OpenRaiser/Intern-Atlas huggingface dataset:
A Time-efficient Prioritised Scheduling Algorithm to Optimise Initial Flock Formation of Drones
Venue: arXiv cs.RO 2025
Year: 2025

Drone applications continue to expand across various domains, with flocking offering enhanced cooperative capabilities but introducing significant challenges during initial formation. Existing flocking algorithms often struggle with efficiency and scalability, particularly when potential collisions force drones into suboptimal trajectories. This paper presents a time-efficient prioritised scheduling algorithm that improves the initial formation process of drone flocks. The method assigns each drone a priority based on its number of potential collisions and its likelihood of reaching its target position without permanently obstructing other drones. Using this hierarchy, each drone computes an appropriate delay to ensure a collision-free path. Simulation results show that the proposed algorithm successfully generates collision-free trajectories for flocks of up to 5000 drones and outperforms the coupling-degree-based heuristic prioritised planning method (CDH-PP) in both performance and computational efficiency.
Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field Study
Venue: arXiv cs.HC 2025
Year: 2025

With the rise of AI-powered coding assistants, firms and programmers are exploring how to optimize their interaction with them. Research has so far mainly focused on evaluating output quality and productivity gains, leaving aside the developers' experience during the interaction. In this study, we take a multimodal, developer-centered approach to gain insights into how professional developers experience the interaction with Generative AI (GenAI) in their natural work environment in a firm. The aim of this paper is (1) to demonstrate a feasible mixed-method study design with controlled and uncontrolled study periods within a firm setting, (2) to give first insights from complementary behavioral and subjective experience data on developers' interaction with GitHub Copilot and (3) to compare the impact of interaction types (no Copilot use, in-code suggestions, chat prompts or both in-code suggestions and chat prompts) on efficiency, accuracy and perceived workload whilst working on different task categories. Results of the controlled sessions in this study indicate that moderate use of either in-code suggestions or chat prompts improves efficiency (task duration) and reduces perceived workload compared to not using Copilot, while excessive or combined use lessens these benefits. Accuracy (task completion) profits from chat interaction. In general, subjective perception of workload aligns with objective behavioral data in this study. During the uncontrolled period of the study, both higher cognitive load and productivity were perceived when interacting with AI during everyday working tasks. This study motivates the use of comparable study designs, in e.g. workshop or hackathon settings, to evaluate GenAI tools holistically and realistically with a focus on the developers' experience.
Community Notes: Crowd Participation and Dependence on Professional Fact-Checking Across Languages
Venue: arXiv cs.SI 2025
Year: 2025

Crowd-sourced fact-checking provides social media platforms with a promising method of managing misinformation at scale. However, the success of fact-checking programs like X's Community Notes requires the participation of a critical mass of note-writers who have the time and epistemic resources necessary to write and rate high-quality notes. As X's Community Notes program was first established in English-speaking countries, much academic research has focused on English-language notes or notes writ large. Relatively little research has investigated how different linguistic communities utilise Community Notes. Thus, it is unclear whether Community Notes or similar crowd-sourced fact-checking initiatives represent a viable alternative to social media platforms' partnerships with professional fact-checking organisations across linguistic contexts. This research identifies how different linguistic communities participate in volunteer fact-checking efforts on X's Community Notes program and addresses volunteers' reliance on professional fact-checking resources differs across languages. We find that while the Community Notes program has had strong uptake in some linguistic communities, the program has failed to catch on in others. Additionally, we confirm findings that notes that cite professional fact-checkers are considered more helpful, but show that reliance on professional fact-checking overall remains minimal.
Efficient Mod Approximation and Its Applications to CKKS Ciphertexts
Venue: arXiv cs.CR 2025
Year: 2025

The mod function plays a critical role in numerous data encoding and cryptographic primitives. However, the widely used CKKS homomorphic encryption (HE) scheme supports only arithmetic operations, making it difficult to perform mod computations on encrypted data. Approximating the mod function with polynomials has therefore become an important yet challenging problem. The discontinuous and periodic characteristics of the mod function make it particularly difficult to approximate accurately under HE. Existing homomorphic mod constructions provide accurate results only within limited subranges of the input range, leaving the problem of achieving accurate approximation across the full input range unresolved. In this work, we propose a novel method based on polynomial interpolation and Chebyshev series to accurately approximate the mod function. Building upon this, we design two efficient data packing schemes, BitStack and CRTStack, tailored for small-integer inputs in CKKS. These schemes significantly improve the utilization of the CKKS plaintext space and enable efficient ciphertext uploads. Furthermore, we apply the proposed HE mod function to implement a homomorphic rounding operation and a general transformation from additive secret sharing to CKKS ciphertexts, achieving accurate ciphertext rounding and complete secret-share-to-CKKS conversion. Experimental results demonstrate that our approach achieves high approximation accuracy (up to 1e-8). Overall, our work provides a practical and general solution for performing mod operations under CKKS, extending its applicability to a broader range of privacy-preserving computations.
Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set images. To obtain these representations, the proposed method extracts features from training dataset images and create clusters, by applying K-Means, with $K$ equals to the number of classes in the dataset. The segmentation model is a customized narrow ViT, built by replacing the patch embedding layer with a frozen DinoV2, pre-trained on the training dataset for individual species classification. This model is trained to reconstruct the class prototypes of the training dataset from the test dataset images. We then use this model to obtain attention scores that enable to identify and localize areas of interest and consequently guide the classification process. The proposed approach enabled a domain-adaptation from multi-class identification with individual species, into multi-label classification from high-resolution vegetation plots. Our method achieved fifth place in the PlantCLEF 2025 challenge on the private leaderboard, with an F1 score of 0.33331. Besides that, in absolute terms our method scored 0.03 lower than the top-performing submission, suggesting that it may achieved competitive performance in the benchmark task. Our code is available at \href{https://github.com/ADAM-UEFS/PlantCLEF2025}{https://github.com/ADAM-UEFS/PlantCLEF2025}.
Towards Analysing Invoices and Receipts with Amazon Textract
Venue: arXiv cs.IR 2025
Year: 2025

This paper presents an evaluation of the AWS Textract in the context of extracting data from receipts. We analyse Textract functionalities using a dataset that includes receipts of varied formats and conditions. Our analysis provided a qualitative view of Textract strengths and limitations. While the receipts totals were consistently detected, we also observed typical issues and irregularities that were often influenced by image quality and layout. Based on the analysis of the observations, we propose mitigation strategies.
A Comprehensive Guide to Mesh Simplification using Edge Collapse
Venue: arXiv cs.CG 2025
Year: 2025

Mesh simplification is the process of reducing the number of vertices, edges and triangles in a three-dimensional (3D) mesh while preserving the overall shape and salient features of the mesh. A popular strategy for this is edge collapse, where an edge connecting two vertices is merged into a single vertex. The edge to collapse is chosen based on a cost function that estimates the error introduced by this collapse. This paper presents a comprehensive, implementation-oriented guide to edge collapse for practitioners and researchers seeking both theoretical grounding and practical insight. We review and derive the underlying mathematics and provide reference implementations for foundational cost functions including Quadric Error Metrics (QEM) and Lindstrom-Turk's geometric criteria. We also explain the mathematics behind attribute-aware edge collapse in QEM variants and Hoppe's energy-based method used in progressive meshes. In addition to cost functions, we outline the complete edge collapse algorithm, including the specific sequence of operations and the data structures that are commonly used. To create a robust system, we also cover the necessary programmatic safeguards that prevent issues like mesh degeneracies, inverted normals, and improper handling of boundary conditions. The goal of this work is not only to consolidate established methods but also to bridge the gap between theory and practice, offering a clear, step-by-step guide for implementing mesh simplification pipelines based on edge collapse.
VNF-Cache: An In-Network Key-Value Store Cache Based on Network Function Virtualization
Venue: arXiv cs.NI 2025
Year: 2025

With the exponential growth of the amount of data available on the Internet, optimizing the response time and resource usage for data access becomes essential. Caches are an effective solution that brings data closer to clients, eliminating repetitive requests to servers. This paper presents VNF-Cache, a caching service for geographically remote key-value databases. VNF-Cache is an NFV-COIN (Network Function Virtualization-Computing In The Network) service, a technology undergoing standardization by the IETF that enables the implementation of arbitrary services directly in the network. VNF-Cache intercepts network packets, processes, stores, and sends values directly to clients when possible. Through a proof-of-concept implementation and experiments conducted with geographically dispersed servers in Brazil, the United States, and Japan, significant reductions in response time and increases in the number of requests processed per second were observed.
Rethinking Knowledge Distillation in Collaborative Machine Learning: Memory, Knowledge, and Their Interactions
Venue: arXiv cs.DC 2025
Year: 2025

Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their privacy concerns. Central to this paradigm is knowledge distillation (KD), a technique that facilitates efficient knowledge transfer among agents. However, the underlying mechanisms by which KD leverages memory and knowledge across agents remain underexplored. This paper aims to bridge this gap by offering a comprehensive review of KD in collaborative learning, with a focus on the roles of memory and knowledge. We define and categorize memory and knowledge within the KD process and explore their interrelationships, providing a clear understanding of how knowledge is extracted, stored, and shared in collaborative settings. We examine various collaborative learning patterns, including distributed, hierarchical, and decentralized structures, and provide insights into how memory and knowledge dynamics shape the effectiveness of KD in collaborative learning. Particularly, we emphasize task heterogeneity in distributed learning pattern covering federated learning (FL), multi-agent domain adaptation (MADA), federated multi-modal learning (FML), federated continual learning (FCL), federated multi-task learning (FMTL), and federated graph knowledge embedding (FKGE). Additionally, we highlight model heterogeneity, data heterogeneity, resource heterogeneity, and privacy concerns of these tasks. Our analysis categorizes existing work based on how they handle memory and knowledge. Finally, we discuss existing challenges and propose future directions for advancing KD techniques in the context of collaborative learning.
S$^3$IT: A Benchmark for Spatially Situated Social Intelligence Test
Venue: arXiv cs.AI 2025
Year: 2025

The integration of embodied agents into human environments demands embodied social intelligence: reasoning over both social norms and physical constraints. However, existing evaluations fail to address this integration, as they are limited to either disembodied social reasoning (e.g., in text) or socially-agnostic physical tasks. Both approaches fail to assess an agent's ability to integrate and trade off both physical and social constraints within a realistic, embodied context. To address this challenge, we introduce Spatially Situated Social Intelligence Test (S$^{3}$IT), a benchmark specifically designed to evaluate embodied social intelligence. It is centered on a novel and challenging seat-ordering task, requiring an agent to arrange seating in a 3D environment for a group of large language model-driven (LLM-driven) NPCs with diverse identities, preferences, and intricate interpersonal relationships. Our procedurally extensible framework generates a vast and diverse scenario space with controllable difficulty, compelling the agent to acquire preferences through active dialogue, perceive the environment via autonomous exploration, and perform multi-objective optimization within a complex constraint network. We evaluate state-of-the-art LLMs on S$^{3}$IT and found that they still struggle with this problem, showing an obvious gap compared with the human baseline. Results imply that LLMs have deficiencies in spatial intelligence, yet simultaneously demonstrate their ability to achieve near human-level competence in resolving conflicts that possess explicit textual cues.
BacAlarm: Mining and Simulating Composite API Traffic to Prevent Broken Access Control Violations
Venue: arXiv cs.CR 2025
Year: 2025

Broken Access Control (BAC) violations, which consistently rank among the top five security risks in the OWASP API Security Top 10, refer to unauthorized access attempts arising from BAC vulnerabilities, whose successful exploitation can impose significant risks on exposed application programming interfaces (APIs). In recent years, learning-based methods have demonstrated promising prospects in detecting various types of malicious activities. However, in real-network operation and maintenance scenarios, leveraging learning-based methods for BAC detection faces two critical challenges. Firstly, under the RESTful API design principles, most systems omit recording composite traffic for performance, and together with ethical and legal bans on directly testing real-world systems, this leads to a critical shortage of training data for detecting BAC violations. Secondly, common malicious behaviors such as SQL injection typically generate individual access traffic that is inherently anomalous. In contrast, BAC is usually composed of multiple correlated access requests that appear normal when examined in isolation. To tackle these problems, we introduce \BAC, an approach for establishing a BAC violation detection model by generating and utilizing API traffic data. The \BAC consists of an API Traffic Generator and a BAC Detector. Experimental results show that \BAC outperforms current state-of-the-art invariant-based and learning-based methods with the $\text{F}_1$ and MCC improving by 21.2\% and 24.1\%.
Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
Venue: arXiv cs.LG 2025
Year: 2025

Class imbalance is a common challenge in machine learning and data mining, often leading to suboptimal performance in classifiers. While deep learning excels in feature extraction, its performance still deteriorates under imbalanced data. In this work, we propose a novel activation function, named OGAB, designed to alleviate class imbalance in deep learning classifiers. OGAB incorporates orthogonality and group-aware bias learning to enhance feature distinguishability in imbalanced scenarios without explicitly requiring label information. Our key insight is that activation functions can be used to introduce strong inductive biases that can address complex data challenges beyond traditional non-linearity. Our work demonstrates that orthogonal transformations can preserve information about minority classes by maintaining feature independence, thereby preventing the dominance of majority classes in the embedding space. Further, the proposed group-aware bias mechanism automatically identifies data clusters and adjusts embeddings to enhance class separability without the need for explicit supervision. Unlike existing approaches that address class imbalance through preprocessing data modifications or post-processing corrections, our proposed approach tackles class imbalance during the training phase at the embedding learning level, enabling direct integration with the learning process. We demonstrate the effectiveness of our solution on both real-world and synthetic imbalanced datasets, showing consistent performance improvements over both traditional and learnable activation functions.
A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments
Venue: arXiv cs.CV 2025
Year: 2025

Despite increasing interest in computer vision-based distracted driving detection, most existing models rely exclusively on driver-facing views and overlook crucial environmental context that influences driving behavior. This study investigates whether incorporating road-facing views alongside driver-facing footage improves distraction detection accuracy in naturalistic driving conditions. Using synchronized dual-camera recordings from real-world driving, we benchmark three leading spatiotemporal action recognition architectures: SlowFast-R50, X3D-M, and SlowOnly-R50. Each model is evaluated under two input configurations: driver-only and stacked dual-view. Results show that while contextual inputs can improve detection in certain models, performance gains depend strongly on the underlying architecture. The single-pathway SlowOnly model achieved a 9.8 percent improvement with dual-view inputs, while the dual-pathway SlowFast model experienced a 7.2 percent drop in accuracy due to representational conflicts. These findings suggest that simply adding visual context is not sufficient and may lead to interference unless the architecture is specifically designed to support multi-view integration. This study presents one of the first systematic comparisons of single- and dual-view distraction detection models using naturalistic driving data and underscores the importance of fusion-aware design for future multimodal driver monitoring systems.
DS-HGCN: A Dual-Stream Hypergraph Convolutional Network for Predicting Student Engagement via Social Contagion
Venue: arXiv cs.MM 2025
Year: 2025

Student engagement is a critical factor influencing academic success and learning outcomes. Accurately predicting student engagement is essential for optimizing teaching strategies and providing personalized interventions. However, most approaches focus on single-dimensional feature analysis and assessing engagement based on individual student factors. In this work, we propose a dual-stream multi-feature fusion model based on hypergraph convolutional networks (DS-HGCN), incorporating social contagion of student engagement. DS-HGCN enables accurate prediction of student engagement states by modeling multi-dimensional features and their propagation mechanisms between students. The framework constructs a hypergraph structure to encode engagement contagion among students and captures the emotional and behavioral differences and commonalities by multi-frequency signals. Furthermore, we introduce a hypergraph attention mechanism to dynamically weigh the influence of each student, accounting for individual differences in the propagation process. Extensive experiments on public benchmark datasets demonstrate that our proposed method achieves superior performance and significantly outperforms existing state-of-the-art approaches.
Scaling Reinforcement Learning for Content Moderation with Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Content moderation at scale remains one of the most pressing challenges in today's digital ecosystem, where billions of user- and AI-generated artifacts must be continuously evaluated for policy violations. Although recent advances in large language models (LLMs) have demonstrated strong potential for policy-grounded moderation, the practical challenges of training these systems to achieve expert-level accuracy in real-world settings remain largely unexplored, particularly in regimes characterized by label sparsity, evolving policy definitions, and the need for nuanced reasoning beyond shallow pattern matching. In this work, we present a comprehensive empirical investigation of scaling reinforcement learning (RL) for content classification, systematically evaluating multiple RL training recipes and reward-shaping strategies-including verifiable rewards and LLM-as-judge frameworks-to transform general-purpose language models into specialized, policy-aligned classifiers across three real-world content moderation tasks. Our findings provide actionable insights for industrial-scale moderation systems, demonstrating that RL exhibits sigmoid-like scaling behavior in which performance improves smoothly with increased training data, rollouts, and optimization steps before gradually saturating. Moreover, we show that RL substantially improves performance on tasks requiring complex policy-grounded reasoning while achieving up to 100x higher data efficiency than supervised fine-tuning, making it particularly effective in domains where expert annotations are scarce or costly.