T-Mobile (United States)

Biometrics-based risk core recognition is identifying a person's unique finger pattern, such as texture and structures. Previous biometric validations have some limitations such as biological false silication paste in finger print forgery and low accuracy detection in reliable classification. The input dataset consists of multiple fingerprint biometric datasets, which are processed using image processing techniques. Initially, the BiHPF (Bilateral High-Pass Filters) pre-processing analyses to reduce intra-edge noise and improve image boundaries. In addition, wavelet analysis of time-domain data allows for multilabel data analysis, while smoothing enables the extraction of detailed point constructions in fingerprint data and texture descriptors in biometric images. The usage of LSTMs (Long Short-Term Memory) assistances to create compact feature representations, reducing the curse of dimensionality and improving classifier generalization. When combined with adversarial training paradigms, this approach creates a secure system capable of distinguishing between authentic and Generative Adversarial Network (GAN) generated biometric inputs. The experiment evaluated the proposed approach, resulting in an accuracy, precision, and recall of 97%. The outcome of this experiment is the ability to identify unique fingerprint patterns, which can improve the accuracy of secure p2p payments.

Artificial Intelligence (AI) enables machines to perform tasks that require human-like intelligence, including the prediction and detection of anomalies.The GRAPH Neural Networks (GNNs) can be successfully used in mobile payment systems to model user-user, device-user, merchant-user, and transaction relationships and detect fraud based on the pattern of relationships. Nevertheless, traditional approaches to AI face the challenges of massive imbalanced data, changing feature usefulness, multi-entity interactions, and real-time detection. To address these obstacles, we combine the best methods in our workflow. The proposed method, Edge-Level Real-Time Graph Neural Network Risk Scoring (ELRLVN2RS), allocates real-time risk ratings on transactions during the streaming mode, which allows the industry to provide high-risk activity alerts immediately. Additionally, synthetic fraud examples produced by SMOTE-GAN seem realistic and balance the PaySim data set, and they improve the acquisition of rare patterns of fraud. Furthermore, Meta-Learning of Feature Selection is a dynamic feature selection model that selects the most predictive features, adapting to the evolving fraud patterns as well as removing noise. Finally, Heterogeneous Graph Neural Networks (HGNNs) learn both local and global patterns of many entities, and this is because they capture the intricate interactions between them. The presented method AI-GNN framework that offers mobile payment fraud detection based on adaptive, heterogeneous, and real-time risks.

Operational experience from the Russo-Ukrainian War revealed frequent use of tourniquets that were not medically indicated and an increase in ischemic complications from prolonged tourniquet application as a consequence of extended evacuation times. In response to this, the Committee on Tactical Combat Casualty Care (CoTCCC) convened a working group to evaluate whether tourniquet reassessment and conversion practices should extend to the All Service Member (ASM) level. With input from NATO and partner nations, the Working Group developed a standardized, time-based algorithm that provides plain language guidance for reassessment, repositioning, and conversion. The proposed change replaces the term replacement with repositioning, affirms a reassessment window within 2 hours for nonmedical personnel, and limits conversion beyond 2 hours to medical personnel. These updates will expand lifesaving capability to nonmedical responders, reduce preventable morbidity and mortality from tourniquet use, and align TCCC principles with current operational realities.

6G aims to provide significant advantages compared to 5G while being designed to be energy-efficient and quantum-safe from the start. Goals for 6G include superior radio performance, ubiquitous connectivity using Satellite (Non-Terrestrial Networks, NTN) and Wi-Fi, native artificial intelligence (AI) support, a modular design enabling delivery of new features in a modular manner and scalable networks for diverse device types. In this paper, we present a modular Non-Access Stratum (NAS) approach allowing new NAS features to be upgraded and deployable without impacting other NAS features that are already deployed. We include a high-level view of the 6G System architecture, and a detailed view of modular NAS protocol stack, including NAS message routing, User equipment (UE) identifiers and NAS security architecture. We conclude with an outlook for future work.

The cellular networks of the next generation will be dependent on virtualized, programmable, and disaggregated architectures and can be entirely cloud-based. Cellular networks are being made by the Open Radio Access Network (O-RAN) prototype and the reference design offered through O-RAN Alliance. In this article, a unique Deep Reinforcement Learning (DRL) agent design that can learn control policies with diverse minimum performance needs and different Service Level Agreements (SLAs) in O-RAN applications is proposed for next generation cellular networks. This method concentrates on RAN slicing and SLAs that define the highest acceptable end-to-end latency levels. For optimal scheduling, pufferfish optimization algorithm (POA) is used. The proposed work has conducted the experiments on Colosseum, the biggest wireless network emulator in the world, to show real-time analytics. Utilizing the O-RAN open interfaces to gather data at the network's edge, the viability of RAN control using xApps operating is demonstrated on the near real-time RAN Intelligent Controller for optimizing coexisting network slices’ scheduling policies.

Contemporary spam filters increasingly rely on resource-intensive, deep learning models. This study evaluates the performance, robustness, and deployability of lightweight learning models. It provides a head-to-head evaluation of probabilistic (Naïve Bayes) and margin-based (Support Vector Machine) classifiers on three feature spaces derived from the 5574-message UCI Short Message Service (SMS) spam collection. Our primary finding shows that a hybrid model, a fusion of bag-of-words (BoW) representation with 22 handcrafted metadata features, achieves the highest accuracy, with SVM peaking at 98.3%. To assess the resilience, the model was tested against adversarial attacks. The hybrid SVM model exhibited strong robustness when faced with altered data and maintained 72.41% accuracy against challenging semantic attacks. Furthermore, the hybrid SVM model demonstrated strong cross-dataset generalization, achieving 74.38% accuracy when trained on the original UCI data and tested on a modern, diverse dataset of SMS, Telegram, and email messages. Deployment analysis confirmed the efficiency of the framework, with processing of ~ 200 requests/s (fastest model) at less than 10ms latency and ~ 1.5% average CPU load on a standard CPU. The results establish three key principles for next-generation spam filters: (i) lexical information remains the dominant signal; (ii) lightweight metadata provides measurable incremental value when paired with text; and (iii) margin-based classifiers exploit multimodal fusion most effectively. Taken together, these findings validate that a lightweight hybrid feature-engineering approach provides a robust, generalizable, and resource-efficient solution for real-time spam mitigation, thereby presenting a compelling and practical alternative to computationally expensive deep learning architectures.

Micro-frontends extend the concept of micro-services to the world of UI. The idea behind Micro Frontends is to develop applications as a composition of features which are owned and developed completely isolated and by independent teams. These experiences are strung together either at runtime or build-time to deliver a single cohesive application experience to the end user. Customer Relationship Management (CRM) is a system that helps businesses manage their interactions with current and potential customers. CRM systems can provide various functions, such as customer service, sales automation, contact management and more. CRM systems are essential for businesses that want to improve their customer satisfaction, retention, and loyalty, as well as increase their sales and revenue. A traditional CRM system is traditionally monolithic, where the system is built as a single unit that shares the same codebase, database, and user interface. Developing and maintaining a CRM system as a monolith can be challenging to scale, especially for large and complex businesses that have multiple teams, departments, and products. These teams often fall back to breaking down the CRM into multiple isolated applications to deal with the maintainability and operability challenges. This whitepaper explores Micro-frontend Architecture in delivering CRM Applications. This allows operational flexibility but helps standardize the experience for users and the tech stack for the enterprise. The paper will attempt to provide an overview of considerations, outline key features, address the challenges in development and illustrate how composable designs can help application teams tackle these obstacles.

Despite all recent progress, it is still challenging to edit and manipulate natural images with modern generative models. When using Generative Adversarial Network (GAN), one major hurdle is in the inversion process mapping a real image to its corresponding noise vector in the latent space, since it is necessary to be able to reconstruct an image to edit its contents. Likewise for Denoising Diffusion Implicit Models (DDIM), the linearization assumption in each inversion step makes the whole deterministic inversion process unreliable. Existing approaches that have tackled the problem of inversion stability often incur in significant trade-offs in computational efficiency. In this work we propose an Accelerated Iterative Diffusion Inversion method, dubbed AIDI, that significantly improves reconstruction accuracy with minimal additional overhead in space and time complexity. By using a novel blended guidance technique, we show that effective results can be obtained on a large range of image editing tasks without large classifier-free guidance in inversion. Furthermore, when compared with other diffusion inversion based works, our proposed process is shown to be more robust for fast image editing in the 10 and 20 diffusion steps’ regimes.