OpenRaiser/Intern-Atlas huggingface dataset:
Active Sybil Attack and Efficient Defense Strategy in IPFS DHT
Venue: arXiv cs.CR 2025
Year: 2025

The InterPlanetary File System (IPFS) is a decentralized peer-to-peer (P2P) storage that relies on Kademlia, a Distributed Hash Table (DHT) structure commonly used in P2P systems for its proved scalability. However, DHTs are known to be vulnerable to Sybil attacks, in which a single entity controls multiple malicious nodes. Recent studies have shown that IPFS is affected by a passive content eclipse attack, leveraging Sybils, in which adversarial nodes hide received indexed information from other peers, making the content appear unavailable. Fortunately, the latest mitigation strategy coupling an attack detection based on statistical tests and a wider publication strategy upon detection was able to circumvent it. In this work, we present a new active attack, with malicious nodes responding with semantically correct but intentionally false data, exploiting both an optimized placement of Sybils to stay below the detection threshold and an early trigger of the content discovery termination in Kubo, the main IPFS implementation. Our attack achieves to completely eclipse content on the latest Kubo release. When evaluated against the most recent known mitigation, it successfully denies access to the target content in approximately 80\% of lookup attempts. To address this vulnerability, we propose a new mitigation called SR-DHT-Store, which enables efficient, Sybil-resistant content publication without relying on attack detection but instead on a systematic and precise use of region-based queries, defined by a dynamically computed XOR distance to the target ID. SR-DHT-Store can be combined with other defense mechanisms resulting in a defense strategy that completely mitigates both passive and active Sybil attacks at a lower overhead, while allowing an incremental deployment.
TActiLE: Tiny Active LEarning for wearable devices
Venue: arXiv cs.LG 2025
Year: 2025

Tiny Machine Learning (TinyML) algorithms have seen extensive use in recent years, enabling wearable devices to be not only connected but also genuinely intelligent by running machine learning (ML) computations directly on-device. Among such devices, smart glasses have particularly benefited from TinyML advancements. TinyML facilitates the on-device execution of the inference phase of ML algorithms on embedded and wearable devices, and more recently, it has expanded into On-device Learning (ODL), which allows both inference and learning phases to occur directly on the device. The application of ODL techniques to wearable devices is particularly compelling, as it enables the development of more personalized models that adapt based on the data of the user. However, one of the major challenges of ODL algorithms is the scarcity of labeled data collected on-device. In smart wearable contexts, requiring users to manually label large amounts of data is often impractical and could lead to user disengagement with the technology. To address this issue, this paper explores the application of Active Learning (AL) techniques, i.e., techniques that aim at minimizing the labeling effort, by actively selecting from a large quantity of unlabeled data only a small subset to be labeled and added to the training set of the algorithm. In particular, we propose TActiLE, a novel AL algorithm that selects from the stream of on-device sensor data the ones that would help the ML algorithm improve the most once coupled with labels provided by the user. TActiLE is the first Active Learning technique specifically designed for the TinyML context. We evaluate its effectiveness and efficiency through experiments on multiple image classification datasets. The results demonstrate its suitability for tiny and wearable devices.
Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings
Venue: arXiv cs.RO 2025
Year: 2025

Diffusion and flow matching policies have recently demonstrated remarkable performance in robotic applications by accurately capturing multimodal robot trajectory distributions. However, their computationally expensive inference, due to the numerical integration of an ODE or SDE, limits their applicability as real-time controllers for robots. We introduce a methodology that utilizes conditional Optimal Transport couplings between noise and samples to enforce straight solutions in the flow ODE for robot action generation tasks. We show that naively coupling noise and samples fails in conditional tasks and propose incorporating condition variables into the coupling process to improve few-step performance. The proposed few-step policy achieves a 4% higher success rate with a 10x speed-up compared to Diffusion Policy on a diverse set of simulation tasks. Moreover, it produces high-quality and diverse action trajectories within 1-2 steps on a set of real-world robot tasks. Our method also retains the same training complexity as Diffusion Policy and vanilla Flow Matching, in contrast to distillation-based approaches.
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks
Venue: arXiv cs.CR 2025
Year: 2025

Hierarchical Federated Learning (HFL) has recently emerged as a promising solution for intelligent decision-making in vehicular networks, helping to address challenges such as limited communication resources, high vehicle mobility, and data heterogeneity. However, HFL remains vulnerable to adversarial and unreliable vehicles, whose misleading updates can significantly compromise the integrity and convergence of the global model. To address these challenges, we propose a novel defense framework that integrates dynamic vehicle selection with robust anomaly detection within a cluster-based HFL architecture, specifically designed to counter Gaussian noise and gradient ascent attacks. The framework performs a comprehensive reliability assessment for each vehicle by evaluating historical accuracy, contribution frequency, and anomaly records. Anomaly detection combines Z-score and cosine similarity analyses on model updates to identify both statistical outliers and directional deviations in model updates. To further refine detection, an adaptive thresholding mechanism is incorporated into the cosine similarity metric, dynamically adjusting the threshold based on the historical accuracy of each vehicle to enforce stricter standards for consistently high-performing vehicles. In addition, a weighted gradient averaging mechanism is implemented, which assigns higher weights to gradient updates from more trustworthy vehicles. To defend against coordinated attacks, a cross-cluster consistency check is applied to identify collaborative attacks in which multiple compromised clusters coordinate misleading updates. Together, these mechanisms form a multi-level defense strategy to filter out malicious contributions effectively. Simulation results show that the proposed algorithm significantly reduces convergence time compared to benchmark methods across both 1-hop and 3-hop topologies.
A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture
Venue: arXiv cs.LG 2025
Year: 2025

To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing transparency and facilitating informed decision-making. By presenting multiple predicted crop scenarios, our system enables farmers to optimize production strategies effectively. This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly.
EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models
Venue: arXiv cs.CL 2025
Year: 2025

As Natural Language Processing (NLP) models continue to evolve and become integral to high-stakes applications, ensuring their interpretability remains a critical challenge. Given the growing variety of explainability methods and diverse stakeholder requirements, frameworks that help stakeholders select appropriate explanations tailored to their specific use cases are increasingly important. To address this need, we introduce EvalxNLP, a Python framework for benchmarking state-of-the-art feature attribution methods for transformer-based NLP models. EvalxNLP integrates eight widely recognized explainability techniques from the Explainable AI (XAI) literature, enabling users to generate and evaluate explanations based on key properties such as faithfulness, plausibility, and complexity. Our framework also provides interactive, LLM-based textual explanations, facilitating user understanding of the generated explanations and evaluation outcomes. Human evaluation results indicate high user satisfaction with EvalxNLP, suggesting it is a promising framework for benchmarking explanation methods across diverse user groups. By offering a user-friendly and extensible platform, EvalxNLP aims at democratizing explainability tools and supporting the systematic comparison and advancement of XAI techniques in NLP.
PREMISE: Matching-based Prediction for Accurate Review Recommendation
Venue: arXiv cs.CL 2025
Year: 2025

We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Distinct to previous fusion-based methods which obtains multimodal representations via cross-modal attention for downstream tasks, PREMISE computes the multi-scale and multi-field representations, filters duplicated semantics, and then obtained a set of matching scores as feature vectors for the downstream recommendation task. This new architecture significantly boosts the performance for such multimodal tasks whose context matching content are highly correlated to the targets of that task, compared to the state-of-the-art fusion-based methods. Experimental results on two publicly available datasets show that PREMISE achieves promising performance with less computational cost.
Thinking Outside the Template with Modular GP-GOMEA
Venue: arXiv cs.NE 2025
Year: 2025

The goal in Symbolic Regression (SR) is to discover expressions that accurately map input to output data. Because often the intent is to understand these expressions, there is a trade-off between accuracy and the interpretability of expressions. GP-GOMEA excels at producing small SR expressions (increasing the potential for interpretability) with high accuracy, but requires a fixed tree template, which limits the types of expressions that can be evolved. This paper presents a modular representation for GP-GOMEA that allows multiple trees to be evolved simultaneously that can be used as (functional) subexpressions. While each tree individually is constrained to a (small) fixed tree template, the final expression, if expanded, can exhibit a much larger structure. Furthermore, the use of subexpressions decomposes the original regression problem and opens the possibility for enhanced interpretability through the piece-wise understanding of small subexpressions. We compare the performance of GP-GOMEA with and without modular templates on a variety of datasets. We find that our proposed approach generally outperforms single-template GP-GOMEA and can moreover uncover ground-truth expressions underlying synthetic datasets with modular subexpressions at a faster rate than GP-GOMEA without modular subexpressions.
Forecasting at Full Spectrum: Holistic Multi-Granular Traffic Modeling under High-Throughput Inference Regimes
Venue: arXiv cs.LG 2025
Year: 2025

Notably, current intelligent transportation systems rely heavily on accurate traffic forecasting and swift inference provision to make timely decisions. While Graph Convolutional Networks (GCNs) have shown benefits in modeling complex traffic dependencies, the existing GCN-based approaches cannot fully extract and fuse multi-granular spatiotemporal features across various spatial and temporal scales sufficiently in a complete manner, proven to yield less accurate results. Besides, as extracting multi-granular features across scales has been a promising strategy across domains such as computer vision, natural language processing, and time-series forecasting, pioneering studies have attempted to leverage a similar mechanism for spatiotemporal traffic data mining. However, additional feature extraction branches introduced in prior studies critically increased model complexity and extended inference time, making it challenging to provide fast forecasts. In this paper, we propose MultiGran-STGCNFog, an efficient fog distributed inference system with a novel traffic forecasting model that employs multi-granular spatiotemporal feature fusion on generated dynamic traffic graphs to fully capture interdependent traffic dynamics. The proposed scheduling algorithm GA-DPHDS, optimizing layer execution order and layer-device scheduling scheme simultaneously, contributes to considerable inference throughput improvement by coordinating heterogeneous fog devices in a pipelined manner. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method over selected GCN baselines.
Micro-Patterns in Solidity Code
Venue: arXiv cs.SE 2025
Year: 2025

Solidity is the predominant programming language for blockchain-based smart contracts, and its characteristics pose significant challenges for code analysis and maintenance. Traditional software analysis approaches, while effective for conventional programming languages, often fail to address Solidity-specific features such as gas optimization and security constraints. This paper introduces micro-patterns - recurring, small-scale design structures that capture key behavioral and structural peculiarities specific to a language - for Solidity language and demonstrates their value in understanding smart contract development practices. We identified 18 distinct micro-patterns organized in five categories (Security, Functional, Optimization, Interaction, and Feedback), detailing their characteristics to enable automated detection. To validate this proposal, we analyzed a dataset of 23258 smart contracts from five popular blockchains (Ethereum, Polygon, Arbitrum, Fantom and Optimism). Our analysis reveals widespread adoption of micro-patterns, with 99% of contracts implementing at least one pattern and an average of 2.76 patterns per contract. The Storage Saver pattern showed the highest adoption (84.62% mean coverage), while security patterns demonstrated platform-specific adoption rates. Statistical analysis revealed significant platform-specific differences in pattern adoption, particularly in Borrower, Implementer, and Storage Optimization patterns.
Model See Model Do: Speech-Driven Facial Animation with Style Control
Venue: arXiv cs.GR 2025
Year: 2025

Speech-driven 3D facial animation plays a key role in applications such as virtual avatars, gaming, and digital content creation. While existing methods have made significant progress in achieving accurate lip synchronization and generating basic emotional expressions, they often struggle to capture and effectively transfer nuanced performance styles. We propose a novel example-based generation framework that conditions a latent diffusion model on a reference style clip to produce highly expressive and temporally coherent facial animations. To address the challenge of accurately adhering to the style reference, we introduce a novel conditioning mechanism called style basis, which extracts key poses from the reference and additively guides the diffusion generation process to fit the style without compromising lip synchronization quality. This approach enables the model to capture subtle stylistic cues while ensuring that the generated animations align closely with the input speech. Extensive qualitative, quantitative, and perceptual evaluations demonstrate the effectiveness of our method in faithfully reproducing the desired style while achieving superior lip synchronization across various speech scenarios.
Toward Teach and Repeat Across Seasonal Deep Snow Accumulation
Venue: arXiv cs.RO 2025
Year: 2025

Teach and repeat is a rapid way to achieve autonomy in challenging terrain and off-road environments. A human operator pilots the vehicles to create a network of paths that are mapped and associated with odometry. Immediately after teaching, the system can drive autonomously within its tracks. This precision lets operators remain confident that the robot will follow a traversable route. However, this operational paradigm has rarely been explored in off-road environments that change significantly through seasonal variation. This paper presents preliminary field trials using lidar and radar implementations of teach and repeat. Using a subset of the data from the upcoming FoMo dataset, we attempted to repeat routes that were 4 days, 44 days, and 113 days old. Lidar teach and repeat demonstrated a stronger ability to localize when the ground points were removed. FMCW radar was often able to localize on older maps, but only with small deviations from the taught path. Additionally, we highlight specific cases where radar localization failed with recent maps due to the high pitch or roll of the vehicle. We highlight lessons learned during the field deployment and highlight areas to improve to achieve reliable teach and repeat with seasonal changes in the environment. Please follow the dataset at https://norlab-ulaval.github.io/FoMo-website for updates and information on the data release.
Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the challenge of mapping polygonal buildings from remote sensing images and introduces a novel algorithm, the Global Collinearity-aware Polygonizer (GCP). GCP, built upon an instance segmentation framework, processes binary masks produced by any instance segmentation model. The algorithm begins by collecting polylines sampled along the contours of the binary masks. These polylines undergo a refinement process using a transformer-based regression module to ensure they accurately fit the contours of the targeted building instances. Subsequently, a collinearity-aware polygon simplification module simplifies these refined polylines and generate the final polygon representation. This module employs dynamic programming technique to optimize an objective function that balances the simplicity and fidelity of the polygons, achieving globally optimal solutions. Furthermore, the optimized collinearity-aware objective is seamlessly integrated into network training, enhancing the cohesiveness of the entire pipeline. The effectiveness of GCP has been validated on two public benchmarks for polygonal building mapping. Further experiments reveal that applying the collinearity-aware polygon simplification module to arbitrary polylines, without prior knowledge, enhances accuracy over traditional methods such as the Douglas-Peucker algorithm. This finding underscores the broad applicability of GCP. The code for the proposed method will be made available at https://github.com/zhu-xlab.
Towards Optimal Deterministic LOCAL Algorithms on Trees
Venue: arXiv cs.DC 2025
Year: 2025

While obtaining optimal algorithms for the most important problems in the LOCAL model has been one of the central goals in the area of distributed algorithms since its infancy, tight complexity bounds are elusive for many problems even when considering \emph{deterministic} complexities on \emph{trees}. We take a step towards remedying this issue by providing a way to relate the complexity of a problem $Π$ on trees to its truly local complexity, which is the (asymptotically) smallest function $f$ such that $Π$ can be solved in $O(f(Δ)+\log^*n)$ rounds. More specifically, we develop a transformation that takes an algorithm $\mathcal A$ for $Π$ with a runtime of $O(f(Δ)+\log^*n)$ rounds as input and transforms it into an $O(f(g(n))+\log^* n)$-round algorithm $\mathcal{A}'$ on trees, where $g$ is the function that satisfies $g(n)^{f(g(n))}=n$. If $f$ is the truly local complexity of $Π$ (i.e., if $\mathcal{A}$ is asymptotically optimal), then $\mathcal{A}'$ is an asymptotically optimal algorithm on trees, conditioned on a natural assumption on the nature of the worst-case instances of $Π$. Our transformation works for any member of a wide class of problems, including the most important symmetry-breaking problems. As an example of our transformation we obtain the first strongly sublogarithmic algorithm for $(\text{edge-degree+1})$-edge coloring (and therefore also $(2Δ-1)$-edge coloring) on trees, exhibiting a runtime of $O(\log^{12/13} n)$ rounds. This breaks through the $Ω(\log n/\log\log n)$-barrier that is a fundamental lower bound for other symmetry-breaking problems such as maximal independent set or maximal matching (that already holds on trees), and proves a separation between these problems and the aforementioned edge coloring problems on trees. We extend a subset of our results to graphs of bounded arboricity.
Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding
Venue: arXiv cs.LG 2025
Year: 2025

If a product deviates from its desired properties in the injection moulding process, its root cause analysis can be aided by models that relate the input machine settings with the output quality characteristics. The machine learning models tested in the quality prediction are mostly black boxes; therefore, no direct explanation of their prognosis is given, which restricts their applicability in the quality control. The previously attempted explainability methods are either restricted to tree-based algorithms only or do not emphasize on the fact that some explainability methods can lead to wrong root cause identification of a product's deviation from its desired properties. This study first shows that the interactions among the multiple input machine settings do exist in real experimental data collected as per a central composite design. Then, the model-agnostic explainable AI methods are compared for the first time to show that different explainability methods indeed lead to different feature impact analysis in injection moulding. Moreover, it is shown that the better feature attribution translates to the correct cause identification and actionable insights for the injection moulding process. Being model agnostic, explanations on both random forest and multilayer perceptron are performed for the cause analysis, as both models have the mean absolute percentage error of less than 0.05% on the experimental dataset.