OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation
Venue: arXiv cs.RO 2025
Year: 2025

Fatigue detection for human operators plays a key role in safety critical applications such as aviation, mining, and long haul transport. While numerous studies have demonstrated the effectiveness of high fidelity sensors in controlled laboratory environments, their performance often degrades when ported to real world settings due to noise, lighting conditions, and field of view constraints, thereby limiting their practicality. This paper formalizes a deployment oriented setting for real world fatigue detection, where high quality sensors are often unavailable in practical applications. To address this challenge, we propose leveraging knowledge from heterogeneous source domains, including high fidelity sensors that are difficult to deploy in the field but commonly used in controlled environments, to assist fatigue detection in the real world target domain. Building on this idea, we design a heterogeneous and multiple source fatigue detection framework that adaptively utilizes the available modalities in the target domain while exploiting diverse configurations in the source domains through alignment across domains and modality imputation. Our experiments, conducted using a field deployed sensor setup and two publicly available human fatigue datasets, demonstrate the practicality, robustness, and improved generalization of our approach across subjects and domains. The proposed method achieves consistent gains over strong baselines in sensor constrained scenarios. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs
Venue: arXiv cs.SI 2025
Year: 2025

Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness of existing detectors against LLM-generated profiles. While highly effective in detecting manually created fake profiles (False Accept Rate: 6-7%), the existing detectors fail to identify GPT-generated profiles (False Accept Rate: 42-52%). We propose GPT-assisted adversarial training as a countermeasure, restoring the False Accept Rate to between 1-7% without impacting the False Reject Rates (0.5-2%). Ablation studies revealed that detectors trained on combined numerical and textual embeddings exhibit the highest robustness, followed by those using numerical-only embeddings, and lastly those using textual-only embeddings. Complementary analysis on the ability of prompt-based GPT-4Turbo and human evaluators affirms the need for robust automated detectors such as the one proposed in this study.
Divisive Decisions: Improving Salience-Based Training for Generalization in Binary Classification Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Existing saliency-guided training approaches improve model generalization by incorporating a loss term that compares the model's class activation map (CAM) for a sample's true-class ({\it i.e.}, correct-label class) against a human reference saliency map. However, prior work has ignored the false-class CAM(s), that is the model's saliency obtained for incorrect-label class. We hypothesize that in binary tasks the true and false CAMs should diverge on the important classification features identified by humans (and reflected in human saliency maps). We use this hypothesis to motivate three new saliency-guided training methods incorporating both true- and false-class model's CAM into the training strategy and a novel post-hoc tool for identifying important features. We evaluate all introduced methods on several diverse binary close-set and open-set classification tasks, including synthetic face detection, biometric presentation attack detection, and classification of anomalies in chest X-ray scans, and find that the proposed methods improve generalization capabilities of deep learning models over traditional (true-class CAM only) saliency-guided training approaches. We offer source codes and model weights\footnote{GitHub repository link removed to preserve anonymity} to support reproducible research.
Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Venue: arXiv cs.CL 2025
Year: 2025

Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the "better" response. This approach can provide feedback for domains where other hard-coded metrics are difficult to obtain (e.g., chat response quality), thereby helping model evaluation or training. However, for some domains high-quality pairwise comparisons can be tricky to obtain - from AI and humans. For example, for responses with many factual statements, annotators may disproportionately weigh writing quality rather than underlying facts. In this work, we explore augmenting standard AI annotator systems with additional tools to improve performance on three challenging response domains: long-form factual, math and code tasks. We propose a tool-using agentic system to provide higher quality feedback on these domains. Our system uses web-search and code execution to ground itself based on external validation, independent of the LLM's internal knowledge and biases. We provide extensive experimental results evaluating our method across the three targeted response domains as well as general annotation tasks, using RewardBench (incl. AlpacaEval and LLMBar), RewardMath, as well as three new datasets for domains with saturated pre-existing datasets. Our results indicate that external tools can indeed improve performance in many, but not all, cases. More generally, our experiments highlight the sensitivity of performance to simple parameters (e.g., prompt) and the need for improved (non-saturated) annotator benchmarks. We share our code at https://github.com/apple/ml-agent-evaluator.
Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and causal analysis, enabling both semantic understanding and structural insight as input for the pretrained GPT-2 model. The resulting textual representation offers a more interpretable view of the complex dynamics underlying the original time series. The reported results confirm the effectiveness of our proposed LLM-based time series forecasting model, as demonstrated across four different multivariate time series datasets. This initiative paves promising future directions in the domain of TSF using LLMs based on FTS.
Ethics through the Facets of Artificial Intelligence
Venue: arXiv cs.CY 2025
Year: 2025

Artificial Intelligence (AI) has received unprecedented attention in recent years, raising ethical concerns about the development and use of AI technology. In the present article, we advocate that these concerns stem from a blurred understanding of AI, how it can be used, and how it has been interpreted in society. We explore the concept of AI based on three descriptive facets and consider ethical issues related to each facet. Finally, we propose a framework for the ethical assessment of the use of AI.
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autonomous driving. However, a critical question remains: How robust are DRL models when exposed to adversarial attacks? While existing defense mechanisms such as adversarial training and distillation enhance the resilience of DRL models, there remains a significant research gap regarding the integration of multiple defenses in autonomous driving scenarios specifically. This paper addresses this gap by proposing a novel ensemble-based defense architecture to mitigate adversarial attacks in autonomous driving. Our evaluation demonstrates that the proposed architecture significantly enhances the robustness of DRL models. Compared to the baseline under FGSM attacks, our ensemble method improves the mean reward from 5.87 to 18.38 (over 213% increase) and reduces the mean collision rate from 0.50 to 0.09 (an 82% decrease) in the highway scenario and merge scenario, outperforming all standalone defense strategies.
Analysis of Post-Quantum Cryptography in User Equipment in 5G and Beyond
Venue: arXiv cs.CR 2025
Year: 2025

The advent of quantum computing threatens the security of classical public-key cryptographic systems, prompting the transition to post-quantum cryptography (PQC). While PQC has been analyzed in theory, its performance in practical wireless communication environments remains underexplored. This paper presents a detailed implementation and performance evaluation of NIST-selected PQC algorithms in user equipment (UE) to UE communications over 5G networks. Using a full 5G emulation stack (Open5GS and UERANSIM) and PQC-enabled TLS 1.3 via BoringSSL and liboqs, we examine key encapsulation mechanisms and digital signature schemes across realistic network conditions. We evaluate performance based on handshake latency, CPU and memory usage, bandwidth, and retransmission rates, under varying cryptographic configurations and client loads. Our findings show that ML-KEM with ML-DSA offers the best efficiency for latency-sensitive applications, while SPHINCS+ and HQC combinations incur higher computational and transmission overheads, making them unsuitable for security-critical but time-sensitive 5G scenarios.
Few-Shot Learning in Video and 3D Object Detection: A Survey
Venue: arXiv cs.CV 2025
Year: 2025

Few-shot learning (FSL) enables object detection models to recognize novel classes given only a few annotated examples, thereby reducing expensive manual data labeling. This survey examines recent FSL advances for video and 3D object detection. For video, FSL is especially valuable since annotating objects across frames is more laborious than for static images. By propagating information across frames, techniques like tube proposals and temporal matching networks can detect new classes from a couple examples, efficiently leveraging spatiotemporal structure. FSL for 3D detection from LiDAR or depth data faces challenges like sparsity and lack of texture. Solutions integrate FSL with specialized point cloud networks and losses tailored for class imbalance. Few-shot 3D detection enables practical autonomous driving deployment by minimizing costly 3D annotation needs. Core issues in both domains include balancing generalization and overfitting, integrating prototype matching, and handling data modality properties. In summary, FSL shows promise for reducing annotation requirements and enabling real-world video, 3D, and other applications by efficiently leveraging information across feature, temporal, and data modalities. By comprehensively surveying recent advancements, this paper illuminates FSL's potential to minimize supervision needs and enable deployment across video, 3D, and other real-world applications.
Assessing Reliability of Statistical Maximum Coverage Estimators in Fuzzing
Venue: arXiv cs.SE 2025
Year: 2025

Background: Fuzzers are often guided by coverage, making the estimation of maximum achievable coverage a key concern in fuzzing. However, achieving 100% coverage is infeasible for most real-world software systems, regardless of effort. While static reachability analysis can provide an upper bound, it is often highly inaccurate. Recently, statistical estimation methods based on species richness estimators from biostatistics have been proposed as a potential solution. Yet, the lack of reliable benchmarks with labeled ground truth has limited rigorous evaluation of their accuracy. Objective: This work examines the reliability of reachability estimators from two axes: addressing the lack of labeled ground truth and evaluating their reliability on real-world programs. Methods: (1) To address the challenge of labeled ground truth, we propose an evaluation framework that synthetically generates large programs with complex control flows, ensuring well-defined reachability and providing ground truth for evaluation. (2) To address the criticism from use of synthetic benchmarks, we adapt a reliability check for reachability estimators on real-world benchmarks without labeled ground truth -- by varying the size of sampling units, which, in theory, should not affect the estimate. Results: These two studies together will help answer the question of whether current reachability estimators are reliable, and defines a protocol to evaluate future improvements in reachability estimation.
Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement
Venue: arXiv cs.IR 2025
Year: 2025

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge across domains. Disentangled representation learning provides an effective solution to model complex user preferences by separating intra-domain features (domain-shared and domain-specific features), thereby enhancing robustness and interpretability. However, disentanglement-based CDR methods employing generative modeling or GNNs with contrastive objectives face two key challenges: (i) pre-separation strategies decouple features before extracting collaborative signals, disrupting intra-domain interactions and introducing noise; (ii) unsupervised disentanglement objectives lack explicit task-specific guidance, resulting in limited consistency and suboptimal alignment. To address these challenges, we propose DGCDR, a GNN-enhanced encoder-decoder framework. To handle challenge (i), DGCDR first applies GNN to extract high-order collaborative signals, providing enriched representations as a robust foundation for disentanglement. The encoder then dynamically disentangles features into domain-shared and -specific spaces, preserving collaborative information during the separation process. To handle challenge (ii), the decoder introduces an anchor-based supervision that leverages hierarchical feature relationships to enhance intra-domain consistency and cross-domain alignment. Extensive experiments on real-world datasets demonstrate that DGCDR achieves state-of-the-art performance, with improvements of up to 11.59% across key metrics. Qualitative analyses further validate its superior disentanglement quality and transferability. Our source code and datasets are available on GitHub for further comparison.
Dynamic Modeling and Dimensional Optimization of Legged Mechanisms for Construction Robot
Venue: arXiv cs.RO 2025
Year: 2025

With the rapid development of the construction industry, issues such as harsh working environments, high-intensity and high-risk tasks, and labor shortages have become increasingly prominent. This drives higher demands for construction robots in terms of low energy consumption, high mobility, and high load capacity. This paper focuses on the design and optimization of leg structures for construction robots, aiming to improve their dynamic performance, reduce energy consumption, and enhance load-bearing capabilities. Firstly, based on the leg configuration of ants in nature, we design a structure for the robot's leg. Secondly, we propose a novel structural optimization method. Using the Lagrangian approach, a dynamic model of the leg was established. Combining the dynamic model with the leg's motion trajectory, we formulated multiple dynamic evaluation metrics and conducted a comprehensive optimization study on the geometric parameters of each leg segment. The results show that the optimized leg structure reduces peak joint torques and energy consumption by over 20%. Finally, dynamic simulation experiments were conducted using ADAMS. The results demonstrate a significant reduction in the driving power of each joint after optimization, validating the effectiveness and rationality of the proposed strategy. This study provides a theoretical foundation and technical support for the design of heavy-load, high-performance construction robots.
Multi-Objective Trajectory Planning for a Robotic Arm in Curtain Wall Installation
Venue: arXiv cs.RO 2025
Year: 2025

In the context of labor shortages and rising costs, construction robots are regarded as the key to revolutionizing traditional construction methods and improving efficiency and quality in the construction industry. In order to ensure that construction robots can perform tasks efficiently and accurately in complex construction environments, traditional single-objective trajectory optimization methods are difficult to meet the complex requirements of the changing construction environment. Therefore, we propose a multi-objective trajectory optimization for the robotic arm used in the curtain wall installation. First, we design a robotic arm for curtain wall installation, integrating serial, parallel, and folding arm elements, while considering its physical properties and motion characteristics. In addition, this paper proposes an NSGA-III-FO algorithm (NSGA-III with Focused Operator, NSGA-III-FO) that incorporates a focus operator screening mechanism to accelerate the convergence of the algorithm towards the Pareto front, thereby effectively balancing the multi-objective constraints of construction robots. The proposed algorithm is tested against NSGA-III, MOEA/D, and MSOPS-II in ten consecutive trials on the DTLZ3 and WFG3 test functions, showing significantly better convergence efficiency than the other algorithms. Finally, we conduct two sets of experiments on the designed robotic arm platform, which confirm the efficiency and practicality of the NSGA-III-FO algorithm in solving multi-objective trajectory planning problems for curtain wall installation tasks.
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards
Venue: arXiv cs.CL 2025
Year: 2025

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying \emph{cognitive} mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce \textbf{CogDual}, a novel RPLA adopting a \textit{cognize-then-respond } reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks.
UNICE: Training A Universal Image Contrast Enhancer
Venue: arXiv cs.CV 2025
Year: 2025

Existing image contrast enhancement methods are typically designed for specific tasks such as under-/over-exposure correction, low-light and backlit image enhancement, etc. The learned models, however, exhibit poor generalization performance across different tasks, even across different datasets of a specific task. It is important to explore whether we can learn a universal and generalized model for various contrast enhancement tasks. In this work, we observe that the common key factor of these tasks lies in the need of exposure and contrast adjustment, which can be well-addressed if high-dynamic range (HDR) inputs are available. We hence collect 46,928 HDR raw images from public sources, and render 328,496 sRGB images to build multi-exposure sequences (MES) and the corresponding pseudo sRGB ground-truths via multi-exposure fusion. Consequently, we train a network to generate an MES from a single sRGB image, followed by training another network to fuse the generated MES into an enhanced image. Our proposed method, namely UNiversal Image Contrast Enhancer (UNICE), is free of costly human labeling. However, it demonstrates significantly stronger generalization performance than existing image contrast enhancement methods across and within different tasks, even outperforming manually created ground-truths in multiple no-reference image quality metrics. The dataset, code and model are available at https://github.com/BeyondHeaven/UNICE.
DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing
Venue: arXiv cs.CV 2025
Year: 2025

Ocular biometrics in the visible spectrum have emerged as a prominent modality due to their high accuracy, resistance to spoofing, and non-invasive nature. However, morphing attacks, synthetic biometric traits created by blending features from multiple individuals, threaten biometric system integrity. While extensively studied for near-infrared iris and face biometrics, morphing in visible-spectrum ocular data remains underexplored. Simulating such attacks demands advanced generation models that handle uncontrolled conditions while preserving detailed ocular features like iris boundaries and periocular textures. To address this gap, we introduce DOOMGAN, that encompasses landmark-driven encoding of visible ocular anatomy, attention-guided generation for realistic morph synthesis, and dynamic weighting of multi-faceted losses for optimized convergence. DOOMGAN achieves over 20% higher attack success rates than baseline methods under stringent thresholds, along with 20% better elliptical iris structure generation and 30% improved gaze consistency. We also release the first comprehensive ocular morphing dataset to support further research in this domain.
Can LLMs Write CI? A Study on Automatic Generation of GitHub Actions Configurations
Venue: arXiv cs.SE 2025
Year: 2025

Continuous Integration (CI) services, such as GitHub Actions, require developers to write YAML-based configurations, which can be tedious and error-prone. Despite the increasing use of Large Language Models (LLMs) to automate software engineering tasks, their ability to generate CI configurations remains underexplored. This paper presents a preliminary study evaluating six LLMs for generating GitHub Actions configurations from natural language descriptions. We assess three general-purpose foundation models (GPT-4o, Llama, and Gemma) and three code-pretrained models (GPT-4.1, Code Llama, and CodeGemma). We also introduce the first labeled dataset of its kind, constructed from GitHub Actions documentation, pairing descriptions with corresponding best-practice YAML configurations. Zero-shot prompting achieves up to 69% similarity with the ground truth, with only 3% perfect matches. Code-pretrained models slightly underperform compared to general-purpose ones in YAML-based CI tasks, revealing LLM limitations for CI configuration generation. Analyzing GPT-4o outputs reveals issues like missing or renamed steps, misinterpreted descriptions, and unnecessary additions that may affect structural and contextual correctness, indicating a gap between generation quality and the precision required for executable CI configurations. Our research offers insights for improving LLM alignment with configuration languages and guiding future efforts on CI automation and tooling support.