Abstract: Data from multiple sources may be gathered continuously to perform reconciliation operations. The data items in a first data set may be matched with those in the second data set using a data matching technique. Based on the matching, a confidence score indicative of an extent of match between the data items in the data sets may be generated. Based on the confidence score and predefined thresholds, it may be ascertained if the data items are reconciled. The non-reconciled items in at least one of the first data set and the second data set may be classified in a classification category, based on an artificial intelligence based technique, the classification category being indicative of an explanation of a non-reconciled data item being non-reconcilable. When the data item is not reconciled and classified, the data item is identified as an open item for further analysis.

Abstract: An AI-based data processing system analyzes a received information request to generate an interactive visualization including data responsive to the information request. The information request is processed to obtain the primary entity and one or more informational items related to the primary entity. Auxiliary entities and informational items related to the primary entity are identified and searches are executed on a knowledge base and the internet. The results from the searches are analyzed to obtain knowledge nuggets which are included into a selected one of a visualization template to generate the interactive visualization. If it is determined via user interactions with the interactive visualization that an informational gap exists between the information request and the data in the interactive visualization, the interactive visualization can be updated to address the informational gap.

Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: A method for reducing reactivation time of services that includes examining page faults that occur during processing of a service after the service has been inactive to provide a plurality of prefetch groups, and formulating a prefetch decision tree from page fault data in the prefetch groups. Pages from an initial page table for the service following a reactivated service request are then compared with the prefetched pages in the resident memory in accordance with the prefetch decision tree. Pages in the page table that are not included in said prefetched pages are paged in. A process to provide to provide the service is executed using the page table. Executing the process substantially avoids page faults.

Abstract: A method and device for provisioning bare-metal servers to tenants of a cloud provider is provided. The cloud provider operating a datacenter receives a profile of a tenant. The cloud provider identifies a set of physical resources subscribed by the tenant based on the tenant's profile and identifies a bare-metal server in the datacenter having sufficient physical resources to provide the subscribed physical resources. The cloud center reports to the tenant a set of physical resources in the BM server that corresponds to the subscribed set of physical resources. The cloud center receives a request from the tenant to provision a tenant bare metal machine in the datacenter. The cloud center enables at least one of the reported physical resources in the bare-metal server for the tenant, based on the request, and disables remaining reported physical resources in the bare-metal.

Abstract: In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: A centralized data reconciliation system processes at least two data streams transmitting data related to one of a plurality of processes and executes a data reconciliation procedure. Unmatched data records identified during the data reconciliation procedure are further categorized into categorized records based on various reason categories and irreconcilable records which could not be categorized into the reason categories. The irreconcilable records are flagged for user input. The user input is recorded to further train the data reconciliation system. The at least two data streams are initially converted into self-describing data streams from which the entities and entity attributes are extracted using the data models received from the data streams. The data records from the first and second self-describing data streams are mapped. The matched pairs and unmatched pairs are selected from the mappings based on respective confidence scores that are estimated in accordance with the rules of data reconciliation.

Abstract: Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.

Abstract: Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.

Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

Abstract: Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Abstract: In an example, in response to occurrence of a report generation event, a report may be generated. An aspect of the report requiring a description may be identified, the aspect being at least one of a trend noted in the report and a discrepancy in the report. A hypothesis explaining the aspect of the report may be generated. Further, an evidence may be collected for the hypothesis, based on a relation between entities of the hypothesis, the relation being determined based on a knowledge data having information pertaining to the entities. The hypothesis may be evaluated generate an evaluation score, based on the collected evidence. Based on the evaluation score, the hypothesis may be selected for explaining the aspect. The selected hypothesis may be provided in natural language text explaining the aspect of the report, based on at least one of the domain and user knowledge and linguistic knowledge.

Abstract: There is a computer program product and computer system that includes program instructions programmed to establish a security container describing a workload and a set of resources in a software-defined environment, the security container including a set of sub-containers that are self-describing sub-containers having associated metadata describing content of a respectively corresponding sub-container, each sub-container of the set of sub-containers respectively corresponds to a resource-divisible portion of the workload, the set of resources being required by the workload, wherein a sub-container of the set of sub-containers is an operating system sub-container; monitor the workload and the set of resources for security events; and responsive to identifying a security event, adjust isolation mechanisms provided by the plurality of sub-containers at various layers of a stack. The set of sub-containers represents an end-to-end run time environment for processing the workload using the set of resources.

Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: There is a computer program product and computer system that includes program instructions programmed to identify, in a software-defined environment, a security container describing a workload and a set of resources required by the workload, the security container including self-describing sub-containers having associated metadata describing content of a respectively corresponding sub-container; determine, for the workload, a set of resource-divisible portions of the workload including a compute-resource portion; generate a plurality of sub-containers within the security container, a sub-container within the plurality of sub-containers being a self-describing sub-container having associated metadata describing the content of the sub-container representing only one resource-divisible portion, the sub-container being an operating system sub-container; and responsive to identifying a security event while processing the workload, adjust a security mechanism associated with the security container.

Abstract: Data from multiple sources may be gathered continuously to perform reconciliation operations. The data items in a first data set may be matched with those in the second data set using a data matching technique. Based on the matching, a confidence score indicative of an extent of match between the data items in the data sets may be generated. Based on the confidence score and predefined thresholds, it may be ascertained if the data items are reconciled. The non-reconciled items in at least one of the first data set and the second data set may be classified in a classification category, based on an artificial intelligence based technique, the classification category being indicative of an explanation of a non-reconciled data item being non-reconcilable. When the data item is not reconciled and classified, the data item is identified as an open item for further analysis.