Some Sort of Tutorial; On YouTube; WHEN? <length?>linear media, feel your life ticking away… there is no transcript?</liength?>
tl;dr → claiming the VL53L0X can be driven with a faster scan rate if a dual processor approach is used.
The increasingly widespread use of mobile phone applications (apps) as research tools and cost-effective means of vast data collection raises new methodological challenges. In recent years, it has become a common practice for scientists to design apps that run only on a single operating system, thereby excluding large numbers of users who use a different operating system. However, empirical evidence investigating any selection biases that might result thereof is scarce. Henceforth, we conducted two studies drawing from a large multi-national (Study 1; N = 1,081) and a German-speaking sample (Study 2; N = 2,438). As such Study 1 compared iOS and Android users across an array of key personality traits (i.e., well-being, self-esteem, willingness to take risks, optimism, pessimism, Dark Triad, and the Big Five). Focusing on Big Five personality traits in a broader scope, in addition to smartphone users, Study 2 also examined users of the main computer operating systems (i.e., Mac OS, Windows). In both studies, very few significant differences were found, all of which were of small or even tiny effect size mostly disappearing after sociodemographics had been controlled for. Taken together, minor differences in personality seem to exist, but they are of small to negligible effect size (ranging from OR = 0.919 to 1.344 (Study 1), ηp2 = .005 to .036 (Study 2), respectively) and may reflect differences in sociodemographic composition, rather than operating system of smartphone users.
tl;dr → QA was performed; observations were noted: data was sent, data was received; not shown: (absence of) consent, harm.
What types of user data are mobile apps sending to third parties? We chose 110 of the most popular free mobile apps as of June-July 2014 from the Google Play Store and Apple App Store, across 9 categories likely to handle potentially sensitive data about users including job information, medical data, and location. For each app, we used a man-in-the-middle proxy to record HTTP and HTTPS traffic that occurred while using the app and looked for transmissions that include personally identifiable information (PII), behavior data such as search terms, and location data, including geo-coordinates. An app that collects these data types may not need to notify the user in current permissions systems.
<quote>to dynamically re-sort your shopping list as you move through the store, like how smartphone maps re-route when you veer off course.</quote>
<quote>We’re also planning to launch a service in the Target app where you can request the help of a store team member right from your phone. Think of it this way: Beacons + Target app = Red-and-Khaki to the Rescue</quote>
We show that the MEMS gyroscopes found on modern smart phones are sufficiently sensitive to measure acoustic signals in the vicinity of the phone. The resulting signals contain only very low-frequency information (< 200 Hz). Nevertheless we show, using signal processing and machine learning, that this information is sufficient to identify speaker information and even parse speech. Since iOS and Android require no special permissions to access the gyro, our results show that apps and active web content that cannot access the microphone can nevertheless eavesdrop on speech in the vicinity of the phone.