OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Pairwise Comparisons: Unveiling Structural Landscape of Mobile Robot Models
Venue: arXiv cs.DC 2025
Year: 2025

Understanding the computational power of mobile robot systems is a fundamental challenge in distributed computing. While prior work has focused on pairwise separations between models, we explore how robot capabilities, light observability, and scheduler synchrony interact in more complex ways. We first show that the Exponential Times Expansion (ETE) problem is solvable only in the strongest model -- fully-synchronous robots with full mutual lights ($\mathcal{LUMT}^F$). We then introduce the Hexagonal Edge Traversal (HET) and TAR(d)* problems to demonstrate how internal memory and lights interact with synchrony: under weak synchrony, internal memory alone is insufficient, while full synchrony can substitute for both lights and memory. In the asynchronous setting, we classify problems such as LP-MLCv, VEC, and ZCC to show fine-grained separations between $\mathcal{FSTA}$ and $\mathcal{FCOM}$ robots. We also analyze Vertex Traversal Rendezvous (VTR) and Leave Place Convergence (LP-Cv), illustrating the limitations of internal memory in symmetric settings. These results extend the known separation map of 14 canonical robot models, revealing structural phenomena only visible through higher-order comparisons. Our work provides new impossibility criteria and deepens the understanding of how observability, memory, and synchrony collectively shape the computational power of mobile robots.
Every Keystroke You Make: A Tech-Law Measurement and Analysis of Event Listeners for Wiretapping
Venue: arXiv cs.CR 2025
Year: 2025

The privacy community has a long track record of investigating emerging types of web tracking techniques. Recent work has focused on compliance of web trackers with new privacy laws such as Europe's GDPR and California's CCPA. Despite the growing body of research documenting widespread lack of compliance with new privacy laws, there is a lack of robust enforcement. Different from prior work, we conduct a tech-law analysis to map decades-old U.S. laws about interception of electronic communications--so-called wiretapping--to web tracking. Bridging the tech-law gap for older wiretapping laws is important and timely because, in cases where legal harm to privacy is proven, they can provide statutory private right of action, are at the forefront of recent privacy enforcement, and could ultimately lead to a meaningful change in the web tracking landscape. In this paper, we focus on a particularly invasive tracking technique: the use of JavaScript event listeners by third-party trackers for real-time keystroke interception on websites. We use an instrumented web browser to crawl a sample of the top-million websites to investigate the use of event listeners that aligns with the criteria for wiretapping, according to U.S. wiretapping law at the federal level and in California. We find evidence that 38.52% websites installed third-party event listeners to intercept keystrokes, and that at least 3.18% websites transmitted intercepted information to a third-party server, which aligns with the criteria for wiretapping. We further find evidence that the intercepted information such as email addresses typed into form fields are used for unsolicited email marketing. Beyond our work that maps the intersection between technical measurement and U.S. wiretapping law, additional future legal research is required to determine when the wiretapping observed in our paper passes the threshold for illegality.
Scalable and consistent few-shot classification of survey responses using text embeddings
Venue: arXiv cs.CL 2025
Year: 2025

Qualitative analysis of open-ended survey responses is a commonly-used research method in the social sciences, but traditional coding approaches are often time-consuming and prone to inconsistency. Existing solutions from Natural Language Processing such as supervised classifiers, topic modeling techniques, and generative large language models have limited applicability in qualitative analysis, since they demand extensive labeled data, disrupt established qualitative workflows, and/or yield variable results. In this paper, we introduce a text embedding-based classification framework that requires only a handful of examples per category and fits well with standard qualitative workflows. When benchmarked against human analysis of a conceptual physics survey consisting of 2899 open-ended responses, our framework achieves a Cohen's Kappa ranging from 0.74 to 0.83 as compared to expert human coders in an exhaustive coding scheme. We further show how performance of this framework improves with fine-tuning of the text embedding model, and how the method can be used to audit previously-analyzed datasets. These findings demonstrate that text embedding-assisted coding can flexibly scale to thousands of responses without sacrificing interpretability, opening avenues for deductive qualitative analysis at scale.
The Power of Regular Constraint Propagation (Technical Report)
Venue: arXiv cs.LO 2025
Year: 2025

The past decade has witnessed substantial developments in string solving. Motivated by the complexity of string solving strategies adopted in existing string solvers, we investigate a simple and generic method for solving string constraints: regular constraint propagation. The method repeatedly computes pre- or post-images of regular languages under the string functions present in a string formula, inferring more and more knowledge about the possible values of string variables, until either a conflict is found or satisfiability of the string formula can be concluded. Such a propagation strategy is applicable to string constraints with multiple operations like concatenation, replace, and almost all flavors of string transductions. We demonstrate the generality and effectiveness of this method theoretically and experimentally. On the theoretical side, we show that RCP is sound and complete for a large fragment of string constraints, subsuming both straight-line and chain-free constraints, two of the most expressive decidable fragments for which some modern string solvers provide formal completeness guarantees. On the practical side, we implement regular constraint propagation within the open-source string solver OSTRICH. Our experimental evaluation shows that this addition significantly improves OSTRICH's performance and makes it competitive with existing solvers. In fact, it substantially outperforms other solvers on random PCP and bioinformatics benchmarks. The results also suggest that incorporating regular constraint propagation alongside other techniques could lead to substantial performance gains for existing solvers.
PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in motion generation show remarkable progress. However, several limitations remain: (1) Existing pose-guided character motion transfer methods merely replicate motion without learning its style characteristics, resulting in inexpressive characters. (2) Motion style transfer methods rely heavily on motion capture data, which is difficult to obtain. (3) Generated motions sometimes violate physical laws. To address these challenges, this paper pioneers a new task: Video-to-Video Motion Personalization. We propose a novel framework, PersonaAnimator, which learns personalized motion patterns directly from unconstrained videos. This enables personalized motion transfer. To support this task, we introduce PersonaVid, the first video-based personalized motion dataset. It contains 20 motion content categories and 120 motion style categories. We further propose a Physics-aware Motion Style Regularization mechanism to enforce physical plausibility in the generated motions. Extensive experiments show that PersonaAnimator outperforms state-of-the-art motion transfer methods and sets a new benchmark for the Video-to-Video Motion Personalization task.
NM-Hebb: Coupling Local Hebbian Plasticity with Metric Learning for More Accurate and Interpretable CNNs
Venue: arXiv cs.LG 2025
Year: 2025

Deep Convolutional Neural Networks (CNNs) achieve high accuracy but often rely on purely global, gradient-based optimisation, which can lead to overfitting, redundant filters, and reduced interpretability. To address these limitations, we propose NM-Hebb, a two-phase training framework that integrates neuro-inspired local plasticity with distance-aware supervision. Phase 1 extends standard supervised training by jointly optimising a cross-entropy objective with two biologically inspired mechanisms: (i) a Hebbian regulariser that aligns the spatial mean of activations with the mean of the corresponding convolutional filter weights, encouraging structured, reusable primitives; and (ii) a learnable neuromodulator that gates an elastic-weight-style consolidation loss, preserving beneficial parameters without freezing the network. Phase 2 fine-tunes the backbone with a pairwise metric-learning loss, explicitly compressing intra-class distances and enlarging inter-class margins in the embedding space. Evaluated on CIFAR-10, CIFAR-100, and TinyImageNet across five backbones (ResNet-18, VGG-11, MobileNet-v2, EfficientNet-V2, DenseNet-121), NM-Hebb achieves consistent gains over baseline and other methods: Top-1 accuracy improves by +2.0-10.0 pp (CIFAR-10), +2.0-9.0 pp (CIFAR-100), and up to +4.3-8.9 pp (TinyImageNet), with Normalised Mutual Information (NMI) increased by up to +0.15. Qualitative visualisations and filter-level analyses further confirm that NM-Hebb produces more structured and selective features, yielding tighter and more interpretable class clusters. Overall, coupling local Hebbian plasticity with metric-based fine-tuning yields CNNs that are not only more accurate but also more interpretable, offering practical benefits for resource-constrained and safety-critical AI deployments.
Walk the Robot: Exploring Soft Robotic Morphological Communication driven by Spiking Neural Networks
Venue: arXiv cs.NE 2025
Year: 2025

Recently, researchers have explored control methods that embrace nonlinear dynamic coupling instead of suppressing it. Such designs leverage dynamical coupling for communication between different parts of the robot. Morphological communication refers to when those dynamics can be used as an emergent data bus to facilitate coordination among independent controller modules within the same robot. Previous research with tensegrity-based robot designs has shown that evolutionary learning models that evolve spiking neural networks (SNN) as robot control mechanisms are effective for controlling non-rigid robots. Our own research explores the emergence of morphological communication in an SNN-based simulated soft robot in theEvoGym environment.
An assessment of estimation models and investment gaps for the deployment of high-speed broadband networks in NUTS3 regions to meet the objectives of the European Gigabit Society
Venue: arXiv cs.CE 2025
Year: 2025

This paper analyses the deployment of high speed broadband networks in the European Union (EU). Its aim is to assess the investment required to meet the targets set by the European Commission (EC) for 2025, within the framework of the European Gigabit Society (EGS). This plan aims to ensure the availability and take up of very high capacity fixed and wireless networks, in both urban and rural areas, among households and the main socioeconomic drivers. The estimation model presented here uses a methodology supported by data at the local (NUTS3) level to give a bottom up estimation of the investment gap for each of the EGS objectives, using three different scenarios depending on the mix of wired and wireless technologies offered. The methodology and estimation model used in the paper are examined against other examples and assumptions available in the literature. We also offer a dynamic perspective on the analysis of the evolution of this investment gap over the years 2017 2019, which includes an assessment of the usefulness of these estimation models.
Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces a novel approach to tackle the challenges of preserving and transferring tacit knowledge--deep, experience-based insights that are hard to articulate but vital for decision-making, innovation, and problem-solving. Traditional methods rely heavily on human facilitators, which, while effective, are resource-intensive and lack scalability. A promising alternative is the use of Socially Interactive Agents (SIAs) as AI-driven knowledge transfer facilitators. These agents interact autonomously and socially intelligently with users through multimodal behaviors (verbal, paraverbal, nonverbal), simulating expert roles in various organizational contexts. SIAs engage employees in empathic, natural-language dialogues, helping them externalize insights that might otherwise remain unspoken. Their success hinges on building trust, as employees are often hesitant to share tacit knowledge without assurance of confidentiality and appreciation. Key technologies include Large Language Models (LLMs) for generating context-relevant dialogue, Retrieval-Augmented Generation (RAG) to integrate organizational knowledge, and Chain-of-Thought (CoT) prompting to guide structured reflection. These enable SIAs to actively elicit knowledge, uncover implicit assumptions, and connect insights to broader organizational contexts. Potential applications span onboarding, where SIAs support personalized guidance and introductions, and knowledge retention, where they conduct structured interviews with retiring experts to capture heuristics behind decisions. Success depends on addressing ethical and operational challenges such as data privacy, algorithmic bias, and resistance to AI. Transparency, robust validation, and a culture of trust are essential to mitigate these risks.
MathBuddy: A Multimodal System for Affective Math Tutoring
Venue: arXiv cs.CL 2025
Year: 2025

The rapid adoption of LLM-based conversational systems is already transforming the landscape of educational technology. However, the current state-of-the-art learning models do not take into account the student's affective states. Multiple studies in educational psychology support the claim that positive or negative emotional states can impact a student's learning capabilities. To bridge this gap, we present MathBuddy, an emotionally aware LLM-powered Math Tutor, which dynamically models the student's emotions and maps them to relevant pedagogical strategies, making the tutor-student conversation a more empathetic one. The student's emotions are captured from the conversational text as well as from their facial expressions. The student's emotions are aggregated from both modalities to confidently prompt our LLM Tutor for an emotionally-aware response. We have evaluated our model using automatic evaluation metrics across eight pedagogical dimensions and user studies. We report a massive 23 point performance gain using the win rate and a 3 point gain at an overall level using DAMR scores which strongly supports our hypothesis of improving LLM-based tutor's pedagogical abilities by modeling students' emotions. Our dataset and code are available at: https://github.com/ITU-NLP/MathBuddy .
Bipartite Matching with Pair-Dependent Bounds
Venue: arXiv cs.DS 2025
Year: 2025

Let $G=(U \cup V, E)$ be a bipartite graph, where $U$ represents jobs and $V$ represents machines. We study a new variant of the bipartite matching problem in which each job in $U$ can be matched to at most one machine in $V$, and the number of jobs that can be assigned to a machine depends on the specific jobs matched to it. These pair-dependent bounds reflect systems where different jobs have varying tolerance for congestion, determined by the specific machine they are assigned to. We define a bipartite PD-matching as a set of edges $M \subseteq E$ that satisfies these job-to-machine tolerance constraints. This variant of matching extends well-known matching problems, however, despite its relevance to real-world systems, it has not been studied before. We study bipartite PD-matchings with the objective of maximizing the matching size. As we show, the problem exhibits significant differences from previously studied matching problems. We analyze its computational complexity both in the general case and for specific restricted instances, presenting hardness results alongside optimal and approximation algorithms.
On the Outage Probability of Multiuser Multiple Antenna Systems with Non-Orthogonal Multiple Access for Air-Ground Communications
Venue: arXiv cs.IT 2025
Year: 2025

This paper explores multiuser multiple antenna systems as a means to enhance the spectral efficiency of aeronautical communications systems. To this end, the outage regime for a multiuser multiple antenna system is studied within a realistic geometry-based stochastic air-ground (AG) channel model. In this application, users (aircraft) transmit air traffic management data to the ground station at a predefined target rate. Due to the nature of the AG propagation, we argue that the relevant performance metric in this context is the information outage probability. We consider the outage probability under three decoding approaches. The first is based on successive interference cancellation (SIC). The second extends the first approach by considering joint group decoding. The third is a version of the second that limits the size of the jointly decoded user groups in order to lower the decoding complexity. The results show that joint group decoding, even in groups of only two, can significantly increase the spectral efficiency in the AG channel by allowing a large number of aircraft to transmit over a non-orthogonal channel with very low outage probabilities.
FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring
Venue: arXiv cs.LG 2025
Year: 2025

Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without considering context. We present FairLoop1, a tool for human-guided bias mitigation in neural network-based prediction models. FairLoop distills decision trees from neural networks, allowing users to inspect and modify unfair decision logic, which is then used to fine-tune the original model towards fairer predictions. Compared to other approaches to fairness, FairLoop enables context-aware bias removal through human involvement, addressing the influence of sensitive attributes selectively rather than excluding them uniformly.
Bridging the Regulatory Divide: Ensuring Safety and Equity in Wearable Health Technologies
Venue: arXiv cs.CY 2025
Year: 2025

As wearable health technologies have grown more sophisticated, the distinction between "wellness" and "medical" devices has become increasingly blurred. While some features undergo formal U.S. Food and Drug Administration (FDA) review, many over-the-counter tools operate in a regulatory grey zone, leveraging health-related data and outputs without clinical validation. Further complicating the issue is the widespread repurposing of wellness devices for medical uses, which can introduce safety risks beyond the reach of current oversight. Drawing on legal analysis, case studies, and ethical considerations, we propose an approach emphasizing distributed risk, patient-centered outcomes, and iterative reform. Without a more pluralistic and evolving framework, the promise of wearable health technology risks being undermined by growing inequities, misuse, and eroded public trust.
Visio-Verbal Teleimpedance Interface: Enabling Semi-Autonomous Control of Physical Interaction via Eye Tracking and Speech
Venue: arXiv cs.RO 2025
Year: 2025

The paper presents a visio-verbal teleimpedance interface for commanding 3D stiffness ellipsoids to the remote robot with a combination of the operator's gaze and verbal interaction. The gaze is detected by an eye-tracker, allowing the system to understand the context in terms of what the operator is currently looking at in the scene. Along with verbal interaction, a Visual Language Model (VLM) processes this information, enabling the operator to communicate their intended action or provide corrections. Based on these inputs, the interface can then generate appropriate stiffness matrices for different physical interaction actions. To validate the proposed visio-verbal teleimpedance interface, we conducted a series of experiments on a setup including a Force Dimension Sigma.7 haptic device to control the motion of the remote Kuka LBR iiwa robotic arm. The human operator's gaze is tracked by Tobii Pro Glasses 2, while human verbal commands are processed by a VLM using GPT-4o. The first experiment explored the optimal prompt configuration for the interface. The second and third experiments demonstrated different functionalities of the interface on a slide-in-the-groove task.
AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 (co-located with EMNLP 2025). This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q&A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA.
A First Look at Inter-Cell Interference in the Wild
Venue: arXiv cs.NI 2025
Year: 2025

In cellular networks, inter-cell interference management has been studied for decades, yet its real-world effectiveness remains under-explored. To bridge this gap, we conduct a first measurement study of inter-cell interference for operational 4G/5G networks. Our findings reveal the prevalence of inter-cell interference and a surprising absence of interference coordination among operational base stations. As a result, user equipments experience unnecessary interference, which causes significant signal quality degradation, especially under frequency-selective channel fading. We examine the inter-cell interference issues from four major perspectives: network deployment, channel assignment, time-frequency resource allocation, and network configuration. In none of these dimensions is inter-cell interference effectively managed. Notably, even when spectrum resources are underutilized and simple strategies could effectively mitigate inter-cell interference, base stations consistently prioritize using the same set of time-frequency resources, causing interference across cells. Our measurements reveal substantial opportunities for improving signal quality by inter-cell interference management.