Retinal vessel segmentation is a vital early detection method for several severe ocular diseases. Despite significant progress in retinal vessel segmentation with the advancement of Neural Networks, there are still challenges to overcome. Specifically, retinal vessel segmentation aims to predict the class label for every pixel within a fundus image, with a primary focus on intra-image discrimination, making it vital for models to extract more discriminative features. Nevertheless, existing methods primarily focus on minimizing the difference between the output from the decoder and the label, but ignore fully using feature-level fine-grained representations from the encoder. To address these issues, we propose a novel Attention U-shaped Kolmogorov-Arnold Network named AttUKAN along with a novel Label-guided Pixel-wise Contrastive Loss for retinal vessel segmentation. Specifically, we implement Attention Gates into Kolmogorov-Arnold Networks to enhance model sensitivity by suppressing irrelevant feature activations and model interpretability by non-linear modeling of KAN blocks. Additionally, we also design a novel Label-guided Pixel-wise Contrastive Loss to supervise our proposed AttUKAN to extract more discriminative features by distinguishing between foreground vessel-pixel pairs and background pairs. Experiments are conducted across four public datasets including DRIVE, STARE, CHASE_DB1, HRF and our private dataset. AttUKAN achieves F1 scores of 82.50%, 81.14%, 81.34%, 80.21% and 80.09%, along with MIoU scores of 70.24%, 68.64%, 68.59%, 67.21% and 66.94% in the above datasets, which are the highest compared to 11 networks for retinal vessel segmentation. Quantitative and qualitative results show that our AttUKAN achieves state-of-the-art performance and outperforms existing retinal vessel segmentation methods. Our code will be available at https://github.com/stevezs315/AttUKAN.

Biological neurons exhibit diverse temporal spike patterns, which are believed to support efficient, robust, and adaptive neural information processing. While models such as Izhikevich can replicate a wide range of these firing dynamics, their complexity poses challenges for directly integrating them into scalable spiking neural networks (SNN) training pipelines. In this work, we propose two probabilistically driven, input-level temporal spike transformations: Poisson-Burst and Delayed-Burst that introduce biologically inspired temporal variability directly into standard Leaky Integrate-and-Fire (LIF) neurons. This enables scalable training and systematic evaluation of how spike timing dynamics affect privacy, generalization, and learning performance. Poisson-Burst modulates burst occurrence based on input intensity, while Delayed-Burst encodes input strength through burst onset timing. Through extensive experiments across multiple benchmarks, we demonstrate that Poisson-Burst maintains competitive accuracy and lower resource overhead while exhibiting enhanced privacy robustness against membership inference attacks, whereas Delayed-Burst provides stronger privacy protection at a modest accuracy trade-off. These findings highlight the potential of biologically grounded temporal spike dynamics in improving the privacy, generalization and biological plausibility of neuromorphic learning systems.

Evaluating machine learning models is crucial not only for determining their technical accuracy but also for assessing their potential societal implications. While the potential for low-sample-size bias in algorithms is well known, we demonstrate the significance of sample-size bias induced by combi-natorics in classification metrics. This revelation challenges the efficacy of these metrics in assessing bias with high resolution, especially when comparing groups of disparate sizes, which frequently arise in social applications. We provide analyses of the bias that appears in several commonly applied metrics and propose a model-agnostic assessment and correction technique. Additionally, we analyze counts of undefined cases in metric calculations, which can lead to misleading evaluations if improperly handled. This work illuminates the previously unrecognized challenge of combinatorics and probability in standard evaluation practices and thereby advances approaches for performing fair and trustworthy classification methods.

A B S T R A C T Deep neural networks have shown potential in analyzing digitized hand-drawn signals for early diagnosis of Parkinson's disease. However, the lack of clear inter-pretability in existing diagnostic methods presents a challenge to clinical trust. In this paper, we propose PointExplainer, an explainable diagnostic strategy to identify hand-drawn regions that drive model diagnosis. Specifically, PointExplainer assigns discrete attribution values to hand-drawn segments, explicitly quantifying their relative contributions to the model's decision. Its key components include: (i) a diagnosis module, which encodes hand-drawn signals into 3D point clouds to represent hand-drawn trajectories, and (ii) an explanation module, which trains an interpretable surrogate model to approximate the local behavior of the black-box diagnostic model. We also introduce consistency measures to further address the issue of faithfulness in explanations. Extensive experiments on two benchmark datasets and a newly constructed dataset show that PointExplainer can provide intuitive explanations with no diagnostic performance degradation. Introduction Parkinson's disease (PD) is one of the most prevalent neurological disorders worldwide, leading to a decrease in functional, cognitive, and behavioral abilities [1, 10]. Despite the unclear etiology and lack of a cure, evidence indicates that early diagnosis, coupled with subsequent neuroprotective interventions, can significantly delay its progression [53]. Hand drawing is a common but complex human activity, requiring fine motor control and involving a sophisticated interplay of cognitive, sensory, and perceptual-motor functions [14]. Dysgraphia is recognized as a crucial biomarker in the early stages of PD [39]. Digitized hand-drawn analysis [6, 26], as a noninvasive and easily accessible biometric technology, has emerged as a promising computer-aided approach for diagnosing PD [23, 30, 22, 72, 47, 21].

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management (IRM) system that integrates behavioral analytics, dynamic risk scoring, and real-time policy enforcement to detect and mitigate insider threats with high accuracy and adaptability. We introduce a hybrid scoring mechanism - transitioning from the static PRISM model to an adaptive AI-based model utilizing an autoencoder neural network trained on expert-annotated user activity data. Through iterative feedback loops and continuous learning, the system reduces false positives by 59% and improves true positive detection rates by 30%, demonstrating substantial gains in detection precision. Additionally, the platform scales efficiently, processing up to 10 million log events daily with sub-300ms query latency, and supports automated enforcement actions for policy violations, reducing manual intervention. The IRM system's deployment resulted in a 47% reduction in incident response times, highlighting its operational impact. Future enhancements include integrating explainable AI, federated learning, graph-based anomaly detection, and alignment with Zero Trust principles to further elevate its adaptability, transparency, and compliance-readiness. This work establishes a scalable and proactive framework for mitigating emerging insider risks in both on-premises and hybrid environments.

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun's atmosphere. These energetic bursts of electromagnetic radiation can release up to 10^32 erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms: Random Forest, k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost) for classifying solar flares into 4 categories (B, C, M, X). Using the dataset of 13 SHARP parameters, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PC), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that Random Forest and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics.

The rise of QR code based phishing ("Quishing") poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing detection methods predominantly focus on URL analysis, which requires the extraction of the QR code payload, and may inadvertently expose users to malicious content. Moreover, QR codes can encode various types of data beyond URLs, such as Wi-Fi credentials and payment information, making URL-based detection insufficient for broader security concerns. To address these gaps, we propose the first framework for quishing detection that directly analyzes QR code structure and pixel patterns without extracting the embedded content. We generated a dataset of phishing and benign QR codes and we used it to train and evaluate multiple machine learning models, including Logistic Regression, Decision Trees, Random Forest, Naive Bayes, LightGBM, and XGBoost. Our best-performing model (XGBoost) achieves an AUC of 0.9106, demonstrating the feasibility of QR-centric detection. Through feature importance analysis, we identify key visual indicators of malicious intent and refine our feature set by removing non-informative pixels, improving performance to an AUC of 0.9133 with a reduced feature space. Our findings reveal that the structural features of QR code correlate strongly with phishing risk. This work establishes a foundation for quishing mitigation and highlights the potential of direct QR analysis as a critical layer in modern phishing defenses.

Given the increasing complexity of omics datasets, a key challenge is not only improving classification performance but also enhancing the transparency and reliability of model decisions. Effective model performance and feature selection are fundamental for explainability and reliability. In many cases, high dimensional omics datasets suffer from limited number of samples due to clinical constraints, patient conditions, phenotypes rarity and others conditions. Current omics based classification models often suffer from narrow interpretability, making it difficult to discern meaningful insights where trust and reproducibility are critical. This study presents a machine learning based classification framework that integrates feature selection with data augmentation techniques to achieve high standard classification accuracy while ensuring better interpretability. Using the publicly available dataset (E MTAB 8026), we explore a bootstrap analysis in six binary classification scenarios to evaluate the proposed model's behaviour. We show that the proposed pipeline yields cross validated perfomance on small dataset that is conserved when the trained classifier is applied to a larger test set. Our findings emphasize the fundamental balance between accuracy and feature selection, highlighting the positive effect of introducing synthetic data for better generalization, even in scenarios with very limited samples availability.

Generative models can unintentionally memorize training data, posing significant privacy risks. This paper addresses the memorization phenomenon in time series imputation models, introducing the Loss-Based with Reference Model (LBRM) algorithm. The LBRM method leverages a reference model to enhance the accuracy of membership inference attacks, distinguishing between training and test data. Our contributions are twofold: first, we propose an innovative method to effectively extract and identify memorized training data, significantly improving detection accuracy. On average, without fine-tuning, the AUROC improved by approximately 40\%. With fine-tuning, the AUROC increased by approximately 60\%. Second, we validate our approach through membership inference attacks on two types of architectures designed for time series imputation, demonstrating the robustness and versatility of the LBRM approach in different contexts. These results highlight the significant enhancement in detection accuracy provided by the LBRM approach, addressing privacy risks in time series imputation models.

Traditional human reliability analysis (HRA) methods, such as IDHEAS-ECA, rely on expert judgment and empirical rules that often overlook the cognitive underpinnings of human error. Moreover, conducting human-in-the-loop experiments for advanced nuclear power plants is increasingly impractical due to novel interfaces and limited operational data. This study proposes a cognitive-mechanistic framework (COGMIF) that enhances the IDHEAS-ECA methodology by integrating an ACT-R-based human digital twin (HDT) with TimeGAN-augmented simulation. The ACT-R model simulates operator cognition, including memory retrieval, goal-directed procedural reasoning, and perceptual-motor execution--under high-fidelity scenarios derived from a high-temperature gas-cooled reactor (HTGR) simulator. To overcome the resource constraints of large-scale cognitive modeling, TimeGAN is trained on ACT-R-generated time-series data to produce high-fidelity synthetic operator behavior datasets. These simulations are then used to drive IDHEAS-ECA assessments, enabling scalable, mechanism-informed estimation of human error probabilities (HEPs). Comparative analyses with SPAR-H and sensitivity assessments demonstrate the robustness and practical advantages of the proposed COGMIF. This work offers a credible and computationally efficient pathway to integrate cognitive theory into industrial HRA practices. Keywords: Human Reliability, Human Digital Twins, IDHEAS-ECA, TimeGAN, Bayesian 1 Introduction Human reliability analysis (HRA) plays a pivotal role in the safety assessment of complex socio-technical systems, particularly in high-risk domains such as nuclear power generation [1]. As a fundamental component of probabilistic risk assessment (PRA), HRA aims to estimate the likelihood of human error under specific operational contexts, thereby supporting risk-informed decision-making and the design of resilient safety systems. Over the past decades, a range of structured methodologies, such as the standardized plant analysis risk-human reliability analysis (SPAR-H) [2], the technique for human error rate prediction (THERP) [3], and more recently, the integrated human event analysis system for event and condition assessment (IDHEAS-ECA) [4], have been developed to quantify human error probabilities (HEPs). While these frameworks offer operational utility, they are primarily grounded in expert judgment, predefined performance shaping factors (PSFs), and empirically derived databases, often lacking a mechanistic understanding of the cognitive processes that drive operator actions and errors. Furthermore, traditional HRA approaches are highly dependent on two major data sources: (1) retrospective analysis of operational events, and (2) human-in-the-loop (HITL) simulation experiments conducted in controlled environments.