Abstract: A processor may receive a route request from a user. The processor may receive data associated with predicted routes of one or more nearby vehicles. The processor may select a recipient vehicle from the one or more nearby vehicles, where the recipient vehicle is selected based on recipient selection criteria. The processor may determine one or more driving change requests for the recipient vehicle. The processor may send the one or more driving change requests and a proposed token to the recipient vehicle. The processor may provide a token to the recipient vehicle in response to the recipient vehicle implementing the driving change requested.

Abstract: A method includes determining a presence of one or more people around a user and determining, based on the presence of the one or more people, an exposure level. The method also includes receiving a message for the user, the message comprising a first portion and a second portion and removing, based on the exposure level, the first and second portions from the message to produce a first message. The method further includes removing, based on the exposure level, the first portion from the message to produce a second message and presenting, based on the exposure level, the first message to the user. The method also includes receiving, from the user, feedback about the first message and presenting the second message to the user in response to the feedback.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: The present disclosure relates to a method comprising receiving an electronic message of an electronic communication system. An image based noisy content may be generated. The image based noisy content is different from a content of the received electronic message and relates to the message intents of the received message. The electronic communication system may be configured to provide the image based noisy to the recipient instead of the received electronic message or provide the received electronic message to the recipient in addition to the image based noisy.

Abstract: The present disclosure relates to a method comprising receiving an electronic message. Message intents of the received electronic message and one or more related intents may be determined. An electronic message may be generated according to a selected subset of the message intents or according to the related intents. The generated electronic message may be provided.

Abstract: Aspects of the present invention disclose a method for selecting a modality to distract a subject from engaging in risk events that minimize disturbances to users within the surrounding of the subject while maximizing an impact of the distraction on the subject. The method includes one or more processors identifying a sequence of actions of a subject within a sensor feed. The method further includes generating a knowledge graph based at least in part on activities of the subject, wherein the knowledge graph includes historical activity data. The method further includes determining that an activity of the subject is hazardous based at least in part on the sequence of actions of the subject. The method further includes initiating a distraction task on an internet of things (IoT) enabled device within a defined area that includes the subject, wherein the distraction task includes an audio-visual event.

Abstract: A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

Abstract: Embodiments provide a computer-implemented method of recommending camera configuration parameters for taking one or more photographs, the method including: acquiring first spatial dimension data, first temporal dimension data, and first manufacturer dimension data of a particular camera for taking the one or more photographs; inputting the first spatial dimension data, the first temporal dimension data, and the first manufacturer dimension data to a trained machine learning model; recommending, by the trained machine learning model, one or more vantage points for taking the one or more photographs, each vantage point corresponding to a different set of predicted camera configuration parameters; receiving a selection of one of the one or more vantage points from the user; and providing a set of predicted camera configuration parameters corresponding to the selected vantage point.

Abstract: A method for selective boundary detection includes identifying a plurality of boundaries for a plurality of subregions in a region of interest utilizing one or more multispectral images for the region of interest. The method further includes analyzing a plurality of adjacent fields to a first field in a first subregion out of the plurality of subregions utilizing a region identification criterion based on a plurality of attributes for the first field and the plurality of adjacent fields. The method further includes determining, based on the analyzing, the first region with the first field requires further analysis of multitemporal remote sensed data over a defined period of time.

Abstract: Aspects of the present disclosure relate to controlling virtual reality (VR) content displayed on a VR head mounted display (HMD). Communication can be established between a computer system, a VR HMD, and a mobile device. A user input configured to control VR content displayed on a display of the VR HMD can be received on the mobile device. The VR content displayed on the VR HMD can then be controlled based on the user input received on the mobile device.

Abstract: A computer-implemented method applies insights from a variety of data sources to each of the data sources. The method includes identifying a set of data sources, wherein each of the data sources are associated with a domain. The method includes analyzing documentation for each of the data sources. The method further includes extracting a set of attributes for each data source, and determining a data schema associated with each data source. The method includes mapping each data schema to a common domain schema. The method also includes linking, based on the mapping and on the set of attributes for each data source, common features across each data source. The method includes generating, in response to the linking, a knowledge graph. The method further includes preparing a visual display for a set of domain insights; and forking the set of domain insights into a first data source.

Abstract: One embodiment provides a method, including: training a plurality of machine-learning models, wherein each of the machine-learning models is trained for a specific farm field region utilizing training data for the plurality of machine-learning models; wherein the utilizing training data includes identifying one of the farm field regions having a similarity to another of the farm field regions and transferring training data; identifying a plurality of types of data needed for updating at least one of the plurality of machine-learning models to address at least one uncertainty; recommending collection of and collecting at least one of the plurality of types of data; and re-training the subset of the plurality of machine-learning models utilizing the at least one of the plurality of types of data, thereby decreasing the cost the of data collection, for example, crowdsourced data, by utilizing data collected from one farm field region in other farm field regions.

Abstract: From a first model parameter, an autoencoder network is generated. A reconstruction error for the autoencoder network is measured, the reconstruction error comprising a difference between an input to the autoencoder network and a corresponding output from the autoencoder network, the input to the autoencoder network comprising a portion of an initial set of data. The reconstruction error and a confidence score corresponding to a complexity level of the autoencoder network are aggregated into a level of difficulty score of the autoencoder network. From the level of difficulty score and an initial data access policy level corresponding to the initial set of data, a derived data access policy level corresponding to the initial data access policy level is generated, the derived data access policy level enforcing access to a transformed set of data generated by applying a transformation to the initial set of data.

Abstract: One embodiment provides a method, including: training a machine-learning model to produce customized farming practices specific to a farm to increase crop yield; wherein the training includes obtaining, from remote sensed data, (i) information corresponding to a crop of each of a plurality of farms and (ii) information corresponding to farming practices of each of the plurality of farms; wherein the training further includes detecting, from the remote sensed data, geographical features and farming characteristics of each of the plurality of farms; wherein the machine-learning model identifies from relationships between (iii) crop information and farming practices and (iv) geographical features and farming characteristics; and discovering, for a specific farm in an identified geographical location, utilizing the trained machine-learning model, and from farm-specific remote-sensed data, farming practices.

Abstract: Embodiments of the present invention describe identifying details of the area around a first vehicle and identifying a potential confusion point in a navigation route of the first vehicle. Additionally, embodiments of the present invention determining based on a threshold, whether to provide a driver of the first vehicle with a visual clue to assist with navigating through the identified potential confusion point, responsive to determining to provide a visual clue, identifying one or more visual clues from an area surrounding the first vehicle; and outputting the identified one or more visual clues to the driver.

Abstract: A supply chain optimization method, system, and computer program product include predicting a risk of a supply chain operation across a supply chain network caused by a term impact analysis of a global hazard, estimating a carbon footprint for the supply chain operation across the supply chain network, generating an alternative resilience plan as an alternative to an existing supply chain plan based on the predicted risk and the estimated carbon footprint.

Abstract: A method includes providing a server executing computer code embodying the chat-bot, receiving, via the server, a requirement for customization of a main computer code, the main computer code including modules, and determining, by the chat-bot, top-k modules of the main computer code potentially affected given the requirement for customization. The determination of the top-k modules includes representing each of the modules of the main computer code in an embedding vector space, representing the requirement for customization as a vector in the embedding space, and fetching the top-k modules nearest the requirement for customization in the embedding space. The method further includes determining a question, communicating the question to an entity providing the requirement for customization, receiving a response to the question, and filtering the top-k modules using the response to determine a set of the modules associated with the requirement for customization.

Abstract: Generation of a set of carbon-aware test cases based on a software resource load includes receiving, by one or more processors, a source code and identifying code changes on the received source code using code analysis technology. The code changes are used to select a plurality of code snippets from the source code. The one or more processors calculate metrics associated with an energy consumption of each of the plurality of code snippets and an amount of carbon dioxide emissions equivalent to the calculated metrics. The calculated equivalent amount of carbon dioxide emissions associated with each of the plurality of code snippets is displayed by the one or more processors on the source code for automatically generating the set of test carbon-aware test cases based on the calculated amount of carbon dioxide emissions and predefined carbon emissions constraints.

Abstract: Methods, systems, and computer program products for content marshaling using biometric data are provided herein. A computer-implemented method includes estimating a cognitive state of a user based at least in part on analyzing one or more sets of biometric data pertaining to the user; dynamically compiling multi-modal content for the user based at least in part on (i) the estimated cognitive state of the user, (ii) one or more user parameters, and (iii) content availability information; and outputting the compiled content to the user via one or more devices.

Abstract: A method for selective boundary detection includes identifying a plurality of boundaries for a plurality of subregions in a region of interest utilizing one or more multispectral images for the region of interest. The method further includes analyzing a plurality of adjacent fields to a first field in a first subregion out of the plurality of subregions utilizing a region identification criterion based on a plurality of attributes for the first field and the plurality of adjacent fields. The method further includes determining, based on the analyzing, the first region with the first field requires further analysis of multitemporal remote sensed data over a defined period of time.