Abstract: Evaluating the presence of training data in generated content by defining a first signature of a portion of the generated content by loading the generated content into memory registers, dividing the generated content into tokens, designating a sequential group of tokens as a signature shingle, and defining the first signature as a hash function value for the signature shingle. The evaluation also including matching the first signature to a training data signature in a training data signature database, updating a database record for the generated content to include the training data associated with the training data signature, and providing an output comprising data associated with the training data signature over a network.

Abstract: Embodiments herein describe techniques for optimizing prompts describing tasks for a large language model (LLM), to enable effective and efficient operations to optimize prompt generation and selection for various tasks in LLMs through human-AI collaboration. In an embodiment, a computing system applies a set of candidate prompts to the LLM, evaluates responses to the candidate prompts received from the LLM and calculates a pairwise similarity matrix of responses based on the received responses to the candidate prompts. The computing system evaluates the pairwise similarity matrix to determine whether or not to present a prompt to receive a user feedback input. The described techniques can enable enhanced processing speed, reducing an overall computer system time typically required for implementing optimized prompt generation, and enhancing performance of the computing system executing the LLM.

Abstract: A computer-implemented method is provided for executing automatic model retraining based on a CO2-based model retraining score. The computer-implemented method includes recognizing that a change in data used to train a set of models occurs, computing an expected model change for each of the models in accordance with the change in the data, predicting a resource consumption level associated with retraining each of the model, computing, over a set of models, an optimal model retraining score for each of the models based on the expected model change and the resource consumption level, comparing the optimal model retraining score with a threshold and executing an automatic model retraining for each model for which the optimal model retraining score exceeds the threshold.

Abstract: Candidate material for polymerization can be received. One or more desired features in the candidate material can be identified. A machine learning model can be trained to generate a new material having one or more of the desired features. Permissively, the candidate material can be determined from running a machine learning classification model that ranks a plurality of material as candidates. Permissively, the generated new material can be input to the machine learning classification model, for the machine learning classification model to include in ranking the plurality of material as candidates.

Abstract: Techniques are provided for training data identification and model selection. In one embodiment, the techniques involve querying a first language learning model with a first query, wherein the first language learning model generates a text output response to the first query, generating groupings of the text output response, wherein the groupings include multiple n-grams, generating a source score of the groupings based on a first training data and a second training data, and identifying the first training data as training data of the first language learning model based on the source score.

Abstract: A set of material candidates expected to yield materials with target properties can be generated. A subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates can be received. Based on the subject matter expert's input, a machine learning model can be trained to replicate the subject matter expert's decision.

Abstract: Disclosed herein are methods, systems, and computer program products for selecting an artificial intelligence (AI) model. Aspects include receiving, by a multi-armed bandit agent, candidate entities from multiple set expansion models and selecting a subset of the candidate entities for a first set expansion task, wherein a first candidate entity of the selected subset of candidate entities is selected from a first model by a subject matter expert. Aspects also include selecting, by the multi-armed bandit agent, based on the first candidate entity selected by the subject matter expert, the first model from the set expansion models for generating further candidate entities.

Abstract: An embodiment includes generating a query prompting a user to select from among a plurality of response options related to a first query set of objects. The embodiment also receives, responsive to the query, user input representative of a selected response option selected by the user from among the plurality of response options. The embodiment also calculates a plurality of weight values for respective ones of a plurality of similarity matrices based on the selected response option, where the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects. The embodiment stores a designated similarity matrix that is selected from among the plurality of similarity matrices based at least in part on a weight value from among the plurality of weight values assigned to the designated similarity matrix.

Abstract: A computer-implemented method according to one embodiment includes receiving snapshot data for a node within a data center; determining one or more candidate labels for one or more software applications running on the node, utilizing the snapshot data; implementing a validation of the one or more candidate labels to determine one or more validated labels; and training a machine learning model, utilizing the one or more validated labels and the snapshot data.

Abstract: A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.

Abstract: Systems, computer-implemented methods, and computer program products to transform a lexicon that describes an information asset are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

Abstract: One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The requested data is encrypted with two keys, including a physical device authentication key and a transient key. Access to the data requires authentication on both the device level and situational level. Device situational data is monitored, which includes selectively enabling access to the requested data and de-activation of the transient key in response to a change in the monitored situational data. The transient key de-activation removes access to the requested data.

Abstract: Embodiments relate to a computer system, computer program product, and method to prevent unauthorized file dissemination and replication. A file parameter is defined, with the defined file parameter including a file dissemination characteristic. The file is encoded with the defined file parameter as file metadata. Dissemination and replication of the file is managed responsive to the encoded file parameter. The defined parameter is assessed along with a physical replication destination. The file is selectively replicated or transmitted responsive to the file parameter and the destination assessment.

Abstract: Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Abstract: Systems, computer-implemented methods, and computer program products to transform a lexicon that describes an information asset are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

Abstract: A verification and authorization method, system, and computer program product include verifying, via a receiving device that receives a verification sound packet, an identity of a trusted caller via the verification sound packet, the verification sound packet including an asymmetrically encrypted payload sent by the trusted caller.

Abstract: In an approach to corpus expansion using lexical signatures, one or more computer processors retrieve a donor corpus of text, wherein the donor corpus includes a plurality of documents. One or more computer processors generate a document signature for each of the plurality of documents in the donor corpus. One or more computer processors retrieve a target corpus of text for expansion. One or more computer processors generate a corpus signature for the target corpus. One or more computer processors compare each document signature to the corpus signature. Based on the comparison, one or more computer processors determine a similarity score for each document signature. One or more computer processors rank the plurality of documents by the similarity score. One or more computer processors add one or more top-ranked documents of the plurality of documents to the target corpus.

Abstract: Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Abstract: An embodiment includes generating a query prompting a user to select from among a plurality of response options related to a first query set of objects. The embodiment also receives, responsive to the query, user input representative of a selected response option selected by the user from among the plurality of response options. The embodiment also calculates a plurality of weight values for respective ones of a plurality of similarity matrices based on the selected response option, where the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects. The embodiment stores a designated similarity matrix that is selected from among the plurality of similarity matrices based at least in part on a weight value from among the plurality of weight values assigned to the designated similarity matrix.