Abstract: Some embodiments of the present disclosure are directed to systems, computer-readable media, and computer-implemented methods for generating graphical icons for taxonomy nodes. Some embodiments are directed to determining, by a computer system, a taxonomy comprising a hierarchical classification for organizing graphical icons associated with digital images from a data set that includes a plurality of files stored in an electronic file format, each file comprising a digital image and descriptive text associated with the digital image. The computer system may further generate, based on the retrieved data set and the taxonomy, a plurality of graphical icons, wherein each respective graphical icon includes a respective text description, and wherein each respective graphical icon corresponds to a respective taxonomy node within the taxonomy. Other embodiments may be disclosed or claimed.

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: A method for managing charging resources of a charging system for plug-in electric vehicles (PEVs), the charging system including a central recording center including a tracking database. The method including initiating a charging session to a first PEV based on detecting that the first PEV has been plugged into a charging station. The first PEV is associated with the charging session in the tracking database. The first PEV is associated, in the tracking database, to a first user and a first PEV profile. The first PEV is charged in accordance with information from the first PEV profile. Charging session data is monitored and stored in the tracking database during the charging session. A machine learning model is generated based on collective charging data. Charge completion time of the first PEV is predicted based on the machine learning model.

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: 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.

Abstract: A method for managing charging resources of a charging system for plug-in electric vehicles (PEVs), the charging system including a central recording center including a tracking database. The method including initiating a charging session to a first PEV based on detecting that the first PEV has been plugged into a charging station. The first PEV is associated with the charging session in the tracking database. The first PEV is associated, in the tracking database, to a first user and a first PEV profile. The first PEV is charged in accordance with information from the first PEV profile. Charging session data is monitored and stored in the tracking database during the charging session. A machine learning model is generated based on collective charging data. Charge completion time of the first PEV is predicted based on the machine learning model.

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: 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: 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: 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: 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.