As virtual reality (VR) devices become increasingly integrated into everyday settings, a growing number of users without prior experience will engage with VR systems. Automatically detecting a user's familiarity with VR as an interaction medium enables real-time, adaptive training and interface adjustments, minimizing user frustration and improving task performance. In this study, we explore the automatic detection of VR familiarity by analyzing hand movement patterns during a passcode-based door-opening task, which is a well-known interaction in collaborative virtual environments such as meeting rooms, offices, and healthcare spaces. While novice users may lack prior VR experience, they are likely to be familiar with analogous real-world tasks involving keypad entry. We conducted a pilot study with 26 participants, evenly split between experienced and inexperienced VR users, who performed tasks using both controller-based and hand-tracking interactions. Our approach uses state-of-the-art deep classifiers for automatic VR familiarity detection, achieving the highest accuracies of 92.05% and 83.42% for hand-tracking and controller-based interactions, respectively. In the cross-device evaluation, where classifiers trained on controller data were tested using hand-tracking data, the model achieved an accuracy of 78.89%. The integration of both modalities in the mixed-device evaluation obtained an accuracy of 94.19%. Our results underline the promise of using hand movement biometrics for the real-time detection of user familiarity in critical VR applications, paving the way for personalized and adaptive VR experiences.

Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single sample is inadequate for understanding causal influence and the underlying computation. Though fully specifying this distribution is intractable, it can be understood by sampling. We present case studies using resampling to investigate model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In "agentic misalignment" scenarios, we resample specific sentences to measure their downstream effects. Self-preservation sentences have small causal impact, suggesting they do not meaningfully drive blackmail. Second, are artificial edits to CoT sufficient for steering reasoning? These are common in literature, yet take the model off-policy. Resampling and selecting a completion with the desired property is a principled on-policy alternative. We find off-policy interventions yield small and unstable effects compared to resampling in decision-making tasks. Third, how do we understand the effect of removing a reasoning step when the model may repeat it post-edit? We introduce a resilience metric that repeatedly resamples to prevent similar content from reappearing downstream. Critical planning statements resist removal but have large effects when eliminated. Fourth, since CoT is sometimes "unfaithful", can our methods teach us anything in these settings? Adapting causal mediation analysis, we find that hints that have a causal effect on the output without being explicitly mentioned exert a subtle and cumulative influence on the CoT that persists even if the hint is removed. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled CoT interventions.

This is what I messaged you about!

Apple today launched a new tool for iPhones to help reduce what a thief with your phone and passcode can access. The feature, called Stolen Device Protection, adds extra layers of protection to your iPhone when someone tries to access or change sensitive settings on your device. If someone tries to access passwords stored in Apple's keychain, for instance, they won't be able to unless they also use a fingerprint or the phone's face recognition to prove they're the legitimate owner. You don't need to look far to find stories of stolen phones. In London, a phone is stolen every six minutes.

A single man from Michigan recounted in a viral video how he nearly gave up on dating entirely and went "mentally insane" after a woman he met on an online dating app committed a heist on the date, earning the nickname "Felony Melanie." After reviewing security footage from the restaurant – he's convinced he may have finally solved the mystery of what really happened and why his eye is slightly red. I may take a sabbatical from going on internet dates," influencer Ryan Michael Annese said. The date nightmare story went viral on TikTok, amassing over 3 million views. "I doubt any of you guys can top it.

Apple is set to add even more protection to the iPhone in the next iOS update, which will stop thieves from accessing smartphones with passcodes. Called'Stolen Device Protection,' the new setting promises to prevent cyber-criminals from locking iPhone users out of their Apple accounts or accessing any of their passwords stored in Apple's Keychain. If the feature detects an unknown location of the iPhone, it will require Apple's FaceID to unlock the device. Stolen Device Protection is set to roll out with Apple's iOS 17.3 but is currently being tested in beta. Apple is rolling out a new feature to protect its customers' passcodes, online banking access, private iCloud photos and videos, and everything else that a stolen, unlocked iPhone leaves vulnerable.

Face ID utilizes facial recognition technology to scan your face and verify your identity. When activated, the feature uses the front-facing camera, or selfie cam, to securely authenticate you are the owner of the iPhone. During the pandemic, Face ID received a lot of scrutiny for not functioning correctly, and it simply did not work whenever you were wearing your mask and attempted to unlock your iPhone. Before that realization, I'm sure you also noticed Face ID was much slower at unlocking your device when compared to its predecessor, Touch ID. Implementing this innovative and seemingly secure way of unlocking your precious iPhone was nothing short of a disaster.

Just a few days after rolling out iOS 15.3, Apple has released the latest iOS developer beta. Among the new features is one that'll come in very handy for unlocking your iPhone while you're out and about in the current climate. The company is testing a way for folks to use Face ID while they're wearing a mask -- without needing an Apple Watch. "Face ID is most accurate when it's set up for full-face recognition only," Apple explains when users set up the feature. "To use Face ID while wearing a mask, iPhone can recognize the unique features around the eye to authenticate."

Apple in 2017: "Nothing has ever been simpler, more natural, more effortless. We call this Face ID." This is an actual executive quote, from back when the company introduced facial recognition on the iPhone X. Apple in 2021: "Nothing has ever been…less natural or more difficult. We call this Face No ID."

This paper describes a system developed to help University students get more from their online lectures, tutorials, laboratory and other live sessions. We do this by logging their attention levels on their laptops during live Zoom sessions and providing them with personalised video summaries of those live sessions. Using facial attention analysis software we create personalised video summaries composed of just the parts where a student's attention was below some threshold. We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not. Attention and usage based video summaries of live classes are a form of personalised content, they are educational video segments recommended to highlight important parts of live sessions, useful in both topic understanding and in exam preparation. The system also allows a Professor to review the aggregated attention levels of those in a class who attended a live session and logged their attention levels. This allows her to see which parts of the live activity students were paying most, and least, attention to. The Help-Me-Watch system is deployed and in use at our University in a way that protects student's personal data, operating in a GDPR-compliant way.