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When you purchase through links in our articles, we may earn a small commission. The service automated dozens of opt-out requests in my testing, but I learned not to take every "Removed" status at face value. Provides guided self-service options when automation isn't possible "Removed" doesn't always mean Aura independently verified that the record is gone Aura Data Removal can take a lot of tedious opt-out work off your hands, but Aura's "Removed" status shouldn't always be taken as definitive proof that your information is gone. Data brokers don't make it easy to get your personal information off their sites. Every broker has its own opt-out process and waiting period, and even if you're successful, keeping your data from resurfacing can quickly turn into a game of whack-a-mole.

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When you purchase through links in our articles, we may earn a small commission. A useful free scan, clear exposure reports, and affordable automated removals make Optery a strong starting point for data cleanup. Dashboard "View" links are not guaranteed matches Optery gives users a clear, useful look at where their personal information is exposed online, but its real cleanup value depends on upgrading to a paid plan and trusting its removal process. Most people don't know how many data brokers have built profiles on them until a service like Optery shows them the list. Names, addresses, phone numbers, and other personal details can end up scattered across people-search sites, public records, and data-broker databases.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

PCWorld reports that Home Assistant has launched a major update to its default overview dashboard, replacing the old cluttered interface with a clean, organized design featuring three main sections. The redesigned dashboard includes Favorites for quick device access, Areas for room-specific control, and Summaries that aggregate devices by type with easy setup options.

Learning Analytics (LA) has rapidly expanded through practical and technological innovation, yet its foundational identity has remained theoretically under-specified. This paper addresses this gap by proposing the first axiomatic theory that formally defines the essential structure, scope, and limitations of LA. Derived from the psychological definition of learning and the methodological requirements of LA, the framework consists of five axioms specifying discrete observation, experience construction, state transition, and inference. From these axioms, we derive a set of theorems and propositions that clarify the epistemological stance of LA, including the inherent unobservability of learner states, the irreducibility of temporal order, constraints on reachable states, and the impossibility of deterministically predicting future learning. We further define LA structure and LA practice as formal objects, demonstrating the sufficiency and necessity of the axioms and showing that diverse LA approaches -- such as Bayesian Knowledge Tracing and dashboards -- can be uniformly explained within this framework. The theory provides guiding principles for designing analytic methods and interpreting learning data while avoiding naive behaviorism and category errors by establishing an explicit theoretical inference layer between observations and states. This work positions LA as a rigorous science of state transition systems based on observability, establishing the theoretical foundation necessary for the field's maturation as a scholarly discipline.

AI based mental health diagnosis is often judged by benchmark accuracy, yet in practice its value depends on how psychologists respond whether they accept, adjust, or reject AI suggestions. Mental health makes this especially challenging: decisions are continuous and shaped by cues in tone, pauses, word choice, and nonverbal behaviors of patients. Current research rarely examines how AI diagnosis interface design influences these choices, leaving little basis for reliable testing before live studies. We present SimClinician, an interactive simulation platform, to transform patient data into psychologist AI collaborative diagnosis. Contributions include: (1) a dashboard integrating audio, text, and gaze-expression patterns; (2) an avatar module rendering de-identified dynamics for analysis; (3) a decision layer that maps AI outputs to multimodal evidence, letting psychologists review AI reasoning, and enter a diagnosis. Tested on the E-DAIC corpus (276 clinical interviews, expanded to 480,000 simulations), SimClinician shows that a confirmation step raises acceptance by 23%, keeping escalations below 9%, and maintaining smooth interaction flow.