Millions of Apple users can now use their device to replace passports, drivers licenses, state IDs, and credit cards. Breakthroughs, discoveries, and DIY tips sent every weekday. Apple users are one step closer to being able to viably travel around the country with nothing but an iPhone in their pocket. On Wednesday, the company announced "Digital ID," a new feature that lets users store a mobile version of their passport in the Wallet app. Once uploaded, iPhone and Apple Watch users can present their Digital ID to pass through TSA security checkpoints at 250 airports in the US.

Try shaking your credit card. Researchers pinpointed nine gestures that may make the technology actually touchless. Breakthroughs, discoveries, and DIY tips sent every weekday. One of the major selling points of supposedly "touchless" payment methods like Apple Pay and modern bank cards has been the promise of less physical contact with potentially germ-covered surfaces. That's sometimes true, but often the initial "tap" for a latte is followed by a prompt to select a tip amount on a shared screen.

We present QZhou-Embedding, a general-purpose contextual text embedding model with exceptional text representation capabilities. Built upon the Qwen2.5-7B-Instruct foundation model, we designed a unified multi-task framework comprising specialized data transformation and training strategies. The data transformation scheme enables the incorporation of more diverse textual training datasets, while the task-specific training strategies enhance model learning efficiency. We developed a data synthesis pipeline leveraging LLM API, incorporating techniques such as paraphrasing, augmentation, and hard negative example generation to improve the semantic richness and sample difficulty of the training set. Additionally, we employ a two-stage training strategy, comprising initial retrieval-focused pretraining followed by full-task fine-tuning, enabling the embedding model to extend its capabilities based on robust retrieval performance. Our model achieves state-of-the-art results on the MTEB and CMTEB benchmarks, ranking first on both leaderboards (August 27 2025), and simultaneously achieves state-of-the-art performance on tasks including reranking, clustering, etc. Our findings demonstrate that higher-quality, more diverse data is crucial for advancing retrieval model performance, and that leveraging LLMs generative capabilities can further optimize data quality for embedding model breakthroughs. Our model weights are released on HuggingFace under Apache 2.0 license. For reproducibility, we provide evaluation code and instructions on GitHub.

--It is difficult for individuals and organizations to protect personal information without a fundamental understanding of relative privacy risks. By analyzing over 5,000 empirical identity theft and fraud cases, this research identifies which types of personal data are exposed, how frequently exposures occur, and what the consequences of those exposures are. We construct an Identity Ecosystem graph--a foundational, graph-based model in which nodes represent personally identifiable information (PII) attributes and edges represent empirical disclosure relationships between them (e.g., the probability that one PII attribute is exposed due to the exposure of another). Leveraging this graph structure, we develop a privacy risk prediction framework that uses graph theory and graph neural networks to estimate the likelihood of further disclosures when certain PII attributes are compromised. The results show that our approach effectively answers the core question: Can the disclosure of a given identity attribute possibly lead to the disclosure of another attribute? Different individuals and organizations have different sets of personally identifiable information (PII), and therefore have different perspectives on which PII attributes are more vulnerable, more valuable, and in greater need of protection. An individual's PII includes personal data in four different categories--What you Know (e.g., name, address, phone number, mother's maiden name), What you Have (e.g., driver's license, Social Security Card, employee ID, passport), What you Are (e.g., fingerprint, voice, facial image), and What you Do (e.g., patterns of life such as websites visited, GPS locations visited, phone logs) [1]. Protecting PII data can be costly and time-consuming. Research has uncovered various strategies to reduce risks of unintended data disclosure [2], including statistical disclosure limitation (SDL) techniques commonly used by national statistical agencies before releasing public-use data sets.

Cybersecurity has become essential worldwide and at all levels, concerning individuals, institutions, and governments. A basic principle in cybersecurity is to be always alert. Therefore, automation is imperative in processes where the volume of daily operations is large. Several cybersecurity applications can be addressed as binary classification problems, including anomaly detection, fraud detection, intrusion detection, spam detection, or malware detection. We present three experiments. In the first experiment, we evaluate single classifiers including Random Forests, Light Gradient Boosting Machine, eXtreme Gradient Boosting, Logistic Regression, Decision Tree, and Gradient Boosting Decision Tree. In the second experiment, we test different sampling techniques including over-sampling, under-sampling, Synthetic Minority Over-sampling Technique, and Self-Paced Ensembling. In the last experiment, we evaluate Self-Paced Ensembling and its number of base classifiers. We found that imbalance learning techniques had positive and negative effects, as reported in related studies. Thus, these techniques should be applied with caution. Besides, we found different best performers for each dataset. Therefore, we recommend testing single classifiers and imbalance learning techniques for each new dataset and application involving imbalanced datasets as is the case in several cyber security applications.

Travel expert Colleen Kelly shares the hottest travel destinations for this summer and provides tips for travelers planning a cruise. As you rush through busy terminals, juggling bags and boarding passes, your credit cards may be at risk, not just from pickpockets, but from digital thieves using high-tech tools like RFID (radio-frequency identification) skimmers. While today's chip-enabled cards are more secure than old magnetic stripes, it's still wise to take extra precautions, especially in crowded places like airports. Here's how to keep your cards protected while traveling. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Katie Drummond: And we obviously know you well on this show because you cohost our Thursday episodes with Mike and Lauren. Katie Drummond: And let's get right into it. So Zoë, two weeks ago on February 20th, you published a story on WIRED.com about a 1 spending limit being placed on government employee credit cards. Walk us through that first story. You've subsequently published more reporting on that topic this week, but tell us sort of where this came from at the outset.

A new platform leverages AI to help potential buyers find an affordable home and earn bonus points on the purchase. Do you know the difference between 550 and 780? Enter here, no purchase necessary! If you don't check yours regularly, now's the time to start. Small mistakes are a lot more common than you think, and they can do some serious damage to your credit score.

On Monday, police arrested 26-year-old Luigi Mangione and charged him in the murder of UnitedHealthcare CEO Brian Thompson. Mangione's five-day run from authorities ended after he was spotted eating at a McDonald's in Altoona, Pennsylvania, about 300 miles from Manhattan, where Thompson was gunned down on the morning of December 4. Authorities say they found Mangione carrying fake IDs and a 3D-printed "ghost gun," the model of which is known as the FMDA, or "Free Men Don't Ask." Meanwhile, a flood of mysterious drone sightings across New Jersey and neighboring states caused so much havoc, it quickly gained federal attention. While many people wondered why the US military couldn't just shoot down the drones, the FBI, Department of Homeland Security, and independent experts say the drone mystery may not be much of a mystery, and the drones are probably mostly just airplanes. As for more terrestrial threats, we dove into the far-right realm of "Active Clubs," small groups of young, fitness-focused men who are steeped in extremist ideology and linked to several violent attacks. While the man who helped invent the Active Club network, Robert Rundo, was sentenced in federal court this week, Active Clubs around the world are proliferating.

Nobody forgets the thrill of their first credit card. Maybe because it tastes like sweet freedom … or because it leaves a permanent mark on your credit score when you forget to pay off those spending sprees. Who else hit the mall feeling like they had "free money?" Well, now that you're a real adult, it's time to improve your credit score. If you want to apply for a home loan, increase your credit limits, or get more credit cards, it's unfortunately necessary.