaneously, creating a multi-level framework. Our method leverages the hierarchical knowledge encoded across different network layers, merging multiple saliency maps into a high-quality final output that functions as a CA ensemble. Benchmarks across two challenging medical datasets demonstrate the competitiveness of our multi-level CA approach compared to established models in the deep SOD literature. △ Less

Submitted 15 April, 2025; originally announced April 2025.

Ankle Exoskeletons in Walking and Load-Carrying Tasks: Insights into Biomechanics and Human-Robot Interaction

Authors: J. F. Almeida, J. André, C. P. Santos

Abstract: Background: Lower limb exoskeletons can enhance quality of life, but widespread adoption is limited by the lack of frameworks to assess their biomechanical and human-robot interaction effects, which are essential for developing adaptive and personalized control strategies. Understanding impacts on kinematics, muscle activity, and HRI dynamics is key to achieve improved usability of wearable robots… ▽ More Background: Lower limb exoskeletons can enhance quality of life, but widespread adoption is limited by the lack of frameworks to assess their biomechanical and human-robot interaction effects, which are essential for developing adaptive and personalized control strategies. Understanding impacts on kinematics, muscle activity, and HRI dynamics is key to achieve improved usability of wearable robots. Objectives: We propose a systematic methodology evaluate an ankle exoskeleton's effects on human movement during walking and load-carrying (10 kg front pack), focusing on joint kinematics, muscle activity, and HRI torque signals. Materials and Methods: Using Xsens MVN (inertial motion capture), Delsys EMG, and a unilateral exoskeleton, three experiments were conducted: (1) isolated dorsiflexion/plantarflexion; (2) gait analysis (two subjects, passive/active modes); and (3) load-carrying under assistance. Results and Conclusions: The first experiment confirmed that the HRI sensor captured both voluntary and involuntary torques, providing directional torque insights. The second experiment showed that the device slightly restricted ankle range of motion (RoM) but supported normal gait patterns across all assistance modes. The exoskeleton reduced muscle activity, particularly in active mode. HRI torque varied according to gait phases and highlighted reduced synchronization, suggesting a need for improved support. The third experiment revealed that load-carrying increased GM and TA muscle activity, but the device partially mitigated user effort by reducing muscle activity compared to unassisted walking. HRI increased during load-carrying, providing insights into user-device dynamics. These results demonstrate the importance of tailoring exoskeleton evaluation methods to specific devices and users, while offering a framework for future studies on exoskeleton biomechanics and HRI. △ Less

Submitted 14 April, 2025; originally announced April 2025.

A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning

Authors: Vitor Martins, Sara M. Cerqueira, Mercedes Balcells, Elazer R Edelman, Cristina P. Santos

Abstract: Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, increased healthcare costs, and long-term disability. This study introduces a human-sensitive robotic system aimed at reintegrating individuals with a history of musculoskeletal disorders into standard job roles, while simultaneously optimizing ergonomic c… ▽ More Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, increased healthcare costs, and long-term disability. This study introduces a human-sensitive robotic system aimed at reintegrating individuals with a history of musculoskeletal disorders into standard job roles, while simultaneously optimizing ergonomic conditions for the broader workforce. This research leverages reinforcement learning (RL) to develop a human-aware control strategy for collaborative robots, focusing on optimizing ergonomic conditions and preventing pain during task execution. Two RL approaches, Q-Learning and Deep Q-Network (DQN), were implemented and tested to personalize control strategies based on individual user characteristics. Although experimental results revealed a simulation-to-real gap, a fine-tuning phase successfully adapted the policies to real-world conditions. DQN outperformed Q-Learning by completing tasks faster while maintaining zero pain risk and safe ergonomic levels, achieving on average 38% shorter task completion times across all tested anthropometries. The structured testing protocol confirmed the system's adaptability to diverse human anthropometries, underscoring the potential of RL-driven cobots to enable safer, more inclusive workplaces. △ Less

Submitted 5 June, 2026; v1 submitted 14 April, 2025; originally announced April 2025.

Revolutionizing Fractional Calculus with Neural Networks: Voronovskaya-Damasclin Theory for Next-Generation AI Systems

Authors: Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

Abstract: This work introduces rigorous convergence rates for neural network operators activated by symmetrized and perturbed hyperbolic tangent functions, utilizing novel Voronovskaya-Damasclin asymptotic expansions. We analyze basic, Kantorovich, and quadrature-type operators over infinite domains, extending classical approximation theory to fractional calculus via Caputo derivatives. Key innovations incl… ▽ More This work introduces rigorous convergence rates for neural network operators activated by symmetrized and perturbed hyperbolic tangent functions, utilizing novel Voronovskaya-Damasclin asymptotic expansions. We analyze basic, Kantorovich, and quadrature-type operators over infinite domains, extending classical approximation theory to fractional calculus via Caputo derivatives. Key innovations include parameterized activation functions with asymmetry control, symmetrized density operators, and fractional Taylor expansions for error analysis. The main theorem demonstrates that Kantorovich operators achieve \(o(n^{-β(N-\varepsilon)})\) convergence rates, while basic operators exhibit \(\mathcal{O}(n^{-βN})\) error decay. For deep networks, we prove \(\mathcal{O}(L^{-β(N-\varepsilon)})\) approximation bounds. Stability results under parameter perturbations highlight operator robustness. By integrating neural approximation theory with fractional calculus, this work provides foundational mathematical insights and deployable engineering solutions, with potential applications in complex system modeling and signal processing. △ Less

Submitted 1 April, 2025; originally announced April 2025.

Comments: 16 pages

Impacto de Treinamento em Programação Competitiva no Ensino Médio: Resultados e Desafios

Authors: Camila da Cruz Santos, Sarah Souto dos Santos, Crishna Irion, Giullia Rodrigues de Menezes, Rafael Dias Araújo, João Henrique de Souza Pereira

Abstract: This article presents an ongoing research aiming to develop an effective methodology for teaching programming, focusing on participation in the Brazilian Informatics Olympiad (OBI), for elementary and high school students. The training conducted with students from the Federal Institute and state schools, demonstrates the importance of programming training programs as a way to promote interest in c… ▽ More This article presents an ongoing research aiming to develop an effective methodology for teaching programming, focusing on participation in the Brazilian Informatics Olympiad (OBI), for elementary and high school students. The training conducted with students from the Federal Institute and state schools, demonstrates the importance of programming training programs as a way to promote interest in computing, stimulate the development of computational skills, and increase participation in competitions such as the OBI. The next steps of the research include conducting more training cycles and analyzing the results obtained in the competitions. △ Less

Submitted 31 January, 2025; originally announced March 2025.

Comments: 10 pages, in Portuguese, 8 figures and 2 tables

Journal ref: Simpósio Brasileiro de Informática na Educação (SBIE) 2024

Promoting Gender Equality in Competitive Programming: Strategies and Impacts of Affirmative Actions in Programming Marathons in Brazil

Authors: Crishna Irion, Camila da Cruz Santos, Luiz Claudio Theodoro, Rafael Dias Araujo, Joao Henrique de Souza Pereira

Abstract: In the context of Computing, competitive programming is a relevant area that aims to have students, usually in teams, solve programming challenges, developing skills and competencies in the field. However, female participation remains significantly low and notably distant compared to male participation, even with proven intellectual equity between genders. This research aims to present strategies… ▽ More In the context of Computing, competitive programming is a relevant area that aims to have students, usually in teams, solve programming challenges, developing skills and competencies in the field. However, female participation remains significantly low and notably distant compared to male participation, even with proven intellectual equity between genders. This research aims to present strategies used to improve female participation in Programming Marathons in Brasil. The developed research is documentary, applied, and exploratory, with actions that generate results for female participation, with affirmative and inclusion actions, an important step towards gender equity in competitive programming. △ Less

Submitted 21 February, 2025; originally announced February 2025.

Comments: 12 pages, SBIE (2024), in Portuguese language

Humanity's Last Exam

Authors: Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, Michael Choi, Anish Agrawal, Arnav Chopra, Adam Khoja, Ryan Kim, Richard Ren, Jason Hausenloy, Oliver Zhang, Mantas Mazeika, Dmitry Dodonov, Tung Nguyen, Jaeho Lee, Daron Anderson, Mikhail Doroshenko, Alun Cennyth Stokes , et al. (1133 additional authors not shown)

Abstract: Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of… ▽ More Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai. △ Less

Submitted 28 July, 2026; v1 submitted 24 January, 2025; originally announced January 2025.

Comments: 29 pages, 6 figures

Extension of Symmetrized Neural Network Operators with Fractional and Mixed Activation Functions

Authors: Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

Abstract: We propose a novel extension to symmetrized neural network operators by incorporating fractional and mixed activation functions. This study addresses the limitations of existing models in approximating higher-order smooth functions, particularly in complex and high-dimensional spaces. Our framework introduces a fractional exponent in the activation functions, allowing adaptive non-linear approxima… ▽ More We propose a novel extension to symmetrized neural network operators by incorporating fractional and mixed activation functions. This study addresses the limitations of existing models in approximating higher-order smooth functions, particularly in complex and high-dimensional spaces. Our framework introduces a fractional exponent in the activation functions, allowing adaptive non-linear approximations with improved accuracy. We define new density functions based on $q$-deformed and $θ$-parametrized logistic models and derive advanced Jackson-type inequalities that establish uniform convergence rates. Additionally, we provide a rigorous mathematical foundation for the proposed operators, supported by numerical validations demonstrating their efficiency in handling oscillatory and fractional components. The results extend the applicability of neural network approximation theory to broader functional spaces, paving the way for applications in solving partial differential equations and modeling complex systems. △ Less

Submitted 17 January, 2025; originally announced January 2025.

Comments: 13 pages

An Empirical Study of Safetensors' Usage Trends and Developers' Perceptions

Authors: Beatrice Casey, Kaia Damian, Andrew Cotaj, Joanna C. S. Santos

Abstract: Developers are sharing pre-trained Machine Learning (ML) models through a variety of model sharing platforms, such as Hugging Face, in an effort to make ML development more collaborative. To share the models, they must first be serialized. While there are many methods of serialization in Python, most of them are unsafe. To tame this insecurity, Hugging Face released safetensors as a way to mitigat… ▽ More Developers are sharing pre-trained Machine Learning (ML) models through a variety of model sharing platforms, such as Hugging Face, in an effort to make ML development more collaborative. To share the models, they must first be serialized. While there are many methods of serialization in Python, most of them are unsafe. To tame this insecurity, Hugging Face released safetensors as a way to mitigate the threats posed by unsafe serialization formats. In this context, this paper investigates developer's shifts towards using safetensors on Hugging Face in an effort to understand security practices in the ML development community, as well as how developers react to new methods of serialization. Our results find that more developers are adopting safetensors, and many safetensor adoptions were made by automated conversions of existing models by Hugging Face's conversion tool. We also found, however, that a majority of developers ignore the conversion tool's pull requests, and that while many developers are facing issues with using safetensors, they are eager to learn about and adapt the format. △ Less

Submitted 3 January, 2025; originally announced January 2025.

Measuring Compliance of Consent Revocation on the Web

Authors: Gayatri Priyadarsini Kancherla, Nataliia Bielova, Cristiana Santos, Abhishek Bichhawat

Abstract: The GDPR requires websites to facilitate the right to revoke consent from Web users. While numerous studies measured compliance of consent with the various consent requirements, no prior work has studied consent revocation on the Web. Therefore, it remains unclear how difficult it is to revoke consent on the websites' interfaces, nor whether revoked consent is properly stored and communicated behi… ▽ More The GDPR requires websites to facilitate the right to revoke consent from Web users. While numerous studies measured compliance of consent with the various consent requirements, no prior work has studied consent revocation on the Web. Therefore, it remains unclear how difficult it is to revoke consent on the websites' interfaces, nor whether revoked consent is properly stored and communicated behind the user interface. Our work aims to fill this gap by measuring compliance of consent revocation on the Web on the top-200 websites. We found that 19.87% of websites make it difficult for users to revoke consent throughout different interfaces, 20.5% of websites require more effort than acceptance, and 2.48% do not provide consent revocation at all, thus violating legal requirements for valid consent. 57.5% websites do not delete the cookies after consent revocation enabling continuous illegal processing of users' data. Moreover, we analyzed 281 websites implementing the IAB Europe TCF, and found 22 websites that store a positive consent despite user's revocation. Surprisingly, we found that on 101 websites, third parties that have received consent upon user's acceptance, are not informed of user's revocation, leading to the illegal processing of users' data by such third parties. Our findings emphasise the need for improved legal compliance of consent revocation, and proper, consistent, and uniform implementation of revocation communication and data deletion practices. △ Less

Submitted 22 May, 2025; v1 submitted 22 November, 2024; originally announced November 2024.

The connected Grundy coloring problem: Formulations and a local-search enhanced biased random-key genetic algorithm

Authors: Mateus C. Silva, Rafael A. Melo, Mauricio G. C. Resende, Marcio C. Santos, Rodrigo F. Toso

Abstract: Given a graph G=(V,E), a connected Grundy coloring is a proper vertex coloring that can be obtained by a first-fit heuristic on a connected vertex sequence. A first-fit coloring heuristic is one that attributes to each vertex in a sequence the lowest-index color not used for its preceding neighbors. A connected vertex sequence is one in which each element, except for the first one, is connected to… ▽ More Given a graph G=(V,E), a connected Grundy coloring is a proper vertex coloring that can be obtained by a first-fit heuristic on a connected vertex sequence. A first-fit coloring heuristic is one that attributes to each vertex in a sequence the lowest-index color not used for its preceding neighbors. A connected vertex sequence is one in which each element, except for the first one, is connected to at least one element preceding it. The connected Grundy coloring problem consists of obtaining a connected Grundy coloring maximizing the number of colors. In this paper, we propose two integer programming (IP) formulations and a local-search enhanced biased random-key genetic algorithm (BRKGA) for the connected Grundy coloring problem. The first formulation follows the standard way of partitioning the vertices into color classes while the second one relies on the idea of representatives in an attempt to break symmetries. The BRKGA encompasses a local search procedure using a newly proposed neighborhood. A theoretical neighborhood analysis is also presented. Extensive computational experiments indicate that the problem is computationally demanding for the proposed IP formulations. Nonetheless, the formulation by representatives outperforms the standard one for the considered benchmark instances. Additionally, our BRKGA can find high-quality solutions in low computational times for considerably large instances, showing improved performance when enhanced with local search and a reset mechanism. Moreover we show that our BRKGA can be easily extended to successfully tackle the Grundy coloring problem, i.e., the one without the connectivity requirements. △ Less

Submitted 21 November, 2024; originally announced November 2024.

ACM Class: F.2; G.2

MojoBench: Language Modeling and Benchmarks for Mojo

Authors: Nishat Raihan, Joanna C. S. Santos, Marcos Zampieri

Abstract: The recently introduced Mojo programming language (PL) by Modular, has received significant attention in the scientific community due to its claimed significant speed boost over Python. Despite advancements in code Large Language Models (LLMs) across various PLs, Mojo remains unexplored in this context. To address this gap, we introduce MojoBench, the first framework for Mojo code generation. Mojo… ▽ More The recently introduced Mojo programming language (PL) by Modular, has received significant attention in the scientific community due to its claimed significant speed boost over Python. Despite advancements in code Large Language Models (LLMs) across various PLs, Mojo remains unexplored in this context. To address this gap, we introduce MojoBench, the first framework for Mojo code generation. MojoBench includes HumanEval-Mojo, a benchmark dataset designed for evaluating code LLMs on Mojo, and Mojo-Coder, the first LLM pretrained and finetuned for Mojo code generation, which supports instructions in 5 natural languages (NLs). Our results show that Mojo-Coder achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet. Furthermore, we provide insights into LLM behavior with underrepresented and unseen PLs, offering potential strategies for enhancing model adaptability. MojoBench contributes to our understanding of LLM capabilities and limitations in emerging programming paradigms fostering more robust code generation systems. △ Less

Submitted 23 October, 2024; originally announced October 2024.

Large Language Models in Computer Science Education: A Systematic Literature Review

Authors: Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos, Marcos Zampieri

Abstract: Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA… ▽ More Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research. △ Less

Submitted 21 October, 2024; originally announced October 2024.

Comments: Accepted at 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025)

Emílias Podcast -- Mulheres na Computação: Ampliando Horizontes e Inspirando Carreiras em STEM

Authors: Nathálya Chaves Dos Santos, Adolfo Gustavo Serra Seca Neto

Abstract: On October 3, 2024, the "Emílias Podcast -- Women in Computing" celebrates its 5th anniversary, standing out as a platform that promotes the participation of women in STEM (an acronym for "science, technology, engineering, and mathematics"). The podcast aims to provide a space for women in computing and related fields to share their experiences and highlight the various opportunities in Informatio… ▽ More On October 3, 2024, the "Emílias Podcast -- Women in Computing" celebrates its 5th anniversary, standing out as a platform that promotes the participation of women in STEM (an acronym for "science, technology, engineering, and mathematics"). The podcast aims to provide a space for women in computing and related fields to share their experiences and highlight the various opportunities in Information and Communication Technology (ICT). The methodology included a feedback survey with interviewees, conducted via Google Forms, to assess their experience and determine whether they would recommend the podcast. In addition, we analyzed audience data, which showed consistent growth over the five years. The results revealed that 100% of the interviewees would recommend "Emílias Podcast," reflecting a high level of satisfaction with the project. The average participation experience rating was 4.7 on a scale of 1 to 5, highlighting positive aspects such as the quality of the script, the interview conduction, and the networking opportunities. The audience data also underscore the podcast's impact: with over 10,000 accumulated downloads and plays, it is primarily listened to by people aged 23 to 44, with 50.9% of the audience being female, demonstrating its relevance and reach. In conclusion, the feedback from interviewees and the audience data reinforce the podcast's positive impact and its crucial role in the inclusion of women in technology. The results highlight the importance of promoting the field and its opportunities, contributing to a more inclusive and inspiring future. The data analysis demonstrates the podcast's effectiveness in engaging and expanding its audience, establishing it as a significant example of social impact in ICT. △ Less

Submitted 6 October, 2024; originally announced October 2024.

Comments: In Portuguese. Accepted for presentation at XIV SEMINÁRIO DE EXTENSÃO E INOVAÇÃO. UTFPR 2024

A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Models

Authors: Beatrice Casey, Joanna C. S. Santos, Mehdi Mirakhorli

Abstract: The development of machine learning (ML) techniques has led to ample opportunities for developers to develop and deploy their own models. Hugging Face serves as an open source platform where developers can share and download other models in an effort to make ML development more collaborative. In order for models to be shared, they first need to be serialized. Certain Python serialization methods a… ▽ More The development of machine learning (ML) techniques has led to ample opportunities for developers to develop and deploy their own models. Hugging Face serves as an open source platform where developers can share and download other models in an effort to make ML development more collaborative. In order for models to be shared, they first need to be serialized. Certain Python serialization methods are considered unsafe, as they are vulnerable to object injection. This paper investigates the pervasiveness of these unsafe serialization methods across Hugging Face, and demonstrates through an exploitation approach, that models using unsafe serialization methods can be exploited and shared, creating an unsafe environment for ML developers. We investigate to what extent Hugging Face is able to flag repositories and files using unsafe serialization methods, and develop a technique to detect malicious models. Our results show that Hugging Face is home to a wide range of potentially vulnerable models. △ Less

Submitted 6 October, 2024; originally announced October 2024.