Abstract: Disclosed herein are system, method, and computer program product embodiments for providing application resiliency using a machine learning model trained to detect potential failures based on computational transaction metrics. A resiliency system may monitor metrics related to an application executing on an enterprise data system. The resiliency system may apply these metrics to a machine learning model trained to identify a potential application failure based on application usage trends. In response to detecting a potential failure of the application, the resiliency system may instruct the application to execute one or more resiliency actions. These may include one or more circuit breaker, rate limiter, time limiter, and/or bulkhead actions. The resiliency actions may aid the application in avoiding failure states. The resiliency actions may also be modified based on feedback metrics to aid the application in quickly restoring service once the failure state has been avoided.

Abstract: Provided are a system, a method, and a device for implementing sensing and communication functions in a telecommunication system. According to embodiments, the system may include at least one sensor node and at least one server node. The at least one sensor node may be configured to obtain one or more sensing data and to provide the one or more sensing data to one or more network functions. Further, the at least one sensor node may include at least one 3GPP node, at least one non-3GPP node, or a combination thereof. The at least one server node may include: a memory storage storing instructions for implementing a sensing function and at least one processor communicatively coupled to the memory storage.

Abstract: Provided are system, method, and device for managing machine learning models in a network. According to embodiments, the system may include: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor may be configured to execute the instructions to: store one or more machine learning model; and distribute the one or more machine learning model to one or more target network element in a network.

Abstract: Provided are a system, method, and device for renewable energy usage within a network. According to embodiments, the system can include a memory storage storing computer-executable instructions, and at least one processor communicatively coupled to the memory storage, wherein the at least one processor is configured to execute the instructions to identify one or more network resources using a renewable energy source; and calculate an energy consumption value for a user using the identified one or more network resources.

Abstract: Provided is a method of monitoring air pollution, the method including: receiving, by a receiver, a wireless communication signal; measuring, by the receiver, attenuation of a millimeter Wave (mm Wave) or terahertz (THz) signal in the received wireless communication signal to obtain a sensing measurement data of the received wireless communication signal; processing the sensing measurement data to obtain air pollution information; and outputting a result of the obtained air pollution information.

Abstract: Provided are a method, system, and an Service Management and Orchestration Framework (SMO) and O-Cloud to drain one or more O-Cloud Nodes based on recommendations from an rApp or manually by an O-Cloud Maintainer via the SMO. In particular, the method may include: receiving, by a SMO function, a first request or recommendation to drain at least one O-Cloud Node, the first request or recommendation being received from an rApp of a Non-Real-Time (Non-RT) RAN Intelligent Controller (RIC) or from an O-Cloud Maintainer; transmitting, by the SMO function to an Infrastructure Management Services (IMS) and/or Deployment Management Services (DMS) via an O2 interface, a second request to drain the at least one O-Cloud Node based on the received first request or recommendation; and receiving, from the IMS/DMS, a first notification as to whether the at least one O-Cloud Node has been drained.

Abstract: An integrated three position rocker switch for vehicles comprises a housing configured to support one or more components of the switch, a knob assembly, a first micro-switch, a second micro-switch and knob. The knob assembly is disposed in the housing, wherein the knob assembly comprises a knob pivotally supported in the housing, and a plunger coupled with the knob through a resilient member. The first micro-switch is coupled with the knob assembly in the housing, and the second micro-switch is disposed in the housing in vicinity of the first micro-switch. The knob is configured to move in multiple positions for actuating the first and second micro-switches to perform more than one operation.

Abstract: In some implementations, a user device (e.g., a computing device) can perform client-side personalization of search results. For example, a computing device can obtain search results matching user specified search parameters from a server device and/or from various services on the user device. The user device can score the search results based on various search result item attributes. After scoring, the user device can promote or demote search results items based on whether the search results item is relevant to recent user behavior. The promotion and/or demotion of search results items can cause search results items scores to be adjusted to generate a personalized score for each search result. The search results can then be ordered and/or presented based on the personalized score for each search results item. When presenting search results items, the user device can present information indicative of the source of the search results items.

Abstract: In some implementations, a mobile device may perform a plurality of signal strength measurements of a radio frequency (RF) signal transmitted by a satellite, wherein performing the plurality of signal strength measurements occurs over a period of time during which the mobile device is subject to a movement. The mobile device may determine, for each signal strength measurement of the plurality of signal strength measurements, a respective orientation of the mobile device corresponding to when the respective signal strength measurement was performed. The mobile device may determine a target orientation for the satellite-based communications based at least in part on a value of a particular signal strength measurement of the plurality of signal strength measurements and the respective orientation of the mobile device corresponding to the particular signal strength measurement. The mobile device may provide guidance for rotating the mobile device to the target orientation.

Abstract: In some implementations, a mobile device may determine a set of target orientations of the mobile device in which an antenna lobe of the mobile device is pointed toward the satellite, the set of target orientations based on: an orientation of the antenna lobe relative to the mobile device, and a location of the satellite relative to the mobile device. The mobile device may determine a current orientation of the mobile device. The mobile device may provide, at a user interface (UI) of the mobile device, guidance for rotating the mobile device from the current orientation to an orientation within the set of target orientations.

Abstract: A context-aware building automation system and a method for enabling the same is disclosed herein. The method comprises detecting, by a processing unit, an event associated with an occupant of a building based on a request received from one or more sources. Further, occupant-context data associated with the occupant is obtained, from an identity graph of the occupant, upon detecting the event. The occupant-context data is used to dynamically determine at least one action to be performed at one or more target devices in the building. The at least one action customizes functioning of the one or more target devices for the occupant. Further, one or more instructions are transmitted to the one or more target devices, for performing the at least one action determined.

Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

Abstract: An online concierge system receives a search query from a client device. The online concierge system identifies a set of matching items from an item database. The matching items correspond to the received search query. The online concierge system obtains, from a hierarchical item taxonomy, a category label for each matching item. The item taxonomy relates each item in the item database to one of a plurality of category labels. The online concierge system groups the matching items by the category labels for each of the matching items into one or more groups. The online concierge system generates instructions for a user interface. The user interface includes a scrollable list of one or more carousels. Each carousel includes a scrollable list of a group of the one or more groups. The online concierge system sends the instructions of the user interface to the client device for display.

Abstract: An online system maintains item embeddings for items. As a number of items maintained by the online system increases, maintaining a single index of the item embeddings is increasingly difficult. To increase scalability, the online system partitions item embeddings into multiple indices, with each index corresponding to a value of a specific attribute maintained by the online system for items. For example, an online system generates indices that each correspond to a different warehouse offering items. To expedite retrieval of item embeddings, the online system allocates each index to one of a number of shards. When the online system receives a query, the online system determines an embedding for the query and retrieves an index from a shard based on metadata received with the query. Based on distances between the query for the embedding and the item embeddings in the retrieved index, the online system selects one or more items.

Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

Abstract: In some implementations, a user device (e.g., a computing device) can perform client-side personalization of search results. For example, a computing device can obtain search results matching user specified search parameters from a server device and/or from various services on the user device. The user device can score the search results based on various search result item attributes. After scoring, the user device can promote or demote search results items based on whether the search results item is relevant to recent user behavior. The promotion and/or demotion of search results items can cause search results items scores to be adjusted to generate a personalized score for each search result. The search results can then be ordered and/or presented based on the personalized score for each search results item. When presenting search results items, the user device can present information indicative of the source of the search results items.

Abstract: An online concierge system improves on methods for presenting content to users. The online concierge system generates a user embedding for a user and recipe embeddings for candidate recipes. The online concierge system generates a context embedding by applying a context embedding model to context data received from a user mobile application. The online concierge system calculates an overall score for each candidate recipe based on a user score and a context score. The user score is calculated based on the user embedding and a recipe embedding for the candidate recipe. The context score is calculated based on the generated context embedding and the recipe embedding for the candidate recipe. The online system selects a recipe for presentation to the user based on the overall scores. The online concierge system trains the context embedding model using a loss function that is based on the user score and the context score.

Abstract: An online concierge system suggests recipes to users to encourage them to include items from the recipes in orders. When suggesting a recipe to a user, the online concierge system maps generic item descriptions in a recipe to specific items for sale at a warehouse identified by the user. The online concierge system maintains a recipe store including mappings of generic item descriptions in recipes to specific items. To determine mappings between generic item descriptions and specific items, the online concierge system determines a category from a taxonomy for a generic item description in a recipe and excludes specific items in different categories than the determined category.

Abstract: In some implementations, a user device (e.g., a computing device) can perform client-side personalization of search results. For example, a computing device can obtain search results matching user specified search parameters from a server device and/or from various services on the user device. The user device can score the search results based on various search result item attributes. After scoring, the user device can promote or demote search results items based on whether the search results item is relevant to recent user behavior. The promotion and/or demotion of search results items can cause search results items scores to be adjusted to generate a personalized score for each search result. The search results can then be ordered and/or presented based on the personalized score for each search results item. When presenting search results items, the user device can present information indicative of the source of the search results items.

Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.