Automated machine learning systems efficiently streamline model selection but often focus on a single best-performing model, overlooking explanation uncertainty--an essential concern in human-centered explainable AI. To address this, we propose a novel framework that incorporates model multiplicity into explanation generation by aggregating partial dependence profiles (PDP) from a set of near-optimal models, known as the Rashomon set. The resulting Rashomon PDP captures interpretive variability and highlights areas of disagreement, providing users with a richer, uncertainty-aware view of feature effects. To evaluate its usefulness, we introduce two quantitative metrics, the coverage rate and the mean width of confidence intervals, to evaluate the consistency between the standard PDP and the proposed Rashomon PDP. Experiments on 35 regression datasets from the OpenML-CTR23 benchmark suite show that in most of the cases, the Rashomon PDP covers less than 70% of the best model's PDP, underscoring the limitations of single-model explanations. Our findings suggest that Rashomon PDP improves the reliability and trustworthiness of model interpretations by adding additional information that would otherwise be neglected. This is particularly useful in high-stakes domains where transparency and confidence are critical.

This paper presents intersection of Physics informed neural networks (PINNs) and Lie symmetry group to enhance the accuracy and efficiency of solving partial differential equation (PDEs). Various methods have been developed to solve these equations. A Lie group is an efficient method that can lead to exact solutions for the PDEs that possessing Lie Symmetry. Leveraging the concept of infinitesimal generators from Lie symmetry group in a novel manner within PINN leads to significant improvements in solution of PDEs. In this study three distinct cases are discussed, each showing progressive improvements achieved through Lie symmetry modifications and adaptive techniques. State-of-the-art numerical methods are adopted for comparing the progressive PINN models. Numerical experiments demonstrate the key role of Lie symmetry in enhancing PINNs performance, emphasizing the importance of integrating abstract mathematical concepts into deep learning for addressing complex scientific problems adequately.

This study introduces an enhanced approach to video super-resolution by extending ordinary Single-Image Super-Resolution (SISR) Super-Resolution Generative Adversarial Network (SRGAN) structure to handle spatio-temporal data. While SRGAN has proven effective for single-image enhancement, its design does not account for the temporal continuity required in video processing. To address this, a modified framework that incorporates 3D Non-Local Blocks is proposed, which is enabling the model to capture relationships across both spatial and temporal dimensions. An experimental training pipeline is developed, based on patch-wise learning and advanced data degradation techniques, to simulate real-world video conditions and learn from both local and global structures and details. This helps the model generalize better and maintain stability across varying video content while maintaining the general structure besides the pixel-wise correctness. Two model variants--one larger and one more lightweight--are presented to explore the trade-offs between performance and efficiency. The results demonstrate improved temporal coherence, sharper textures, and fewer visual artifacts compared to traditional single-image methods. This work contributes to the development of practical, learning-based solutions for video enhancement tasks, with potential applications in streaming, gaming, and digital restoration.

Arabic dialect recognition presents a significant challenge in speech technology due to the linguistic diversity of Arabic and the scarcity of large annotated datasets, particularly for underrepresented dialects. This research investigates hybrid modeling strategies that integrate classical signal processing techniques with deep learning architectures to address this problem in low-resource scenarios. Two hybrid models were developed and evaluated: (1) Mel-Frequency Cepstral Coefficients (MFCC) combined with a Convolutional Neural Network (CNN), and (2) Discrete Wavelet Transform (DWT) features combined with a Recurrent Neural Network (RNN). The models were trained on a dialect-filtered subset of the Common Voice Arabic dataset, with dialect labels assigned based on speaker metadata. Experimental results demonstrate that the MFCC + CNN architecture achieved superior performance, with an accuracy of 91.2% and strong precision, recall, and F1-scores, significantly outperforming the Wavelet + RNN configuration, which achieved an accuracy of 66.5%. These findings highlight the effectiveness of leveraging spectral features with convolutional models for Arabic dialect recognition, especially when working with limited labeled data. The study also identifies limitations related to dataset size, potential regional overlaps in labeling, and model optimization, providing a roadmap for future research. Recommendations for further improvement include the adoption of larger annotated corpora, integration of self-supervised learning techniques, and exploration of advanced neural architectures such as Transformers. Overall, this research establishes a strong baseline for future developments in Arabic dialect recognition within resource-constrained environments.

--This study presents TSLFormer, a light and robust word-level T urkish Sign Language (TID) recognition model that treats sign gestures as ordered, string-like language. In contrast to working with raw RGB or depth videos, our method only works with 3D joint positions--articulation points--extracted using Google's Mediapipe library, which focuses on the hand and torso skeletal locations. This creates efficient input dimensionality reduction with significant preservation of important semantic information of the gesture. Our approach revisits sign language recognition as sequence-to-sequence translation, drawing inspiration from sign languages' linguistic nature and transformer's success at natural language translation. Since TSLFormer adapts the transformers' self-attention mechanism, it effectively represents the temporal co-occurrence of a sign sequence, stressing significant movement habits over time as words are referenced in a sentence. Experimented and validated on the AUTSL dataset holding over 36,000 sign samples of over 226 different words, the TSLFormer achieves competitive performance and with minimal computational demands. From the experimentation, rich spatiotemporal understanding of signs is evidenced, and using only joint landmarks, it is possible within any real-time, mobile, and assistive technology facilitating communication between hearing-impaired members. Sign language is an essential communication method for the hearing impaired to express ideas and sentiments through hand gestures, facial expressions, and body movement. Unlike spoken languages, which employ auditory and verbal modalities, sign language utilizes visual and spatial modalities to express meaning. However, despite the limited number of sign language proficient individuals, communication gaps still exist to hinder inclusion--particularly in social interaction on a daily basis and in employment, educational, and healthcare environments.

--Electrocardiogram (ECG) interpretation is fundamental to cardiac diagnosis, but deep learning models often lack transparency, hindering clinical trust. We introduce Car-dioPatternFormer, a novel transformer-based architecture that reframes ECG interpretation through the lens of pattern recognition, treating cardiac patterns as a vocabulary learned from data. CardioPatternFormer integrates several innovations: (1) a Cardiac Pattern T okenizer that decomposes ECG signals into learned, multi-scale patterns; (2) Physiologically Guided Attention mechanisms incorporating adaptable, domain-specific constraints based on cardiac electrophysiology; (3) Multi-Resolution T emporal Encoding to capture diverse temporal dynamics; and (4) specialized classification heads providing class-specific attention visualizations for detailed diagnostic explanations. Evaluated on the Chapman-Shaoxing dataset across major diagnostic categories, CardioPatternFormer demonstrates strong classification performance, particularly for rhythm disorders, with results aligning with clinical experience regarding diagnostic difficulty gradients. More significantly, CardioPatternFormer enhances in-terpretability by visualizing physiologically relevant ECG regions influencing each diagnosis, bridging automated analysis and clinical reasoning. This pattern-centric approach advances ECG classification and establishes a foundation for more transparent and clinically integrated cardiac signal analysis. I. INTRODUCTION The electrocardiogram (ECG) stands as a cornerstone of cardiovascular diagnostics, offering a non-invasive window into the heart's electrical activity. Annually, hundreds of millions of ECGs are performed worldwide, underscoring its fundamental role in identifying a wide spectrum of cardiac conditions [20]. Despite its ubiquity and the wealth of information it provides, accurate ECG interpretation demands considerable expertise, typically cultivated over years of rigorous training and clinical practice. Even amongst seasoned cardiologists, inter-reader variability remains a notable challenge, with studies reporting significant discrepancies in the identification of specific cardiac abnormalities. This variability highlights not only the inherent complexities of ECG analysis but also the persistent need for advanced computational tools that can deliver consistent, precise, and interpretable analyses.

This paper investigates the tunability of the Random Survival Forest (RSF) model in predictive maintenance, where accurate time-to-failure estimation is crucial. Although RSF is widely used due to its flexibility and ability to handle censored data, its performance is sensitive to hyperparameter configurations. However, systematic evaluations of RSF tunability remain limited, especially in predictive maintenance contexts. We introduce a three-level framework to quantify tunability: (1) a model-level metric measuring overall performance gain from tuning, (2) a hyperparameter-level metric assessing individual contributions, and (3) identification of optimal tuning ranges. These metrics are evaluated across multiple datasets using survival-specific criteria: the C-index for discrimination and the Brier score for calibration. Experiments on four CMAPSS dataset subsets, simulating aircraft engine degradation, reveal that hyperparameter tuning consistently improves model performance. On average, the C-index increased by 0.0547, while the Brier score decreased by 0.0199. These gains were consistent across all subsets. Moreover, ntree and mtry showed the highest average tunability, while nodesize offered stable improvements within the range of 10 to 30. In contrast, splitrule demonstrated negative tunability on average, indicating that improper tuning may reduce model performance. Our findings emphasize the practical importance of hyperparameter tuning in survival models and provide actionable insights for optimizing RSF in real-world predictive maintenance applications.

In many applications, especially those involving prediction, models may yield near-optimal performance yet significantly disagree on individual-level outcomes. This phenomenon, known as predictive multiplicity, has been formally defined in binary, probabilistic, and multi-target classification, and undermines the reliability of predictive systems. However, its implications remain unexplored in the context of survival analysis, which involves estimating the time until a failure or similar event while properly handling censored data. We frame predictive multiplicity as a critical concern in survival-based models and introduce formal measures -- ambiguity, discrepancy, and obscurity -- to quantify it. This is particularly relevant for downstream tasks such as maintenance scheduling, where precise individual risk estimates are essential. Understanding and reporting predictive multiplicity helps build trust in models deployed in high-stakes environments. We apply our methodology to benchmark datasets from predictive maintenance, extending the notion of multiplicity to survival models. Our findings show that ambiguity steadily increases, reaching up to 40-45% of observations; discrepancy is lower but exhibits a similar trend; and obscurity remains mild and concentrated in a few models. These results demonstrate that multiple accurate survival models may yield conflicting estimations of failure risk and degradation progression for the same equipment. This highlights the need to explicitly measure and communicate predictive multiplicity to ensure reliable decision-making in process health management.

The prediction of the Remaining Useful Life of aircraft engines is a critical area in high-reliability sectors such as aerospace and defense. Early failure predictions help ensure operational continuity, reduce maintenance costs, and prevent unexpected failures. Traditional regression models struggle with censored data, which can lead to biased predictions. Survival models, on the other hand, effectively handle censored data, improving predictive accuracy in maintenance processes. This paper introduces a novel approach based on the Rashomon perspective, which considers multiple models that achieve similar performance rather than relying on a single best model. This enables uncertainty quantification in survival probability predictions and enhances decision-making in predictive maintenance. The Rashomon survival curve was introduced to represent the range of survival probability estimates, providing insights into model agreement and uncertainty over time. The results on the CMAPSS dataset demonstrate that relying solely on a single model for RUL estimation may increase risk in some scenarios. The censoring levels significantly impact prediction uncertainty, with longer censoring times leading to greater variability in survival probabilities. These findings underscore the importance of incorporating model multiplicity in predictive maintenance frameworks to achieve more reliable and robust failure predictions. This paper contributes to uncertainty quantification in RUL prediction and highlights the Rashomon perspective as a powerful tool for predictive modeling.

Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2^n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations.