Abstract: Examples, methods and systems described herein may include determining, by a payment processing system, whether a second payment mechanism may be used to perform a payment transaction when a first payment mechanism is preferred and/or when the first payment mechanism has failed. Determining to use the second payment mechanism can be based on (i) an analysis of a communication status indicator received from a point-of-sale (POS) terminal that indicates which payment mechanisms are capable of communicating with the POS terminal, (ii) data received from the POS terminal that is associated with a read of a payment object, and/or (iii) an allowance criterion.

Abstract: A user identifies products offered by the user to an online system. The online system identifies a product offered by the user in an image by applying a identification model to the image. If the online system identifies a product in the image with at least a maximum confidence value, the online system automatically tags the post with metadata about the product or suggests a tag to the user. If an object in the image could be one of multiple products, the online system identifies the multiple products to the user, which may be ordered based on confidences of matching the object, allowing the user to select which product is in the image. If the unlisted identifies a product in the image with less than a minimum confidence value, the online system identifies the user's offered products and suggests that the user select a product.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

Abstract: Techniques are described that enable a user to edit and customize captions generated by a social networking system, such as transcriptions of an audio clip. In some cases, a social networking system receives, from a first user account, a video and an audio clip associated with the video, and determines that the audio clip contains speech. The social networking system may leverage a speech-to-text component to generate a first text caption based at least in part on the speech in the audio clip. The social networking system provides the first text caption to the first user account, and receives a user input to modify a word included in the first text caption. The social networking system generates a second text caption based at least in part on the user input, and provides the video, including the second text caption, to a second user account.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: A request to perform a compute task is received. A plurality of compute processor resources eligible to perform the compute task is identified, wherein the plurality of compute processor resources includes two or more of the following: a field-programmable gate array, an application-specific integrated circuit, a graphics processing unit, or a central processing unit. Based on an optimization metric, one of the compute processor resources is dynamically selected to perform the compute task.

Abstract: Techniques are described that enable a user to share videos having dynamic overlays on a social networking system. In some examples, a server computing device may receive, at a first time from a first client device, a first video. For instance, the server computing device may begin receiving the first video after the first client device has concluded capturing the first video, but has not yet completed customizing the first video. The server computing device may receive, at a second time after the first time and from the first client device, a dynamic overlay including customizations to be applied to the first video. The dynamic overlay may be transmitted separately from the first video. In examples, the server computing device generates a second video by applying the dynamic overlay to the first video. The server computing device then provides the second video to a second client device.

Abstract: Techniques are described that enable a user to edit and customize captions generated by a social networking system, such as transcriptions of an audio clip. In some cases, a social networking system receives, from a first user account, a video and an audio clip associated with the video, and determines that the audio clip contains speech. The social networking system may leverage a speech-to-text component to generate a first text caption based at least in part on the speech in the audio clip. The social networking system provides the first text caption to the first user account, and receives a user input to modify a word included in the first text caption. The social networking system generates a second text caption based at least in part on the user input, and provides the video, including the second text caption, to a second user account.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Abstract: Techniques are described that enable a user to edit and customize captions generated by a social networking system, such as transcriptions of an audio clip. In some cases, a social networking system receives, from a first user account, a video and an audio clip associated with the video, and determines that the audio clip contains speech. The social networking system may leverage a speech-to-text component to generate a first text caption based at least in part on the speech in the audio clip. The social networking system provides the first text caption to the first user account, and receives a user input to modify a word included in the first text caption. The social networking system generates a second text caption based at least in part on the user input, and provides the video, including the second text caption, to a second user account.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Abstract: An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

Abstract: An online system accesses and applies a trained item detection model to attributes of each of one or more content items received from one or more content-providing users to predict a probability that each content item includes an image of an item associated with an entity. If the probability is at least a threshold probability for each of a set of content items, the online system identifies a content-providing user from whom the set of content items was received and determines a number of content items included among the set of content items. The online system computes a score for the content-providing user based on the number and ranks the content-providing user among additional content-providing users based on the score. The online system provides an option to the entity to select a set of content-providing users from the ranking authorized to create content items on behalf of the entity.

Abstract: Utilization metrics associated with a plurality of eligible compute tasks executable on a plurality of field-programmable gate arrays are determined. The utilization metrics are evaluated to dynamically identify a reallocation of an identified field-programmable gate array resource of the field-programmable gate arrays to handle a selected eligible compute task of the eligible compute tasks. The identified field-programmable gate array resource is automatically reprogrammed to handle the selected eligible compute task.