Abstract: Embodiments for providing enhanced location-aware protection of latches in a computing environment are provided. One or more latches are combined in one or more of a plurality of bounding boxes on a two-dimensional circuit design layout based on one or more rules. A location-aware interleaving of error correction codes (“ECC”) and burst error correction codes may be selectively applied to one or more latches in those of the plurality of bounding boxes, where multiple bit errors are corrected.

Abstract: An embodiment includes receiving, at database connectivity (DBC) layer, a request to access data of a data source, wherein the data source is registered with a data catalog that includes data governance artifacts, and wherein the request is made via a connection that bypasses the data catalog. The embodiment also includes comparing, by a governance manager at the DBC layer, an access privilege level for a credential associated with the request to access requirements of requested data included in data protection rules of the governance artifacts. The embodiment also includes masking, by the governance manager, a portion of the data provided in response to the request, wherein the portion of the data has an access requirement that is not met by the access privilege level for the credential associated with the request.

Abstract: An embodiment includes detecting an interface element and an element attribute of the interface element in a series of views of a user interface, and then after an update of the user interface, detecting a candidate element and a candidate element attribute in a series of views of the updated user interface. The embodiment then determines that the updated user interface lacks any errors using a decision tree that includes comparisons of all interface elements of the user interface to corresponding candidate elements of the updated user interface. The embodiment then generates an optimized decision tree based at least in part on an analysis of the comparisons of the user interface to the updated user interface resulting in a condition that allows for the determining of a lack of errors based on comparisons of a subset of the interface elements to corresponding candidate elements.

Abstract: Embodiments for dynamic bandwidth allocation in cloud network switches in a cloud computing environment are provided. Quality of service (QoS) policies may be dynamically changed in one or more cloud network switches based on dynamically estimating expected traffic demands for each of a plurality of traffic classes, wherein bandwidth is dynamically allocated among queues based on changing the QoS policies.

Abstract: An embodiment includes receiving an explanation request that includes an undesired output resulting from an input case of a hierarchical rule-based decision policy specified by an acyclic dependency graph, and further includes an alternative desired output from the hierarchical rule-based decision policy. The embodiment also includes computing a network of intermediate explanations for required ranges of respective decision nodes that achieve the desired output from the hierarchical rule-based decision policy. The embodiment also includes computing a user-facing explanation that includes a range constraint for achieving the desired output by aggregating the intermediate explanations. The embodiment also includes transmitting, as a response to the explanation request, an explanation for achieving the desired output from the hierarchical rule-based decision policy based on the user-facing explanation.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: An embodiment detects, by a packet detection module on a first worker node, a first synchronization message (SYN) issued from a client pod on the first worker node and directed to a first server pod on a second worker node and generates a first SYN record in a connection table stored on the first worker node. The embodiment detects, by the packet detection module, a second SYN issued from the client pod and directed to the first server pod and generates a server pod status record stored in memory on the first worker node indicative of the first server pod having an unhealthy status. The embodiment identifies an available server pod having a healthy status and generates a detour routing rule in a routing table on the first worker node indicative of a revised connection in which the identified available server pod replaces the first server pod.

Abstract: Using a set of menu to key process mappings, historical menu usage data for an application is aggregated into aggregated key process usage data. A set of key process association rules, each comprising a consequent key process given a particular antecedent key process, is generated. From the set of key process association rules and a set of ranked menus by frequency of usage within each key process, a set of model menu recommendations is generated. According to an application usage history, a menu frequency ratio, and a confidence value of a modelled next menu, the set of menu recommendations is scored. A scored menu recommendation having a rank below a threshold rank is pruned from a set of menu items of the application ranked according to their scores. The pruned set of scored menu recommendations is presented for selection instead of the set of menu items.

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: By analyzing a natural language content concurrently with a presentation of the natural language content, a first language of the natural language content is detected. By analyzing the natural language content concurrently with the presentation of the natural language content, it is detected that a subset of the natural language content is expressed in a second language, wherein the second language is different from the first language, wherein a set of known languages comprises the first language, wherein the set of known languages excludes the second language. From the subset of the natural language content, a modified subset expressed in the first language is generated. The modified subset is inserted into the natural language content.

Abstract: At a first chatbot, a query expressed in natural language form is received. It is determined that responding to the query requires data external to the first chatbot. From a data source external to the first chatbot, response data corresponding to the query is obtained. Using the response data, the query is responded to in natural language form. Using the response data, the first chatbot is updated.

Abstract: A system, computer program product, and method are provided to graph neural network (GNN) ensemble learning. Training data is represented in a graph format, from which two or more subgraphs are sampled. Two or more GNNs are training from feature space sampled from the subgraphs. The GNN ensemble is built from the trained GNNs, and subject to testing data. Application of the testing data to the GNN ensemble generates output in the form of an ensemble value, with the output configured to interface with and selectively control an operatively coupled physical hardware device or software.

Abstract: By monitoring an in-person interaction using a first Internet of Things (IoT) device, an entity relationship between parties in the in-person interaction is identified. Using data resulting from the monitoring, a habit workflow corresponding to the entity relationship is generated, wherein the habit workflow comprises a sequence of habits and a habit in the sequence of habits comprises performance of a behavior. A second IoT device able to monitor execution of the habit workflow is identified. Using the second IoT device, execution of the habit workflow is monitored. A motivational method corresponding to the execution is identified from the monitored execution.

Abstract: Feedback-directed pass pipeline optimization in static compilation includes receiving a set of source input of source code by a static compiler. The static compiler initiates a compilation operation on the source code in which, in a pass pipeline during the compilation operation that creates an intermediate representation (IR) representing the source code, a set of optimization diagnostics identified during a first pass of the pass pipeline is emitted into a side file. The pass pipeline is then optimized using the set of optimization diagnostics.

Abstract: A method of detecting deceptive web activity is implemented in an intermediary located between a requesting client device, and a server that hosts a web application. Following a bootstrap phase used to generate a database of information identifying characteristics of clients, the method begins by receiving a page directed to the client from the server. The server injects an invisible DOM element having a set of style properties associated therewith, with one of the set of style properties assigned a random value, to generate a modified page, which is returned to the client. As the client interacts with the modified page, the intermediary tracks the device's styles and uses them to identify the client from information in the database. Once the device is identified, the intermediary then detects whether a spoofing attack has occurred. By leveraging the tracked styles, a spoofing attack on the DOM element's styles may also be detected.

Abstract: Embodiments for providing enhanced adversarial robustness of machine learning models using certification for federated learning in a computing environment by a processor. Machine learning model updates, a dataset, and a set of hyperparameters may be received. One or more certification parameters and one or more filtered machine learning model updates for a machine learning model may be generated by certifying each of plurality of data points using one or more abstract representations in a machine learning operation and filtering the plurality of machine learning model updates.

Abstract: Adversarial attack detection operations may be applied on one or more deep generative models for defending deep generative models from adversarial attacks. The adversarial attack may be detected on the one or more deep generative models based on the one or more of a plurality of adversarial attack detection operations. The one or more deep generative models may be sanitized based on the adversarial attack.

Abstract: A method, apparatus and computer program product for automated security and regulatory compliance in association with an enterprise. A set of security and compliance controls that operate in association with the enterprise are provided. One or more compliance policies that are enforced by the set of security and compliance controls are encapsulated according to a common data format. One or more customer-specific security/compliance requirements associated with the enterprise are collected. Using microservices-based modular components, the customer-specific security/compliance requirements are then transformed into machine-readable representations having the common data format and that conform to the one or more compliance policies being enforced by the set of security and compliance controls. The one or more compliance policies including the one or more transformed security/compliance requirements are then activated to facilitate the security and regulatory compliance.

Abstract: Embodiments are provided for enabling visual editing of machine learning models in a computing environment by a processor. A multidimensional dataset may be received. The multidimensional dataset may be processed. Visualization and exploration of an interactive representation of a plurality of datasets and decision boundaries of one or more machine learning models built upon multidimensional dataset are provided. Behavior of the one or more machine learning models may be edited via the interactive representation using one or more logical rules or moving the decision boundaries of one or more machine learning models.

Abstract: Embodiments facilitating enhanced synergy between machine learning models and annotators in a computing environment by a processor. Annotation tasks may be coordinated between one or more annotators and machine learning models based on one or more annotator preferences and data annotation requirements of a machine learning model. The one or more annotator preferences and the data annotation requirements for coordinating the annotation tasks may be learned over a period of time.

Abstract: A compliance discovery and integration process is implemented in association with a cloud-based security and compliance platform and associated CI/CD framework. The process assumes an existing DevOps-based deployment of a product, such as an enterprise application that executes in a runtime production environment. The technique of this disclosure addresses the problem of misalignment between a compliance policy and the product’s post-deployment regulation posture by providing tools and methods that enable pro-active augmentation of governance and compliance policy during the pre-deployment phase and with respect to a next deployment of the product (e.g., a next or updated version). Thus, when the product is later deployed in its next deployment, its regulation posture (post-deployment) is already consistent with the compliance policy.

Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.

Abstract: An embodiment generates a candidate retraining dataset in response to a retraining request. The embodiment generates first metadata comprising attribute information for data points in the candidate retraining dataset and second metadata comprising a range of attributes from training data previously used to train the model. The embodiment determines whether the candidate retraining dataset includes new information for the machine learning model based on a comparison of the first metadata to the second metadata. In a first case in which the candidate retraining dataset is determined to include new information, the embodiment initiates retraining of the machine learning model using the candidate retraining dataset. In a second case in which the candidate retraining dataset is determined to not include new information, the embodiment generates a response to the retraining request indicating that retraining the machine learning model using the candidate retraining dataset is not recommended.

Abstract: Embodiments are provided for providing a continuous knowledge graph in a computing system by a processor. One or more weighted values of an edge between a pair of entities in a knowledge graph may be predicted based on one or more candidate statements. A confidence score may be generated for the one or more predicted weighted values.

Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning pipeline, is represented in a data flow graph (DFG) with nodes and edges. The represented nodes are configured to be annotated with an operational semantic. On order of execution of the pipeline is discovered through the node annotation(s) represented in the annotated DFG, and execution of the pipeline is based on the discovered order. A control signal formatted based on the executed pipeline is configured to dynamically and selectively control an operatively coupled physical hardware device.

Abstract: Embodiments are provided for upgrading operating application in a multi-device ecosystem in a computing environment. Various types of computing devices are determined to be connected to a multi-device computing network. A collaboration plan is generated between the computing devices to execute an operating application operation event on each of the computing devices without interrupting user activities executing on each of the computing devices. Operating applications on each of the computing devices are upgraded according to the collaboration plan without interrupting each of the f user activities on each of the computing devices.

Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is implemented over a data flow graph (DFG) with nodes configured to support rich semantics. The rich semantics include two or more operational semantics, and at least one lineage semantic to selectively combine features that trace lineage to a common input object. The lineage semantic is leveraged to associate training and testing data set pairs in cross validation of the trained ML models produced from parallelizing the selection of ML pipelines.

Abstract: From a set of point data, a set of scattered rays is constructed. From the set of scattered rays, a set of ray slopes is computed. The set of ray slopes is mapped to a corresponding set of trigonometric functions. Using an optimization method, a parameter of the set of trigonometric functions is selected. Using an inverse of the set trigonometric functions, a vehicle mass corresponding to the set of point data is computed. Based on the vehicle mass, a threshold braking distance of a collision avoidance system of the vehicle is adjusted, the threshold braking distance comprising a distance from an object predicted to collide with the vehicle. By braking the vehicle at least the threshold braking distance from the object, a predicted collision between the vehicle and the object is avoided.

Abstract: A route from a first physical location to a second physical location is divided into a set of segments using a route optimization engine. Using a user response analysis model, a response to a physical environment associated with a segment in the set of segments is scored, the scoring resulting in a score. Using a content generation model and the score, the physical environment is augmented, the augmenting combining the physical environment and a generated environment.

Abstract: Trustworthiness of an accelerator in heterogenous systems is increased. A workload of an application is offloaded to an accelerator for the accelerator to perform the workload. The accelerator is ensured to generate an output of the workload based on offloading the workload. The accelerator is identified as generating an output of the workload based on offloading the workload. Both an input and the output of the workload are ensured to be authentic based on offloading the workload to the accelerator. Both the input and the output of the workload are ensured to be securely transmitted based on offloading the workload to the accelerator.

Abstract: Various embodiments are provided for identifying relevant graph patterns in a knowledge graph in a computing environment by a processor. Data elements may be identified from a knowledge graph and associated datasets that is related to one or more nodes of the knowledge graph and external to the knowledge graph. One or more subgraphs may be selected and created based on missing data elements and inferred knowledge data. The knowledge graph may be modified with one or more subgraphs.

Abstract: Embodiments are provided for providing increased performance of various industrial systems and processes in a computing system by a processor. Each of a plurality of dependencies of a plurality of entities in a knowledge graph are modeled as a graph neural network (“GNN”). A reference graph model is generated based on the modeling. One or more anomalies are monitored and detected for a plurality of process based on the reference graph model.

Abstract: Various embodiments are provided for improving machine learning model integration using one or more processors in a computing system. One or more artifacts of one or more machine learning models may be inspected. A degree of compatibility may be determined between the one or more machine learning models and an application based on inspecting the one or more artifacts. One or more adjustments may be recommended to the one or more artifacts based on the degree of compatibility for integrating the one or more machine learning models into the application.

Abstract: In an embodiment, a method includes measuring a first number of control qubits in a quantum algorithm, wherein a quantum circuit representation of the quantum algorithm includes a multiple-controlled-NOT gate. In an embodiment, a method includes measuring a second number of ancilla qubits in a quantum computer. In an embodiment, a method includes comparing the first number and the second number to determine an optimum compilation method for a quantum circuit. In an embodiment, a method includes compiling, in response to the comparison determining the second number is greater than one and less than the difference of the first number and 2, a quantum circuit from the quantum algorithm using a hybrid method.

Abstract: Embodiments facilitating data exploration in a computing environment by a processor. A multidimensional dataset may be received. The multidimensional dataset may be processed according to booting operation parameters. A visualization and exploration of an interactive representation of one or more probabilistic models for each one of a set of instances using the multidimensional dataset.

Abstract: A first version of a model specified by a model execution request is executed, producing a first execution result. A second version of the model is selected according to an input data attribute specified by the model execution request. The second version of the model is executed, producing a first execution result. Using a natural language processing engine, responsive to the first execution result and the second execution result differing by more than a threshold amount, a natural language explanation of a difference between the first execution result and the second execution result is constructed.

Abstract: An embodiment generates a training batch of data points from training data for a plurality of classes and builds a multi-class classifier having a series of binary classifiers arranged in a first order. Each of the binary classifiers is associated with a respective class. The embodiment trains the multi-class classifier with the binary classifiers arranged in a first order and, at each binary classifier, the embodiment identifies data points as belonging to the class associated with the respective classifier and updates the training batch to exclude the classified data points. The embodiment then modifies the multi-class classifier by changing the order of classifiers and repeats the training of the multi-class classifier with the series of binary classifiers arranged in a second order. The embodiment then selects a final configuration of the multi-class classifier based at least in part on a comparison of first training results to the second training results.

Abstract: Embodiments for using learned physical knowledge to guide feature engineering in a computing environment by a processor. Physical knowledge data associated with a dataset may be learned. The physical knowledge data may be translated into a plurality of features for one or more automated feature engineering models to execute for one or more prediction and monitoring operations, wherein the plurality of features represent relationships between the physical knowledge data.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Abstract: Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.

Abstract: A multi-purpose server cache directory in a computing environment is provided. One of a plurality of operation modes may be selectively enabled or disabled, by a cache directory, based on a computation phase, data type, and data pattern for caching data in a cache having a plurality of address tags in the cache directory greater than a number of data lines in a cache array.

Abstract: A method, a system, and a non-transitory computer readable medium for measuring a local critical dimension uniformity of an array of two-dimensional structural elements, the method may include obtaining an acquired optical spectrometry spectrum of the array; feeding the acquired optical spectrometry spectrum of the array to a trained machine learning process, wherein the trained machine learning process is trained to map an optical spectrometry spectrum to an average critical dimension (CD) and a local critical dimension uniformity (LCDU); and outputting, by the trained machine learning process, the average CD and the LCDU of the array.

Abstract: Various embodiments are provided for managing performance of a data processing system in a computing environment using one or more processors in a computing system. A drift may be dynamically detected in one or more machine learning models generating a plurality of predictions and deployed in a computing system. A plurality of metrics and data may be collected of the one or more machine learning models based on the drift. One or more additional machine learning models may be trained based of the drift and the plurality of metrics and data.

Abstract: A first type memory and a second type memory may be identified in a computing system. The second type memory is slower than the first type memory while having a greater storage capacity compared to the first type memory. An application process executing in the computing system may be identified. A region of the first type memory may be provided as a cache of the second type memory for the application process.

Abstract: Embodiments relate to an intelligent computer platform to support knowledge graph (KG) driven content generation. A KG is created from one or more knowledge articles. The created KG includes individual nodes representing individual physical object and individual edges representing a hardware state characteristic of a physical object represented in a corresponding node. A trained computer vision model is leveraged to recognize one or more physical components and localize an active state of the recognized physical components. Content is generated responsive to the localized active state and the hardware state characteristic represented in the KG, and a control signal is dynamically issued to an operatively coupled device associated with the generated content. The control signal is configured to selectively control an event injection responsive to synchronization of the recognized one or more physical components and the generated content.

Abstract: As part of a container initialization procedure, a maximum number of hardware threads per processor core in a set of cores of a computer system are enabled, the container initialization procedure configuring an operating system executing on the computer system for container execution and configuring a first container for execution on the operating system. From a set of available cores in the set of cores, an execution core is selected. In the selected execution core, a number of threads per core to be used during execution of the first container is configured, the number of threads per core specified for the container initialization procedure by a first simultaneous multithreading (SMT) parameter. Using the configured execution core, the first container is executed, the executing virtualizing the operating system.

Abstract: A computer system, computer program product, and computer-implemented method are provided that includes learning a tree ordered graphical event model from an event dataset. Temporal relationships between one or more events in received temporal event data is modeled, and an ordered graphical event model (OGEM) graph is learned. The learned OGEM graph is configured to capture ordinal historical dependence. Leveraging the learned OGEM graph, a parameter sharing architecture is learned, including order dependent statistical and causal co-occurrence relationships among event types. A control signal to an operatively coupled event device that is associated with at least one event type reflected in the learned parameter sharing environment is dynamically issued. The control signal is configured to selectively control an event injection.

Abstract: Using a trained image classification model, a first image captured using an image capture device is classified, the trained image classification model executing in the same system as the image capture device, the classifying producing an inappropriate content classification of the first image. Using a trained feature extraction model, a pattern within a portion of the first image is recognized from the first image, the trained feature extraction model executing in the same system as the image capture device, the pattern predefined as inappropriate content. Based on the classification and the pattern, the first image is adjusted.