Abstract: Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.

Abstract: In one embodiment of systems and methods described herein, a computing device is configured to generate and display a user interface including one or more selectable generic options; receive a selection of a data analysis function to perform machine learning; translate the selected function into pre-configured commands for each back-end tool that are specific to each back-end tool for performing the selected function, wherein the pre-configured commands identify and access specific schema with a compatible format for reading the specific schema from each back end tool to expose retail-specific data sets in accordance with the specific schema; execute the preconfigured commands based on the specific schema and retrieve corresponding retail-specific data from a plurality of the back-end tools based on the specific schema; and execute the selected data analysis function on the retrieved retail-specific data from the plurality of back-end tools and generate a data analysis result using Artificial Intelligence-Ena

Abstract: Techniques are disclosed for managing aspects of identifying and/or deploying hardware of a dedicated cloud to be hosted at a customer location (a “DRCC”). A DRCC may comprise cloud infrastructure components provided by a cloud provider but hosted by computing devices located at the customer's (a “cloud owner's”) location. Services of the central cloud-computing environment may be similarly executed at the DRCC. A number of user interfaces may be hosted within the central cloud-computing. These interfaces may be used to track deployment and region data of the DRCC. A deployment state may be transitioned from a first state to a second state based at least in part on the tracking and the deployment state may be presented at one or more user interfaces. Using the disclosed user interfaces, a user may manage the entire lifecycle of a DRCC and its corresponding hardware components.

Abstract: Techniques described herein include frameworks and models for identifying, analyzing, and addressing hangs within distributed and heterogenous computing environments. A hang detection framework may model a distributed computing environment as a complex forest of interrelated requests. The hang detection framework may generate hang graphs based upon requests that are being processed and/or waited upon within the distributed environment. For example, a node within an acyclic graph may represent an execution entity that is currently processing one or more requests. Directed edges that connect one node to another may represent requests upon which an execution entity is waiting for another execution node to fulfill. The model may be used to isolate and address the root cause of hangs within the computing environment.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: Techniques for generating text content arranged in a consistent read order from a source document including text corresponding to different read orders are disclosed. A system parses a binary file representing an electronic document to identify characters and metadata associated with the characters. The system pre-sorts a character order of characters in each line of the electronic document to generate an ordered list of characters arranged according to the right-to-left reading order. The system performs a layout-mirroring operation to change a position of characters within the modified document relative to a right edge of the document and a left edge of the document. Subsequent to performing layout-mirroring, the system identifies native left-to-right reading-order text in-line with the native right-to-left reading-order text.

Abstract: The present disclosure relates generally to determining intent based upon speech input using a dialog system. More particularly, techniques are described using matching-based machine learning techniques to identify an intent corresponding to speech input in a dialog system. These procedures do not require training when intents are added or removed from the set of possible intents.

Abstract: In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

Abstract: Systems, devices, and methods of the present invention involve discourse trees. In an example, a method involves generating a discourse tree. The method includes identifying, from the discourse tree, a central entity that is associated with a rhetorical relation of type elaboration and corresponds to a topic node that identifies a central entity of the text. The method includes determining a subset of elementary discourse units of the discourse tree that are associated with the central entity. The method includes forming generalized phrases from the subset of elementary discourse units. The method includes forming tuples from the generalized phrases, where a tuple is an ordered set of words in normal form. The method involves responsive to successfully converting an elementary discourse unit associated with an identified tuple into a logical representation, updating the ontology with an entity from the identified tuple.

Abstract: A tool may provide a real-time analysis of potential bottlenecks while threads wait on locks held by other threads. For each job currently operating on the server instance, the tool may access a list of threads and retrieve call stacks associated with those threads. The call stacks may then be analyzed to identify threads that are holding a lock, along with any corresponding threads that are waiting on the lock. The locks may be held on memory resources or any other type of computing resource. These bottlenecks may be identified and an adjustment of the configuration of the server instance may be triggered in response that is configured to reduce the likelihood that these types of bottlenecks may occur in the future.

Abstract: A computer comprising one or more processors and memory may implement multiple threads that perform a lock operation using a data structure comprising an allocation field and a grant field. Upon entry to a lock operation, a thread allocates a ticket by atomically copying a ticket value contained in the allocation field and incrementing the allocation field. The thread compares the allocated ticket to the grant field. If they are unequal, the thread determines a number of waiting threads. If the number is above the threshold, the thread enters a long term wait operation comprising determining a location for long term wait value and waiting on changes to that value. If the number is below the threshold or the long term wait operation is complete, the thread waits for the grant value to equal the ticket to indicate that the lock is allocated.

Abstract: New data structures for analyzing a log are generated. A hierarchical data structure includes a plurality of hierarchical nodes. Each node is associated with data and metadata. Each node may also be associated with analysis data. Information (data, metadata, and/or analysis data) of an ancestor node is imputed to a descendant node; a descendant node inherits the information of an ancestor node. When determining analysis data for a particular hierarchical node, information from any ancestor node to the particular hierarchical node may be used; however, information from non-ancestor nodes is not necessarily used. A timeline data structure includes a reference to a hierarchical node within a hierarchical data structure and a reference to an event type. The timeline data structure is thereby associated with the information of the referenced hierarchical node and information of any ancestor nodes to the referenced hierarchical node.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: Methods, systems, and computer readable media for processing binding requests in a telecommunications network. A method includes receiving, at a binding support function (BSF), a first request to create a first binding between a protocol data unit (PDU) session and a first policy and control function (PCF). The method includes creating, at the BSF, the first binding between the PDU session and the first PCF and storing, with the first binding, a first PCF session acceptance timestamp from a first header of the first request. The method includes receiving, at the BSF, a second request to create a second binding between the PDU session and a second PCF. The method includes determining, at the BSF, whether to accept or reject the second request based on a difference between the first PCF session acceptance timestamp and a second PCF session acceptance timestamp from a second header of the second request.

Abstract: Systems that determine relationships between network components of a cluster using packet filters are disclosed. A system can identify objects that implement services of a cluster and network connections associated with respective pairs of the objects. The system can also filter out network connections from the identified network connections. The filtering can remove connections between source objects and destination objects based on the destination objects lacking any components that implement a service in cluster. The filtering can also retain network connections between source objects and destination objects based on the source objects including components that each implement at least one service, and based on the second destination object including components that each implement at least one service. Additionally, the system can generate relationship maps and network topologies using the determined relationships.

Abstract: Systems and methods for using InfiniBand routing algorithms for Ethernet fabrics in a high performance computing environment. The method can provide, at a computer comprising one or more microprocessors, a plurality of switches, a plurality of hosts, a topology provider (TP) module, a routing engine (RE) module, and a switch initializer (SI) module. The method can perform a discovery sweep, by the TP, of the plurality of hosts and the plurality of switches and assigns an address to each of the plurality of hosts and the plurality of switches. The method can calculate, by the routing engine, a routing map, based upon a routing scheme, for the plurality of hosts and the plurality of switches, the routing map comprising a plurality of forwarding tables. The method can configure, each of the plurality of switches with a forwarding table of the plurality of forwarding tables calculated by the routing engine.

Abstract: Methods, systems, and computer readable media for enabling forwarding of subsequent network function subscription updates. A method includes forwarding, from a bootstrapped network function repository function (NRF) in a first region, a subscription creation request from a consumer network function to a target NRF in a second region. The method includes creating, at the target NRF, a subscription for the consumer network function. The method includes generating, at the target NRF, a hint comprising data indicating one or more alternate NRFs each sharing subscription state data with the target NRF. The method includes sending, from the target NRF to the consumer network function, a subscription response message including the hint.

Abstract: Techniques for managing a software build using a machine learning model are disclosed. A system obtains historical data associated with historical software builds. The historical data includes attribute data for a plurality of development stages associated with a historical software build and labels indicating success or failure for the plurality of development stages. The system trains a machine learning model using the historical data associated with the historical software builds to generate predictions of success or failure of the plurality of development stages. The system receives attributes of a target software build and a selection of a first target development stage of the target software build. The system applies the machine learning model to the target software build to generate a first prediction of success or failure of the first target development stage.

Abstract: Disclosed systems relate to generating questions from text. In an example, a method includes forming a first semantic tree from a first reference text and second semantic tree from a second reference text. The method includes identifying a set of semantic nodes that are in the first semantic tree but not in the second semantic tree. The method includes forming a first syntactic tree for the first reference text and a second syntactic tree for the second reference text. The method includes identifying a set of syntactic nodes that are in the first syntactic tree but not in the second syntactic tree. The method includes mapping the set of semantic nodes to the set of syntactic nodes by identifying a correspondence between a semantic node and a syntactic node, forming a question fragment from a normalized word, and providing the question fragment to a user device.