Abstract: A computerized method classifies vegetation in geographic regions. Target coordinates that describe a geographic region are obtained and vegetation data associated with the geographic region is obtained. In some examples, the vegetation data includes satellite imagery data of the geographic region. Vegetation index data points are generated using the obtained vegetation data (e.g., Normalized Difference Vegetation Index (NDVI) data points). A signature curve associated with the geographic region is plotted using the generated vegetation index data points and the vegetation in the geographic region is classified using the plotted signature curve. Then, a vegetation management action is caused to be performed in association with the geographic region based on the classified vegetation. The method enables the identification of crops in the geographic location and/or monitoring of forest growth or decline in the geographic location.

Abstract: A method of repairing crashed applications includes detecting a crash in an application operating in a host computing device. The application is migrated to a remote computer server. The remote computer server provisions computing resources to the application, while the application is resident in the remote computer server. Resumed operation of the application is executed, using the provisioned computing resources, in the remote computer server. Execution results are generated from the application, in the remote computer server. The generated execution results are migrated from the application back to the host computing device.

Abstract: Embodiments of the invention are directed to a computer-implemented method for matching a graph-under-analysis to a technique for embedding the graph-under-analysis. In a non-limiting example, the computer-implemented method includes receiving, using a processor, graph data representing the graph-under-analysis, wherein the graph-under-analysis represents a network. The graph data is analyzed, using the processor, to extract graph property data representing properties of the graph-under-analysis. Based at least in part on a result of analyzing the graph property data, one or more embedding techniques are selected, wherein at least one of the one or more embedding techniques is configured to transform the graph data to a graph embedding that is used by a task algorithm to perform a task.

Abstract: The present disclosure relates to systems and methods for creating a copilot. The copilot uses plugins to provide additional features and functionalities to the copilot. The systems and methods use a large language model (LLM) pipeline to generate a knowledge resource used by the plugins and/or an LLM in the copilot to answer queries from a user.

Abstract: A topic for building a new intent on which to train a chatbot can be received. A database of chatbot training data can be searched for a candidate intent having meta-knowledge similar to the received topic. Utterances associated with the candidate intent can be extracted. The received topic and the extracted utterances can be input to a trained machine learning model. The trained machine learning model generates example utterances for the new intent. The new intent with the generated example utterances can be used as training data for training the chatbot.

Abstract: A supply chain tracking system utilizes tracking codes to track products through a supply chain. A tracking code is assigned to each product. If the product is grouped with other products at a stage in the supply chain, a tracking code is assigned to the group, and the tracking code for each of the products in the group is associated with the tracking code for the group. If the group of products is further aggregated with groups of other products, such as in a shipping container, a tracking code is assigned to the aggregated groups of products, and the tracking code for each of the groups of products is associated with the tracking code for the aggregated groups of products. The tracking codes are used to generate a supply chain graph which maps the travel of each product through the supply chain.

Abstract: An approach to identifying architectures of machine learning models meeting a user defined constraint. The approach can receive input associated with evaluating machine learning models from a user. The approach can determine acceptable architectural templates to evaluate the machine learning models based on the input and determine a list of architectures and metrics based on a calculation of maximum neural network sizes of the acceptable architectural templates not exceeding the constraint. The approach can send the list of architectures and metrics to the user for selection.

Abstract: A computing system for achieving traceability in a food commodity supply chain is provided, including a server computing device configured to receive a message indicating an optical code associated with a unit of a food product has been scanned by a camera-equipped computing device at a location, in which the message includes a product unit-specific identifier encoded in the optical code. The server computing device is further configured to identify in a database, a merchant record including the product unit-specific identifier and a merchant identifier of a merchant at the location. The server computing device is further configured to trace a supply chain path of the unit of the food product through additional records in the database that are linked to the product unit-specific identifier, and output supply chain derived information on the unit of the food product based on the traced supply chain path.

Abstract: One or more processors may detect that an extreme event that occurred in a first region. One or more processors may determine that the extreme event creates a drift in a probability distribution that is an output of a model associated with the first region. One or more processors may retrain the model associated with the first region using data associated with the extreme event. One or more processors may identify a second region similar to the first region according to a defined correlation threshold. One or more processors may generate data representing potential extreme scenarios for the second region based on the detected extreme event in the first region. One or more processors may retrain a model associated with the second region based on the generated data representing potential extreme scenarios.

Abstract: A method of conducting computing experiments includes executing a set of jobs based on user-selected parameters, learning a user strategy by checking the user-selected parameters during the executing of the set of jobs, and refining the user strategy by refining the set of jobs.

Abstract: The present disclosure provides for crop yield estimation by identifying, via image processing, a field in which a crop is grown; identifying a plurality of regions within the field; identifying, by processing growth metrics via a model, a plurality of data collection points in the plurality of regions, wherein a given data collection point of the plurality of data collection points within a given region of the plurality of regions is identified by multivariate analysis as representative of growing conditions in the given region; receiving in-field data linked to the data collection points of the plurality; and predicting a yield for the crop in the field based on the in-field data.

Abstract: A method, computer system, and a computer program product for generating instructions to highlight software change may be provided. In one embodiment, the technique comprises obtaining information about a requested modification to an original code of a software program and classifying it based on the type of change requested by the modification. The unit tests available are then identified. At least one of the identified unit tests are selected and customized based on classification of the type of code modification requested. Using the at least one selected and customized unit test, the differences between the original and modified code may be identified. One or more test execution stories are then generated related to the modification of the code, to highlight the changes. Test execution stories are further analyzed to provide any additional missing information.

Abstract: In an approach to composing a machine learning model for complex data sources, a computer receives data and associated metadata corresponding to a machine learning task from a user. A computer determines a task context and a problem domain. A computer identifies the machine learning task. A computer evaluates a match between the problem domain and one or more pre-compiled models. A computer selects at least two of the one or more pre-compiled models. A computer generates one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models. A computer executes the multimodal model combinations with the data and associated metadata. A computer displays the results of the executed one or more multimodal model combinations to the user. A computer determines whether a level of error associated with the results is acceptable to the user based on a response from the user.

Abstract: Aspects of the invention include receiving a first instructional video describing a first method of using a function of a first software application; determining each step of the first method described in the first instructional video; determining, by the processor, a second method of using a first function of a second software application, wherein the first function of the first software application is substantially the same as the first function of the second software application; and creating a second instructional video describing each step of the second method.

Abstract: Described are techniques for application migration. The techniques include migrating an application to a target cloud infrastructure and generating a cost-aware code dependency graph during execution of the application on the target cloud infrastructure. The techniques further include modifying the application by removing source code corresponding to unused nodes according to the cost-aware code dependency graph and replacing identified source code of a high-cost subgraph of the cost-aware code dependency graph with calls to a generated microservice configured to provide functionality similar to the identified source code. The techniques further include implementing the modified application on one or more virtual machines of the target cloud infrastructure.

Abstract: A mobile device may generate a plurality of signals based on sensor data received from a plurality of sensors associated with the mobile device. Based on a logic combination of the plurality of signals, the mobile device may detect its status indicating one of different scenarios. Based on the detected mobile device status and a configured policy, the mobile device may determine a type of deletion action for deleting data on the mobile device. The mobile device may perform the determined type of deletion action.

Abstract: Chatbot logs can be received. The chatbot logs can be clustered or classified into clusters of potential intents. Trending topics can be detected by analyzing at least social media content. Semantic similarities between the potential intents and the trending topics can be computed. Based on the computed semantic similarities, at least one of the potential intents to at least one of the trending topics can be correlated. Training of the chatbot using the chatbot logs associated with the correlated potential intent can be triggered.

Abstract: One or more processors may detect that an extreme event that occurred in a first region. One or more processors may determine that the extreme event creates a drift in a probability distribution that is an output of a model associated with the first region. One or more processors may retrain the model associated with the first region using data associated with the extreme event. One or more processors may identify a second region similar to the first region according to a defined correlation threshold. One or more processors may generate data representing potential extreme scenarios for the second region based on the detected extreme event in the first region. One or more processors may retrain a model associated with the second region based on the generated data representing potential extreme scenarios.

Abstract: A method of repairing crashed applications includes detecting a crash in an application operating in a host computing device. The application is migrated to a remote computer server. The remote computer server provisions computing resources to the application, while the application is resident in the remote computer server. Resumed operation of the application is executed, using the provisioned computing resources, in the remote computer server. Execution results are generated from the application, in the remote computer server. The generated execution results are migrated from the application back to the host computing device.

Abstract: A topic for building a new intent on which to train a chatbot can be received. A database of chatbot training data can be searched for a candidate intent having meta-knowledge similar to the received topic. Utterances associated with the candidate intent can be extracted. The received topic and the extracted utterances can be input to a trained machine learning model. The trained machine learning model generates example utterances for the new intent. The new intent with the generated example utterances can be used as training data for training the chatbot.