LA school hid student's gender switch from parents before teen's suicide, lawsuit claims I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. America's heartland to see huge population plunge by 2050 - professor has a controversial visa plan to fix it Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare I'm raising my two-year-old on a cruise ship These are the harsh realities of life at sea Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite I ignored my itchy legs and cold-like symptoms. Then doctors discovered something horrifying on a scan... I'm terrified I'm going to die I made a 34-page dress code for my wedding guests... critics say I'm controlling but I want it to be perfect Trump's religious inner circle implodes as beauty queen's firing sparks revolt... and'spiritual adviser' faces shocking Israel claims China's sinister'Trojan horse' that has already breached America's gates and scooped up YOUR data We fled Trump to chase the REAL American dream in the most idyllic European hotspot... here's why we're coming back to a red state Harry and Meghan explode at claim the Queen accused Markle of'brainwashing' Iran's deadly drone arsenal is a'wake-up call for America': Expert warns US defenses may be unprepared for swarm attacks A US military drone expert has warned that Iranian attack drones could potentially slip through America's defenses and strike targets on US soil. Brett Velicovich, a former US Army intelligence and special operations soldier who spent years using drones to hunt ISIS leaders before founding drone company PowerUs, said the threat comes from a new type of warfare that the US is still struggling to defend against. 'These new asymmetric threats, where you've got low-cost, cheap, small drones, in some cases, that are able to be sent in massive waves, don't have the same signature of an intercontinental ballistic missile,' Velicovich explained.

Scammers are using Apple iPhone Calendar subscriptions to send phishing alerts directly to users' devices, Kurt "CyberGuy" Knutsson writes.

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When you purchase through links in our articles, we may earn a small commission. What video doorbells see (and what they don't): Here's what you can expect Don't assume these gadgets will capture everything that happens on your porch. Understand these critical specs and you'll avoid a disappointing purchase. With a camera at the front door and an app on their phone, they jump to the conclusion that they'll capture faces on the sidewalk, license plates at the curb, and anybody cutting across the lawn. Most doorbell cameras deliver far more modest real-world performances. They have a tight field of view that sees what's directly in front of their lens; they're built to frame a visitor's face standing in front of the door, not the entire space the door.

Amazon Ring introduces AI-powered facial recognition to identify friends and delivery drivers, while privacy advocates warn of surveillance risks despite convenience benefits.

Abstract--Security Operations Centers face massive, heterogeneous alert streams under minute-level service windows, creating the Alert Triage Latency Paradox: verbose reasoning chains ensure accuracy and compliance but incur prohibitive latency and token costs, while minimal chains sacrifice transparency and auditability. Existing solutions fail: signature systems are brittle, anomaly methods lack actionability, and fully cloud-hosted LLMs raise latency, cost, and privacy concerns. We propose AIDR, a hybrid cloud-edge framework that addresses this trade-off through constrained information-density optimization. The core innovation is gradient-based compression of reasoning chains to retain only decision-critical steps--minimal evidence sufficient to justify predictions while respecting token and latency budgets. We demonstrate that this approach preserves decision-relevant information while minimizing complexity. We construct compact datasets by distilling alerts into 3-5 high-information bullets (68% token reduction), train domain-specialized experts via LoRA, and deploy a cloud-edge architecture: a cloud LLM routes alerts to on-premises experts generating SOAR-ready JSON. Experiments demonstrate AIDR achieves higher accuracy and 40.6% latency reduction versus Chain-of-Thought, with robustness to data corruption and out-of-distribution generalization, enabling auditable and efficient SOC triage with full data residency compliance.

Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices.

Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

Abstract-- We study automated intrusion prediction in an IT system using statistical learning methods. The focus is on developing online attack predictors that detect attacks in real time and identify the current stage of the attack. While such predictors have been proposed in the recent literature, these works typically rely on constructing a monolithic predictor tailored to a specific attack type and scenario. Given that hundreds of attack types are cataloged in the MITRE framework, training a separate monolithic predictor for each of them is infeasible. In this paper, we propose a modular framework for rapidly assembling online attack predictors from reusable components. Using public datasets for training and evaluation, we provide many examples of modular predictors and show how an effective predictor can be dynamically assembled during training from a network of modular components. Traditional intrusion detection systems (IDS), such as Snort [1] or Suricata [2], rely on rule-based configurations that are manually crafted and maintained by domain experts. The growing complexity and rapid evolution of IT systems make the maintenance of these rules increasingly challenging and time-consuming. As a response, research efforts into automated cyberdefence have started, based on the idea that attack patterns can be dynamically learned. The rules are no longer defined by humans, but automatically inferred from observing systems under attack. Over the last decade, various approaches have been proposed for automated cyberdefence, most of them based on statistical learning, e.g., [3], [4], [5], [6]. We follow this direction in the paper. We are specifically interested in predicting the stage of an ongoing attack in real time, based on current and earlier observations of an IT system.

We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need clear, preemptive criteria to abort the maneuver in advance of safety violation. To solve this problem, we use offline stochastic trajectory simulation to learn a calibrated statistical model of the short-term safety risk facing pilots. We use flight testing as a motivating example for data-driven learning/monitoring of safety due to its inherent safety risk, uncertainty, and human-interaction. However, our approach consists of three broadly-applicable components: a model to predict future state from recent observations, a nearest neighbor model to classify the safety of the predicted state, and classifier calibration via conformal prediction. We evaluate our method on a flight dynamics model with uncertain parameters, demonstrating its ability to reliably identify unsafe scenarios, match theoretical guarantees, and outperform baseline approaches in preemptive classification of risk.