Abstract: The present disclosure is directed to systems and methods of managing remote devices. The system can include a server with memory, a detection module, and a collection module. The memory can store a management information base (MIB) having a hierarchical tree of object identifiers and corresponding object values. The detection module can query devices and receive a first object identifier and its first object value, which can vary from those in the MIB; and use patterns to match the first object identifier and object value; and generate an identification of the device from the matches. The collection module can use the identification to select a collection template, which can indicate a subtree of the MIB and a collection pattern; traverse the subtree and identify a second object identifier that matches the collection pattern, and its second object value; and associate the second object value with the first object value.

Abstract: Systems and methods are described to automatically generate candidate questions and responses to speed the process of response creation and editing for commercial IVAs and chatbots. Rather than create the questions and responses from scratch for a new IVA, the system uses existing questions and responses from a previous or related IVA to train a model that can generate proposed responses to provided questions. The model, or a different model, can further be trained to generate responses using data taken from company or entity-specific data sources such as websites and knowledge bases. After a set of questions and responses have been generated for an IVA they may be reviewed by one or more human reviewers to ensure they are of a suitable quality. Where no previous or related IVA exists to provide example responses, the model may be trained solely using the company or entity-specific data.

Abstract: Techniques to leveraging distributed ledgers for integrating heterogeneous database systems independent of underlying legacy systems are described herein. Heterogeneous database systems may be integrated to generate a desired analytics report. In one embodiment, DApps (i.e., smart contracts) and database drivers may be deployed to implement just-in-time data mapping in smart contracts to save substantial time in integrating these heterogeneous database systems. With the smart contract approach, the database drivers may be configured to port only the key data that are needed to be processed based upon, for example, on business considerations. As a result, only data fields that are needed may be mapped and converted into smart contracts. Because the data needed may be dynamically mapped to the inputs specified for the smart contract, which in turn handles posting to the distributed ledgers, changes to the inputs are isolated to the smart contract, rather than forcing a schema redesign.

Abstract: Examples described herein provide a computer-implemented method that includes defining a key performance indicator associated with a non-instrumented object of a processing system. The method further includes determining a current anomaly level of the key performance indicator for an instrumented object having a relationship with the non-instrumented object. The method further includes assigning an anomaly level to the non-instrumented object based on the current anomaly level.

Abstract: An information providing system includes a content association database storing external information, in which content is divided in a chunk structure, and chunk reference information, in which reference information having a different attribute than the content is divided in a chunk structure, in association with each other. An external information similarity calculation database is built on machine learning using the external information. A processor acquires specific external information, and compares the external information with the acquired specific external information. The processor looks up the external information similarity calculation database, and calculates a similarity between the external information and the specific external information, when the external information does not match with the acquired specific external information.

Abstract: Systems for processing queries may first determine correspondence between the parameters of the query and a set of existing data entries, a set of previous queries that have been received, or both the existing data entries and the previous queries. If the query parameters do not correspond to the data entries or pervious queries, correspondence is determined between the query parameters and group data that associates at least a subset the query parameters with a particular group that may generate a response to the query. The same group or the generated response may be used when similar queries are received. If the group transmits the query to a different group or if negative user feedback is received, the group data may be modified to indicate the different group or to remove the association with the initial group that received the query.

Abstract: A method for ingesting data artifacts into a recovery pod may include: identifying, by a first controller, a data artifact for ingestion; pulling, by the first controller, the data artifacts into the first datastore; confirming, by second controller, that a first airlock between the first zone and the second zone and a second airlock between a third zone and the second zone are closed; opening, by the second controller, the first airlock; identifying, by the second controller, the data artifacts in the first datastore; pulling, by the second controller, the data artifacts into a second datastore; confirming that the first airlock and the second airlock are closed; opening, by the second controller, the second airlock; identifying, by a third controller, the data artifacts in the second zone datastore; pulling, by the third controller, the data artifacts into a third zone datastore; and closing, by the second controller, the second airlock.

Abstract: Distributed ledger-based networks (DLNs) employ self-executing codes, also known as smart contracts, to manage interactions occurring on the networks, which may result in the generation of a massive amount of DLN state data representing the interactions and participants thereof. The instant disclosure discloses systems, apparatus and methods that allow interactions to occur on the DLNs without modification to stored data, thereby improving the storage capabilities of the networks.

Abstract: The approaches presented herein may include loading a plurality of metadata instances for a plurality of data blocks stored in a database, each metadata instance of the plurality of metadata instances indicating a respective data block structure applied to aggregate a set of data messages. The approaches may include processing a first metadata instance corresponding to a first set of one or more data blocks of the plurality of data blocks and a second metadata instance corresponding to a second set of one or more data blocks of the plurality of data blocks to perform an anomaly storage check between the first set of one or more data blocks of the plurality of data blocks and the second set of one or more data blocks of the plurality of data blocks. The approaches may include generating an output based at least in part on the anomaly storage check.

Abstract: Some embodiments employ a novel procedure of training an artificial intelligence system (e.g., set of deep neural networks) for anomaly detection in applications such as natural language processing and computer security. Token sequences selected from a training corpus are distorted according to at least one of a plurality of pre-determined sequence transformations, before being fed to a sequence analyzer. In turn, the sequence analyzer is trained to correctly guess which transformation was used to generate the respective input token sequence.

Abstract: The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

Abstract: Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels.

Abstract: Systems, methods, and computer-readable storage media are described for processing data change requests. In one exemplary embodiment, a computer-implemented method receives one or more requests in a software production environment for one or more data changes to data from a requestor. The method executes the request(s) in a rollback mode. The method generates a snapshot of the executed request(s). The method also transmits the snapshot to an approver. In response to an approval of the snapshot from the approver, the method also executes the request(s) in a commit mode.

Abstract: Embodiments of the present technology provide systems, methods, and computer storage media for facilitating anomaly detection. In some embodiments, a prediction model is generated using a training data set. The prediction model is used to predict an expected value for a latest (current) timestamp, which is used to determine that the incoming observed data value is an anomaly. Based on the incoming observed data value determined to be the anomaly or not, a corrected data value is generated to be included in the training data set. Thereafter, the training data set having the corrected data value is used to update the prediction model for use in determining whether a subsequent observed data value is anomalous. Such a process may be performed in an iterative manner to maintain optimized training data and prediction model.

Abstract: Disclosed are systems and methods restoring a computing system. The described method includes emulating a virtual disk using a backup of the computing device and generating an ancillary virtual machine (VM). The method includes determining and writing, to a delta disk linked to the virtual disk, configuration modifications to the ancillary VM that enable booting of the ancillary VM using the virtual disk on a different device with dissimilar hardware as the computing device. Responsive to receiving a request to perform recovery of the computing device, the method comprises restoring the backup by creating on the different device a recovery virtual machine (VM) having a base virtual disk emulated from the backup, and modifying the recovery VM by attaching the delta disk having the configuration modifications.

Abstract: Certain example embodiments relate to techniques generating a database of virtual families for real-time generation of part numbers for configurable products. For instance, certain example embodiments may include receiving a search request for part numbers and determining virtual families including entities matching the search request. The number of matching products matching the search request and the part numbers may be determined based on the virtual families and the independent groups stored in the virtual families that match the search request. The part numbers may be determined based on allowed characters, number and/or symbols for the part number positions defined in the independent groups of the virtual families including the entities matching the one or more configurable properties of the user input search request. Facet counts for the configurable product may also be determined based on the results of the user input search request.

Abstract: Techniques regarding code retrieval tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a code retrieval component that can execute a code retrieval machine learning task by computing an amount of similarity between neural network embeddings of graph representations of a query text and at least a portion of a computer program code.

Abstract: Systems and techniques are provided for flexible data ingestion. Data including a file including a database table may be received at a computing device. The file may be in a non-standard binary format. The data including the file may be stored unaltered as a source data chunk. A processed data chunk may be generated from the source data chunk by converting the file to a standard binary format and storing the file in the processed data chunk without altering the source data chunk. A materialized data chunk may be generated from the processed data chunk by performing, with a database server engine of the computing device, a database operation on the database table of the file of the processed data chunk and storing the file in the materialized data chunk without altering the processed data chunk. The database table of the file of the materialized data chunk may be made available for querying by the database server engine.

Abstract: Systems and methods are described for updating text files for a processing pipeline without restarting the processing pipeline. A processing pipeline may include a frontend thread and a backend thread. The frontend thread of the processing pipeline may generate transformed data using the text file. A backend thread of the processing pipeline may periodically determine whether an updated text file has been uploaded. The backend thread can determine that an updated text file has been uploaded and cause the frontend thread to pause generating transformed data. The backend thread can validate the updated text file by comparing the text file and the updated text file. Based on validating the updated text file, the backend thread can cause the frontend thread to resume transforming data using the updated text file.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for managing asynchronous data replication in a multiple availability zone cloud environment. Metadata for files for asynchronous replication at a second availability zone is stored at an in-memory data grid of a first instance of a storage service at a first availability zone at a multiple availability cloud platform that provides storage services. The in-memory data grid includes a queue data structure of metadata records and a map of metadata records. In response to determining that connection from the first availability zone to the second availability zone is available, asynchronous data replication for files identified at the map is executed. A file for replication is identified at the map and provided for replication at a second file storage at the second availability zone through a replication interface of a second instance of the storage service at the second availability zone.