OpenRaiser/Intern-Atlas huggingface dataset:
Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

The rapid proliferation of artificial intelligence (AI) models and methods presents growing challenges for research software engineers and researchers who must select, integrate, and maintain appropriate models within complex research workflows. Model selection is often performed in an ad hoc manner, relying on fragmented metadata and individual expertise, which can undermine reproducibility, transparency, and overall research software quality. This work proposes a structured and evidence-driven approach to support AI model selection that aligns with both technical and contextual requirements. We conceptualize AI model selection as a Multi-Criteria Decision-Making (MCDM) problem and introduce an evidence-based decision-support framework that integrates automated data collection pipelines, a structured knowledge graph, and MCDM principles. Following the Design Science Research methodology, the proposed framework (ModelSelect) is empirically validated through 50 real-world case studies and comparative experiments against leading generative AI systems. The evaluation results show that ModelSelect produces reliable, interpretable, and reproducible recommendations that closely align with expert reasoning. Across the case studies, the framework achieved high coverage and strong rationale alignment in both model and library recommendation tasks, performing comparably to generative AI assistants while offering superior traceability and consistency. By framing AI model selection as an MCDM problem, this work establishes a rigorous foundation for transparent and reproducible decision support in research software engineering. The proposed framework provides a scalable and explainable pathway for integrating empirical evidence into AI model recommendation processes, ultimately improving the quality and robustness of research software decision-making.
Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
Venue: arXiv cs.CR 2025
Year: 2025

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.
Semantic-Drive: Democratizing Long-Tail Data Curation via Open-Vocabulary Grounding and Neuro-Symbolic VLM Consensus
Venue: arXiv cs.CV 2025
Year: 2025

The development of robust Autonomous Vehicles (AVs) is bottlenecked by the scarcity of "Long-Tail" training data. While fleets collect petabytes of video logs, identifying rare safety-critical events (e.g., erratic jaywalking, construction diversions) remains a manual, cost-prohibitive process. Existing solutions rely on coarse metadata search, which lacks precision, or cloud-based VLMs, which are privacy-invasive and expensive. We introduce Semantic-Drive, a local-first, neuro-symbolic framework for semantic data mining. Our approach decouples perception into two stages: (1) Symbolic Grounding via a real-time open-vocabulary detector (YOLOE) to anchor attention, and (2) Cognitive Analysis via a Reasoning VLM that performs forensic scene analysis. To mitigate hallucination, we implement a "System 2" inference-time alignment strategy, utilizing a multi-model "Judge-Scout" consensus mechanism. Benchmarked on the nuScenes dataset against the Waymo Open Dataset (WOD-E2E) taxonomy, Semantic-Drive achieves a Recall of 0.966 (vs. 0.475 for CLIP) and reduces Risk Assessment Error by 40% ccompared to the best single scout models. The system runs entirely on consumer hardware (NVIDIA RTX 3090), offering a privacy-preserving alternative to the cloud.
Adaptive federated learning for ship detection across diverse satellite imagery sources
Venue: arXiv cs.CV 2025
Year: 2025

We investigate the application of Federated Learning (FL) for ship detection across diverse satellite datasets, offering a privacy-preserving solution that eliminates the need for data sharing or centralized collection. This approach is particularly advantageous for handling commercial satellite imagery or sensitive ship annotations. Four FL models including FedAvg, FedProx, FedOpt, and FedMedian, are evaluated and compared to a local training baseline, where the YOLOv8 ship detection model is independently trained on each dataset without sharing learned parameters. The results reveal that FL models substantially improve detection accuracy over training on smaller local datasets and achieve performance levels close to global training that uses all datasets during the training. Furthermore, the study underscores the importance of selecting appropriate FL configurations, such as the number of communication rounds and local training epochs, to optimize detection precision while maintaining computational efficiency.
Towards Channel-Robust and Receiver-Independent Radio Frequency Fingerprint Identification
Venue: arXiv cs.CR 2025
Year: 2025

Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices. RFFI exploits the intrinsic and unique hardware imperfections for classifying IoT devices. Deep learning-based RFFI has shown excellent performance. However, there are still remaining research challenges, such as limited public training datasets as well as impacts of channel and receive effects. In this paper, we proposed a three-stage RFFI approach involving contrastive learning-enhanced pretraining, Siamese network-based classification network training, and inference. Specifically, we employed spectrogram as signal representation to decouple the transmitter impairments from channel effects and receiver impairments. We proposed an unsupervised contrastive learning method to pretrain a channel-robust RFF extractor. In addition, the Siamese network-based scheme is enhanced by data augmentation and contrastive loss, which is capable of jointly mitigating the effects of channel and receiver impairments. We carried out a comprehensive experimental evaluation using three public LoRa datasets and one self-collected LoRa dataset. The results demonstrated that our approach can effectively and simultaneously mitigate the effects of channel and receiver impairments. We also showed that pretraining can significantly reduce the required amount of the fine-tuning data. Our proposed approach achieved an accuracy of over 90% in dynamic non-line-of-sight (NLOS) scenarios when there are only 20 packets per device.
Dynamic SLA-aware Network Slice Monitoring
Venue: arXiv cs.NI 2025
Year: 2025

Next-generation networks increasingly rely on network slices - logical networks tailored to specific application requirements, each with distinct Service-Level Agreements (SLAs). Ensuring compliance with these SLAs requires continuous, real-time monitoring of end-to-end performance metrics for each slice, within a limited telemetry budget. However, we find that existing solutions face two fundamental limitations: they either lack end-to-end visibility (e.g., sketches, probabilistic sampling) or provide visibility but lack the control mechanisms to dynamically allocate monitoring resources according to slice SLAs. We address this through a formal framework that reframes slice monitoring as a closed-loop control problem, and defines the minimal data plane requirements for SLA-aware slice monitoring via a telemetry primitive contract. We then present SliceScope, a realization of this framework that combines: (1) a control strategy that dynamically allocates the monitoring resources across diverse slices according to their SLA criticality, and (2) a data-plane based on change-triggered INT that provides per-packet end-to-end visibility with tunable accuracy-overhead trade-offs, satisfying the telemetry contract. Our evaluation results on programmable switches and in large-scale simulations with a mixture of different slice types, demonstrate that SliceScope tracks critical slices up to 4x more accurately compared to static baselines, while showing that change-triggered INT outperforms alternative primitives for realizing the telemetry primitive contract.
Open Horizons: Evaluating Deep Models in the Wild
Venue: arXiv cs.CV 2025
Year: 2025

Open-world deployment requires models to recognize both known categories and remain reliable when novel classes appear. We present a unified experimental study spanning open-set recognition (OSR) and few-shot class-incremental learning (FSCIL) on CIFAR-10. For OSR, we compare three pretrained frozen visual encoders: ResNet-50, ConvNeXt-Tiny and CLIP ViT-B/16,using a linear probe and four post-hoc scoring functions, namely MSP, Energy, Mahalanobis and kNN. Across metrics,such as, AUROC, AUPR, FPR@95, and OSCR, CLIP consistently yields the strongest separability between known and unknown samples, with Energy providing the most stable performance across backbones. For FSCIL, we compare modified SPPR, OrCo, and ConCM using partially frozen ResNet-50 across 1-, 5-, and 10-shot scenarios. ConCM achieves 84.7% accuracy in the 10-shot setting with the cleanest confusion matrix, while all methods show saturation beyond 5 shots. Our controlled evaluation reveals how the backbone architecture and scoring mechanisms affect unknown detection and how prototype-based methods mitigate catastrophic forgetting during incremental adaptation.
EIP-7702 Phishing Attack
Venue: arXiv cs.CR 2025
Year: 2025

EIP-7702 introduces a delegation-based authorization mechanism that allows an externally owned account (EOA) to authenticate a single authorization tuple, after which all subsequent calls are routed to arbitrary delegate code. We show that this design enables a qualitatively new class of phishing attacks: instead of deceiving users into signing individual transactions, an attacker can induce a victim to sign a single authorization tuple that grants unconditional and persistent execution control over the account. Through controlled experiments, we identify three reliable trigger pathways: user-driven, attacker-driven, and protocol-triggered. Each can lead to full account takeover and complete asset drainage. We further propose two extended attack surfaces. First, ERC-4337's EntryPoint pipeline enables remote and repeated activation of the delegated code without further victim involvement. Second, the chain-agnostic authorization mode permits replay-like compromises across independent networks. We also present the first empirical measurement of EIP-7702 usage across major EVM chains. Analyzing over 150k authorization and execution events involving 26k addresses and hundreds of delegator contracts, we assess the protocol's real-world footprint. Our findings show that EIP-7702 authorizations are highly centralized, dominated by a small number of contract families linked to criminal activity and repeatedly reused across incidents. Corresponding loss data reveals substantial theft of ETH, ERC-20 tokens, and NFTs. These results provide practical evidence that the attack surface we identify is not merely theoretical, but is already being exploited at scale. We conclude by proposing protocol-level defenses to mitigate the delegation-based phishing vector introduced by EIP-7702.
The Ideological Turing Test for Moderation of Outgroup Affective Animosity
Venue: arXiv cs.CY 2025
Year: 2025

Rising animosity toward ideological opponents poses critical societal challenges. We introduce and test the Ideological Turing Test, a gamified framework requiring participants to adopt and defend opposing viewpoints, to reduce affective animosity and affective polarization. We conducted a mixed-design experiment ($N = 203$) with four conditions: modality (debate/writing) x perspective-taking (Own/Opposite side). Participants engaged in structured interactions defending assigned positions, with outcomes judged by peers. We measured changes in affective animosity and ideological position immediately post-intervention and at 2-6 week follow-up. Perspective-taking reduced out-group animosity and ideological polarization. However, effects differed by modality (writing vs. debate) and over time. For affective animosity, writing from the opposite perspective yielded the largest immediate reduction ($Δ=+0.45$ SD), but the effect was not detectable at the 4-6 week follow-up. In contrast, the debate modality maintained a statistically significant reduction in animosity immediately after and at follow-up ($Δ=+0.37$ SD). For ideological position, adopting the opposite perspective led to significant immediate movement across modalities (writing: $Δ=+0.91$ SD; debate: $Δ=+0.51$ SD), and these changes persisted at follow-up. Judged performance (winning) did not moderate these effects, and willingness to re-participate was similar across conditions (~20-36%). These findings challenge assumptions about adversarial methods, revealing distinct temporal patterns: non-adversarial engagement fosters short-term empathy gains, while cognitive engagement through debate sustains affective benefits. The Ideological Turing Test demonstrates potential as a scalable tool for reducing polarization, particularly when combining perspective-taking with reflective adversarial interactions.
SMRABooth: Subject and Motion Representation Alignment for Customized Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Customized video generation aims to produce videos that faithfully preserve the subject's appearance from reference images while maintaining temporally consistent motion from reference videos. Existing methods struggle to ensure both subject appearance similarity and motion pattern consistency due to the lack of object-level guidance for subject and motion. To address this, we propose SMRABooth, which leverages the self-supervised encoder and optical flow encoder to provide object-level subject and motion representations. These representations are aligned with the model during the LoRA fine-tuning process. Our approach is structured in three core stages: (1) We exploit subject representations via a self-supervised encoder to guide subject alignment, enabling the model to capture overall structure of subject and enhance high-level semantic consistency. (2) We utilize motion representations from an optical flow encoder to capture structurally coherent and object-level motion trajectories independent of appearance. (3) We propose a subject-motion association decoupling strategy that applies sparse LoRAs injection across both locations and timing, effectively reducing interference between subject and motion LoRAs. Extensive experiments show that SMRABooth excels in subject and motion customization, maintaining consistent subject appearance and motion patterns, proving its effectiveness in controllable text-to-video generation.
Anticipatory Governance in Data-Constrained Environments: A Predictive Simulation Framework for Digital Financial Inclusion
Venue: arXiv cs.CY 2025
Year: 2025

Financial exclusion remains a major barrier to digital public service delivery in resource-constrained and archipelagic nations. Traditional policy evaluations rely on retrospective data, limiting the ex-ante intelligence needed for agile resource allocation. This study introduces a predictive simulation framework to support anticipatory governance within government information systems. Using the UNCDF Pacific Digital Economy dataset of 10,108 respondents, we apply a three-stage pipeline: descriptive profiling, interpretable machine learning, and scenario simulation to forecast outcomes of digital financial literacy interventions before deployment. Leveraging cross-sectional structural associations, the framework projects intervention scenarios as prioritization heuristics rather than causal estimates. A transparent linear regression model with R-squared of 95.9 identifies modifiable policy levers. Simulations indicate that foundational digital capabilities such as device access and expense tracking yield the highest projected gains, up to 5.5 percent, outperforming attitudinal nudges. The model enables precision targeting, highlighting young female caregivers as high-leverage responders while flagging non-responders such as urban professionals to prevent resource misallocation. This research demonstrates how static survey data can be repurposed into actionable policy intelligence, offering a scalable and evidence-based blueprint for embedding predictive analytics into public-sector decision-support systems to advance equity-focused digital governance.
Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Venue: arXiv cs.CV 2025
Year: 2025

Reasoning-augmented vision language models (VLMs) generate explicit chains of thought that promise greater capability and transparency but also introduce new failure modes: models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction. Standard evaluations that only measure final-answer accuracy cannot distinguish these behaviors. We introduce the visual faithfulness of reasoning chains as a distinct evaluation dimension, focusing on whether the perception steps of a reasoning chain are grounded in the image. We propose a training- and reference-free framework that decomposes chains into perception versus reasoning steps and uses off-the-shelf VLM judges for step-level faithfulness, additionally verifying this approach through a human meta-evaluation. Building on this metric, we present a lightweight self-reflection procedure that detects and locally regenerates unfaithful perception steps without any training. Across multiple reasoning-trained VLMs and perception-heavy benchmarks, our method reduces Unfaithful Perception Rate while preserving final-answer accuracy, improving the reliability of multimodal reasoning.
Comparison of different segmentation algorithms on brain volume and fractal dimension in infant brain MRIs
Venue: arXiv cs.CV 2025
Year: 2025

Accurate segmentation of infant brain MRI is essential for quantifying developmental changes in structure and complexity. However, ongoing myelination and reduced tissue contrast make automated segmentation particularly challenging. This study systematically compared segmentation accuracy and its impact on volumetric and fractal dimension (FD) estimates in infant brain MRI using the Baby Open Brains (BOB) dataset (71 scans, 1-9 months). Two methods, SynthSeg and SamSeg, were evaluated against expert annotations using Dice, Intersection over Union, 95th-percentile Hausdorff distance, and Normalised Mutual Information. SynthSeg outperformed SamSeg across all quality metrics (mean Dice > 0.8 for major regions) and provided volumetric estimates closely matching the manual reference (mean +4% [-28% - 71%]). SamSeg systematically overestimated ventricular and whole-brain volumes (mean +76% [-12% - 190%]). Segmentation accuracy improved with age, consistent with increasing tissue contrast during myelination. Fractal dimension a(FD) nalyses revealed significant regional differences between SynthSeg and expert segmentations, and Bland-Altman limits of agreement indicated that segmentation-related FD variability exceeded most group differences reported in developmental cohorts. Volume and FD deviations were positively correlated across structures, indicating that segmentation bias directly affects FD estimation. Overall, SynthSeg provided the most reliable volumetric and FD results for paediatric MRI, yet small morphological differences in volume and FD should be interpreted with caution due to segmentation-related uncertainty.
A Geometric Theory of Cognition
Venue: arXiv cs.AI 2025
Year: 2025

Human cognition spans perception, memory, intuitive judgment, deliberative reasoning, action selection, and social inference, yet these capacities are often explained through distinct computational theories. Here we present a unified mathematical framework in which diverse cognitive processes emerge from a single geometric principle. We represent the cognitive state as a point on a differentiable manifold endowed with a learned Riemannian metric that encodes representational constraints, computational costs, and structural relations among cognitive variables. A scalar cognitive potential combines predictive accuracy, structural parsimony, task utility, and normative or logical requirements. Cognition unfolds as the Riemannian gradient flow of this potential, providing a universal dynamical law from which a broad range of psychological phenomena arise. Classical dual-process effects--rapid intuitive responses and slower deliberative reasoning--emerge naturally from metric-induced anisotropies that generate intrinsic time-scale separations and geometric phase transitions, without invoking modular or hybrid architectures. We derive analytical conditions for these regimes and demonstrate their behavioural signatures through simulations of canonical cognitive tasks. Together, these results establish a geometric foundation for cognition and suggest guiding principles for the development of more general and human-like artificial intelligence systems.
OMUDA: Omni-level Masking for Unsupervised Domain Adaptation in Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised domain adaptation (UDA) enables semantic segmentation models to generalize from a labeled source domain to an unlabeled target domain. However, existing UDA methods still struggle to bridge the domain gap due to cross-domain contextual ambiguity, inconsistent feature representations, and class-wise pseudo-label noise. To address these challenges, we propose Omni-level Masking for Unsupervised Domain Adaptation (OMUDA), a unified framework that introduces hierarchical masking strategies across distinct representation levels. Specifically, OMUDA comprises: 1) a Context-Aware Masking (CAM) strategy that adaptively distinguishes foreground from background to balance global context and local details; 2) a Feature Distillation Masking (FDM) strategy that enhances robust and consistent feature learning through knowledge transfer from pre-trained models; and 3) a Class Decoupling Masking (CDM) strategy that mitigates the impact of noisy pseudo-labels by explicitly modeling class-wise uncertainty. This hierarchical masking paradigm effectively reduces the domain shift at the contextual, representational, and categorical levels, providing a unified solution beyond existing approaches. Extensive experiments on multiple challenging cross-domain semantic segmentation benchmarks validate the effectiveness of OMUDA. Notably, on the SYNTHIA->Cityscapes and GTA5->Cityscapes tasks, OMUDA can be seamlessly integrated into existing UDA methods and consistently achieving state-of-the-art results with an average improvement of 7%.