Measuring success in global health diplomacy: lessons from marketing food to children in India.

Global health diplomacy (GHD) focuses on international negotiation; principally between nation states, but increasingly non-state actors However, agreements made at the global level have to be enacted at the national, and in some cases the sub-national level. This presents two related problems: (1) how can success be measured in global health diplomacy and (2) at what point should success be evaluated? This commentary highlights these issues through examining the relationship between India and the WHO Set of Recommendations on the Marketing of Food and Non-alcoholic Beverages to Children, endorsed by Resolution WHA63.14 at the 63rd World Health Assembly in 2010

DETECTION OF AND RESPONSE TO ACCESSIBILITY-RELATED BEHAVIORAL CUES

A system is described that enables a computing device (e.g., a mobile phone, smart watch, tablet computer, etc.) to detect behavioral cues from a user and recommend relevant accessibility settings for the user based on the detected behavioral cues. The computing device may use various sensors to detect accessibility-related behavioral cues and analyze the detected behavioral cues to identify a relevant accessibility setting. For example, a computing device may use a radio detection and ranging (radar) system to detect behavioral cues from a user as inputs and may identify a relevant accessibility setting that benefits the user based on the detected behavioral cues. A behavioral cue input refers to any touch input or non-touch input detected by the computing device including, for example, any behavioral cues performed by a user using any finger, hand, body part, stylus, or any other object that may be detected by the computing device as described herein. In response to detecting a behavioral cue, the computing device may make the user aware of a relevant accessibility setting (e.g., identified based on the detected behavioral cue). For instance, the computing device may output a notification recommending activation of the identified relevant accessibility setting and/or may direct the user to an accessibility setting page of the identified relevant accessibility setting. In some examples, the accessibility setting page may contain a recommended setting determined based on the detected behavioral cue. In some examples, the accessibility setting page may contain a tutorial which may interact with the user to walk the user through the accessibility setting

Automatic Simulation and Display of Functions Within Developer Tools

Mathematical functions are frequently implemented in a variety of code, e.g., video games, web publishing (CSS/HTML), scientific programming, etc. Developers of such code often benefit from visualizations of the output of in-code functions for various inputs. At present, to obtain visualizations, developers need to utilize alternative tools, e.g., graphing or mathematical software/websites. Using such alternative tools outside the code-development environment breaks the development flow. This disclosure describes techniques that find functions within code that a developer is currently viewing and automatically simulate and display a visualization of the results. Such instant visualization of functions present in code enables developers to ensure that the function and its parameters, as entered by the developers, are indeed accurate and as intended. The introduction of bugs is forestalled, and developer time is optimized

Improved Color Compression for People with Color Blindness

Color blindness is estimated to affect around 9% of the population. Streaming media requires large bandwidth due to the size of the video which depends in part on the color information in the video. This disclosure describes techniques that leverage, with user permission, a user’s unique visual acuity to automatically adjust the parameters and the encoding of a video stream to incorporate suitably modified color information to reduce bandwidth and/or to improve quality of the video stream for the particular user

IN-VEHICLE SYSTEM FOR INDICATING DIRECTIONALITY OF DETECTED SOUNDS

A vehicle system is described that enables an in-vehicle device (e.g., a steering wheel, a steering wheel cover, etc.) to provide visual and/or haptic alerts indicating the direction from which sounds are captured via various microphones or other audio sensors of the vehicle originated. The vehicle system may, after receiving explicit permission from a user (e.g., a driver, a hearing-impaired person, etc.), use user equipment (e.g., a mobile phone, a smartphone, a cellular phone, a portable device, a handheld device, a mobile terminal, a portable terminal, etc.) to detect noises (e.g., honking, sirens, crashes, accidents, pedestrian shouts/yells, etc.) outside the vehicle and cause the in-vehicle device to generate warning lights and/or vibrations based on the detected noises outside the vehicle. For example, the vehicle system may analyze audio data captured by microphones to determine if the audio captured is indicative of an alert-triggering event. Based on the analysis of audio data, the vehicle system may further determine a direction of the alert-triggering event and cause the in-vehicle device to generate warning lights and/or vibrations in a portion that corresponds to the direction of the triggering event for alerting

Enabling an Energy-Saving Mode on a Mobile Device After an Unexpected Charging Interruption

This publication describes methods of enabling an energy-saving mode on a mobile device after an unexpected charging interruption. The power manager application implemented on the mobile device uses sensor data from sensors on the device and/or power quality data to determine if the charging interruption was expected. If the sensor data and/or power quality data indicates that the charging interruption was unexpected, the power manager application may automatically enable an energy-saving mode on the mobile device to conserve battery life. The user may also receive a notification from the mobile device that the energy-saver mode was enabled

HEIGHTENED SECURITY MEASURES FOR MARKED APPLICATIONS

A system is described that enables a computing device (e.g., a mobile phone, a camera, a tablet computer, etc.) to associate an application with a security level (e.g., a high-sensitive level, a middle-sensitive level, a low-sensitive level, etc.), and provide additional security mechanisms (e.g., auto-lock, biometric authentication, password authentication, etc.) to the application based on the security level. A user may set a security level for an application, and the computing device may provide additional security mechanisms to the application based on the selected security level. For example, a user may associate a banking application with a high-sensitive security level, and based on the high-sensitive security level, the computing device may periodically (e.g., every second, every 10 seconds, every minute, etc.) require the user to provide biometric inputs to verify user identity

Automatically Changing Accessibility Settings on an Electronic Device Responsive to Detecting the Presence of a Hearing Device

This publication describes techniques and procedures for automatically changing accessibility settings on an electronic device responsive to detecting the presence of a hearing device. For example, detecting a hearing device connected to an electronic device and then performing one or more actions to enhance accessibility services. The actions may include providing a prompt to the user to suggest using different accessibility settings or automatically toggling to a user’s hearing device-dependent accessibility settings. The hearing device-dependent settings may turn on audio enhancement features of the electronic device, such as a screen reader application (screen reader) and speech-to-text functionality. The hearing device-dependent settings may decrease the use of visibility enhancement settings such as large font and captions. The electronic device can quickly toggle between the current settings to the hearing device-dependent settings when the hearing device is detected or removed

AUTOMATIC SWITCHING BETWEEN INTERNET RADIO AND TRADITIONAL RADIO

An infotainment system of a vehicle (e.g., an automobile, a motorcycle, a bus, a recreational vehicle (RV), a semi-trailer truck, etc.) is configured to automatically switch from an Internet radio application (e.g., a music streaming application) to a radio station of a traditional radio (e.g., an AM/FM radio built into the vehicle) in certain situations, such as when the Internet is unavailable. In examples, the infotainment system uses machine learning to generate a personalized model of the music preferences of a user (e.g., a driver). Responsive to determining that a connection to the Internet (e.g., via communication components of the vehicle) is unreliable, poor, nonexistent, and/or the like, the infotainment system scans radio channels of the traditional radio for a radio station that is broadcasting relevant media content (e.g., media content predicted or otherwise determined to be desirable to the user based on the personalized model). Responsive to identifying a radio station broadcasting relevant media content, the infotainment system automatically stops playing the media content from the Internet radio application and starts playing relevant media content that is broadcast by the radio station. Thus, rather than the driver manually switching from the Internet radio to the traditional radio and then trying to find a radio station that is broadcasting relevant media content, which may be distracting to the driver, the techniques described herein automate such tasks. In this way, the techniques described may improve the user experience by reducing distractions to the driver while the driver is operating the vehicle, thereby potentially promoting driving safety and improving the driving experience

PREDICTED APPLICATION PRELOAD BASED ON CONTEXT

A computing device (e.g., a mobile phone, camera, tablet computer, etc.) uses an application preload prediction model (e.g., an artificial intelligence model) for preloading an application. The computing device may execute an application that employs a user interface to facilitate human-machine interaction. The computing device may collect contextual user data and use a prediction model (e.g., an application preload prediction module) to analyze the collected contextual data to determine and predict an application to preload in background mode even with the computing device’s screen locked. For example, a user device (e.g. mobile phone) with a touch screen predicts a user’s intention to use the device and intelligently preloads applications (apps) (e.g., camera app, browser, terminal application, map, virtual assistant, emergency dialer, etc.) in the background of the device, even when the screen is off. The device includes a machine learned model that predicts the user’s intention to use the device based on the information from one or more sensors (such as light sensor, accelerometer, gyroscope, GPS, and proximity sensor) and location information, and preloads the necessary app before the user unlocks the screen or opens the app. This allows for rapid launching of the preloaded app in response to the user selecting the app