PayPal (United States)

Recognizing the complexities of real-world financial data, including missing values, class imbalance, and scale variability, the methodology begins with an extensive preprocessing pipeline that standardizes inputs and constructs essential financial indicators like credit utilization and debt-toincome ratios. The framework employs sensitivity analysis for effective feature selection and applies synthetic minority oversampling to address class distribution issues. Principal Component Analysis is optionally used for dimensionality reduction to streamline computation. The model training phase leverages binary classification techniques, with probabilistic outputs computed via logistic activation. Optimization is governed by a cross-entropy loss function and enhanced with gradient descent, regularization, dynamic learning rates, and early stopping. A rigorous hyperparameter tuning process using grid search and K-fold cross-validation ensures optimal performance. The final model demonstrates superior accuracy, precision, recall, and AUC when compared to traditional methods like Logistic Regression and advanced ones like Gradient Boosting and Neural Networks. In deployment, the system provides real-time credit risk scores using incoming customer data, supporting decisions with continuous performance monitoring. A drift detection module ensures longterm adaptability by triggering retraining when data distributions change. Mini-batch updates further enable model refinement without full retraining.

Modern IT infrastructures are increasingly complex and prone to frequent failures that demand real-time diagnosis and resolution. This paper presents a hybrid big data analytics framework for automated root cause analysis (RCA), combining deep learning and machine learning techniques with SHAP-based interpretability. Using the LEMMA-RCA dataset, comprising multi-modal system logs and performance metrics, the framework processes heterogeneous data through a Bi-LSTM for log sequences and a CNN for metric patterns, followed by fusion and classification using a Random Forest model. The system achieves high predictive performance with an accuracy of 94.8 %, precision of 93.5 %, recall of 92.7 %, F1-score of 93.1 %, and AUC-ROC of 96.2%. SHAP values reveal that Error Code, CPU Usage, and Memory Usage are the most influential features. The results confirm the framework's effectiveness in both detecting root causes and offering interpretable, actionable insights for large-scale IT infrastructure monitoring.

The intermittent uncertainties and low inertia cause the instability intricacy in a micro-grid (MG). So, to subside this issue, integration of robust controller is indispensable. In this paper, a non-linear sliding mode controller (SMC) has been employed to a MG load frequency control (LFC) model. Here, frequency of the system has been controlled by designing the SMC controller endorsing Selfish Herd Optimization. Simultaneously, to die out the chattering in the response, a saturation function has been added to the SMC controller. Further, a time delay has been considered to realize the MG more practical. In both, system without time delay and system with time delay, a comparative study of SMC and PID controller has been presented. Under time delay and abrupt load change, the SMC controller has perceived its robust performance.

The advent of Large Language Models (LLMs) has transformed traditional practices in product data science. In this paper, we explore the complete lifecycle of GenAI tools within product data science teams, using the PayPal digital wallet data science team as an example. Specifically, we focus on the GenAI-powered text-to-SQL model we developed to support data scientists. This tool significantly reduces the time spent on ad-hoc data retrieval tasks-critical for business operations but often resource-intensive. We will delve into our modeling approach and demonstrate the tool's fast and secure implementation. Additionally, we will discuss our user data collection and feedback processes, and how periodic measurement of the tool performance using a unique question bank for PayPal data has ensured the tool's continuous improvement. Finally, we will address key challenges in adopting GenAI tools in large organizations, including gaps in data catalogs and the inherent complexities of data structures and lifecycles.

The businesses, governments, societies, and individuals are all very concerned about data security when it comes to applications of the Internet of Things and data traveling over the internet. For reasons of national security, the any government prohibited company from working on the 5G and 6G etc mobile phone network. This demonstrates that there is ongoing debate over 5G technology security. Nonetheless, many in the sector believe that network security and privacy will only get better and more stable with the introduction of 5G Internet. It is anticipated that when the 5G network goes into commercial use, which is anticipated to will truly become a "Internet of Everything." The 5G network is supposed to support telemedicine services, smart city service systems, autopilot cars, mobile phones, smart homes, and more. There is a claim that the 5G network will subtly improve and enhance our quality of life. This article's goal is to provide an overview of the 5G network's security features.

Orthopaedic surgery remains a competitive surgical subspecialty with more applicants than spots each year. As a result, numerous students fail to match into these competitive positions each year with a growing number of reapplicants in consecutive application cycles. We sought to understand the socioeconomic factors at play between this growing reapplicant pool compared with first-time applicants to better understand potential discrepancies between these groups. Our hypothesis is that reapplicants would have higher socioeconomic status and have less underrepresented minority representation compared with successful first-time applicants. A retrospective review of deidentified individual orthopaedic surgery applicant data from the American Association of Medical Colleges was reviewed from 2011 to 2021. Individual demographic and application data as well as self-reported socioeconomic and parental data were analyzed using descriptive and advanced statistics. Of the 12,112 applicants included in this data set, 77% were first-time applicants (61% versus 17% successfully entered into an orthopaedic surgery residency vs versus unmatched, respectively), whereas 22% were reapplicants. In successful first-time applicants, 12% identified as underrepresented minorities in medicine. The proportion of underrepresented minorities was significantly higher among unmatched first-time applicants (20%) and reapplicants (25%) ( P < 0.001). Reapplicants (mean = $83,364) and unmatched first-time applicants (mean = $80,174) had less medical school debt compared with first time applicants (mean = $101,663) ( P < 0.001). More than 21% of reapplicants were found to have parents in healthcare fields, whereas only 16% of successful first-time applicants and 15% of unsuccessful first-applicants had parents in health care ( P < 0.001). Reapplicants to orthopaedic surgery residency have less educational debt and are more likely to have parental figures in a healthcare field compared with first-time applicants. This suggests the discrepancies in socioeconomic status between reapplicants and first-time applicants and the importance of providing resources for reapplicants.

FIDO2 is a suite of protocols that combines the usability of local authentication (e.g., biometrics) with the security of public-key cryptography to deliver passwordless authentication. It eliminates shared authentication secrets (i.e., passwords, which could be leaked or phished) and provides strong security guarantees assuming the benign behavior of the client-side protocol components.