Techniques for generating sample code for a configured computing environment are disclosed. A system receives a plurality of user-selected component types for a computing environment. The system selects a first set of code for a first user-selected component type and a second set of code for a second user-selected component type. A selected set of code for a user-selected component type may include a first subset of code that is statically associated with the user-selected component type and a second subset of code that is selected based on a compatibility between the user-selected component type and another user-selected component type. The system generates sample code that is executable in a computing environment corresponding to the first and second user-selected component types from the first and second sets of code.
Systems, computer-implemented methods, and computer-readable media for facilitating resource balancing based on resource capacities and resource assignments are disclosed. Electronic communications, received via interfaces, from monitoring devices to identify resource descriptions of resources may be monitored. A resource descriptions data store may be updated to associate each entity of the entities and resource capacities of each resource type of resource types. A first electronic communication, from resource-controlling systems, may be detected. Model data from a model data store may be accessed based on the identified resource descriptions. A first model may be identified based on the model data. A resources assessment corresponding may be generated based on whether a threshold is satisfied based on the first model, a first resource capacity of a first resource type, and the first electronic communication. An electronic notification may be transmitted to the client devices to identify the resources assessment.
H04L 47/76 - Admission controlResource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
Techniques are disclosed for bootstrapping a secure data center using a cross domain system with a static sidecar node. The cross domain system can be implemented at the secure data center to provide one-way ingress and egress channels for network traffic to the target data center. The cross domain system is connected to a host data center and can receive configuration data from the host data center to configure the static sidecar node. The static sidecar node can receive bootstrapping data from the host data center and store the bootstrapping data. The bootstrapping data can include software resources for provisioning services in the secure data center. The received bootstrapping data passes into the secure data center via the ingress channel.
The present disclosure relates to dynamically generating incident-remediation runbooks. An input configuration may be parsed, where the input configuration specifies a plurality of interface component sequences. A context comprising a plurality of context variable may be initialized. A dynamic runbook may be generated based at least in part on the input configuration and the context. A value of a particular context variable from the context may be modified to comprise an updated value. In response to modifying the value of the particular context variable, the dynamic runbook may be modified to comprise one or more interface components corresponding to one or more of the plurality of interface component sequences.
Disclosed herein is a programming framework that addresses the challenges of managing breaking changes in API versioning. A breaking change between a first version of an application programming interface (API) and a second version of the API may be detected. One or more mapper functions associated with the first version of the API and the second version of the API may be generated. A first version request may be received from a client computing device, wherein the first version request is compatible with the first version of the API. The first version request may be converted, via the one or more mapper functions, to a second version request, wherein the second version request is compatible with the second version of the API. The second version request may be provided to an endpoint of the API, and a second version response may be received from the endpoint of the API. The second version response may be compatible with the second version of the API. The second version response may be converted, via the one or more mapper functions, to a first version response, wherein the first version response is compatible with the first version of the API. The first version response may be provided to the client computing device,
METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR SYNTHETIC MONITORING OF NETWORK FUNCTION (NF) SERVICE INSTANCES AND AUTOMATED UPDATING OF ROUTING RULES BY SERVICE COMMUNICATION PROXY (SCP)
A method for synthetic monitoring and updating of routing rules by a service communication proxy (SCP) includes identifying, by an SCP, candidate producer NF service instances for synthetic monitoring. The method further includes for each of the candidate producer NF service instances: testing, by the SCP, the candidate producer NF service instance using the synthetic monitoring; determining, by the SCP, results of the synthetic monitoring; and updating, by the SCP and based on the results of the synthetic monitoring, routing rules in a routing database maintained by the SCP.
Examples provide an electronic device including at least one electronic processor configured to receive, via an accessibility application programming interface (“API”), first accessibility data associated with a first user interface (“UI”) displayed by a user device and including information identifying one or more UI elements and/or UI events in the first UI; identify, based on the first accessibility data, a first action performed by a user of the user device and defining a first set of operations; train, based on the first accessibility data and the first set of operations, an artificial intelligence (“AI”) model; receive, via the accessibility API, second accessibility data associated with a second UI displayed by the user device; determine, based on the second accessibility data and the AI model, a recommended action defining a second set of operations; and output a command to perform the recommended action on the user device.
Examples provide an electronic device including at least one electronic processor configured to request first accessibility data associated with a first user interface (“UI”) displayed by a first device and including at least one of (a) information identifying one or more UI elements in the first UI or (b) information identifying one or more UI events in the first UI; train, based on at least the first accessibility data and an error state associated with the first device, an artificial intelligence (“AI”) model; request second accessibility data associated with a second UI displayed by a second device and including (a) information identifying one or more UI elements in the second UI or (b) information identifying one or more UI events in the second UI; and detect, based on at least the second accessibility data and the AI model, the error state on the second device.
A system identifies a secure entry point for accessing a target resource of a virtual cloud network. The system determines a mapping that maps the secure entry point to a particular addressable network entity of a plurality of addressable network entities. Based on the mapping, the system selects the particular addressable network entity as a destination for requests associated with the target resource. The system transmits a network address corresponding to the particular addressable network entity to a computing entity as a destination address for the requests associated with the target resource. The computing entity transmits the requests associated with the target resource to the network address corresponding to the particular addressable network entity, and the addressable network entity forwards the requests to the secure entry point.
A system selects a first garbage collection process from a group of garbage collection processes. When a first thread stores a first set of objects to a first private memory region that is exclusive of any shared objects accessible by one or more additional threads, the system executes a sweeping thread-local garbage collection process upon termination of the first thread, including reclaiming the first private memory region. When a second thread stores to a second private memory region at least one shared object accessible by one or more additional threads, the system executes the selective garbage collection process upon termination of the second thread. The selective garbage collection process includes selectively reclaiming a second subset of memory blocks from the second private memory region allocated for a subset of private objects that are inaccessible from any thread.
A reader of a set of data accessors that includes readers and writer detects that a particular lock of a first collection of non-global locks associated with a data object of a computing environment is held by another accessor. After checking a blocking indicator, the reader uses a second lock (which is not part of the first collection) to obtain read access to the data object and implements its reads without acquiring the particular lock. Prior to implementing a write on the data object, a writer acquires at least some locks of the first collection, and sets the blocking indicator to prevent readers from using the second lock to obtain read access to the data object.
A system executes a first virtual thread, including storing a first shared object in a first private memory region, storing a pointer in a shared memory region, and determining that the pointer includes a reference to a location of the first shared object in the first private memory region. Responsive to determining that the pointer includes the reference, the system designates the first virtual thread as disqualified from eligibility for execution of a sweeping thread-local garbage collection process and augments the pointer to indicate that the pointer includes the reference to the location of the first shared object. The system executes a second virtual thread, including loading the pointer and determining that the pointer has been augmented. Responsive to determining that the pointer has been augmented, the system designates the second virtual thread as disqualified from eligibility for execution of the sweeping thread-local garbage collection process.
A system and computer-implemented method includes accessing a set of clusters, where each cluster is defined to cover a portion of a multi-dimensional feature space, and each cluster is associated with a label of a plurality of labels. A plurality of content items is received from a plurality of data sources. The label is assigned to each of the plurality of content items using one or more label machine learning models. Using a feature-vector machine learning model, a set of feature vectors for the plurality of content items are identified. A feature vector is associated with a respective portion in the multi-dimensional feature space. Portions in the multi-dimensional feature space corresponding to the plurality of labels are identified. A cluster from the plurality of clusters is assigned to the feature vector based on a proximity of the feature vector to the plurality of clusters in the multi-dimensional feature space.
Techniques are disclosed for cryptographically securing data on devices of a region data center built in the first facility and installed at a data center. A plurality of server devices and a fleet of key server devices can be configured for transit by encrypting a storage device of each server device using a corresponding data encryption key, storing and encrypting each data encryption key at a corresponding key storage element of each server device using a key encryption key, and storing the key encryption key at the fleet of key server devices. After transit, the fleet of key server devices can be enabled and a server device booted using an initialization disk image stored at a boot volume of the first server device. The first server device can obtain the key encryption key, decrypt the encrypted data encryption key, and then decrypt the storage device.
G06F 21/78 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure storage of data
15.
PROVISIONING AND MANAGING SERVERLESS DATABASE RESOURCES WITHIN A MULTI-CLOUD INFRASTRUCTURE
Techniques described herein include receiving, by a first cloud environment and from a second cloud environment, a request to provision a cloud service from among a plurality of cloud services provided by a cloud service provider associated with the first cloud environment. The techniques further include, performing a set of operations associated with provisioning the cloud service in the second cloud environment, wherein at least one operation of the set of operations comprises identifying one or more resource locations of a plurality of private clouds of the first cloud environment for executing the cloud service. The techniques further include, provisioning the cloud service in the plurality of private clouds, wherein the provisioning enables data pertaining to the cloud service to flow between a resource location of a first private cloud and a resource location of one or more second private clouds of the plurality of private clouds.
H04L 47/76 - Admission controlResource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
16.
OPERATION SYSTEM-LEVEL INSTRUMENTATION TO AUTOMATICALLY INFER SOFTWARE DEPENDENCIES
Herein is software dependency reporting with an innovative framework to analytically generate a software bill of materials (SBOM) that is a list of dependencies of a software application that was built in an opaque (i.e. black-box) way. This approach makes minimal assumptions about the build framework(s) and language ecosystem(s) involved with building a software application. For building a deliverable based on source files defined in separate programing languages, source files are read and deliverable files are generated and copied. Unique identifiers of accessed files are recorded by novel probes into a probe log having unprecedented accuracy. The probe log is analyzed to generate a provenance graph that contains a vertex for each file accessed by the build. By graph analytics, a dependency report having unprecedented accuracy is generated for the deliverable from the provenance graph.
Techniques include accessing a set of clusters, where each cluster is defined to cover a portion of a multi-dimensional feature space, and each cluster is associated with a label of a plurality of labels. A plurality of content items is received from a plurality of data sources. The label is assigned to each of the plurality of content items using one or more label machine learning models. Using a feature-vector machine learning model, a set of feature vectors for the plurality of content items are identified. A feature vector is associated with a respective portion in the multi-dimensional feature space. Portions in the multi-dimensional feature space corresponding to the plurality of labels are identified. A cluster from the plurality of clusters is assigned to the feature vector based on a proximity of the feature vector to the plurality of clusters in the multi-dimensional feature space.
G06F 16/908 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Here is acceleration of Python based on novel notebook instrumentation that offloads a computationally intensive paragraph for remote execution. Python notebook paragraphs may be offloaded to custom virtual machines (VMs) by seamless snapshotting of notebook state. This enables the user to selectively execute paragraph(s) locally or on different VMs that cater to the specific computational requirements of each notebook paragraph. Diverse paragraphs may demand varying levels of computational resources. A notebook may run in the usual manner on a chosen VM shape (i.e. configuration of capabilities and capacities) and offload the execution of the most computationally intensive paragraphs to VMs with more powerful shapes when desired, while being able to resume execution in the starting VM in a seamless manner. In this elastic way, the user is able to vertically scale the execution and, for reliability, obtain an isolated (i.e. dedicated, not multitenant) environment.
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
19.
TECHNIQUES FOR BUILDING DATA CENTERS IN CLOUD REGIONS WITH VERSION SETS
Techniques are described for performing an automated region build using a version set that identifies versions of configuration files and/or artifacts with which the region build is to be performed. A Multi-Flock Orchestrator (MFO) may be configured to maintain multiple version sets identifying a respective set of configuration files associated with various services to be bootstrapped. The MFO may execute a validation process using one version set. A second version set may be identified from the first based on identifying configuration files that successfully passed the validation process. The automated region build can be performed using the second version set.
METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR SYNTHETIC MONITORING OF NETWORK FUNCTION (NF) SERVICE INSTANCES AND AUTOMATED UPDATING OF ROUTING RULES BY SERVICE COMMUNICATION PROXY (SCP)
A method for synthetic monitoring and updating of routing rules by a service communication proxy (SCP) includes identifying, by an SCP, candidate producer NF service instances for synthetic monitoring. The method further includes for each of the candidate producer NF service instances: testing, by the SCP, the candidate producer NF service instance using the synthetic monitoring; determining, by the SCP, results of the synthetic monitoring; and updating, by the SCP and based on the results of the synthetic monitoring, routing rules in a routing database maintained by the SCP.
Techniques for automatic assignment of patch panel ports are disclosed. The system accesses a first set of information identifying a plurality of source patch panel ports and a second set of information identifying a plurality of destination patch panel ports. The system accesses, based on the first set of information, a first directed acyclic graph representing a first source patch panel. Additionally, the system accesses, based on the second set of information, a second directed acyclic graph representing a first destination patch panel. The system selects a port assignment strategy from a plurality of port assignment strategies for mapping the plurality of source ports to the plurality of destination ports. The system traverses the first directed acyclic graph and the second directed acyclic graph in accordance with the selected port assignment strategy to generate a mapping of the plurality of source port nodes to the plurality of destination port nodes.
Various embodiments of the present technology generally relate to systems and methods for remote mobile network capability sharing, and delegation of service discovery and selection to the remote mobile network, such as from a visited or roaming network to a home network. A first public land mobile network (PLMN) system may send, via a local network repository function (NRF), a request for capability details of a second PLMN, the capability details including an ability of the second PLMN to handle delegated service discovery and selection. The first PLMN may receive the capability details, and send, from the first PLMN to the second PLMN, a delegated discovery and network function (NF) selection request directing a service communications proxy (SCP) of the second PLMN to determine a producer NF of the second PLMN to process a service request from a consumer NF of the first PLMN.
Techniques are disclosed for cryptographically securing data on devices of a region data center built in the first facility and installed at a data center. A plurality of server devices and a fleet of key server devices can be configured for transit by encrypting a storage device of each server device using a corresponding data encryption key, storing and encrypting each data encryption key at a corresponding key storage element of each server device using a key encryption key, and storing the key encryption key at the fleet of key server devices. After transit, the fleet of key server devices can be enabled and a server device booted using an initialization disk image stored at a boot volume of the first server device. The first server device can obtain the key encryption key, decrypt the encrypted data encryption key, and then decrypt the storage device.
Techniques for facilitating efficient polling using machine learning are disclosed. A system uses historical data associated with execution of tasks to train a machine learning model to predict execution times. After receiving a request for execution of a task, the system provides a polling configuration to the requesting device that includes a polling frequency based on a prediction for when the task execution will be completed. This prediction is generated by the machine learning model.
Techniques are described for providing personalized content in applications without revealing personal details to content providers. A base machine learning model is loaded to a cloud environment that is private to a tenant organization. The base machine learning model is used to generate a custom machine learning model specific to the tenant organization based on user interactions within the tenant organization and content categories specific to the tenant organization. The custom machine learning model is used to select content categories for which to provide instances of content items to users based on user interactions with applications. Engagement with the instances of content items is tracked in a user-specific manner privately by the tenant organization, in a tenant-specific manner privately by a cloud services provider, and in a content-specific manner by the content provider. Feedback is provided to the custom machine learning model based on engagement with instances of content items.
Various techniques can include accessing a master tree that was generated using a plurality of protocol definitions. The plurality of protocol definitions can identifies an ordered set of actions and specifies, for each sequential pair of actions in the ordered set of actions, an action-advancement condition that identifies a criterion for advancing across the sequential pair of actions in the ordered set of actions so as to trigger a later of the sequential pair of actions. A master tree includes a set of dynamic nodes and a set of static nodes. The technique can include accessing a partial protocol definition that includes at least one action. The technique can include generating an auto-completion of the partial protocol definition using the master tree, at least some of the dynamic-node weights, and at least some of the static-node weights. The technique can output a representation of an auto-completed protocol definition.
H04L 69/00 - Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
H04L 69/08 - Protocols for interworkingProtocol conversion
27.
Estimating Query Execution Performance Using A Sampled Counter
Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.
Techniques for enabling efficient data migration between data stores with no downtime are disclosed. A distributed computing system can be implemented with an initial data store and a target data store. During the migration of a portion of the data from the initial data store to the target data store, the distributed computing system can receive requests to create data entities or launch workflow instances at the data stores. The system can determine whether the initial data store or the target data store has been designated the primary data store for handling the requests. The system can also determine whether the initial data store or the target data store contain a key associated with the request. If the key is present in either of the data stores, the system may abort the creation of the data entity.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
Systems and methods of interface-based ACLs in a virtual Layer-2 network. The method can include sending a packet from source compute instance in a virtual network to a destination compute instance via a destination virtual network interface card (destination VNIC) within a first virtual layer 2 network and evaluating an access control list (ACL) for the packet with a source virtual network interface card (source VNIC). ACL information relevant to the packet can be embedded in the packet. The VSRS can receive the packet and can identify the destination VNIC within the first virtual layer 2 network for delivery of the packet based on information received with the packet and mapping information contained within a mapping table. The VSRS can access ACL information from the packet and can apply the ACL information to the packet.
H04L 61/103 - Mapping addresses of different types across network layers, e.g. resolution of network layer into physical layer addresses or address resolution protocol [ARP]
H04L 61/4552 - Lookup mechanisms between a plurality of directoriesSynchronisation of directories, e.g. metadirectories
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
H04L 101/622 - Layer-2 addresses, e.g. medium access control [MAC] addresses
30.
TECHNIQUES FOR CREATING SNAPSHOTS AND PERFORMING RECOVERY OF A HARDWARE SECURITY MODULE
Techniques are described for creating snapshots and performing recovery of a hardware security module (HSM) using write-ahead logs (WALs) in a cloud infrastructure. In some embodiments, any changes to a crypto key in an HSM partition may be stored in a temporary storage (e.g., a write-ahead log (WAL)). Snapshots for the HSM partition can be created from the temporary storage. The process of creating the snapshots may be performed in parallel with the process of making changes to the crypto keys in the HSM partition. In further embodiments, the snapshots for the HSM partition are chained together to form a multi-version snapshot linked list to become portable snapshot files. In some embodiments, creating the snapshots and restoring the crypto keys in the snapshots follow the same security boundary.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
Here is out of distribution (OOD) detection of an input token sequence for a large language model (LLM). A finetuning factor matrix is used for Mahalanobis measurement of transformer layer activation in the LLM. A computer generates a first token embedding based on: a factor matrix and an output of a first neural layer that represents a first token in a sequence of tokens. Into a multi-token embedding, the first token embedding is combined with a second token embedding that is based on: the factor matrix and a second token in the sequence of tokens. Based on a second neural layer, a second multi-token embedding that represents the sequence of tokens is generated. Into a multilayer embedding, the first multi-token embedding and the second multi-token embedding are concatenated. The sequence of tokens is classified as OOD based on statistical analysis of the multilayer embedding.
A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.
Systems and methods for providing policy tags to detect and reconcile inconsistencies between a network function (NF) producer and an NF consumer are provided herein. In an example, a system includes instructions for a NF producer to establish a first state with an NF consumer, where the NF producer and the NF consumer are in a 5G network, generate a first policy tag corresponding to the first state, and store the first policy tag as the latest stored policy tag. The latest policy tag is then transmitted in subsequent signaling from/to the NF consumer and signaling including the latest received policy tag is received from the NF consumer. A validation process is then performed with the latest received policy tag from the NF consumer and the communication is processed based on the validation process of the received policy tag.
Systems and methods for a virtual layer-2 network are described herein. The method can include providing a virtual Layer 3 network in a virtualized cloud environment. The virtual Layer 3 network can be hosted by an underlying physical network. The method can include providing a virtual Layer 2 network in the virtualized cloud environment. The virtual Layer 2 network can be hosted by the underlying physical network.
In accordance with an embodiment, described herein is a system and method for providing support for extensibility and customization in an analytic applications environment. An extract, transform, load (ETL) or other data pipeline or process provided by the analytic applications environment, can operate in accordance with an analytic applications schema and/or a customer schema associated with a customer (tenant), to receive data from the customer's enterprise software application or data environment, for loading into a data warehouse instance. A semantic layer enables the use of custom semantic extensions to extend a semantic model, and provide custom content at a presentation layer. Extension wizards or development environments can guide users in using the custom semantic extensions to extend or customize the semantic model, through a definition of branches and steps, followed by promotion of the extended or customized semantic model to a production environment.
Here is out of distribution (OOD) detection of an input token sequence for a large language model (LLM). A finetuning factor matrix is used for Mahalanobis measurement of transformer layer activation in the LLM. A computer generates a first token embedding based on: a factor matrix and an output of a first neural layer that represents a first token in a sequence of tokens. Into a multi-token embedding, the first token embedding is combined with a second token embedding that is based on: the factor matrix and a second token in the sequence of tokens. Based on a second neural layer, a second multi-token embedding that represents the sequence of tokens is generated. Into a multilayer embedding, the first multi-token embedding and the second multi-token embedding are concatenated. The sequence of tokens is classified as OOD based on statistical analysis of the multilayer embedding.
Disclosed are machine learning techniques directed to training a machine learning model for the combined learning of multiple natural language processing (NLP) tasks. The NLP tasks may be named entity recognition (NER), relation extraction (RE), and assertion detection (AD) tasks. The machine learning model may be a multi-layer transformer model. Training the machine learning model may involve first training the NER module on the NER task, and thereafter training the RE module on the RE task while the AD module is simultaneously trained on the AD task. Training the machine learning model may alternatively involve training the NER module on the NER task concurrently with training the RE module on the RE task and training the AD module on the AD task. The trained machine learning model can predict entities and entity types in newly provided text, along with relations between the entities and assertions associated with the entities.
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
38.
METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR IMPROVED NETWORK FUNCTION (NF) DISCOVERY BETWEEN FORWARDED AND FORWARDING NF REPOSITORY FUNCTIONS (NRFs) TO AVOID PREEMPTION OF NF PROFILES OF PRODUCER NFs LOCAL TO FORWARDING NRF
A method for NF discovery between forwarded and forwarding NRFs include receiving, at a forwarding NRF, an NF discovery request message generated by a consumer NF or an SCP, determining, by the forwarding NRF, not to process the NF discovery request message locally and transmitting the NF discovery request message to a forwarded NRF. The forwarded NRF receives the discovery request message, applies empty-list functionality to promote the producer NFs having NF profiles that match query parameters in the NF discovery request message from a SUSPENDED state to a REGISTERED state, and generates and transmits an NF discovery response message and an indication of application if the empty-list functionality to the forwarding NRF. The forwarding NRF applies a local policy to process the indication of application of the empty-list functionality and transmits the NF discovery response to the consumer NF or SCP.
Techniques are disclosed for provisioning and managing a virtual edge device that is configured to emulate a physical edge device that executes within an isolated computing environment. The isolated computing environment may be separate from a centralized cloud computing environment that provides a plurality of services for executing customer workloads. In one example, a computer system receives a request to provision a virtual edge device. The computer system identifies a physical computing device to be provisioned as the virtual edge device based on the request. The computer system generates a set of data containers that containerizes a set of services configured to execute subsequent workloads, and then the system provisions the physical computing system with the set of data containers. In response to the customer request, the computer system provides a user interface operable for accessing and managing the virtual edge device.
A distributed event processing system is disclosed that receives a batch of events via a continuous data stream and performs the serialization of data in the batch of events. In certain embodiments, the system identifies a first data type of a first attribute for each event in a batch of events and determines a first type of data compression to be performed on data values represented by the first attribute. The system determines a first type of data compression to be performed on data values represented by the first attribute based on the first data type of the first attribute. The system then generates a first set of serialized data values for the first attribute. The system processes the first set of serialized data values against a set of one or more continuous queries to generate a first set of output events.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
A computer-implemented method to determine form factors of a tree includes building an input tree, wherein the input tree includes nodes. The method further includes implementing a first top-down pass to determine a universal number for each node in the input tree. The method further includes implementing a second top-down pass to determine form factors for each node of the input tree, wherein a form factor includes a depth and a width of the tree with a root in a corresponding node. The method further includes storing the form factors as part of node metadata or in a separate table.
Techniques are provided for generating and maintaining Precomputed Result Tables ("PRTs") that are used by a database server to improve performance of SPARQL queries that target an RDF graph set. Each PRT corresponds to a particular pattern, which may be simple-triple, star, or chain. The techniques support the creation of PRTs for chain patterns that contain multiple instances of the same property, and PRTs for star and chain patterns that include reverse properties. Techniques are also described for maintaining such PRTs as Data Manipulation Language (DML) operations make changes to the RDF graph tables that belong to the RDF graph set associated with the PRTs.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Arranging, organizing, and conducting trade shows, exhibitions, and business networking events in the fields of computer software, computer hardware, computer peripherals, cloud-based services, cloud computing, cloud-based platforms, cloud services, computer and cloud applications, business automation, computer networking, technology planning, business management, and product and technology demonstrations; arranging and conducting in-person and virtual business conferences featuring business leaders, developers, IT management, and business end-users of enterprise technology, cloud computing, cloud platforms, cloud services, computer and cloud applications, and business automation technology. Arranging and conducting educational conferences, seminars, speeches, and entertainment, namely, in-person and virtual presentations, all in the fields of computer software, computer hardware, computer peripherals, cloud-based services, cloud computing, cloud-based platforms, cloud services, computer and cloud applications, business automation, computer networking, technology planning, business management, and product and technology demonstrations; arranging and conducting educational conferences featuring business leaders, developers, IT management, and business end-users of enterprise technology, cloud computing, cloud platforms, cloud services, computer and cloud applications, and business automation technology.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Arranging, organizing, and conducting trade shows, exhibitions, and business networking events in the fields of computer software, computer hardware, computer peripherals, cloud-based services, cloud computing, cloud-based platforms, cloud services, computer and cloud applications, business automation, computer networking, technology planning, business management, and product and technology demonstrations; arranging and conducting in-person and virtual business conferences featuring business leaders, developers, IT management, and business end-users of enterprise technology, cloud computing, cloud platforms, cloud services, computer and cloud applications, and business automation technology. Arranging and conducting educational conferences, seminars, speeches, and entertainment, namely, in-person and virtual presentations, all in the fields of computer software, computer hardware, computer peripherals, cloud-based services, cloud computing, cloud-based platforms, cloud services, computer and cloud applications, business automation, computer networking, technology planning, business management, and product and technology demonstrations; arranging and conducting educational conferences featuring business leaders, developers, IT management, and business end-users of enterprise technology, cloud computing, cloud platforms, cloud services, computer and cloud applications, and business automation technology.
45.
MEASURING AND VISUALIZING TOPIC MODEL TRAINING CONVERGENCE
A topic modeling system may include a stability monitor to obtain topic probability distributions for vocabulary items for multiple topics during training iterations of a topic model. For a training iteration and topic, the stability monitor may select a top number of vocabulary elements according to a probability distribution of the topic for the training iteration and a previous training iteration, where the selected vocabulary elements have higher probabilities than vocabulary elements not selected. Then, using a similarity function, top vocabulary elements of the training iteration are compared to top vocabulary elements of the previous training iteration to generate a stability metric indicating an amount of similarity between the probability distributions of the training iteration and the previous training iteration. Additional metrics may be derived and the cumulative metrics may be used to analyze or visualize the convergence or divergence of training of individual topics of the topic model.
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
46.
Privacy-Preserving Decision Trees Using Secure Multiparty Computation and Differential Privacy
Privacy-preserving decision trees may be generated using secure multiparty computation and differential privacy. A decision tree generation protocol may determine class partitions and frequencies from a data set, determine a stopping condition is satisfied for determining the plurality of class partitions and the plurality of frequencies, determine a majority class for the data set, based on the plurality of class partitions and the frequencies, determine respective quality scores for the plurality of class partitions based on respective attributes for the plurality of class partitions, select an attribute of the respective attributes according to the respective quality scores, and partition the data set recursively to build a plurality of sub-trees according to an encoding determined for the selected attribute may be partitioned, in some embodiments.
Novel techniques for end-to-end file storage replication and security between file systems in different cloud infrastructure regions are disclosed herein. In one embodiment, a file storage service generates deltas between snapshots in a source file system, and transfers the deltas and associated data through a high-throughput object storage to recreate a new snapshot in a target file system located in a different region during disaster recovery. The file storage service utilizes novel techniques to achieve scalable, reliable, and restartable end-to-end replication. Novel techniques are also described to ensure a secure transfer of information and consistency during the end-to-end replication.
H04L 9/14 - Arrangements for secret or secure communicationsNetwork security protocols using a plurality of keys or algorithms
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
G06F 16/11 - File system administration, e.g. details of archiving or snapshots
G06F 16/174 - Redundancy elimination performed by the file system
G06F 16/176 - Support for shared access to filesFile sharing support
G06F 16/178 - Techniques for file synchronisation in file systems
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
48.
SYSTEM AND METHOD FOR PROVIDING CROSS-MICROSERVICE QUERY OPTIMIZATION
In accordance with an embodiment, described herein is a system and method for providing cross-microservice query processing. The system provides an object service framework that supports the use of microservices that may be loosely-coupled but related in some way, for example in that they interoperate together or require access to each other's data in order to process queries. Each microservice can be developed, deployed and evolve independently, and interact with the other microservices through contracts or interfaces that are defined as public APIs and are then exposed via the framework. The object service framework can be used, for example to provide a cross-microservice layer that automatically transforms queries that join objects in different microservices into a single database query that is optimized for use with the database.
Here is accelerated debug of more accurate replay of recorded Java execution. Nondeterministic execution, arguments, results, and side effects of subroutines are recorded by instrumentation at various architectural levels for increased accuracy of instrumented replay. In an embodiment, a computer configures for nondeterminism recording all of: a recorded subroutine, a native subroutine that is not a Java method, and a Java method that invokes the native subroutine. The native subroutine is invoked by invoking the Java method. For the native subroutine, instrumentation detects that the Java method already has activated nondeterminism recording. After the Java method finishes, the recorded subroutine is invoked. For the recorded subroutine, instrumentation detects that nondeterminism recording is inactive and should be activated.
Techniques for initiating commands in a user interface by disambiguating user input terms are disclosed. As a system displays a set of data, the system receives a user input that includes a set of terms. The system determines the terms correspond to multiple different interpretations. The system selects a particular interpretation for the terms based on context data. The context data includes data, such as user profile data and display data. The user profile data includes data for a user entering the terms and data of other users related to the user entering the terms. The system selects and executes a command based on selecting the particular interpretation for the terms.
Aspects of the present disclosure provide security compliance in integration connectors. In one embodiment, a system receives a definition data specifying implementation details of an integration connector. The system checks by inspecting the definition data, whether the implementation details are in compliance with a set of security rules and computes a security score for the integration connector based on the compliance determined by the checking. According to an aspect, the system displays to a user, the security score for the integration connector along with a set of warnings about the compliance. According to another aspect, the system monitors a run-time operation of the integration connector. In response to identifying, based on the monitoring, that a security rule of the set of security rules has been violated, the system stops the operation of the integration connector and sends an error indicating that the security rule has been violated.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
52.
PERSISTENT CONTEXT FOR REUSABLE PIPELINE COMPONENTS
Techniques are provided for managing and isolating build process pipelines. An example method can include determining an identifier of a version commit event. The method can further include obtaining a build pipeline having a plurality of build steps comprising at least a first build step and a second build step based at least in part on the identifier of the version commit event. The method can further include executing the first build step of the build pipeline to access a first object from a repository. The method can further include generating a build context comprising at least a second object output based at least in part on the first build step. The method can further include executing the second build step of the build pipeline based at least in part on the build context, the build context accessible to the second build step.
A security tool is provided to check for security issues with a progression candidate version of code. The security tool identifies a progressed version of code that addresses a documented security issue that was present in a prior version of code and has a same ancestor version of code as the progression candidate version of code. Based on a difference between the progressed version of code and the prior version of code, the security tool determines whether a similar difference has been made between other versions of code in a lineage of the progression candidate version of code. Based on determining that a similar difference has not been made, the security tool stores an indication that the progression candidate version of code is associated with the documented security issue. The security tool may also determine a proposed change to the progression candidate version of code based on the difference.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 8/71 - Version control Configuration management
Machine learning based static profiling includes obtaining an intermediate representation (IR) graph of source code of a program, extracting multiple control flow split node features of a control flow split node in the IR graph, and processing, by a regression machine learning model, the control flow split node features to generate a branch frequency value of a branch from the control flow split node. The machine learning based static profiling further includes adding the branch frequency value to a profile for the program and executing an optimizer on the program according to the profile to generate optimized code.
Data structures and methods are described to provide a unique extendable group identifier for efficient aggregation query processing using multiple grouping keys. A method comprises retrieving a database query comprising an aggregate function of a selected column from a database table, grouped by a plurality of columns. The method further comprises maintaining a plurality of hash tables that map column values to grouping keys and identify a maximum grouping key. The method further comprises updating a plurality of bitmasks that define per-column grouping key bit positions, capable of storing the maximum grouping key, within a combined index value. The method further comprises allocating a result array sized according to the plurality of bitmasks. The method further comprises using the plurality of bitmasks and the plurality of hash tables to determine the combined index value to apply the aggregate function on the selected column for each row in the database table.
The system and methods for audience and return recommendation for targeted content items using machine learning and similarity models. The method includes receiving an input dataset comprising of an input audience or specified constraints on the targeted content items. A join operation augments the input dataset by adding individual characteristics data. A trained machine model generates an engagement score for each individual and by using a threshold on the engagement score each individual is classified into a tier category. Based on the engagement level a target audience is generated. Using a similarity model, an expanded audience is generated which increases the size of the target audience by adding similar individuals from the reference audiences. A return is computed using the individuals in the expanded audience while meeting the specified constraints. The expanded audience and return for the targeted content items are returned to the client device.
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
G06Q 30/0202 - Market predictions or forecasting for commercial activities
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
An input dataset corresponding to an audience is augmented by joining a first set of individual IDs of the audience with a second set of individual IDs in a set of reference audiences stored within a cloud system. An engagement score is generated for each individual in the first set of individual IDs by processing the input dataset with augmented data using a trained machine learning model. Each ID in the first set of individual IDs is assigned to a tier category. A target audience (a subset of the first set of individual IDs belonging to a particular tier category) is identified. A similarity score is calculated for each individual belonging to the target audience with the second set of individual IDs in the set of reference audiences. An expanded audience is generated based on the similarity scores.
Techniques for automatically generating tests using a large language model (LLM) are provided. In one technique, a set of positive training samples for training a first LLM is stored. Based on that set, a set of correction training samples is generated, each sample including an error from processing a faulty test of particular code. A second LLM is trained based on those samples. A first test, of code, that was generated by the first LLM is received. A first result of processing the first test is generated. In response to determining that the first result indicates an error in processing the first test, a first correction prompt is generated based on the first result. The first correction prompt is input into the second LLM that outputs a second test that is a corrected version of the first test. A second result of processing the second test is generated.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment. The first cloud infrastructure receives a request from a user associated with an account in the second cloud infrastructure. The request corresponding to using a service provided by the first cloud infrastructure. A tenancy is created for the user in the first cloud infrastructure to enable the user to utilize the service, and a link-resource object is created that includes information linking the tenancy of the user in the first cloud infrastructure to the account of the user in the second cloud infrastructure, the link-resource object enabling the user to utilize the service provided by the first cloud infrastructure.
G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
Novel graph analytics herein detect security violations in machine code. Here is static validation of machine code for detecting policy violations in an accelerated way that analytically generates a control flow graph from misaligned sequences of instructions that may, for example, partially overlap (i.e. share some of the bytes) in a memory buffer. In a first analytic phase, entry points into machine code are discovered. The entry points are starting points of control flow analysis that generates a directed control flow graph (CFG) in a second analytic phase. In a third analytic phase, interchangeable and combinable security policies implement the graph analytics in a flexible way that can be customized for a processor and its instruction set architecture (ISA). This approach will quantifiably increase the reliability of a computer that executes untrusted code such as an open source library or tenant logic in a multitenant environment such as a public cloud.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Here is postprocessing calibration of class probabilities inferred by a machine learning (ML) model, and this calibration is improved by generation of novel initial points that accelerate a genetic algorithm that optimizes fairness and accuracy of the ML model. Tri-objective optimization for multiplier tuning is enhanced by adding a “warm start” mechanism. Innovative designed points are high performance as follows. An identity point has all group multipliers set to 1.0, corresponding to the original model. By definition, this solution will have high accuracy scores and no outcome regression. A parity point has multipliers that have near-perfect disparity and outcome regression. This entails finding multipliers that provide every subgroup approximately the outcome rate of the subgroup with the highest outcome rate. An opportunity point has multipliers that give an approximation of the outcome rate given by an Equality of Opportunity algorithm. This solution provides near-optimal values for accuracy and disparity.
A system deploys an instance of a secure element (SE) application object to each of a plurality of secure containers of an SE platform runtime environment. The system generates an SE proxy application that includes an extension component that redirects to an executable component of an SE application installation file. The system additionally generates a secure container in the SE platform runtime environment. The secure container includes a partition that logically isolates the secure container from other secure containers of the SE platform runtime environment. The system deploys an SE application object to the secure container based on the extension component of the SE proxy application. Upon having deployed the SE application object to the secure container, the system executes the SE application object within the secure container.
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
The present disclosure pertains to natural language techniques for querying data stored in feature stores using zero shot learning. In a particular aspect, a computer-implemented method includes receiving a natural language query for retrieving features from a feature store, generating an input prompt by appending a script to the natural language query, and then using a large language model to determine tables or databases from the feature store that are relevant to the natural language query, retrieve metadata for the tables or databases from the feature store, determine feature groups comprising features relevant to the natural language query, and generate a programming language query based on the input prompt, the metadata, and the groups. A list of features within the feature groups that are accessible within the feature store may then be retrieved by executing the programming language query on the feature store.
A method can include receiving requests to perform actions in a file system environment. The method can include populating a first bucket with first tokens. The first bucket can be associated with actions in the file system environment. The method can include populating second buckets, which can correspond to different tenants, with corresponding second tokens based on priorities of the tenants. The second tokens may correspond to allowable actions on behalf of the tenants. Each token of the first tokens and the second tokens may be in one-to-one correspondence with a single action. The method can include prioritizing the second buckets. The method can include generating an execution list for executing the requests. The method can include executing the execution list based on the first tokens and the second tokens.
Techniques for diagnosing and repairing provisioned physical machines are disclosed. The system identifies a provisioned physical machine for diagnosing an issue. The system executes respective tests on a subset of components comprising the physical machine to generate a target component log. The system obtains a set of base component logs generated by executing the test on a set of base physical machines and evaluates the target component log based on the set of base component logs to identify an anomalous portion of the target component log. The system selects a remediation operation based on the anomalous portion of the target component log and executes the remediation operation for the provisioned physical machine for addressing the issue.
Techniques are disclosed herein for generating dynamic vocabularies for conditioning a language model. A dynamic vocabulary is constructed from an input prompt, database schema information for a database to be queried, and programming language information for a programming language to be used for querying the database to condition the language model to predict an output statement in the programming language. The dynamic vocabulary can be included in prompt information that is provided to the language model. The number of tokens in the dynamic vocabulary can be different than a number of tokens included in a vocabulary of the language model. By utilizing a dynamic vocabulary, the language model can be conditioned to predict tokens for the output statement that are contextually consistent with the tokens included the dynamic vocabulary.
Techniques are described herein are directed toward techniques for data partitioning. The method can include receiving a plurality of data sets sampled from data stored in a source system. The method can further include determining a respective partitioning column, each respective partitioning column comprising a plurality of discrete values. The method can further include determining, for each data set, a respective set of discrete values from the plurality of discrete values of the respective partitioning column. The method can further include comparing a number of partitions to a number of discrete values of a first subset of discrete values of the respective set of discrete values. The method can further include determine that the number of partitions is different from the number of discrete values of the first subset of discrete values. The method can further include partitioning the data based on the determining the number of partitions.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/25 - Integrating or interfacing systems involving database management systems
68.
Smoothing Univariate Signal Edges with Stable and Unstable Subsequence Detection and Geometric Extrapolation
Techniques for smoothing a signal are disclosed. The system partitions the portion of the data sequence into a stable subsequence and an unstable subsequence of data points. The system applies a smoothing function that replaces the unstable subsequence of data points with another subsequence of data points, based at least in part on the stable subsequence, to create a smoothed, more stable subsequence.
A parametric constant resolves to different values in different contexts, but a single value within a particular context. An anchor constant is a parametric constant that allows for a degree of parametricity for an API point. The context for the anchor constant is provided by a caller to the API point. The anchor constant resolves to an anchor value that records specialization decisions for the API point within the provided context. Specialization decisions may include type restrictions, memory layout, and/or memory size. The anchor value together with an unspecialized type of the API point result in a specialized type of the API point. A class object representing the specialized type is created. The class object may be accessible to the caller, but the full value of the anchor value is not accessible to the caller. The API point is executed based on the specialization decisions embodied in the anchor value.
Disclosed is a custom framework for fine-grained human activity recognition. Input video(s) may be accessed comprising a plurality of frames depicting actor(s) and object(s). A plurality of object-pose interaction graphs may be generated for individual frames from the input video(s) on object(s) of interest and joint keypoint(s) of the actor(s) in the individual frames. A first graph neural network may be trained based at least in part on the object-pose interaction graphs to identify spatial information for the actor(s), the object(s) of interest, and interaction(s) between the actor(s) and the object(s) of interest. A second graph neural network may be trained based on the object-pose interaction graphs and keyframes from the plurality of frames to identify temporal information for the actor(s), the object(s) of interest, and the interaction(s). A classifier may be trained to identify action(s) in the input video(s) based on the spatial information and the temporal information.
Techniques are disclosed for building a region data center using image-based resource deployment. A manager service executing in a distributed computing system can deploy software resources to a first set of physical resources within a data center and generate an image set for the first set of physical resources. The image set can include a software image corresponding to each physical resource in the first set of physical resources. The manager service can also determine whether a second set of physical resources is compatible with the image set and deploy the image set to the second set of physical resources. The manager service can then configure identifiers associated with the software resources of the image set deployed to the second set of physical resources.
Techniques are disclosed for building a region data center using image-based resource deployment. A manager service executing in a distributed computing system can deploy software resources to a first set of physical resources within a data center and generate an image set for the first set of physical resources. The image set can include a software image corresponding to each physical resource in the first set of physical resources. The manager service can also determine whether a second set of physical resources is compatible with the image set and deploy the image set to the second set of physical resources. The manager service can then configure identifiers associated with the software resources of the image set deployed to the second set of physical resources.
Techniques are described herein that provide high-level views of roles, access permissions, and responsibilities for individuals within an organization. Organizational data, including detailed, low-level information on access permissions and attributes of individuals, are used to generate human-comprehensible role names and descriptions that individuals may be classified into. A list of outlier individuals is determined and presented, where outlier individuals have access permissions that do not align with the responsibilities of their assigned role.
A security tool is provided to check for security issues with a progression candidate version of code. The security tool identifies a progressed version of code that addresses a documented security issue that was present in a prior version of code and has a same ancestor version of code as the progression candidate version of code. Based on a difference between the progressed version of code and the prior version of code, the security tool determines whether a similar difference has been made between other versions of code in a lineage of the progression candidate version of code. Based on determining that a similar difference has not been made, the security tool stores an indication that the progression candidate version of code is associated with the documented security issue. The security tool may also determine a proposed change to the progression candidate version of code based on the difference.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
75.
Multi-Tenant Secure Element Platform Runtime Environments
A system deploys an instance of a secure element (SE) application object to each of a plurality of secure containers of an SE platform runtime environment. The system generates an SE proxy application that includes an extension component that redirects to an executable component of an SE application installation file. The system additionally generates a secure container in the SE platform runtime environment. The secure container includes a partition that logically isolates the secure container from other secure containers of the SE platform runtime environment. The system deploys an SE application object to the secure container based on the extension component of the SE proxy application. Upon having deployed the SE application object to the secure container, the system executes the SE application object within the secure container.
G06F 21/51 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems at application loading time, e.g. accepting, rejecting, starting or inhibiting executable software based on integrity or source reliability
76.
Compilation techniques for algorithmic graph processing in a relational database
A method and apparatus for graph processing in a relational database environment is provided. Preprocessing transformations that enforce restrictions on a high-level domain-specific language (DSL) input is performed to generate one or more graph iterations. At least one query intermediate representation (IR) is generated by lowering the one or more graph iterations into at least one query. The query IR is/are mapped to one or more corresponding relational queries with procedural extensions. The relational queries with procedural extensions are run against relational database management system (RDBMS) tables representing at least one property graph.
A computer system is described for receiving a request for secondary content to be included in an active user session with a content distribution service. In response to the request, the computer system determines whether the content distribution service is included in a set of labeled content distribution services. If not, the computer system retrieves primary content from the content distribution service in an automated second active session with the content distribution service. The computer system then generates a vector embedding of the primary content for comparison with vector embeddings of contextual personality categories. The content distribution service is labeled with a contextual personality category based on the comparison, and the labeled content distribution service is added to the set of labeled content distribution services for use in providing targeted secondary content for inclusion in active user sessions. Contextual personality labels for sites may be used to update contextual personality labels for candidate consumers visiting the sites, and vice versa.
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/84 - Generation or processing of descriptive data, e.g. content descriptors
78.
Evaluating replication credentials against replication tags to determine whether to grant replication requests
A system receives a replication credential associated with a request to replicate a dataset to a destination partition of a cloud environment. The replication credential includes a destination identifier that identifies the destination partition as a destination for replicating the dataset. The system accesses a replication tag associated with the dataset that defines a replication policy for the dataset. The replication tag includes a destination key that identifies the destination as being a permissible destination for replicating the dataset in accordance with the replication policy. The system determines that replication is permissible based on successfully validating that the destination key corresponds to the destination partition identified by the destination identifier of the replication credential. Responsive to determining that replication is permissible, the system initiates a set of one or more operations to replicate the dataset to the destination partition.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/38 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
Generative artificial intelligence-based techniques are disclosed herein to conduct conversations of various types with multiple Large Language Model Services at once. In one aspect, a method is provided that includes identifying large language model services that qualify to take part in the conversation based, at least in part, on a conversation type and profile information provided by a user, user-selected terms selected by the user specific to the conversation, or both, rendering a conversation screen in a graphical user interface, receiving a prompt input into a dialog box of the conversation screen, communicating the prompt input to each of the large language model services, receiving responses from the large language model services based on the prompt input, and rendering the responses in a dialog box of the conversation screen with an indication of which of the large language model services provided each of the responses.
Techniques for updating a sentiment analysis model in response to feedback for operations that are executed based on an output of the sentiment analysis model are disclosed. The sentiment analysis model analyzes a chat conversation to determine user sentiment. The system executes an operation based on the user sentiment determined by the sentiment analysis model. The operation may be the transfer of the chat conversation from a chatbot to a human agent or the generation of an outbound message by the chatbot. The system receives positive or negative feedback regarding the appropriateness and/or timeliness of the operation. The positive or negative feedback is attributed to the sentiment analysis model. The system generates training data using the feedback. The system then retrains the sentiment analysis model based on the training data.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
Systems and methods are disclosed for implementing a virtual IP for a container pod. In certain embodiments, a method may comprise operating a cloud based network system in a containerized software environment to assign a virtual internet protocol (VIP) address to an application pod, the VIP being directly reachable from a network external to the containerized software environment. The method may include assigning a first VIP address to route traffic to a first fixed IP address assigned to a first application pod, and in response to the first application pod becoming unavailable, switching a second application pod having a second fixed IP address from a standby role to the active role, and assigning the first VIP address to route traffic to the second fixed IP address, enabling continued access to a service offered by the first application pod and the second application pod through the first VIP address.
A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.
A system grants access for a computing entity to execute a requested operation upon a target resource based on a set of one or more access policies associated with a different computing entity. The access control service receives a surrogate access request from a first computing entity. The surrogate access request represents a request for the first computing entity to execute a requested operation upon a target resource based on a set of one or more access policies corresponding to a principal associated with a second computing entity. The system obtains a set of one or more access policies respectively, including a set of one or more authorized operations associated with the principal, and determines whether the requested operation corresponds to at least one authorized operation. Responsive to determining that the requested operation corresponds to at least one authorized operation, the system authorizes execution of the requested operation.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
The technology disclosed herein enables resiliency of routing between NFs when degraded 5G NF topology information is provided to an SCP by an NRF. In a particular example, a method includes transmitting requests for NRF status from a Service Communications Proxy (SCP) to NRFs in a 5G network. The NRFs exchange messages with each other to determine whether Network Function (NF) topology information is available from the NRFs. The method further includes receiving responses to the requests in the SCP. The responses indicate a number of the NRFs from which the NF topology information is available. The method also includes identifying one or more failed NRFs of the NRFs that are in a failed state based on the responses. The NF topology information is aggregated from operative NRFs should one or more of the NRFs still be operative.
An efficient join processing technique is provided that improves multi-level hash join performance by decomposing join operations into a set of opcodes that describe any complex hash join, pipelining execution of these opcodes across join levels, and sharing join operand metadata across opcodes. Complex multi-level joins are easier to describe and execute when decomposed into opcodes. The join technique decomposes multi-level join operations into a minimal set of opcodes such that the join work at each node of the multi-level join can be fully described as an execution of a sequence of opcodes. Operand metadata is shared across the opcodes of all join levels that reference the operand, thereby obviating the need to copy or transmit rows between the join nodes.
The present disclosure relates to a custom framework for fine-grained human activity recognition. One or more input videos may be accessed, where the one or more input videos comprise one or more frames depicting one or more actors and one or more objects. A plurality of object-pose interaction graphs may be generated for individual frames from the one or more input videos based at least in part on one or more objects of interest from the one or more objects and on one or more joint keypoints of the one or more actors. A first graph neural network may be trained based at least in part on the plurality of object-pose interaction graphs to identify spatial information for the one or more actors, the one or more objects of interest, and one or more interactions between the one or more actors and the one or more objects of interest. A second graph neural network may be trained based at least in part on the plurality of object-pose interaction graphs and one or more keyframes from the plurality of frames to identify temporal information for the one or more actors, the one or more objects of interest, and the one or more interactions between the one or more actors and the one or more objects of interest. A classifier may be trained to identify one or more actions in the one or more input videos based at least in part on the spatial information and the temporal information.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
87.
EXTRACTING DIAGNOSTIC METADATA TO IDENTIFY PROBLEMS IN ACCESS-RESTRICTED COMPUTING ENVIRONMENTS
A computer system performs tasks in an access restricted environment. Data is logged in diagnostic files about logical resources in use by the computer system as the computer system attempts to perform the tasks. Occasionally, a problem may prevent the computer system from correctly performing a task. A machine authenticates a user in the access-restricted environment and receives error metadata to initiate an automated process for generating a troubleshooting signature. The automated process involves selecting a metadata extraction policy based on a category of error and using the metadata extraction policy to extract metadata from a diagnostic file. The extracted metadata is analyzed to determine troubleshooting components including the problem, a source of the problem, and/or a version of software that encountered the problem. These troubleshooting components are combined in the troubleshooting signature, which is consumed in a diagnostic tool environment that is separate from the access-restricted environment.
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
88.
METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR DETECTING AND PROCESSING INTER-PUBLIC LAND MOBILE NETWORK (PLMN) SERVICE-BASED INTERFACE (SBI) MESSAGES WITHOUT 3GPP-SBI-ORIGINATING-NETWORK-ID HEADERS
A method for detecting and processing egress inter-PLMN SBI request messages without 3gpp-Sbi-Originating-Network-Id headers includes receiving, by a proxy NF serving a plurality of PLMNs, an egress inter-PLMN SBI request message without an 3gpp-Sbi-Originating-Network-Id header. The method further includes determining an originating network identifier from the message, from DNS, or from a database record. The method further includes adding a 3gpp-Originating-Network-Id header to the message, populating the header with the originating network identifier, and forwarding the message to or towards a target PLMN.
A unified schema, such as a common metrics schema, is provided that can universally cater to different kinds of ML metrics generated by different ML pipelines and platforms. In certain implementations, a metrics management system is provided. The metrics management system is based upon the unified schema and provides a repository for storing ML-related metrics in which the metrics may be generated by different disparate pipelines or platforms. The metrics management system may include adapters, converters, layers, libraries, or combinations thereof that can receive metric data and can provide generalized data that can be consumed by various different types of downstream systems. The generalized data may be provided to a downstream system, such as a user interface, an adjustment module, etc.
Operations include identifying a cryptographic Application Programming Interface (API) call corresponding to a Cryptography Architecture API. A cryptographic security ruleset may be applied to match one or more rules based on attributes of a cryptographic operation identified by the cryptographic API call. The system may perform operations associated with the one or more matched rules. As an example, an operation may include generating a risk analysis metric for the cryptographic API call. The system may generate a cryptographic health report based on the risk analysis metric.
Techniques are provided for optimizing resources (e.g., CPU, memory, IO) allocated to a database server using one or more machine learning models. A database management system executes a database workload for the database server. During execution of the workload, a monitoring service collects metrics for the database workload and sends the metrics to a resource allocation prediction service. The resource allocation prediction service implements one or more machine learning models to generate optimized resource allocation predictions. A generated resource allocation prediction is sent to a change recommendation generation service that generates change instructions for updating the resources allocated to the database server in order to align the current resource allocation of the database server with the resource allocation prediction.
Systems and methods for converting data flow to data processing code. One example system includes an electronic processor configured to receive a data flow for processing a set of source data on a target runtime, determine a characteristic associated with the set of source data, determine a target configuration of the target runtime, generate data processing code at least by adding an operator to the data flow at a point based at least on the characteristic associated with the set of source data and the target configuration of the target runtime, and output the data processing code to a compiler for generation of machine executable code.
Systems, methods, and other embodiments associated with automatic configuration of a circular buffer for ingesting a stream and generating ML estimates in real-time are described. In one embodiment, an example method includes loading a stream of multivariate time series observations into a circular buffer at a real-time pace of input from a target asset. The circular buffer is configured with a buffer configuration that specifies buffer length and choice of arrangement as a single-buffer or dual-buffer. The method then adjusts the buffer configuration until generation of machine learning estimates of the multivariate time series observations that are in the circular buffer satisfies a threshold test for generation at the real-time pace. And, at the real time pace, the method loads additional multivariate time series observations into the circular buffer that is in the adjusted configuration and generates additional machine learning estimates of the additional multivariate time series observations.
Techniques are disclosed herein for resolving date/time expressions while transforming natural language to a logical form such as a meaning representation language. A class label for a token in a natural language utterance and a meaning representation for the natural language utterance can be predicted. The class label can be associated with a date/time expression. The meaning representation can include an operator and a value. When the value associated with the class label matches a predetermined value type or the operator matches a predetermined operator, the value and/or the operator can be modified, and an executable statement can be generated for the meaning representation. A query on a computing system can be executed using the executable statement.
G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
95.
METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR OUT-OF-BAND TRANSPORT LAYER SECURITY (TLS) VERSION AND PARAMETER NEGOTIATION USING A NETWORK FUNCTION REPOSITORY FUNCTION (NRF)
An example method includes registering, at a network function repository function (NRF) of a telecommunications network, a producer network function, including receiving a first transport layer security (TLS) version for the producer network function; providing, by the NRF, the first TLS version of the producer network function to a consumer network function in a network function discovery response; and establishing, by the consumer network function, a service based interface (SBI) communication with the producer network function based on the first TLS version and a second TLS version for the consumer network function.
Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.
An electronic form management system is programmed to: (i) provide a planning UI configured to enable a planning user to assign conditions of approval to a planning application during a planning phase, each condition of approval includes a completion status and one or more conditions to which a permit application is subject during a permitting phase; (ii) provide a permitting UI configured to enable a permitting user to administer the permit application during the permitting phase; (iii) update a completion status of at least one condition of approval data element of the plurality of conditions of approval data elements in response to a condition being satisfied; (iv) calculate an aggregate completion status of a set of conditions of approval data elements; and (v) cause to be displayed at least one graphical interface element representing the calculated aggregate completion status of the set of conditions of approval data elements.
A computer-implemented method includes receiving a first input for a deterministic model and generating, in accordance with the model, a first output corresponding to the first input; the generating is performed in a first computation time. The method also includes storing the first input and the first output as a first input-output pair in a cache; the first input-output pair has a priority in the cache according to the first computation time. The method further includes subsequently receiving a second input for the model that is a duplicate of the first input; and generating a second output corresponding to the second input by retrieving the first output from the cache.
Techniques are described herein that provide machine learning-augmented report summarization. One or more embodiments train and apply a machine learning model to generate a summary report for an entity that is associated with a particular hierarchical level in an organization utilizing base reports from entities at another hierarchical level in the organization. A training data set used for training the machine learning model includes base reports at a particular hierarchical level in the organization and identification of content from the base reports that is to be used for generating a summary report. The machine learning model may then be applied to any set of base reports to generate a corresponding summary report.
Techniques for natural language processing include accessing an input string comprising a natural language utterance and a database schema representation for a database; providing the natural language utterance to a first encoder to generate one or more embeddings of the natural language utterance; providing the database schema representation to the first encoder to generate one or more embeddings of the database schema representation; encoding, by a second encoder, relations between elements in the database schema representation and words in the natural language utterance based on the one or more embeddings of the natural language utterance and the one or more embeddings of the database schema representation; and generating a logical form for the natural language utterance based on the encoded relations, the one or more embeddings of the natural language utterance, and the one or more embeddings of the database schema representation.
