Abstract: A plurality of best practice rules pertaining to best practices for interacting with a system can be received. A first user interaction from a user can received. A best practice determiner can determine whether the first user interaction fits at least one of the best practice rules. Responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, a modified user interaction can be generated by modifying the first user interaction based, at least in part, on the received plurality of best practice rules. The modified user interaction can be presented to the user.

Abstract: The present invention relates to crop breeding. More particularly, the present invention relates to targeted modification of root to enhance abiotic stress tolerance in maize. In one aspect, the invention provides recombinant maize exhibiting increased root cortical aerenchyma (RCA). Methods of making the recombinant maize and various methods of plant selection and breeding are further provided.

Abstract: In an approach to improve prompt variations to enhance user-based experiences while interacting with artificial intelligent (AI) systems, embodiments generate variations of the prompts associated with the initial input by automatically leveraging machine learning techniques and collect the prompts from a user resulting from an interaction with an artificial intelligent (AI) system. Further, embodiments, identify a heuristic to organize suggestions and the variations of the prompts, and determine and utilize the heuristic to organize the variations of the prompts in systematic categories to produce an efficient display of the variations of the prompts, in a graphic user interface (GUI), for the user to view. Additionally, embodiments, update the variations of the prompts based on identified interaction data from the user, and dynamically output the updated variations of the prompts in response to the identified and collected user data.

Abstract: Provided are techniques for moderating Artificial Intelligence (AI) agent interlocution in group dialog environments. Under control of an interlocution module that has been trained with dialog content and dialog turns, an indication that interlocution is to be determined for a group dialog is received. Under control of the interlocution module, it is determined whether an AI agent is to participate in the group dialog based on a current dialog context and a dialog response. Under control of the interlocution module, in response to determining that the AI agent is to participate in the group dialog, the AI agent is triggered to post the dialog response to the group dialog.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating responses to prompts by utilizing a context engine and a logic engine. In one or more embodiments, the disclosed systems can determine a prompt received from a client device involves one or more logical problems with a prompt classification model. Based on identifying the one or more logical problems, the disclosed systems can generate a logic code segment by processing the prompt with one or more large language models within a context engine. The disclosed systems can generate a logic result for the prompt by processing the logic code segment with a logic engine that solves one or more logical problems within the prompt according to the structure of the logic code segment. The disclosed systems can generate a response to the prompt based, at least in part, on the logic result.

Abstract: Embodiments are provided that related to a computer system, a computer program product, and a computer-implemented method for dynamically managing knowledge graphs and their corresponding datasets. Embodiments include identifying a neologism from a virtual environment, and leveraging a virtual environment exploration to resolve a meaning of the identified neologism. The resolved meaning of the neologism is applied to a dynamic expansion of a dataset and a corresponding knowledge graph.

Abstract: A method, computer program, and computer system are provided for explaining generation of a flow from natural language utterances. Data corresponding to a natural language utterance is received. One or more constraints corresponding to a flow to be generated are determined based on the received natural language utterance. A flow is constructed based on the determined constraints. An explanation associated with the constructed flow is provided, and the explanation identifies parameters corresponding to constructing the flow.

Abstract: A plurality of best practice rules pertaining to best practices for interacting with a system can be received. A first user interaction from a user can received. A best practice determiner can determine whether the first user interaction fits at least one of the best practice rules. Responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, a modified user interaction can be generated by modifying the first user interaction based, at least in part, on the received plurality of best practice rules. The modified user interaction can be presented to the user.

Abstract: A method or system for testing and benchmarking commits made on source code. The system extracts commits from a history of a first code history that records a sequence of commits made thereon. The extracted commits are then combined into a sequence of patches, each of which includes changes made during consecutive commits. The system also establishes a connection with a system under test (SUT) having a second code repository corresponding to a historical version of the first code repository before the extracted commits were made, and sequentially applies the sequence of the patches to the second code repository. Performance of the SUT is monitored during the application of the sequence of the patches to determine whether the SUT performs as expected after the application of each patch.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating context engine outputs by utilizing an interpreter purpose-built to execute code generated by large language models. For example, the disclosed systems generate computer code executable for responding to a query by utilizing a large language model. In addition, the disclosed systems execute the model-generated computer code utilizing an interpreter integrated with the context engine that further includes swappable logic interchangeable across multiple executors. Moreover, the disclosed systems can further generate as part of executing the computer code utilizing the interpreter, a first context engine output by implementing the interpreter at a first executor. Additionally, the disclosed systems can further generate a second context engine output by implementing the interpreter at a second executor.

Abstract: In an approach to improve prompt variations to enhance user-based experiences while interacting with artificial intelligent (AI) systems, embodiments generate variations of the prompts associated with the initial input by automatically leveraging machine learning techniques and collect the prompts from a user resulting from an interaction with an artificial intelligent (AI) system. Further, embodiments, identify a heuristic to organize suggestions and the variations of the prompts, and determine and utilize the heuristic to organize the variations of the prompts in systematic categories to produce an efficient display of the variations of the prompts, in a graphic user interface (GUI), for the user to view. Additionally, embodiments, update the variations of the prompts based on identified interaction data from the user, and dynamically output the updated variations of the prompts in response to the identified and collected user data.

Abstract: A method or system for testing and benchmarking commits made on source code. The system extracts commits from a history of a first code history that records a sequence of commits made thereon. The extracted commits are then combined into a sequence of patches, each of which includes changes made during consecutive commits. The system also establishes a connection with a system under test (SUT) having a second code repository corresponding to a historical version of the first code repository before the extracted commits were made, and sequentially applies the sequence of the patches to the second code repository. Performance of the SUT is monitored during the application of the sequence of the patches to determine whether the SUT performs as expected after the application of each patch.

Abstract: A system and method is disclosed for evaluating a plurality of cryptocurrencies, the method comprising monitoring and storing volatility data for a plurality of cryptocurrency exchanges; analyzing the stored volatility data to generate one or more risk forecasts indicating risks associated with purchasing cryptocurrency through each of the cryptocurrency exchanges; determining a number of transaction factors for purchasing cryptocurrencies and selecting a cryptocurrency based on the transaction factors.

Abstract: Provided are techniques for moderating Artificial Intelligence (AI) agent interlocution in group dialog environments. Under control of an interlocution module that has been trained with dialog content and dialog turns, an indication that interlocution is to be determined for a group dialog is received. Under control of the interlocution module, it is determined whether an AI agent is to participate in the group dialog based on a current dialog context and a dialog response. Under control of the interlocution module, in response to determining that the AI agent is to participate in the group dialog, the AI agent is triggered to post the dialog response to the group dialog.

Abstract: A system or a method for analyzing a software project for vulnerabilities. The system extracts scopes of source code, each of which is a source code block that contains a definition of an entity. The system also receives a vulnerability report relating to the source code. The vulnerability report identifies a vulnerability at a line of the source code. The system identifies a subset of the scopes of source code that contains the line of source code where the vulnerability is identified. The system identifies, based on smatch values, a minimum scope among the subset of the scopes that contains the line of source code where the vulnerability is identified, and generates a scoped vulnerability report recording the minimum scope and the vulnerability.

Abstract: A present invention embodiment analyzes user input via natural language processing. A natural language utterance from a user is analyzed to determine one or more computing tasks. the natural language utterance is analyzed using a knowledge base to identify one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance. An indication that the user accepted at least one modification of the one or more modifications is received, wherein the at least one modification modifies the one or more computing tasks. The modified one or more computing tasks are executed.

Abstract: A system and method to adaptively generate a program model. Source code of a program to be tested for code issues, and a set of predefined patterns to be tested in the source code are received. Feature configuration data is generated by determining a set of features corresponding to the received set of predefined patterns. A set of program models is identified by selecting, for each feature in the set of features, a program model from among a plurality of program models that is optimized for the feature. A dynamic program model is built based on the identified set of program models, the dynamic program model being adapted to resolve each of the patterns included in the received set of predefined patterns. And the source code is tested for code issues by extracting from the dynamic program model instances of each of the set of predefined patterns.

Abstract: A system and method to adaptively generate a program model. Source code of a program to be tested for code issues, and a set of predefined patterns to be tested in the source code are received. Feature configuration data is generated by determining a set of features corresponding to the received set of predefined patterns. A set of program models is identified by selecting, for each feature in the set of features, a program model from among a plurality of program models that is optimized for the feature. A dynamic program model is built based on the identified set of program models, the dynamic program model being adapted to resolve each of the patterns included in the received set of predefined patterns. And the source code is tested for code issues by extracting from the dynamic program model instances of each of the set of predefined patterns.

Abstract: A method or system for testing and benchmarking commits made on source code. The system extracts commits from a history of a first code history that records a sequence of commits made thereon. The extracted commits are then combined into a sequence of patches, each of which includes changes made during consecutive commits. The system also establishes a connection with a system under test (SUT) having a second code repository corresponding to a historical version of the first code repository before the extracted commits were made, and sequentially applies the sequence of the patches to the second code repository. Performance of the SUT is monitored during the application of the sequence of the patches to determine whether the SUT performs as expected after the application of each patch.

Abstract: A system and method is disclosed for evaluating a plurality of cryptocurrencies, the method comprising monitoring and storing volatility data for a plurality of cryptocurrency exchanges; analyzing the stored volatility data to generate one or more risk forecasts indicating risks associated with purchasing cryptocurrency through each of the cryptocurrency exchanges; determining a number of transaction factors for purchasing cryptocurrencies and selecting a cryptocurrency based on the transaction factors.