Abstract: In accordance with the present approach, a library management system identifies third-party libraries that developers request to incorporate into a software release. The library management system may determine whether a master ticket or usage ticket for a new third-party library exists. If a master or usage ticket is not already existing and approved for the third-party library, the third-party library management system may automatically analyze the third-party library to determine whether it corresponds to third-party libraries that are already approved and stored in a central repository. After approval of a master ticket, the third-party library may be incorporated into the central repository and referenced by subsequent usage tickets that are particular to an individual software release. If not approved, the library management system provides the third-party library to a manual approval system.

Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.

Abstract: Systems and methods are directed to release tools and deployment tools for software applications. A computing device includes a processor operatively coupled to a memory. The processor identifies one or more environments and a plurality of deployment operations to be performed to deploy a software application to the one or more environments. The processor generates a deployment plan having instructions to execute the one or more deployment operations. The processor executes the deployment plan to deploy the one or more release environments. The processor sends signals to a display to display progression through the deployment plan during deployment of the one or more release environments.

Abstract: An embodiment may involve a database containing a first user profile that specifies a first preferred language of a first user and a second user profile that specifies a second preferred language of a second user. The embodiment may also involve one or more processors configured to: receive, from the first user and within a chat session, a first set of messages in the first preferred language; cause the first set of messages to be translated into the second preferred language; provide, to the second user and within the chat session, the first set of messages as translated; receive, from the second user and within the chat session, a second set of messages in the second preferred language; cause the second set of messages to be translated into the first preferred language; and provide, to the first user and within the chat session, the second set of messages as translated.

Abstract: Persistent storage may contain an input discovery payload that contains entries representing configuration items and relationships therebetween, wherein the configuration items contain attributes defining devices, components, or applications on a network.

Abstract: A discovery computing system may receive an account identifier (ID) and a set of credentials required to access a first service account. The discovery computing system may transmit a first API query to a remote computing system. The discovery computing system may receive an organization identifier (ID) of the organization from the remote computing system. The discovery computing system may further transmit a second API query to the remote computing system. The discovery computing system may be further configured to receive information about a set of projects, in the organization, from the remote computing system. The discovery computing system may further generate a set of service accounts and further determine the set of resources, in the remote computing system, associated with each of the generated set of service accounts. The discovery computing system may further store the determined set of resources, as configuration items in a persistent storage.

Abstract: A discovery application on a computing system is provided. The discovery application receives a user input, which is for discovery of resources associated with a cloud operating system of a cloud computing system. The user input includes an authentication credential and account information associated with the cloud operating system. Based on the received input, the discovery application executes a discovery pattern comprising operations for the discovery of resources. The cloud operating system includes a group of services to access such resources. At least one of the operations corresponds to an API call to an API endpoint associated with a service of the group of services. The discovery application receives a response to the API call from the cloud operating system. The response includes a payload of information associated with the resources. The discovery application updates, based on the received response, one or more configuration items in a configuration management database.

Abstract: A database contains a corpus of incident reports, a machine learning (ML) model trained to calculate paragraph vectors of the incident reports, and a look-up set table that contains a list of paragraph vectors respectively associated with sets of the incident reports. A plurality of ML worker nodes each store the look-up set table and are configured to execute the ML model. An update thread is configured to: determine that the look-up set table has expired; update the look-up set table by: (i) adding a first set of incident reports received since a most recent update of the look-up set table, and (ii) removing a second set of incident reports containing timestamps that are no longer within a sliding time window; store, in the database, the look-up set table as updated; and transmit, to the ML worker nodes, respective indications that the look-up set table has been updated.

Abstract: Decoding a partially encrypted data stream may include receiving and scanning the partially encrypted data stream. Scanning the partially encrypted data stream may include identifying an encrypted portion sentinel in the partially encrypted data stream subsequent to a first portion, identifying an encrypted portion in the partially encrypted data stream subsequent to the encrypted portion sentinel, and generating a decrypted data portion by decrypting the encrypted portion. Decrypting the encrypted portion may include identifying an encrypted data portion in the encrypted portion, the encrypted data portion omitting an end encrypted portion sentinel, decrypting the encrypted data portion, and identifying an end encrypted portion sentinel in the encrypted portion subsequent to the encrypted data portion.

Abstract: Systems and methods are disclosed for obtaining network security threat information and mitigating threats to improve computing network operations. For example, methods may include receiving a message from a central instance; from outside of a private network, invoking a search of data associated with the private network, wherein the search is based on the message and the search is performed by an agent device within the private network; receiving a search result of the search from the agent device; transmitting the search result to the central instance, wherein the central instance is configured to generate network security threat information based in part on the search result and share the network security threat information with a plurality of customer instances that are associated with a group of customers; and receiving an alert message from the central instance, wherein the alert message includes information that identifies a network security threat.

Abstract: Systems, methods, and media are used to identify phishing attacks. A notification of a phishing attempt with a parameter associated with a recipient of the phishing attempt is received at a security management node. In response, an indication of the phishing attempt is presented in a phishing attempt search interface. The phishing attempt search interface may be used to search for additional recipients, identify which recipients have been successfully targeted, and provide a summary of the recipients. Using this information, appropriate security measures in response to the phishing attempt for the recipients may be performed.

Abstract: The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

Abstract: A method of managing alerts in a client instance associated with a configuration management database (CMDB) platform is disclosed. The method includes: receiving a request identifying a particular CI and a particular alert; identifying related CIs from a plurality of CIs associated with the client instance based on the particular CI and the particular alert; identifying alerts, incidents (INTs), changes (CHGs), and problems (PRBs) of the client instance that are associated with either the particular CI or the related CIs; determining frequency data for the alerts, INTs, CHGs, and PRBs associated with the particular CIs and frequency data for the alerts, INTs, CHGs, and PRBs associated with the related CIs; and sending a response that includes the frequency data for the alerts, INTs, CHGs, and PRBs associated with the particular CIs to be presented alongside the frequency data for the alerts, INTs, CHGs, PRBs associated with the related CIs.

Abstract: A specification of a client-side user interface behavior for a view of a computer application is received from a representative of an organization. The received specification is stored. A request for content of the view is received via a network from the computer application associated with a user that belongs to the organization. The received specification is identified as being associated with the requested content of the view. The requested content of the view and an identifier of the identified specification of the client-side user interface behavior are provided via the network. The computer application utilizes the received identifier of the identified specification of the client-side user interface behavior to modify the view of the computer application.

Abstract: Properties associated with computing resources are received. At least a portion of the received properties is used to cluster the computing resources into one or more operating groups. At least a portion of the received properties is used to determine a recommendation of an operation schedule for at least one of the one or more operating groups. The recommendation is provided. A feedback is received in response to the recommendation.

Abstract: Client instance data including a plurality of incidents and a plurality of knowledge elements comprising information relating to resolving one or more of the plurality of incidents is obtained. A validation set is built based on the obtained client instance data, the validation set including fingerprint data of plural fingerprints of known incident-knowledge relationships, each of fingerprint representing a link between one of the incidents and one of the knowledge elements used for resolving the incident. A knowledge element class is predicted from among plural knowledge element classes for each of knowledge element based on the built validation set, the plural knowledge element classes being defined based on respective threshold values indicating a quality of coverage provided by a knowledge element for resolving an incident. Classification data of the plural knowledge elements classified into the plural knowledge element classes is presented with the obtained client instance data.

Abstract: A embodiment may involve receiving a contact tracing request for a first user identifier that corresponds to a first portable device identifier of a first portable device. The second example embodiment may also involve requesting and receiving, from a first computing device associated with the first user identifier, device adjacency data, wherein the device adjacency data contains a plurality of contact entries, wherein one of the contact entries identifies a second portable device identifier of a second portable device that was wirelessly detected by the first portable device and a timestamp of when the wireless detection of the second portable device occurred. The second example embodiment may involve determining, from the mappings, a second user identifier that corresponds to the second portable device identifier. The second example embodiment may further involve transmitting, to a second computing device associated with the second user identifier, a contact tracing notification.

Abstract: Systems and methods are disclosed for computer network threat assessment. For example, methods may include receiving from client networks respective threat data and storing the respective threat data in a security event database; maintaining affiliations for groups of the client networks; detecting correlation between a network threat and one of the groups; identifying an indicator associated with the network threat, and, dependent on the affiliation for the group, identifying a client network and generating a message, which conveys an alert to the client network, comprising the indicator; responsive to the message, receiving, from the client network, a report of detected correlation between the indicator and security event data maintained by the client network; and updating the security event database responsive to the report of detected correlation.

Abstract: A system may include memory containing: (i) a master data set representable in columns and rows, and (ii) a query expression. The system may include a software application configured to apply a machine learning (ML) pipeline to an input data set. The system may include a computing device configured to: obtain the master data set and the query expression; apply the query expression to the master data set to generate a test data set, where applying the query expression comprises, based on content of the query expression, generating the test data set to have one or more columns or one or more rows fewer than the master data set; apply the ML pipeline to the test data set, where applying the ML pipeline results in either generation of a test ML model from the test data set or indication of an error in the test data set; and delete the test data set from the memory.

Abstract: A computing system includes a processor and memory. The memory includes instruction code that causes the processor to generate first and second parser instances and associate the first parser and the second parser with respective first and second search queries. The processor controls the first parser to repeatedly obtain data from the data stream in blocks until the first parser finishes identifying elements in the data stream associated with its search path. The processor controls the second parser to repeatedly obtain blocks from the first parser when the blocks obtained by the first parser have not been searched by the second parser, and controls the second parser to obtain blocks from the data stream when the blocks obtained by the first parser have been searched by the second parser and the first parser has finished searching.