Abstract: Techniques for power savings in communications equipment are provided. The computer-implemented method can comprise identifying, by an electronic device operatively coupled to a processing unit, one or more connectivity requirements of one or more servers associated with a data center. The computer-implemented method can also comprise determining, by the electronic device, a defined graph that satisfies the one or more connectivity requirements. The computer-implemented method can further comprise powering down, by the electronic device, one or more elements of the data center that are not required by the defined graph; and powering up, by the device one or more nodes of the data center, which are in any state other than power up, that are required by the defined graph.

Abstract: An example operation may include one or more of hosting a first virtual node in a blockchain-as-a-service (Baas) provider, hosting a second virtual node in the Baas provider, and controlling transmission of information between the first virtual node and the second virtual node along an internal signal path of the Baas provider, wherein the information corresponds to a block in a blockchain that includes an entry for the first and second virtual nodes.

Abstract: An apparatus for automating inventory procedures for items stored on shelves in a closed environment includes a mobile mechanical device having a movable appendage including a camera and additional sensory modalities. The camera and additional sensory modalities are used to position the movable appendage to take camera images of the items on the shelves from many different perspectives. Camera vision and the additional sensory modalities can be used to rotate, lower and raise movable appendage to position the appendage over and along the sides of the items on the shelf. The context of the mobile mechanical device is determined and edge AI computing retrieves AI context specific models based on the context. The AI context specific models may be downloaded from a cloud service. The edge AI computing uses the AI context specific models to identify and count the items on the shelf.

Abstract: The method provides for receiving a plurality of trained models from a corresponding plurality of clients, wherein a respective trained model predicts a condition of an asset and is based on a data set associated with the asset of a respective client. The trained model is based on a seed model that includes a canonical set of features. The trained model includes a component that converts the data at a site to the canonical set of features used by the seed model. The plurality of trained models from the corresponding plurality of clients is assigned to two or more groupings, wherein a grouping includes trained models providing similar analysis. The one or more processors generate an improved model for a client with a limited amount of training data, obtaining the improvement by using multiple models that belong to the same grouping of the first client's model.

Abstract: A computer-implemented method of determining an agent data attribution and selection to perform a collaborative data-related task includes computing an agent data attribution score for each agent of the plurality of agents associated with the collaborative data-related task. A subset of the plurality of agents that participate in the collaborative data-related task is selected based on the agent data attribution score. An instruction is transmitted to the selected subset of the plurality of agents for each agent to conduct a respective portion of the collaborative data-related task.

Abstract: A set of network traffic among a plurality of legacy applications is monitored. From the set of network traffic, a communication graph is generated for the plurality. From the communication graph, a set of migratable applications within the plurality is identified. The set of migratable applications is migrated to a cloud edge layer, and a user is notified of the migration.

Abstract: A cloud-enterprise resource management system enables sharing of computing resources belonging to different datacenters by one or more clients of a resource pooling and sharing service. Each datacenter of includes a first partition of computing resources and a second partition of computing resources. The first partition is designated as reserved for use by an enterprise operating the datacenter. The second partition is designated as available for use by one or more clients of the resource pooling and sharing service. A workload manager in each datacenter predicts workload and transfers (i) a first computing resource from the first partition to the second partition wherein when the predicted workload is below a first threshold and (ii) a second computing resource from the second partition to the first partition when the predicted workload is above a second threshold.

Abstract: Aspects of the invention include methods of performing beam management in a base station of a cellular network. A method includes obtaining, at the base station, information indicating whether one or more terminals of the cellular network have data to transmit, and determining, using the base station, during a sweep by the base station if an upcoming terminal has data to transmit based on the information. The sweep by the base station is a sequential movement of the beam over a coverage area. The method also includes foregoing, by the base station, any communication with the upcoming terminal during the sweep based on the information indicating that the upcoming terminal has no data to transmit, and communicating, using the base station, with the upcoming terminal during the sweep based on the information indicating that the upcoming terminal has data to transmit.

Abstract: An example operation may include one or more of determining data at a first node satisfies a condition, obtaining a pointer to a shared storage area for the data, controlling generation of a block including the pointer, and appending the block to a blockchain without the data, wherein the first node corresponds to first virtual node hosted by blockchain-as-a-service (Baas) provider and wherein the first virtual node receives the data from a network coupled to the Baas provider.

Abstract: Techniques for determining system performance without ground truth include receiving a trained model and one or more generator models, the trained model having been trained on training data. The trained model is used on testing data to produce labeled testing data, and the labeled testing data is used to train a proxy model. The one or more generator models are used to produce synthetic training data that is representative of the training data. The proxy model is used on the synthetic training data to produce predictions, and performance of the trained model is determined based on the predictions by the proxy model.

Abstract: Techniques for non-iterative federated learning include receiving local models from agents, generating synthetic datasets for the local models, and producing outputs using the local models and the synthetic datasets. A global model is trained based on the synthetic datasets and the outputs.

Abstract: A computer-implemented method for delivering network connectivity includes receiving, by an edge server, a set of communication packets from a communication device. The method further includes storing, by the edge server, the set of communication packets as part of outbound data. The method further includes determining, by the edge server, that a mobile access point is within a communicable range of the edge server. The mobile access point travels back and forth between the edge server and a base station. The method further includes transmitting, by the edge server, the outbound data to the mobile access point that is within the communicable range.

Abstract: A data mining method, system, and non-transitory computer readable medium include obtaining a subset of public records of data in a public domain and performing data mining, via private domain data, within the subset of the public records of data to find data in the public domain corresponding to a particular individual.

Abstract: Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Abstract: A GAN includes a first device and a second device. A discriminator model in the first device is trained to discriminate samples from a transmitter in the first device from samples from other transmitters, by collaborating by the first device with the second device to train the discriminator model to discriminate between samples from its transmitter and spoofed samples received from a generator model in the second device and to train the generator model in the second device to produce more accurate spoofed samples received by the first device during the training. The training results in a trained discriminator model, which is distributed to another device for use by the other device to discriminate samples received by the other device in order to perform authentication of the transmitter in the first device. The other device performs authentication of the transmitter of the first device using the distributed model.

Abstract: A computing system for learning a device type and message formats used by a device is provided. The computing system includes an interface and a processor. The interface is receptive of documents describing identification information and communication and application protocols of devices. The processor is coupled with the interface to obtain rules of network packet analysis using document analytics and identify identification information and communication and application protocols of network messages from devices using the rules.

Abstract: Regulating a flow of data from an electronic device comprises generating a user profile associated with a user. Further, a resource profile is generated and is associated with one or more resources external to the electronic device. Additionally, a context profile is generated and describes an outcome of one or more previous interactions between the user of the electronic device and one or more resources. Rules are generated based on a comparison the user profile, the context profile and the site profile. Further, data sent from an electronic device is modified based on the set of rules.

Abstract: Methods and systems for detecting events. A satellite image is obtained and the satellite image is processed using a first convolutional neural network (CNN) to produce a satellite vector that identifies at least one fire. A mobile sensor is automatically directed to a mobile location based on the satellite vector.

Abstract: Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Abstract: A computer-implemented method includes: receiving, using a processor, multiple data session records (DSRs); storing the multiple DSRs in a memory communicatively coupled to the processor; analyzing, using the processor, the stored multiple DSRs for temporal and spatial data; and determining, using the processor, quality degradation by using the temporal and spatial data for the stored multiple DSRs.