Abstract: A snapshot handler is registered with an event monitoring subsystem of a process virtual machine, the registering specifying a trigger event of the snapshot handler, wherein the trigger event comprises execution of a specified portion of an application executing in the process virtual machine, the trigger event specified externally from a source code of the application. Responsive to the event monitoring subsystem detecting an occurrence of the trigger event, the snapshot handler is executed, storing data of an execution state of the process virtual machine at a time of occurrence of the trigger event.

Abstract: Privacy-preserving homomorphic inferencing utilizes batch processing on encrypted data records. Each data record has a private data portion of interest against which the inferencing is carried out. Batch processing is enabled with respect to a set of encrypted data records by techniques that ensure that each encrypted data record has its associated private data portion in a unique location relative to the other data records. The set of encrypted data records are then summed to generate a single encrypted data record against which the inferencing is done. In a first embodiment, the private data portions of interest are selectively and uniquely positioned at runtime (when the inferencing is being applied). In a second embodiment, the private data portions of interest are initially positioned with the data-at-rest, preferably in an off-line process; thereafter, at runtime individual encrypted data records are processed as necessary to adjust the private data portions to unique positions prior to batching.

Abstract: Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity.

Abstract: Ground rule verification (“GRV”) design layouts may be automatically generated based on one or more design macros. The GRV design layout may be tested based on the one or more design macros by violating one or more ground rules using one or more GRV ranges. The testing may include electrical testing of the one or more GRV design layouts based on the one or more design macros. The one or more ground rules may be automatically selected and approved the based upon a degree of violation acceptability.

Abstract: Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity.

Abstract: A method, apparatus and computer program product that provides multi-phase privacy-preserving inferencing in a high throughput data environment, e.g., to facilitate fraud prediction, detection and prevention. In one embodiment, two (2) machine learning models are used, a first model that is trained in the clear on first transaction data, and a second model that is trained in the clear but on the first transaction data, and user data. The first model is used to perform inferencing in the clear on the high throughput received data. In this manner, the first model provides a first level evaluation of whether a particular transaction might be fraudulent. If a transaction is flagged in this first phase, a second more secure inference is then carried out using the second model. The inferencing performed by the second model is done on homomorphically encrypted data. Thus, only those transactions marked by the first model are passed to the second model for secure evaluation.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: An embodiment includes executing, by a hypervisor, a bootloader with access to a first logical partition of a non-volatile memory, the first logical partition storing a keystore. The embodiment also includes loading, by the bootloader, a kernel with access to the first logical partition of the non-volatile memory. The embodiment also includes receiving, by the bootloader, an encryption key from the keystore. The embodiment also includes performing, by the bootloader, a cryptographic algorithm using the encryption key on the kernel. The embodiment also includes executing, by the bootloader in an event that the performing of the cryptographic algorithm produces a first result, the kernel with access to the first logical partition of the non-volatile memory. The embodiment also includes halting, by the bootloader in an event that the performing of the cryptographic algorithm fails to produce the first result, booting of the kernel and generating an error message.

Abstract: A method includes receiving a plurality of files where each file represents an object. The method further combines selected files of the plurality of files into an aggregation file and stores the aggregation file on low-latency storage media, such as disk storage media. The method represents the aggregation file with a single inode that is stored on the low-latency storage media. The method migrates the aggregate file to high-latency storage media, such as tape storage media, while leaving the single inode representing the aggregation file on the low-latency storage media. By collecting files into larger aggregation files, the number of files archived is significantly reduced and the files archived are of larger size. The reduced number of files and increased size of the files improves archival and retrieval performance due to reduced metadata operation overhead. A corresponding system and computer program product are also disclosed.

Abstract: From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

Abstract: A system, computer program product, and method are provided for probing model signal awareness. An iterative process is employed to systematically isolate one or more relevant tokens of an input sequence to generate a reduced input sequence. The reduced input sequence is validated and presented to a trained artificial intelligence (AI) model and prediction output is generated. The reduction process is continued while the prediction output stays the same as that of the input sequence, and until a minimal sub-sequence is identified. A signal existence in the minimal sub-sequence is verified and signal awareness of the trained AI model is evaluated. The evaluation includes measuring the verified signal existence against an original signal from the input sentence.

Abstract: Local memory and disaggregated memory may be identified and monitored for integrating disaggregated memory in a computing system. Candidate data may be migrated between the local memory and disaggregated memory to optimize allocation of disaggregated memory and migrated data according to a dynamic set of migration criteria.

Abstract: Embodiments relate to an input-encoding technique in conjunction with federation. Participating entities are arranged in a collaborative relationship. Each participating entity trains a machine learning model with an encoder on a training data set. The performance of each of the models is measured and at least one of the models is selectively identified based on the measured performance. An encoder of the selectively identified machine learning model is shared with each of the participating entities. The shared encoder is configured to be applied by the participating entities to train the first and second machine learning models, which are configured to be merged and shared in the federated learning environment.

Abstract: An audio file format of an audio portion of a natural language content is determined. Using a trained audio language identification model, a human language included in the audio portion is identified. Using a trained audio to text model trained on the human language, the audio portion is converted to a corresponding set of text data. The set of text data is indexed. Using the indexed set of text data responsive to a search query, a search result is generated, the search query specifying a search including a non-textual portion of the natural language content.

Abstract: An embodiment includes processing a dataset to generate a set of feature vectors that include a first feature vector corresponding to a first concept within a user's areas of interest and a second feature vector corresponding to a second concept within the user's areas of study. The embodiment identifies clusters of the feature vectors and identifies key features that most contribute to influencing the clustering algorithm. The embodiment selects the first feature vector in response to a user query, and then selects the second feature vector based on an overlap between key features of the first and second feature vectors and a degree of dissimilarity between the first and second concepts. The embodiment outputs a query response that includes the second concept. The embodiment also determines an effectiveness value based on sensor data indicative of a user action responsive to the outputting of the response to the query.

Abstract: An embodiment includes parsing an input dataset associated with a first node of a decision tree, where the input dataset includes a set of profile values for a set of projected usage parameters for a computing environment. The embodiment identifies a structure of the dataset using a recursive neural network that predicts a question sequence in a hierarchical tree format. The embodiment calculates a first deviation from the predicted question sequence and determines whether the deviation exceeds a threshold value. The embodiment generates a modified input dataset using a disambiguation rule and calculates a second deviation of the modified structure from the predicted question sequence and determines whether the deviation exceeds the threshold value. The embodiment assembles a customized hierarchical path using a generative model and assembles the customized hierarchical path by performing iterations of generating a series of candidate questions until a leaf node is reached.

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Abstract: A system, product, and method are provided for improving knowledge graph (KG) link prediction using transformer-based artificial neural networks. A first topic model is leveraged against a first dataset derived from a KG containing a plurality of first triples. The first triples include first entities and first edges connecting the first entities to represent relationships between the first connected entities. A first similarity function is applied to the first connected entities of the first triples to provide respective first similarity scores. A first subset of one of more first triples is selected from the plurality of first triples based upon the first similarity scores. An artificial neural network is trained using the selected first subset of one or more first triples.

Abstract: An embodiment includes initiating a search engine to conduct a first search of a data resource for information associated with an acquisition candidate using search criteria provided via a user interface. The embodiment receives a first search result from the search engine comprising an information dataset associated with the acquisition candidate. The embodiment generates a candidate rating for the acquisition candidate indicative of a performance metric for the acquisition candidate relative to another acquisition candidate. The embodiment initiates the search engine to conduct a second search of the data resource using search criteria extracted from the information dataset. The embodiment receives a second search result from the search engine comprising due-diligence data. The embodiment calculates an updated candidate rating for the acquisition candidate based on the cognitive analysis of the due-diligence data and uses the updated rating to provide a recommendation regarding the acquisition candidate.

Abstract: An embodiment includes activating, responsive to receiving an update notification, an update mode of a mobile device, wherein the activating of the update mode includes disabling a primary communication interface and enabling a secondary communication interface, and wherein the update notification includes notification of a software update available for the mobile device. The embodiment also includes initiating execution of the software update on the mobile device while the mobile device remains in the update mode. The embodiment also includes deactivating, responsive to completing the software update, the update mode of the mobile device, wherein the deactivating of the update mode includes enabling the primary communication interface and disabling the secondary communication interface.

Abstract: By executing a natural language processing model on a set of natural language text describing a first engagement, a set of characteristics of the first engagement is generated. By executing the natural language processing model on a set of natural language text describing a future engagement, a set of characteristics of the future engagement is generated. The first engagement is determined to be above a threshold similarity with the future engagement. Using the skillset used in performing the first engagement, a required skillset of the future engagement is forecasted. By executing the natural language processing model on a set of natural language text describing a current skillset, a set of characteristics of the current skillset is generated. Using the required skillset of the future engagement and the set of characteristics of the current skillset, a learning path is generated.

Abstract: A system, computer program product, and method are provided for entity linking in a logical neural network (LNN). A set of features are generated for one or more entity-mention pairs in an annotated dataset. The generated set of features is evaluated against an entity linking LNN rule template having one or more logically connected rules and corresponding connective weights organized in a tree structure. An artificial neural network is leveraged along with a corresponding machine learning algorithm to learn the connective weights. The connective weights associated with the logically connected rules are selectively updated and a learned model is generated with learned thresholds and the learned weights for the logically connected rules.

Abstract: Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for automating scenario planning. Embodiments involve machine learning (ML) and an artificial intelligence (AI) planner to capture a general scenario planning (GSP) problem and provide a solution to the GSP problem in the form of trajectories.

Abstract: From a set of natural language text documents, a concept tree is constructed. For a node in the concept tree a polarity of the subset represented by the node is scored. A second set of natural language text documents is added to the subset, the adding resulting in a modified subset of natural language text documents having a polarity score within a predefined neutral polarity score range. From the modified subset, a bin of sentences is selected according to a sentence selection parameter, a sentence in the bin of sentences being extracted from a selected document in the modified subset. A sentence having a factuality score below a threshold factuality score is removed from the bin of sentences. From the filtered bin of sentences a new natural language text document corresponding to the filtered bin of sentences is generated using a transformer deep learning narration generation model.

Abstract: At a sidecar portion sharing a container with a first application portion, metadata describing a modification to a communications protocol is received. At the sidecar portion, a communication of the first application portion to a second application outside the container is received. The modifying is performed according to the metadata. The modified communication is transmitted to the second application.

Abstract: An embodiment includes receiving a request to delete a specified document from a primary shard of a sharded database. The embodiment also includes inserting a soft-deletion document to a soft-deletion shard, wherein the soft-deletion document identifies the specified document, which remains in the primary shard. The embodiment also includes receiving a query from a client application, wherein the specified document satisfies the query. The embodiment also includes blocking the specified document from being returned in response to the query while the soft-deletion document associated with the specified document remains in the soft-deletion shard.

Abstract: A quantum computing device is formed using a first chip and a second chip, the first chip having a first substrate, a first set of pads, and a set of Josephson junctions disposed on the first substrate. The second chip has a second substrate, a second set of pads disposed on the second substrate opposite the first set of pads, and a second layer formed on a subset of the second set of pads. The second layer is configured to bond the first chip and the second chip. The subset of the second set of pads corresponds to a subset of the set of Josephson junctions selected to avoid frequency collision between qubits in a set of qubits. A qubit is formed using a Josephson junction from the subset of Josephson junctions and another Josephson junction not in the subset being rendered unusable for forming qubits.

Abstract: A computer-implemented method and system for resilient adaptive biased locking. The method includes adding, in a system including an adaptive lock reservation scheme having a learning state, a component comprising a per class counter that counts, collectively, a number of learning failures and a number of revocation failures. An embodiment includes initializing the per class counter upon loading a class with a predetermined value representing at least one of a maximum number of learning failures and cancellation instances associated with the class. An embodiment includes initializing, based on a determination of an operational state of the per class counter for an object transitioning from one of the learning state and a biased state to a flatlock state, a lock word of the object directly to the flatlock state while bypassing the biased state.

Abstract: A system configuration template is configured according to a first configuration command received from a configuration management system executing on a control computer system. A node configuration command is generated for a node computer system. The node configuration command adjusts a configuration of a node computer system to conform to the system configuration template.

Abstract: By intercepting a natural language communication of a protected party, the communication is monitored, wherein the protected party is a human being. Within the monitored communication using a natural language processing engine, a natural language interaction between the protected party and a second party is detected. To determine an interaction pattern, the natural language interaction is analyzed. The interaction pattern includes data derived from the monitored communication, metadata of the protected party, and metadata of the second party. Using the interaction pattern and an interaction behavior model, an adverse result of the natural language interaction is predicted, wherein the adverse result comprises an economic loss to the protected party. By notifying the protected party, the predicted adverse result is intercepted.

Abstract: An artificial intelligence (AI) platform to support optimization of workload scheduling in a distributed computing environment. Unstructured data corresponding to one or more application artifacts related to a workload in the distributed computing environment is leveraged. NLP is applied to the unstructured data to identify one or more host requirements corresponding to the application artifacts. One or more hosts in the computing environment compatible with the identified host requirements are selectively identified and compatibility between the application artifacts and the identified hosts is assessed. The workload is selectively scheduled responsive to the selective host identification based on the assessed compatibility. The scheduled workload is selectively executed on at least one of the selectively identified hosts responsive to the assessment workload compatibility.

Abstract: A system, computer program product, and method are provided for jointly learning dictionary based rules and dictionary candidates. Natural language text is received and parsed into subsets, with the subset being subjected to natural language processing to identify one or more verbs within the subset. The identified verbs are evaluated with respect to a dictionary and one or more rules. The evaluation is directed at each predicate in the rules with respect to the identified verbs. A neural network is leveraged to jointly induce modification of the rules and one or more dictionaries responsive to the evaluation.

Abstract: A set of nodes of a content delivery network are weighted according to an effect of a node on a network. A data points parameter specifying a number of nodes constituting a cluster is set according to a policy. A subset of the weighted nodes is clustered according to the data points parameter. A cluster comprises nodes having a content access history similarity greater than a threshold similarity. A structured representation of a natural language document is positioned at a node within the cluster, the positioning determined by evaluating a similarity between the structured representation and a content access history of the node.

Abstract: From an unstructured content using an ontology, a forward materialization graph is generated. The forward materialization graph is converted to a set of vector representations comprising multidimensional numbers representing elements of the forward materialization graph. A set of inference paths is computed for the set of vector representations. An inference path in the set of inference paths connecting a first vector representation with a second vector representation. Based on a set of features, the set of vector representations is formed into clusters, a feature in the set of features comprising a relevance probability, the relevance probability corresponding to a relevance of a portion of the unstructured content according to a relevance metric. A structured representation of the unstructured content is placed at an edge location of a content delivery network determined using the set of clusters.

Abstract: To rank time series forecasting in machine learning pipelines, time series data may be incrementally allocated from a time series data set for testing by candidate machine learning pipelines based on seasonality or a degree of temporal dependence of the time series data. Intermediate evaluation scores may be provided by each of the candidate machine learning pipelines following each time series data allocation. One or more machine learning pipelines may be automatically selected from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores.

Abstract: An embodiment trains a machine-learning model using a first training corpus of general items indicative of varying levels of confidence. The embodiment also prepares a second training corpus that includes domain-specific items indicative of varying levels of confidence extracted from communications from members of a project group associated with a project. The embodiment retrains the machine-learning model using the second training corpus and generates a confidence score for the project based on confidence values assigned by the machine-learning model to each of a plurality of project-related communication items from members of the project group. The embodiment also detects that the confidence score is below a predetermined threshold confidence level and, in response, initiates a communication to members of the project group conveying information regarding an automated remedial action for the project.

Abstract: A set of sentences within a natural language text document are parsed, generating a word-level graph corresponding to a sentence in the set of sentences. Within the word-level graph using a trained entity identification model, a set of entity candidates are identified. From a set of graphs modelling relationships between portions of the set of sentences, a set of embeddings is generated. From a set of pairs of embeddings in the set of embeddings using a set of deconvolution layers, a set of links between entity candidates within the set of entity candidates is extracted. From the set of links and the set of entity candidates, an output graph modelling linkages between portions of the set of sentences within the natural language text document is generated.

Abstract: Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for improving performance of a virtual dialog agent system employing an automated virtual dialog agent. Embodiments involve generating ground truth (GT) from a user's knowledge base, and leveraging the GT to evaluate how the virtual dialog agent performs with the GT. The evaluation measures quality of a multi-turn virtual dialog, and generates a remediation plan directed at an algorithmic improvement of the virtual dialog agent.

Abstract: An embodiment includes identifying, from a training dataset for training a model, a first unlabeled datapoint to present for labelling according to a first query strategy. The embodiment also includes issuing a query requesting a label for the first unlabeled datapoint. The embodiment also includes receiving a labeled datapoint in response to the query, the labeled datapoint comprising the first unlabeled datapoint as labeled by an oracle. The embodiment also includes generating a causal network based on labeled datapoints from the training dataset. The embodiment also includes receiving an instruction to modify the causal network. The embodiment also includes replacing the first query strategy with a second query strategy based on the instruction to modify the causal network. The embodiment also includes identifying, from the training dataset, a second unlabeled datapoint to present for labelling according to the second query strategy.

Abstract: Model data comprising a model object and model metadata is extracted from a trained model. The model data is integrated within a function executable from within a database system environment. The integrated function is deployed within the database system environment, the deploying activating the trained model for execution within the database system environment.

Abstract: An embodiment includes extracting, responsive to an update request from a remote requesting system, technical descriptor data from a data source. The embodiment also includes forming a new graph data structure using the technical descriptor data extracted from the data source. The embodiment also includes augmenting the new graph data structure to include a concept based on a value from instance data from the data source. The embodiment also includes identifying a first pair of concepts that are connected in a pre-existing ontology that correspond with a second pair of concepts that lack a connection therebetween in the new graph structure. The embodiment also includes augmenting the new graph data structure to include a connection between the second pair of concepts. The embodiment also includes outputting the new graph data structure as part of a response to the update request from the requesting system.

Abstract: By analyzing execution of a set of transactions by an application, a set of actual code execution paths of the application are determined. From the set of actual code execution paths, a set of predicted execution paths of the application are predicted using an execution prediction model. The set of predicted execution paths includes the set of actual code execution paths. By determining that paths in the set of predicted execution paths have above a threshold similarity to each other, a cluster of predicted execution paths is identified. The cluster of predicted execution paths is recommended, using a recommendation model, for implementation as a single execution path in a revised version of the application.

Abstract: A database protection system (DPS) mitigates injection attacks. DPS receives an unrestricted database query, extract a syntax tree, and evaluates whether it recognizes the query. To this end, DPS applies a hash function over the extracted syntax tree, and then determines whether the resulting hash has been seen by DPS before. If so, DPS retrieves a previously-generated prepared statement associated with the syntax tree, and that prepared statement is then forward to the database server in lieu of sending the original query. If the syntax tree is not recognized, DPS creates a new prepared statement, generates a hash of the syntax tree, and stores the hash and the new prepared statement, and forwards the new prepared statement. The prepared statements are configured based on the native wire protocol used by the database server, and DPS includes additional functionality by which it can learn the semantics of this protocol if necessary.

Abstract: An insight comprising a fact about a portion of data in a dataset is detected in the dataset. A context is determined by analyzing a natural language input, the context identifying an intent and an entity referenced by the natural language input. A relevance score is computed for the insight, the relevance score comprising a measure of similarity between a vector representation of the insight and a vector representation of the context, the vector representation of the insight comprising an encoding of the insight in multidimensional numerical form, the vector representation of the context comprising an encoding of the context in multidimensional numerical form. A selected insight is presented, the selected insight selected from a set of insights including the insight, the selected insight selected based on the relevance score.

Abstract: An embodiment includes generating a first concept representation based on a first portion of a knowledge base using a first processing path of a neural network that includes a hyperbolic graph convolution layer. The embodiment also includes generating a second concept representation based on a second portion of the knowledge base using a second processing path of the neural network, the second processing path comprising a heterogenous graph convolution layer. The embodiment also includes generating a unified concept representation including concatenating the first concept representation with the second concept representation. The embodiment also includes generating a prediction score using a using a predictive matching module, where the predictive score is indicative of an extent of a match between the unified concept representation and a concept representation from a second knowledge base.

Abstract: A method for verifying semiconductor wafers includes receiving a semiconductor wafer including a plurality of layers. A first set of measurement data is obtained for at least one layer of the plurality of layers, where the first set of measurement data includes at least one previously recorded thickness measurement for one or more portions of the at least one layer. The first set of measurement data is compared to a second set of measurement data for the at least one layer. The second set of measurement data includes at least one new thickness measurement for the one or more portions of the at least one layer. The semiconductor wafer is determined to be an authentic wafer based on the second set of measurement data corresponding to the first set of measurement data, otherwise the semiconductor is determined to not be an authentic wafer.

Abstract: An embodiment of a summarization application divides collected conversation data into media and text components. The application implements respective machine learning mechanisms to enhance modeling operations of the text and media components to identify key elements from the conversation. The application generates a headline banner from a group of key elements based on an analysis involving first predetermined criteria. The application also combines additional key elements to the group of key elements to form a second group of key elements. The application generates a summary from the second group of key elements based on a second analysis involving second predetermined criteria. The application presents, via a display, the headline banner according to a first output of the first key element analysis and the summary according to a second output of the second key element analysis.

Abstract: A repository is configured in a hybrid data processing environment comprising a classical computing system and a quantum computing system, to hold a plurality of quantum circuit components (QCC(s)). A degree of difficulty in simulating the received QCC in the classical computing system is transformed into a classical hardness score. A degree of difficulty in implementing the received QCC in the quantum computing system is transformed into a quantum hardness score. A first parameter in a metadata data structure associated with the received QCC is populated with the classical hardness score. A second parameter in the metadata data structure associated with the received QCC is populated with the quantum hardness score. The received QCC is transformed into a library element by at least augmenting the received QCC with the metadata data structure. The library element is added to the repository.

Abstract: An embodiment includes determining, in response to receiving data at a source node for transmission as streaming data on a network, whether a characteristic of the data indicates that an attribute of the data includes sensitive information. The embodiment also includes calculating, responsive to determining that the attribute includes sensitive information, an entropy value for the attribute. The embodiment also includes determining, responsive to the calculating of the entropy value, a likelihood that the attribute is encrypted by comparing the entropy value to a threshold value. The embodiment also includes generating a parameter value for a cryptographic parameter for the attribute.

Abstract: A computer system, a computer program product, and a computer-implemented system are provided for leveraging a national language processing (NLP) system for dynamically generating natural language output data. A NLP module investigates text or communication data from one or more platforms and derives global characteristic data from the investigated data. The derived global characteristic data is subject to modulation with a captured local writing style corresponding to a captured relationship. Contextual information is dynamically identified. Real-time natural language output communication data is generated that incorporates communication dynamics from the modulated global characteristic data and the dynamically identified contextual information.