OpenRaiser/Intern-Atlas huggingface dataset:
Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are adapting, we conducted semi-structured interviews with 42 instructors from 33 institutions in 10 countries in June and July 2025. Our qualitative analysis reveals a pragmatic mix of optimism and concern. Many respondents view LLMs as inevitable classroom tools -- comparable to calculators or Wikipedia -- while others worry about de-skilling, misplaced confidence, and uneven integration across institutions. Around 58 per cent have already introduced demonstrations, guided activities, or make extensive use of LLMs in their courses, though most expect change to remain slow and uneven. That said, 31 per cent have not used LLMs to teach students and do not plan to. We highlight some instructional innovations, including AI-aware assessments, reflective use of LLMs as tutors, and course-specific chatbots. By sharing these perspectives, we aim to help data science educators adapt collectively to ensure curricula keep pace with technological change.
An End to End Edge to Cloud Data and Analytics Strategy
Venue: arXiv cs.DC 2025
Year: 2025

There is an exponential growth of connected Internet of Things (IoT) devices. These have given rise to applications that rely on real time data to make critical decisions quickly. Enterprises today are adopting cloud at a rapid pace. There is a critical need to develop secure and efficient strategy and architectures to best leverage capabilities of cloud and edge assets. This paper provides an end to end secure edge to cloud data and analytics strategy. To enable real life implementation, the paper provides reference architectures for device layer, edge layer and cloud layer.
GhostNetV3-Small: A Tailored Architecture and Comparative Study of Distillation Strategies for Tiny Images
Venue: arXiv cs.CV 2025
Year: 2025

Deep neural networks have achieved remarkable success across a range of tasks, however their computational demands often make them unsuitable for deployment on resource-constrained edge devices. This paper explores strategies for compressing and adapting models to enable efficient inference in such environments. We focus on GhostNetV3, a state-of-the-art architecture for mobile applications, and propose GhostNetV3-Small, a modified variant designed to perform better on low-resolution inputs such as those in the CIFAR-10 dataset. In addition to architectural adaptation, we provide a comparative evaluation of knowledge distillation techniques, including traditional knowledge distillation, teacher assistants, and teacher ensembles. Experimental results show that GhostNetV3-Small significantly outperforms the original GhostNetV3 on CIFAR-10, achieving an accuracy of 93.94%. Contrary to expectations, all examined distillation strategies led to reduced accuracy compared to baseline training. These findings indicate that architectural adaptation can be more impactful than distillation in small-scale image classification tasks, highlighting the need for further research on effective model design and advanced distillation techniques for low-resolution domains.
Evaluating the printability of stl files with ML
Venue: arXiv cs.LG 2025
Year: 2025

3D printing has long been a technology for industry professionals and enthusiasts willing to tinker or even build their own machines. This stands in stark contrast to today's market, where recent developments have prioritized ease of use to attract a broader audience. Slicing software nowadays has a few ways to sanity check the input file as well as the output gcode. Our approach introduces a novel layer of support by training an AI model to detect common issues in 3D models. The goal is to assist less experienced users by identifying features that are likely to cause print failures due to difficult to print geometries before printing even begins.
Privacy-Driven Network Data for Smart Cities
Venue: arXiv cs.SI 2025
Year: 2025

A smart city is essential for sustainable urban development. In addition to citizen engagement, a smart city enables connected infrastructure, data-driven decision making and smart mobility. For most of these features, network data plays a critical role, particularly from public Wi-Fi infrastructures, where cities can benefit from optimized services such as public transport management and the safety and efficiency of large events. One of the biggest concerns in developing a smart city is using secure and private data. This is particularly relevant in the case of Wi-Fi network data, where sensitive information can be collected. This paper specifically addresses the problem of sharing secure data to enhance the quality of the Wi-Fi network in a city. Despite the high importance of this type of data, related work focuses on improving the safety of mobility patterns, targeting only the protection of MAC addresses. On the opposite side, we provide a practical methodology for safeguarding all attributes in real Wi-Fi network data. This study was developed in collaboration with a multidisciplinary team of legal experts, data custodians and technical privacy specialists, resulting in high-quality data. On top of that, we show how to integrate the legal considerations for secure data sharing. Our approach promotes data-driven innovation and privacy awareness in the context of smart city initiatives, which have been tested in a real scenario.
Prompt Commons: Collective Prompting as Governance for Urban AI
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, community-maintained repository of prompts with governance metadata, licensing, and moderation - to steer model behaviour toward pluralism. Using a Montreal dataset (443 human prompts; 3,317 after augmentation), we pilot three governance states (open, curated, veto-enabled). On a contested policy benchmark, a single-author prompt yields 24 percent neutral outcomes; commons-governed prompts raise neutrality to 48-52 percent while retaining decisiveness where appropriate. In a synthetic incident log, a veto-enabled regime reduces time-to-remediation for harmful outputs from 30.5 +/- 8.9 hours (open) to 5.6 +/- 1.5 hours. We outline licensing (CC BY/BY-SA for prompts with optional OpenRAIL-style restrictions for artefacts), auditable moderation, and safeguards against dominance capture. Prompt governance offers a practical lever for cities to align AI with local values and accountability.
Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Venue: arXiv cs.HC 2025
Year: 2025

Joint visual attention (JVA) provides informative cues on human behavior during social interactions. The ubiquity of egocentric eye-trackers and large-scale datasets on everyday interactions offer research opportunities in identifying JVA in multi-user environments. We propose a novel approach utilizing spatiotemporal tubes centered on attention rendered by individual gaze and detect JVA using deep-learning-based feature mapping. Our results reveal object-focused collaborative tasks to yield higher JVA (44-46%), whereas independent tasks yield lower (4-5%) attention. Beyond JVA, we analyze attention characteristics using ambient-focal attention coefficient K to understand the qualitative aspects of shared attention. Our analysis reveals $\mathcal{K}$ to converge instances where participants interact with shared objects while diverging when independent. While our study presents seminal findings on joint attention with egocentric commodity eye trackers, it indicates the potential utility of our approach in psychology, human-computer interaction, and social robotics, particularly in understanding attention coordination mechanisms in ecologically valid contexts.
Cott-ADNet: Lightweight Real-Time Cotton Boll and Flower Detection Under Field Conditions
Venue: arXiv cs.CV 2025
Year: 2025

Cotton is one of the most important natural fiber crops worldwide, yet harvesting remains limited by labor-intensive manual picking, low efficiency, and yield losses from missing the optimal harvest window. Accurate recognition of cotton bolls and their maturity is therefore essential for automation, yield estimation, and breeding research. We propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions. Building on YOLOv11n, Cott-ADNet enhances spatial representation and robustness through improved convolutional designs, while introducing two new modules: a NeLU-enhanced Global Attention Mechanism to better capture weak and low-contrast features, and a Dilated Receptive Field SPPF to expand receptive fields for more effective multi-scale context modeling at low computational cost. We curate a labeled dataset of 4,966 images, and release an external validation set of 1,216 field images to support future research. Experiments show that Cott-ADNet achieves 91.5% Precision, 89.8% Recall, 93.3% mAP50, 71.3% mAP, and 90.6% F1-Score with only 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet.
QKD Oracles for Authenticated Key Exchange
Venue: arXiv cs.CR 2025
Year: 2025

Authenticated Key Exchange (AKE) establishes shared ('symmetric') cryptographic keys which are essential for secure online communication. AKE protocols can be constructed from public-key cryptography like Key Encapsulation Mechanisms (KEMs). Another approach is to use Quantum Key Distribution (QKD) to establish a symmetric key, which uses quantum communication. Combining post-quantum AKE and QKD appropriately may provide security against quantum attacks even if only one of the two approaches turns out to be secure. We provide an extensive review of existing security analyses for combined AKE and their formal security models, and identify some gaps in their treatment of QKD key IDs. In particular, improper handling of QKD key IDs leads to Dependent-Key attacks on AKE. As our main conceptual contribution, we model QKD as an oracle that closely resembles the standard ETSI 014 QKD interface. We demonstrate the usability of our QKD oracle for cryptographic security analyses by integrating it into a prominent security model for AKE, called CK+ model, thereby obtaining a security model for combined AKE that catches Dependent-Key attacks. In this model, we formally prove security of a new protocol that combines QKD with a triple-KEM handshake. This is the first provably secure hybrid protocol that maintains information-theoretic security of QKD.
Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model
Venue: arXiv cs.LG 2025
Year: 2025

Time-series forecasting and causal discovery are central in neuroscience, as predicting brain activity and identifying causal relationships between neural populations and circuits can shed light on the mechanisms underlying cognition and disease. With the rise of foundation models, an open question is how they compare to traditional methods for brain signal forecasting and causality analysis, and whether they can be applied in a zero-shot setting. In this work, we evaluate a foundation model against classical methods for inferring directional interactions from spontaneous brain activity measured with functional magnetic resonance imaging (fMRI) in humans. Traditional approaches often rely on Wiener-Granger causality. We tested the forecasting ability of the foundation model in both zero-shot and fine-tuned settings, and assessed causality by comparing Granger-like estimates from the model with standard Granger causality. We validated the approach using synthetic time series generated from ground-truth causal models, including logistic map coupling and Ornstein-Uhlenbeck processes. The foundation model achieved competitive zero-shot forecasting fMRI time series (mean absolute percentage error of 0.55 in controls and 0.27 in patients). Although standard Granger causality did not show clear quantitative differences between models, the foundation model provided a more precise detection of causal interactions. Overall, these findings suggest that foundation models offer versatility, strong zero-shot performance, and potential utility for forecasting and causal discovery in time-series data.
Digital Twin-Assisted Resilient Planning for mmWave IAB Networks via Graph Attention Networks
Venue: arXiv cs.NI 2025
Year: 2025

Digital Twin (DT) technology enables real-time monitoring and optimization of complex network infrastructures by creating accurate virtual replicas of physical systems. In millimeter-wave (mmWave) 5G/6G networks, the deployment of Integrated Access and Backhaul (IAB) nodes faces highly dynamic urban environments, necessitating intelligent DT-enabled optimization frameworks. Traditional IAB deployment optimization approaches struggle with the combinatorial complexity of jointly optimizing coverage, connectivity, and resilience, often leading to suboptimal solutions that are vulnerable to network disruptions. With this consideration, we propose a novel Graph Attention Network v2 (GATv2)-based reinforcement learning approach for resilient IAB deployment in urban mmWave networks. Specifically, we formulate the deployment problem as a Markov Decision Process (MDP) with explicit resilience constraints and employ edge-conditioned GATv2 to capture complex spatial dependencies between heterogeneous node types and dynamic connectivity patterns. The attention mechanism enables the model to focus on critical deployment locations to maximize coverage and ensure fault tolerance through redundant backhaul connections. To address the inherent vulnerability of mmWave links, we train the GATv2 policy using Proximal Policy Optimization (PPO) with a carefully designed balance between coverage, cost, and resilience. Comprehensive simulations across three urban scenarios demonstrate that our method achieves 98.5-98.7 percent coverage with 14.3-26.7 percent fewer nodes than baseline approaches, while maintaining 87.1 percent coverage retention under 30 percent link failures, representing 11.3-15.4 percent improvement in fault tolerance compared to state-of-the-art methods.
Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Venue: arXiv cs.CY 2025
Year: 2025

Computational developments--particularly artificial intelligence--are reshaping social scientific research and raise new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis (QDA), we revisit possibilities and dangers in an era of automation, Large Language Model (LLM) chatbots, and 'big data.' We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science (CSS), we describe methodological choices across study lifecycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address longstanding methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet a pragmatic approach--moving beyond technological optimism and dismissal--is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand--rather than displace--the irreducible insights of qualitative research.
Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design
Venue: arXiv cs.LG 2025
Year: 2025

Large language models often produce confident but incorrect outputs, creating a critical need for reliable uncertainty quantification with formal abstention guarantees. We introduce information-lift certificates that compare model probabilities to a skeleton baseline, accumulating evidence through sub-gamma PAC-Bayes bounds that remain valid under heavy-tailed distributions where standard concentration inequalities fail. On eight diverse datasets, our method achieves 77.0\% coverage at 2\% risk, outperforming recent baselines by 10.0 percentage points on average. In high-stakes scenarios, we block 96\% of critical errors compared to 18-31\% for entropy-based methods. While our frequency-based certification does not guarantee severity-weighted safety and depends on skeleton quality, performance degrades gracefully under distributional shifts, making the approach practical for real-world deployment.
Cross-Modal Deep Metric Learning for Time Series Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

To effectively address the issues of low sensitivity and high time consumption in time series anomaly detection, we propose an anomaly detection method based on cross-modal deep metric learning. A cross-modal deep metric learning feature clustering model is constructed, composed of an input layer, a triplet selection layer, and a loss function computation layer. The squared Euclidean distances between cluster centers are calculated, and a stochastic gradient descent strategy is employed to optimize the model and classify different time series features. The inner product of principal component direction vectors is used as a metric for anomaly measurement. The von Mises-Fisher (vMF) distribution is applied to describe the directional characteristics of time series data, and historical data is used to train and obtain evaluation parameters. By comparing the principal component direction vector of actual time series data with the threshold, anomaly detection is performed. Experimental results demonstrate that the proposed method accurately classifies time series data with different attributes, exhibits high sensitivity to anomalies, and achieves high detection accuracy, fast detection speed, and strong robustness.
Yet Another Watermark for Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Existing watermarking methods for large language models (LLMs) mainly embed watermark by adjusting the token sampling prediction or post-processing, lacking intrinsic coupling with LLMs, which may significantly reduce the semantic quality of the generated marked texts. Traditional watermarking methods based on training or fine-tuning may be extendable to LLMs. However, most of them are limited to the white-box scenario, or very time-consuming due to the massive parameters of LLMs. In this paper, we present a new watermarking framework for LLMs, where the watermark is embedded into the LLM by manipulating the internal parameters of the LLM, and can be extracted from the generated text without accessing the LLM. Comparing with related methods, the proposed method entangles the watermark with the intrinsic parameters of the LLM, which better balances the robustness and imperceptibility of the watermark. Moreover, the proposed method enables us to extract the watermark under the black-box scenario, which is computationally efficient for use. Experimental results have also verified the feasibility, superiority and practicality. This work provides a new perspective different from mainstream works, which may shed light on future research.
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
Venue: arXiv cs.RO 2025
Year: 2025

We present LightVLA, a simple yet effective differentiable token pruning framework for vision-language-action (VLA) models. While VLA models have shown impressive capability in executing real-world robotic tasks, their deployment on resource-constrained platforms is often bottlenecked by the heavy attention-based computation over large sets of visual tokens. LightVLA addresses this challenge through adaptive, performance-driven pruning of visual tokens: It generates dynamic queries to evaluate visual token importance, and adopts Gumbel softmax to enable differentiable token selection. Through fine-tuning, LightVLA learns to preserve the most informative visual tokens while pruning tokens which do not contribute to task execution, thereby improving efficiency and performance simultaneously. Notably, LightVLA requires no heuristic magic numbers and introduces no additional trainable parameters, making it compatible with modern inference frameworks. Experimental results demonstrate that LightVLA outperforms different VLA models and existing token pruning methods across diverse tasks on the LIBERO benchmark, achieving higher success rates with substantially reduced computational overhead. Specifically, LightVLA reduces FLOPs and latency by 59.1% and 38.2% respectively, with a 2.6% improvement in task success rate. Meanwhile, we also investigate the learnable query-based token pruning method LightVLA* with additional trainable parameters, which also achieves satisfactory performance. Our work reveals that as VLA pursues optimal performance, LightVLA spontaneously learns to prune tokens from a performance-driven perspective. To the best of our knowledge, LightVLA is the first work to apply adaptive visual token pruning to VLA tasks with the collateral goals of efficiency and performance, marking a significant step toward more efficient, powerful and practical real-time robotic systems.
DisorientLiDAR: Physical Attacks on LiDAR-based Localization
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Even this poses a serious security challenge for the localization of self-driving cars, there has been very little exploration of attack on it, as most of adversarial attacks have been applied to 3D perception. In this work, we propose a novel adversarial attack framework called DisorientLiDAR targeting LiDAR-based localization. By reverse-engineering localization models (e.g., feature extraction networks), adversaries can identify critical keypoints and strategically remove them, thereby disrupting LiDAR-based localization. Our proposal is first evaluated on three state-of-the-art point-cloud registration models (HRegNet, D3Feat, and GeoTransformer) using the KITTI dataset. Experimental results demonstrate that removing regions containing Top-K keypoints significantly degrades their registration accuracy. We further validate the attack's impact on the Autoware autonomous driving platform, where hiding merely a few critical regions induces noticeable localization drift. Finally, we extended our attacks to the physical world by hiding critical regions with near-infrared absorptive materials, thereby successfully replicate the attack effects observed in KITTI data. This step has been closer toward the realistic physical-world attack that demonstrate the veracity and generality of our proposal.