OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design
Venue: arXiv cs.LG 2025
Year: 2025

We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the corresponding design variables. By leveraging SHapley Additive exPlanations (SHAP), a machine learning interpretability tool, we identified the key parameters influencing the objective function and gained insights into how these parameters affect sound absorption. The insights derived from the SHAP analysis were subsequently used to automatically refine the bounds of the optimization problem automatically, enabling a more targeted and efficient exploration of the design space. The proposed approach was applied to two polyurethane materials with distinct hardness levels, resulting in improved optimal solutions compared to those obtained without SHAP-informed guidance. Notably, these enhancements were achieved without increasing the number of simulation iterations. Our findings demonstrate the potential of SHAP to streamline optimization processes by uncovering hidden parameter relationships and guiding the search toward promising regions of the design space. This work underscores the effectiveness of combining interpretability techniques with Bayesian optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problems.
Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection
Venue: arXiv cs.CV 2025
Year: 2025

Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset.
The Malaysian Election Corpus (MECo): Federal and State-Level Election Results from 1955 to 2025
Venue: arXiv cs.CY 2025
Year: 2025

Empirical research and public knowledge on Malaysia's elections have long been constrained by a lack of high-quality open data, particularly in the absence of a Freedom of Information framework. This paper introduces the Malaysian Election Corpus (MECo), an open-access panel database covering all federal and state general elections since 1955, as well as by-elections since 2008. MECo includes candidate- and constituency-level data for 9,704 electoral contests across seven decades, standardised with unique identifiers for candidates, parties, and coalitions. The database also provides summary statistics for each contest (electorate size, voter turnout, majority size, rejected ballots, unreturned ballots), key demographic data for candidates (age, gender, ethnicity), and lineage data for political parties. MECo is the most well-curated open database on Malaysian elections to date, and will unlock new opportunities for research, data journalism, and civic engagement.
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors
Venue: arXiv cs.CV 2025
Year: 2025

Protecting power transmission lines from potential hazards involves critical tasks, one of which is the accurate measurement of distances between power lines and potential threats, such as large cranes. The challenge with this task is that the current sensor-based methods face challenges in balancing accuracy and cost in distance measurement. A common practice is to install cameras on transmission towers, which, however, struggle to measure true 3D distances due to the lack of depth information. Although 3D lasers can provide accurate depth data, their high cost makes large-scale deployment impractical. To address this challenge, we present ElectricSight, a system designed for 3D distance measurement and monitoring of potential hazards to power transmission lines. This work's key innovations lie in both the overall system framework and a monocular depth estimation method. Specifically, the system framework combines real-time images with environmental point cloud priors, enabling cost-effective and precise 3D distance measurements. As a core component of the system, the monocular depth estimation method enhances the performance by integrating 3D point cloud data into image-based estimates, improving both the accuracy and reliability of the system. To assess ElectricSight's performance, we conducted tests with data from a real-world power transmission scenario. The experimental results demonstrate that ElectricSight achieves an average accuracy of 1.08 m for distance measurements and an early warning accuracy of 92%.
Two-Stage Random Alternation Framework for One-Shot Pansharpening
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning has substantially advanced pansharpening, achieving impressive fusion quality. However, a prevalent limitation is that conventional deep learning models, which typically rely on training datasets, often exhibit suboptimal generalization to unseen real-world image pairs. This restricts their practical utility when faced with real-world scenarios not included in the training datasets. To overcome this, we introduce a two-stage random alternating framework (TRA-PAN) that performs instance-specific optimization for any given Multispectral(MS)/Panchromatic(PAN) pair, ensuring robust and high-quality fusion. TRA-PAN effectively integrates strong supervision constraints from reduced-resolution images with the physical characteristics of the full-resolution images. The first stage introduces a pre-training procedure, which includes Degradation-Aware Modeling (DAM) to capture spectral degradation mappings, alongside a warm-up procedure designed to reduce training time and mitigate the adverse effects of reduced-resolution data. The second stage employs Random Alternation Optimization (RAO), randomly alternating between reduced- and full-resolution images to refine the fusion model progressively. This adaptive, per-instance optimization strategy, operating in a one-shot manner for each MS/PAN pair, yields superior high-resolution multispectral images. Experimental results demonstrate that TRA-PAN outperforms state-of-the-art (SOTA) methods in quantitative metrics and visual quality in real-world scenarios, underscoring its enhanced practical applicability and robustness.
Compact and Efficient Neural Networks for Image Recognition Based on Learned 2D Separable Transform
Venue: arXiv cs.CV 2025
Year: 2025

The paper presents a learned two-dimensional separable transform (LST) that can be considered as a new type of computational layer for constructing neural network (NN) architecture for image recognition tasks. The LST based on the idea of sharing the weights of one fullyconnected (FC) layer to process all rows of an image. After that, a second shared FC layer is used to process all columns of image representation obtained from the first layer. The use of LST layers in a NN architecture significantly reduces the number of model parameters compared to models that use stacked FC layers. We show that a NN-classifier based on a single LST layer followed by an FC layer achieves 98.02\% accuracy on the MNIST dataset, while having only 9.5k parameters. We also implemented a LST-based classifier for handwritten digit recognition on the FPGA platform to demonstrate the efficiency of the suggested approach for designing a compact and high-performance implementation of NN models. Git repository with supplementary materials: https://github.com/Mak-Sim/LST-2d
An $\tilde{O}$ptimal Differentially Private Learner for Concept Classes with VC Dimension 1
Venue: arXiv cs.LG 2025
Year: 2025

We present the first nearly optimal differentially private PAC learner for any concept class with VC dimension 1 and Littlestone dimension $d$. Our algorithm achieves the sample complexity of $\tilde{O}_{\varepsilon,δ,α,δ}(\log^* d)$, nearly matching the lower bound of $Ω(\log^* d)$ proved by Alon et al. [STOC19]. Prior to our work, the best known upper bound is $\tilde{O}(VC\cdot d^5)$ for general VC classes, as shown by Ghazi et al. [STOC21].
Emergent Multi-View Fidelity in Autonomous UAV Swarm Sport Injury Detection
Venue: arXiv cs.RO 2025
Year: 2025

Accurate, real-time collision detection is essential for ensuring player safety and effective refereeing in high-contact sports such as rugby, particularly given the severe risks associated with traumatic brain injuries (TBI). Traditional collision-monitoring methods employing fixed cameras or wearable sensors face limitations in visibility, coverage, and responsiveness. Previously, we introduced a framework using unmanned aerial vehicles (UAVs) for monitoring and real time kinematics extraction from videos of collision events. In this paper, we show that the strategies operating on the objective of ensuring at least one UAV captures every incident on the pitch have an emergent property of fulfilling a stronger key condition for successful kinematics extraction. Namely, they ensure that almost all collisions are captured by multiple drones, establishing multi-view fidelity and redundancy, while not requiring any drone-to-drone communication.
Evaluating LLM-Generated Q&A Test: a Student-Centered Study
Venue: arXiv cs.CL 2025
Year: 2025

This research prepares an automatic pipeline for generating reliable question-answer (Q&A) tests using AI chatbots. We automatically generated a GPT-4o-mini-based Q&A test for a Natural Language Processing course and evaluated its psychometric and perceived-quality metrics with students and experts. A mixed-format IRT analysis showed that the generated items exhibit strong discrimination and appropriate difficulty, while student and expert star ratings reflect high overall quality. A uniform DIF check identified two items for review. These findings demonstrate that LLM-generated assessments can match human-authored tests in psychometric performance and user satisfaction, illustrating a scalable approach to AI-assisted assessment development.
Using External knowledge to Enhanced PLM for Semantic Matching
Venue: arXiv cs.CL 2025
Year: 2025

Modeling semantic relevance has always been a challenging and critical task in natural language processing. In recent years, with the emergence of massive amounts of annotated data, it has become feasible to train complex models, such as neural network-based reasoning models. These models have shown excellent performance in practical applications and have achieved the current state-ofthe-art performance. However, even with such large-scale annotated data, we still need to think: Can machines learn all the knowledge necessary to perform semantic relevance detection tasks based on this data alone? If not, how can neural network-based models incorporate external knowledge into themselves, and how can relevance detection models be constructed to make full use of external knowledge? In this paper, we use external knowledge to enhance the pre-trained semantic relevance discrimination model. Experimental results on 10 public datasets show that our method achieves consistent improvements in performance compared to the baseline model.
Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations
Venue: arXiv cs.HC 2025
Year: 2025

There is a growing demand for the use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, particularly as clinical decision support systems to assist medical professionals. However, the complexity of many of these models, often referred to as black box models, raises concerns about their safe integration into clinical settings as it is difficult to understand how they arrived at their predictions. This paper discusses insights and recommendations derived from an expert working group convened by the UK Medicine and Healthcare products Regulatory Agency (MHRA). The group consisted of healthcare professionals, regulators, and data scientists, with a primary focus on evaluating the outputs from different AI algorithms in clinical decision-making contexts. Additionally, the group evaluated findings from a pilot study investigating clinicians' behaviour and interaction with AI methods during clinical diagnosis. Incorporating AI methods is crucial for ensuring the safety and trustworthiness of medical AI devices in clinical settings. Adequate training for stakeholders is essential to address potential issues, and further insights and recommendations for safely adopting AI systems in healthcare settings are provided.
VTutor: An Animated Pedagogical Agent SDK that Provide Real Time Multi-Model Feedback
Venue: arXiv cs.HC 2025
Year: 2025

Pedagogical Agents (PAs) show significant potential for boosting student engagement and learning outcomes by providing adaptive, on-demand support in educational contexts. However, existing PA solutions are often hampered by pre-scripted dialogue, unnatural animations, uncanny visual realism, and high development costs. To address these gaps, we introduce VTutor, an open-source SDK leveraging lightweight WebGL, Unity, and JavaScript frameworks. VTutor receives text outputs from a large language model (LLM), converts them into audio via text-to-speech, and then renders a real-time, lip-synced pedagogical agent (PA) for immediate, large-scale deployment on web-based learning platforms. By providing on-demand, personalized feedback, VTutor strengthens students' motivation and deepens their engagement with instructional material. Using an anime-like aesthetic, VTutor alleviates the uncanny valley effect, allowing learners to engage with expressive yet comfortably stylized characters. Our evaluation with 50 participants revealed that VTutor significantly outperforms the existing talking-head approaches (e.g., SadTalker) on perceived synchronization accuracy, naturalness, emotional expressiveness, and overall preference. As an open-source project, VTutor welcomes community-driven contributions - from novel character designs to specialized showcases of pedagogical agent applications - that fuel ongoing innovation in AI-enhanced education. By providing an accessible, customizable, and learner-centered PA solution, VTutor aims to elevate human-AI interaction experience in education fields, ultimately broadening the impact of AI in learning contexts. The demo link to VTutor is at https://vtutor-aied25.vercel.app.
A Novel Framework for Significant Wave Height Prediction based on Adaptive Feature Extraction Time-Frequency Network
Venue: arXiv cs.LG 2025
Year: 2025

Precise forecasting of significant wave height (Hs) is essential for the development and utilization of wave energy. The challenges in predicting Hs arise from its non-linear and non-stationary characteristics. The combination of decomposition preprocessing and machine learning models have demonstrated significant effectiveness in Hs prediction by extracting data features. However, decomposing the unknown data in the test set can lead to data leakage issues. To simultaneously achieve data feature extraction and prevent data leakage, a novel Adaptive Feature Extraction Time-Frequency Network (AFE-TFNet) is proposed to improve prediction accuracy and stability. It is encoder-decoder rolling framework. The encoder consists of two stages: feature extraction and feature fusion. In the feature extraction stage, global and local frequency domain features are extracted by combining Wavelet Transform (WT) and Fourier Transform (FT), and multi-scale frequency analysis is performed using Inception blocks. In the feature fusion stage, time-domain and frequency-domain features are integrated through dominant harmonic sequence energy weighting (DHSEW). The decoder employed an advanced long short-term memory (LSTM) model. Hourly measured wind speed (Ws), dominant wave period (DPD), average wave period (APD) and Hs from three stations are used as the dataset, and the four metrics are employed to evaluate the forecasting performance. Results show that AFE-TFNet significantly outperforms benchmark methods in terms of prediction accuracy. Feature extraction can significantly improve the prediction accuracy. DHSEW has substantially increased the accuracy of medium-term to long-term forecasting. The prediction accuracy of AFE-TFNet does not demonstrate significant variability with changes of rolling time window size. Overall, AFE-TFNet shows strong potential for handling complex signal forecasting.
RuleGenie: SIEM Detection Rule Set Optimization
Venue: arXiv cs.CR 2025
Year: 2025

SIEM systems serve as a critical hub, employing rule-based logic to detect and respond to threats. Redundant or overlapping rules in SIEM systems lead to excessive false alerts, degrading analyst performance due to alert fatigue, and increase computational overhead and response latency for actual threats. As a result, optimizing SIEM rule sets is essential for efficient operations. Despite the importance of such optimization, research in this area is limited, with current practices relying on manual optimization methods that are both time-consuming and error-prone due to the scale and complexity of enterprise-level rule sets. To address this gap, we present RuleGenie, a novel large language model (LLM) aided recommender system designed to optimize SIEM rule sets. Our approach leverages transformer models' multi-head attention capabilities to generate SIEM rule embeddings, which are then analyzed using a similarity matching algorithm to identify the top-k most similar rules. The LLM then processes the rules identified, utilizing its information extraction, language understanding, and reasoning capabilities to analyze rule similarity, evaluate threat coverage and performance metrics, and deliver optimized recommendations for refining the rule set. By automating the rule optimization process, RuleGenie allows security teams to focus on more strategic tasks while enhancing the efficiency of SIEM systems and strengthening organizations' security posture. We evaluated RuleGenie on a comprehensive set of real-world SIEM rule formats, including Splunk, Sigma, and AQL (Ariel query language), demonstrating its platform-agnostic capabilities and adaptability across diverse security infrastructures. Our experimental results show that RuleGenie can effectively identify redundant rules, which in turn decreases false positive rates and enhances overall rule efficiency.
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images
Venue: arXiv cs.CV 2025
Year: 2025

Various multi-instance learning (MIL) based approaches have been developed and successfully applied to whole-slide pathological images (WSI). Existing MIL methods emphasize the importance of feature aggregators, but largely neglect the instance-level representation learning. They assume that the availability of a pre-trained feature extractor can be directly utilized or fine-tuned, which is not always the case. This paper proposes to pre-train feature extractor for MIL via a weakly-supervised scheme, i.e., propagating the weak bag-level labels to the corresponding instances for supervised learning. To learn effective features for MIL, we further delve into several key components, including strong data augmentation, a non-linear prediction head and the robust loss function. We conduct experiments on common large-scale WSI datasets and find it achieves better performance than other pre-training schemes (e.g., ImageNet pre-training and self-supervised learning) in different downstream tasks. We further show the compatibility and scalability of the proposed scheme by deploying it in fine-tuning the pathological-specific models and pre-training on merged multiple datasets. To our knowledge, this is the first work focusing on the representation learning for MIL.
Perspectives on Unsolvability in the Roommates Problem
Venue: arXiv cs.GT 2025
Year: 2025

In the well-studied Stable Roommates problem, we seek a stable matching of agents into pairs, where no two agents prefer each other over their assigned partners. However, some instances of this problem are unsolvable, lacking any stable matching. A long-standing open question posed by Gusfield and Irving (1989) asks about the behavior of the probability function Pn, which measures the likelihood that a random instance with n agents is solvable. This paper provides a comprehensive analysis of the landscape surrounding this question, combining structural, probabilistic, and experimental perspectives. We review existing approaches from the past four decades, highlight connections to related problems, and present novel structural and experimental findings. Specifically, we estimate Pn for instances with preferences sampled from diverse statistical distributions, examining problem sizes up to 5,001 agents, and look for specific sub-structures that cause unsolvability. Our results reveal that while Pn tends to be low for most distributions, the number and lengths of "unstable" structures remain limited, suggesting that random instances are "close" to being solvable. Additionally, we present the first empirical study of the number of stable matchings and the number of stable partitions that random instances admit, using recently developed algorithms. Our findings show that the solution sets are typically small. This implies that many NP-hard problems related to computing optimal stable matchings and optimal stable partitions become tractable in practice, and motivates efficient alternative solution concepts for unsolvable instances, such as stable half-matchings and maximum stable matchings.
Modelling and Study of t , Peak and Effective Diameter in Temporal Networks
Venue: arXiv cs.SI 2025
Year: 2025

Understanding how information, diseases, or influence spread across networks is a fundamental challenge in complex systems. While network diameter has been extensively studied in static networks, its definition and behavior in temporal networks remain underexplored due to their dynamic nature. In this study, we present a formal mathematical framework for analyzing diameter in temporal networks and introduce three time-aware metrics: Effective Diameter , Peak Diameter (*D), and t-Diameter (tD), each capturing distinct temporal aspects of connectivity and diffusion. Our approach combines theoretical analysis with empirical validation using four real-world datasets: high school, hospital, conference, and workplace contact networks. We simulate flow propagation on temporal networks and compare the observed diameters with the proposed theoretical Equations. Across all datasets, our model demonstrates high accuracy, with low RMSE and absolute error values. Furthermore, we observe that the effective diameter decreases with increasing average degree and increases with network size. The results also show that tD and *D are more sensitive to node removal, highlighting their relevance for applications such as epidemic modeling. By bridging formal modeling and empirical data, our framework offers new insights into the temporal dynamics of networked systems and provides tools for assessing robustness, controlling information spread, and optimizing interventions in time-sensitive environments.