OpenRaiser/Intern-Atlas huggingface dataset:
Navigating Speech Recording Collections with AI-Generated Illustrations
Venue: arXiv cs.IR 2025
Year: 2025

Although the amount of available spoken content is steadily increasing, extracting information and knowledge from speech recordings remains challenging. Beyond enhancing traditional information retrieval methods such as speech search and keyword spotting, novel approaches for navigating and searching spoken content need to be explored and developed. In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models. We demonstrate our approach with a Web application that organizes data into a structured format using interactive mind maps and image generation tools. The system is implemented using the TED-LIUM~3 dataset, which comprises over 2,000 speech transcripts and audio files of TED Talks. Initial user tests using a System Usability Scale (SUS) questionnaire indicate the application's potential to simplify the exploration of large speech collections.
An explicit formulation of the learned noise predictor $ε_θ({\bf x}_t, t)$ via the forward-process noise $ε_{t}$ in denoising diffusion probabilistic models (DDPMs)
Venue: arXiv cs.LG 2025
Year: 2025

In denoising diffusion probabilistic models (DDPMs), the learned noise predictor $ ε_θ ( {\bf x}_t , t)$ is trained to approximate the forward-process noise $ε_t$. The equality $\nabla_{{\bf x}_t} \log q({\bf x}_t) = -\frac 1 {\sqrt {1- {\bar α}_t} } ε_θ ( {\bf x}_t , t)$ plays a fundamental role in both theoretical analyses and algorithmic design, and thus is frequently employed across diffusion-based generative models. In this paper, an explicit formulation of $ ε_θ ( {\bf x}_t , t)$ in terms of the forward-process noise $ε_t$ is derived. This result show how the forward-process noise $ε_t$ contributes to the learned predictor $ ε_θ ( {\bf x}_t , t)$. Furthermore, based on this formulation, we present a novel and mathematically rigorous proof of the fundamental equality above, clarifying its origin and providing new theoretical insight into the structure of diffusion models.
Normalized Iterative Hard Thresholding for Tensor Recovery
Venue: arXiv cs.LG 2025
Year: 2025

Low-rank recovery builds upon ideas from the theory of compressive sensing, which predicts that sparse signals can be accurately reconstructed from incomplete measurements. Iterative thresholding-type algorithms-particularly the normalized iterative hard thresholding (NIHT) method-have been widely used in compressed sensing (CS) and applied to matrix recovery tasks. In this paper, we propose a tensor extension of NIHT, referred to as TNIHT, for the recovery of low-rank tensors under two widely used tensor decomposition models. This extension enables the effective reconstruction of high-order low-rank tensors from a limited number of linear measurements by leveraging the inherent low-dimensional structure of multi-way data. Specifically, we consider both the CANDECOMP/PARAFAC (CP) rank and the Tucker rank to characterize tensor low-rankness within the TNIHT framework. At the same time, we establish a convergence theorem for the proposed TNIHT method under the tensor restricted isometry property (TRIP), providing theoretical support for its recovery guarantees. Finally, we evaluate the performance of TNIHT through numerical experiments on synthetic, image, and video data, and compare it with several state-of-the-art algorithms.
WSCoach: Wearable Real-time Auditory Feedback for Reducing Unwanted Words in Daily Communication
Venue: arXiv cs.HC 2025
Year: 2025

The rise of wearable smart devices raises unprecedented opportunities for self-improvement through ubiquitous behavior tracking and guidance. However, the design of effective wearable behavior intervention systems remains relatively unexplored. To address this gap, we conducted controlled studies focusing on the reduction of unwanted words (e.g., filler words, swear words) in daily communication through auditory feedback using wearable technology. We started with a design space exploration, considering various factors such as the type, duration, and timing of the auditory feedback. Then, we conducted pilot studies to reduce the space of design choices and prototyped a system called WSCoach (Wearable Speech Coach), which informs users when they utter unwanted words in near-real-time. To evaluate WSCoach, we compared it with a state-of-the-art mobile application supporting post-hoc conversation analysis. Both approaches were effective in reducing the occurrence of unwanted words, but WSCoach appears to be more effective in the long run. Finally, we discuss guidelines for the design of wearable audio-based behavior monitoring and intervention systems and highlight the potential of wearable technology for facilitating behavior correction and improvement. For supplementary material, please see the META Appendix and our OSF project at https://osf.io/6vhwn/?view_only=489498d3ac2d4703a17475fc6ca65dfa.
ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments
Venue: arXiv cs.LG 2025
Year: 2025

High dimensionality in datasets produced by microarray technology presents a challenge for Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid ensemble feature selection techniques with majority voting classifier for micro array classi f ication. Here we have considered both filter and wrapper-based feature selection techniques including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier that combines multiple machine learning models, such as Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide more reliable and effective diagnostic predictions. The efficacy of the proposed model has been tested in both local and cloud environments. In the cloud environment, three virtual machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used to demonstrate the model performance. From the experiment it has been observed that, virtual Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%, 97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian, andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud environments.
SeqTex: Generate Mesh Textures in Video Sequence
Venue: arXiv cs.CV 2025
Year: 2025

Training native 3D texture generative models remains a fundamental yet challenging problem, largely due to the limited availability of large-scale, high-quality 3D texture datasets. This scarcity hinders generalization to real-world scenarios. To address this, most existing methods finetune foundation image generative models to exploit their learned visual priors. However, these approaches typically generate only multi-view images and rely on post-processing to produce UV texture maps -- an essential representation in modern graphics pipelines. Such two-stage pipelines often suffer from error accumulation and spatial inconsistencies across the 3D surface. In this paper, we introduce SeqTex, a novel end-to-end framework that leverages the visual knowledge encoded in pretrained video foundation models to directly generate complete UV texture maps. Unlike previous methods that model the distribution of UV textures in isolation, SeqTex reformulates the task as a sequence generation problem, enabling the model to learn the joint distribution of multi-view renderings and UV textures. This design effectively transfers the consistent image-space priors from video foundation models into the UV domain. To further enhance performance, we propose several architectural innovations: a decoupled multi-view and UV branch design, geometry-informed attention to guide cross-domain feature alignment, and adaptive token resolution to preserve fine texture details while maintaining computational efficiency. Together, these components allow SeqTex to fully utilize pretrained video priors and synthesize high-fidelity UV texture maps without the need for post-processing. Extensive experiments show that SeqTex achieves state-of-the-art performance on both image-conditioned and text-conditioned 3D texture generation tasks, with superior 3D consistency, texture-geometry alignment, and real-world generalization.
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Venue: arXiv cs.CY 2025
Year: 2025

Effective feedback is essential for student learning but is time-intensive for teachers. We present LearnLens, a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education. LearnLens comprises three components: (1) an error-aware assessment module that captures nuanced reasoning errors; (2) a curriculum-grounded generation module that uses a structured, topic-linked memory chain rather than traditional similarity-based retrieval, improving relevance and reducing noise; and (3) an educator-in-the-loop interface for customisation and oversight. LearnLens addresses key challenges in existing systems, offering scalable, high-quality feedback that empowers both teachers and students.
DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Atmospheric Turbulence (AT) degrades the clarity and accuracy of surveillance imagery, posing challenges not only for visualization quality but also for object classification and scene tracking. Deep learning-based methods have been proposed to improve visual quality, but spatio-temporal distortions remain a significant issue. Although deep learning-based object detection performs well under normal conditions, it struggles to operate effectively on sequences distorted by atmospheric turbulence. In this paper, we propose a novel framework that learns to compensate for distorted features while simultaneously improving visualization and object detection. This end-to-end training strategy leverages and exchanges knowledge of low-level distorted features in the AT mitigator with semantic features extracted in the object detector. Specifically, in the AT mitigator a 3D Mamba-based structure is used to handle the spatio-temporal displacements and blurring caused by turbulence. Optimization is achieved through back-propagation in both the AT mitigator and object detector. Our proposed DMAT outperforms state-of-the-art AT mitigation and object detection systems up to a 15% improvement on datasets corrupted by generated turbulence.
Attention Slipping: A Mechanistic Understanding of Jailbreak Attacks and Defenses in LLMs
Venue: arXiv cs.CR 2025
Year: 2025

As large language models (LLMs) become more integral to society and technology, ensuring their safety becomes essential. Jailbreak attacks exploit vulnerabilities to bypass safety guardrails, posing a significant threat. However, the mechanisms enabling these attacks are not well understood. In this paper, we reveal a universal phenomenon that occurs during jailbreak attacks: Attention Slipping. During this phenomenon, the model gradually reduces the attention it allocates to unsafe requests in a user query during the attack process, ultimately causing a jailbreak. We show Attention Slipping is consistent across various jailbreak methods, including gradient-based token replacement, prompt-level template refinement, and in-context learning. Additionally, we evaluate two defenses based on query perturbation, Token Highlighter and SmoothLLM, and find they indirectly mitigate Attention Slipping, with their effectiveness positively correlated with the degree of mitigation achieved. Inspired by this finding, we propose Attention Sharpening, a new defense that directly counters Attention Slipping by sharpening the attention score distribution using temperature scaling. Experiments on four leading LLMs (Gemma2-9B-It, Llama3.1-8B-It, Qwen2.5-7B-It, Mistral-7B-It v0.2) show that our method effectively resists various jailbreak attacks while maintaining performance on benign tasks on AlpacaEval. Importantly, Attention Sharpening introduces no additional computational or memory overhead, making it an efficient and practical solution for real-world deployment.
Implicit Dual-Control for Visibility-Aware Navigation in Unstructured Environments
Venue: arXiv cs.RO 2025
Year: 2025

Navigating complex, cluttered, and unstructured environments that are a priori unknown presents significant challenges for autonomous ground vehicles, particularly when operating with a limited field of view(FOV) resulting in frequent occlusion and unobserved space. This paper introduces a novel visibility-aware model predictive path integral framework(VA-MPPI). Formulated as a dual control problem where perceptual uncertainties and control decisions are intertwined, it reasons over perception uncertainty evolution within a unified planning and control pipeline. Unlike traditional methods that rely on explicit uncertainty objectives, the VA-MPPI controller implicitly balances exploration and exploitation, reducing uncertainty only when system performance would be increased. The VA-MPPI framework is evaluated in simulation against deterministic and prescient controllers across multiple scenarios, including a cluttered urban alleyway and an occluded off-road environment. The results demonstrate that VA-MPPI significantly improves safety by reducing collision with unseen obstacles while maintaining competitive performance. For example, in the off-road scenario with 400 control samples, the VA-MPPI controller achieved a success rate of 84%, compared to only 8% for the deterministic controller, with all VA-MPPI failures arising from unmet stopping criteria rather than collisions. Furthermore, the controller implicitly avoids unobserved space, improving safety without explicit directives. The proposed framework highlights the potential for robust, visibility-aware navigation in unstructured and occluded environments, paving the way for future advancements in autonomous ground vehicle systems.
Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Venue: arXiv cs.RO 2025
Year: 2025

Safe trajectory planning in complex environments must balance stringent collision avoidance with real-time efficiency, which is a long-standing challenge in robotics. In this work, we present a diffusion-based trajectory planning framework that is both rapid and safe. First, we introduce a scene-agnostic, MPC-based data generation pipeline that efficiently produces large volumes of kinematically feasible trajectories. Building on this dataset, our integrated diffusion planner maps raw onboard sensor inputs directly to kinematically feasible trajectories, enabling efficient inference while maintaining strong collision avoidance. To generalize to diverse, previously unseen scenarios, we compose diffusion models at test time, enabling safe behavior without additional training. We further propose a lightweight, rule-based safety filter that, from the candidate set, selects the trajectory meeting safety and kinematic-feasibility requirements. Across seen and unseen settings, the proposed method delivers real-time-capable inference with high safety and stability. Experiments on an F1TENTH vehicle demonstrate practicality on real hardware. Project page: https://rstp-comp-diffuser.github.io/.
Tractable Representation Learning with Probabilistic Circuits
Venue: arXiv cs.LG 2025
Year: 2025

Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference tasks. While dominant in neural networks, representation learning with PCs remains underexplored, with prior approaches relying on external neural embeddings or activation-based encodings. To address this gap, we introduce autoencoding probabilistic circuits (APCs), a novel framework leveraging the tractability of PCs to model probabilistic embeddings explicitly. APCs extend PCs by jointly modeling data and embeddings, obtaining embedding representations through tractable probabilistic inference. The PC encoder allows the framework to natively handle arbitrary missing data and is seamlessly integrated with a neural decoder in a hybrid, end-to-end trainable architecture enabled by differentiable sampling. Our empirical evaluation demonstrates that APCs outperform existing PC-based autoencoding methods in reconstruction quality, generate embeddings competitive with, and exhibit superior robustness in handling missing data compared to neural autoencoders. These results highlight APCs as a powerful and flexible representation learning method that exploits the probabilistic inference capabilities of PCs, showing promising directions for robust inference, out-of-distribution detection, and knowledge distillation.
Exploring React Library Related Questions on Stack Overflow: Answered vs. Unanswered
Venue: arXiv cs.SE 2025
Year: 2025

React is a popular JavaScript framework in modern web application development. Due to its high performance and efficiency, many developers use this framework. Although React library offers many advantages, it is not without its challenges. When using React library, developers often face problems where they often seek solutions through question-and-answer forums, such as Stack Overflow (SO). However, despite its high popularity, many React-related questions on SO remain unanswered. Thus, this study aims to analyze the factors associated with question answerability and difficulty levels of React-related questions on SO. To facilitate our study, Exploratory Data Analysis was applied to 534,820 questions, where they are filtered based on 23 React-related tags. We implemented a quantitative approach through text mining and statistical analysis. A logistic regression model was used to identify attributes associated with question answerability, while a simple linear regression model was employed to examine the correlation between user reputations and performance difficulty scores (PD Score). The results show that some attributes, such as number of views, code snippet inclusion, number of lines of code, and user reputation, positively affect the likelihood of question answerability. In contrast, the number of comments, question lengths, and presence of images in React-related questions reduce the probability of a question receiving responses from users. Further investigation indicates a negative correlation between user reputations and PD Score, where reputation increase corresponds to -0.092 reduction in PD score, signaling experienced users tend to propose more complex technical inquiries. This study provides insights into the characteristics of technical question-and-answer platforms, such as SO, that users need to consider the answerability factors when posting questions related to React.
The Agency Gap: How Generative AI Literacy Shapes Independent Writing after AI Support
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI (GenAI) tools are rapidly transforming higher education, yet little is known about how students' GenAI literacy shapes their ability to perform independently once such support is removed. This study investigates what we term the agency gap, introduced as the extent to which GenAI literacy predicts student writing performance in contexts that require self-initiation and regulation. Seventy-nine medical and nursing students completed multimodal academic writing tasks based on visual data, supported either by a reactive or proactive GenAI chatbot, followed by a parallel task without AI support. Writing was evaluated across insightfulness, visual data integration, organisation, linguistic quality, and critical thinking. Results showed that GenAI literacy predicted independent writing performance only in the reactive condition, where students had to actively mobilise their own strategies. Mediation analyses revealed no indirect effect via in-task performance, indicating that GenAI literacy acts as a direct, task-general competence rather than a proxy for domain knowledge or other literacies. By contrast, proactive scaffolding equalised outcomes across literacy levels, reducing reliance on learners' GenAI literacy. The agency gap highlights when GenAI literacy matters most, with implications for designing equitable AI-supported learning environments that both leverage and mitigate differences in students' GenAI literacy.
Enhancing Text-Based Hierarchical Multilabel Classification for Mobile Applications via Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

A hierarchical labeling system for mobile applications (apps) benefits a wide range of downstream businesses that integrate the labeling with their proprietary user data, to improve user modeling. Such a label hierarchy can define more granular labels that capture detailed app features beyond the limitations of traditional broad app categories. In this paper, we address the problem of hierarchical multilabel classification for apps by using their textual information such as names and descriptions. We present: 1) HMCN (Hierarchical Multilabel Classification Network) for handling the classification from two perspectives: the first focuses on a multilabel classification without hierarchical constraints, while the second predicts labels sequentially at each hierarchical level considering such constraints; 2) HMCL (Hierarchical Multilabel Contrastive Learning), a scheme that is capable of learning more distinguishable app representations to enhance the performance of HMCN. Empirical results on our Tencent App Store dataset and two public datasets demonstrate that our approach performs well compared with state-of-the-art methods. The approach has been deployed at Tencent and the multilabel classification outputs for apps have helped a downstream task--credit risk management of user--improve its performance by 10.70% with regard to the Kolmogorov-Smirnov metric, for over one year.
NourID+: A Digital Energy Identity Framework for Efficient Subsidy Allocation in Morocco
Venue: arXiv cs.CY 2025
Year: 2025

We introduce NourID+, a digital energy identity framework that addresses Morocco's need for trusted energy subsidy allocation through authenticated digital identity integration. NourID+ creates a strong foundation for future subsidy programs by unifying three government-issued and digitalized credentials: Moroccan national identity cards (CIN), cadastral plans, and property ownership certificates are transformed into unique digital energy IDs (DE-IDs) that map authenticated identities with specific properties and their energy consumption patterns. The system supports three property ownership profiles: farmers (landowners), entrepreneurs (factory or company owners), and households (house owners), as energy consumption is directly related to land ownership. NourID+ provides dual access through a government portal allowing officials to process DE-ID generation requests, as well as a citizen portal for DE-ID usage and energy monitoring. Our framework supports CIN upload with facial biometric matching, automated property retrieval through government APIs, and government officer approval workflow for DE-ID generation. After evaluation of the system, we demonstrate a reduction in verification time from weeks to minutes, with 98% accuracy of document validation. The proposed solution allows for targeted subsidy allocation of electricity based on actual consumption needs rather than estimations, potentially improving the efficiency of Morocco's significant energy subsidy expenditure.