--Despite the ample availability of graph data, obtaining vertex labels is a tedious and expensive task. Therefore, it is desirable to learn from a few labeled vertices only. Existing few-shot learners assume a class oracle, which provides labeled vertices for a desired class. However, such an oracle is not available in a real-world setting, i. e., when drawing a vertex for labeling it is unknown to which class the vertex belongs. Few-shot learners are often combined with prototypical networks, while classical semi-supervised vertex classification uses discriminative models, e. g., Graph Convolutional Networks (GCNs). In this paper, we train our models by iteratively prompting a human annotator with vertices to annotate. We perform three experiments where we continually relax our assumptions. First, we assume a class oracle, i. e., the human annotator is provided with an equal number of vertices to label for each class. In the subsequent experiment, "Unbalanced Sampling," we replace the class oracle with k - medoids clustering and draw vertices to label from the clusters. In the last experiment, the "Unknown Number of Classes," we no longer assumed we knew the number and distribution of classes. Our results show that prototypical models outperform discriminative models in all experiments when fewer than 20 samples per class are available. While dropping the assumption of the class oracle for the "Unbalanced Sampling" experiment reduces the performance of the GCN by 9%, the prototypical network loses only 1% on average. For the "Unknown Number of Classes" experiment, the average performance for both models decreased further by 1%. I NTRODUCTION In many fields, data is organized as networks or graphs, where vertices are connected by links. For example, in citation networks, a link exists between two vertices if one paper cites another. In social networks, people represent vertices, and links are formed based on relationships or shared interests. While collecting such graphs is often inexpensive, obtaining labels for vertices from human annotators is tedious and expensive.

The Xeno -Canto bird audio repository is an invaluable resource for those interested in vocalizations and other sounds made by birds around the world. This is particularly the case for machine learning researchers attempting to improve on the bird species r ecognition accuracy of classification models. However, the task of extracting labeled datasets from th e recordings found in this crowd -sourced repository faces several challenges. One challenge of particular significance to machine learning practitioners i s that one bird species label is applied to each audio recording, but frequently other sounds are also captured including other bird species, other animal sounds, anthropogenic and other ambient sounds . These non -target bird species sounds can result in dataset labeling discrepanc ies referred to as label noise . In this work we present a cleaning process consisting of audio preprocessing followed by dimensionality reduction and unsupervised outlier detection (UOD) to reduce the label noise in a dataset derived from Xeno -Canto recordings . We investigate three neural network dimensionality reduction techniques: two flavors of convolutional autoencoder s and variational deep embedding (VaDE (Jiang, 2017)) . While both methods show some degree of effectiveness at detecting outliers for most bird species datasets, we f ound significant variation in the performance of the methods from one species to the next. We believe that the results of this investigation demonstrate that the application of our cleaning process can meaningfully reduce the label noise of bird species datasets derived from Xeno-Canto audio repository but results vary across species.

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high accuracy and explainability, either failing to detect novel attacks or operating as opaque black-box models. To address this critical gap, we propose a novel phishing URL detection system based on a first-order Takagi-Sugeno-Kang (TSK) fuzzy inference model optimized through gradient-based techniques. Our approach intelligently combines the interpretability and human-like reasoning capabilities of fuzzy logic with the precision and adaptability provided by gradient optimization methods, specifically leveraging the Adam optimizer for efficient parameter tuning. Experiments conducted using a comprehensive dataset of over 235,000 URLs demonstrate rapid convergence, exceptional predictive performance (accuracy averaging 99.95% across 5 cross-validation folds, with a perfect AUC i.e. 1.00). Furthermore, optimized fuzzy rules and membership functions improve interoperability, clearly indicating how the model makes decisions - an essential feature for cybersecurity applications. This high-performance, transparent, and interpretable phishing detection framework significantly advances current cybersecurity defenses, providing practitioners with accurate and explainable decision-making tools.

With the timely formation of personalized intervention plans based on high-dimensional heterogeneous time series information has become an important challenge in the medical field today . As electronic medical records, wearables and other multi-source medical data are increasingly generated and diversified. In this work, we develop a system to generate personalized medical intervention strategies based on Group Relative Policy Optimization (GRPO) and Time-Series Data Fusion: First by incorporating relative policy constraints among the groups during policy gradient updates adaptive balance the individual gain and group gain distribution. To improve the robustness and interpretability of decision-making, the multi-layer neural network structure was employed to group code the patient characteristics. Secondly, for the rapid multi-modal fusion of multi -source heterogeneous time series, a multi -channel neural network combined with self -attention mechanism was employed for dynamic feature extraction, the key feature screening and aggregation were further achieved through the differentiable gating network. Finally, a collaborative search process was proposed to find the ideal candidate intervention strategy based on the combination of genetic algorithm and Monte Carlo tree search so that a global optimization of the candidate intervention strategies was achieved, which greatly enhanced the accuracy of the system as well a s the system's personalization level. The experimental results show that model achieves significant improvement in aspects of accuracy, coverage and decision -making benefits of intervention effect compared with existing methods.

Chronic obstructive pulmonary disease (COPD) is a major global health concern, with accurate severity assessment crucial for effective management, especially in intensive care units (ICUs). This study presents a novel approach to COPD sever - ity classification using machine learning algorithms applied to the MIMIC - III dataset. Our work presents a new application of the MIMIC - III dataset and con - tributes to the growing field of artificial intelligence in critical care medicine. We developed a model to classify COPD severity based on available ICU parameters, including blood gas measurements and vital signs. Our methodology incorpo - rated semi - supervised learning techniques to leverage unlabeled data, enhancing model robustness. A random forest classifier demonstrated superior performance, achieving 92.51% accuracy and 0.98 ROC AUC distinguishing between mild - to - moderate and severe COPD cases. This approach offers a practical, accurate, and accessible tool for rapid COPD severity assessment in ICU settings, poten - tially improving clinical decision - making and patient outcomes. Future research should focus on external validation and integration into clinical decision support systems to enhance COPD management in the ICUs.

Dynamic quality of service (QoS) data exhibit rich temporal patterns in user - service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users' choice of services. To predict unobserved QoS data, we propose a Non - negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor - based, nonnegative multiplication update o n tensor (SLF - NMUT) for parameter learning . Empirical results demonstrate that the proposed model more accurately learns dynamic user - service interaction patterns, thereby yielding improved predictions for missing QoS data.

Background: Emergency department (ED) overcrowding remains a major challenge, causing delays in care and increased operational strain. Hospital management often reacts to congestion after it occurs. Machine learning predictive modeling offers a proactive approach by forecasting patient flow metrics, such as waiting count, to improve resource planning and hospital efficiency. Objective: This study develops machine learning models to predict ED waiting room occupancy at two time scales. The hourly model forecasts the waiting count six hours ahead (e.g., a 1 PM prediction for 7 PM), while the daily model estimates the average waiting count for the next 24 hours (e.g., a 5 PM prediction for the following day's average). These tools support staffing decisions and enable earlier interventions to reduce overcrowding. Methods: Data from a partner hospital's ED in the southeastern United States were used, integrating internal metrics and external features. Eleven machine learning algorithms, including traditional and deep learning models, were trained and evaluated. Feature combinations were optimized, and performance was assessed across varying patient volumes and hours. Results: TSiTPlus achieved the best hourly prediction (MAE: 4.19, MSE: 29.32). The mean hourly waiting count was 18.11, with a standard deviation of 9.77. Accuracy varied by hour, with MAEs ranging from 2.45 (11 PM) to 5.45 (8 PM). Extreme case analysis at one, two, and three standard deviations above the mean showed MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, XCMPlus performed best (MAE: 2.00, MSE: 6.64), with a daily mean of 18.11 and standard deviation of 4.51. Conclusions: These models accurately forecast ED waiting room occupancy and support proactive resource allocation. Their implementation has the potential to improve patient flow and reduce overcrowding in emergency care settings.

Distributed Denial of Service (DDoS) attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and intelligent feature selection techniques for effective detection. Traditional methods such as filter-based, wrapper-based, and embedded approaches, each offer strengths but struggle with scalability or adaptability in complex attack environments. In this study, we explore these existing techniques through a detailed comparative analysis and highlight their limitations when applied to large-scale DDoS detection tasks. Building upon these insights, we introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method that leverages adversarial learning dynamics to identify the most informative features. By training a GAN exclusively on attack traffic and employing a perturbation-based sensitivity analysis on the Discriminator, GANFS effectively ranks feature importance without relying on full supervision. Experimental evaluations using the CIC-DDoS2019 dataset demonstrate that GANFS not only improves the accuracy of downstream classifiers but also enhances computational efficiency by significantly reducing feature dimensionality. These results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.

Virtual screening plays a critical role in modern drug discovery by enabling the identification of promising candidate molecules for experimental validation. Traditional machine learning methods such, as Support Vector Machines (SVM) and XGBoost, rely on predefined molecular representations, often leading to information loss and potential bias. In contrast, deep learning approaches-particularly Graph Convolutional Networks (GCNs)-offer a more expressive and unbiased alternative by operating directly on molecular graphs. Meanwhile, Large Language Models (LLMs) have recently demonstrated state-of-the-art performance in drug design, thanks to their capacity to capture complex chemical patterns from large-scale data via attention mechanisms. In this paper, we propose a hybrid architecture that integrates GCNs with LLM-derived embeddings to combine localized structural learning with global chemical knowledge. The LLM embeddings can be precomputed and stored in a molecular feature library, removing the need to rerun the LLM during training or inference and thus maintaining computational efficiency. We found that concatenating the LLM embeddings after each GCN layer-rather than only at the final layer-significantly improves performance, enabling deeper integration of global context throughout the network. The resulting model achieves superior results, with an F1-score of (88.8\%), outperforming standalone GCN (87.9%), XGBoost (85.5%), and SVM (85.4%) baselines.

Quality and diversity are two critical metrics for the training data of large language models (LLMs), positively impacting performance. Existing studies often optimize these metrics separately, typically by first applying quality filtering and then adjusting data proportions. However, these approaches overlook the inherent trade-off between quality and diversity, necessitating their joint consideration. Given a fixed training quota, it is essential to evaluate both the quality of each data point and its complementary effect on the overall dataset. In this paper, we introduce a unified data selection framework called QuaDMix, which automatically optimizes the data distribution for LLM pretraining while balancing both quality and diversity. Specifically, we first propose multiple criteria to measure data quality and employ domain classification to distinguish data points, thereby measuring overall diversity. QuaDMix then employs a unified parameterized data sampling function that determines the sampling probability of each data point based on these quality and diversity related labels. To accelerate the search for the optimal parameters involved in the QuaDMix framework, we conduct simulated experiments on smaller models and use LightGBM for parameters searching, inspired by the RegMix method. Our experiments across diverse models and datasets demonstrate that QuaDMix achieves an average performance improvement of 7.2% across multiple benchmarks. These results outperform the independent strategies for quality and diversity, highlighting the necessity and ability to balance data quality and diversity.