Abstract: A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, selecting a subset of users' public records of data from a filtered set of data from a public domain that is common with the users' private records of data, and creating a data file including the matched user of the private domain to the public records of the user of the private domain, where the set of the filter constraints comprises a function that captures the subset of the users' public records of data who are of interest to the private domain, and only performs data mining with that set of information from the public domain.

Abstract: Embodiments of the invention are directed to a computer-implemented method of distributed learning using a fusion-based approach. The method includes determining data statistics at each system node of a plurality of system nodes, wherein each system node respectively comprises an artificial intelligence model. The method further includes determining a set of control and coordination instructions for training each artificial intelligence model at each system node of the plurality of system nodes. The method further includes directing an exchange of data between the plurality of system nodes based on the data statistics of each system node of the plurality of system nodes. The method further includes fusing trained artificial intelligence models from the plurality of system nodes into a fused artificial intelligence model, wherein the trained artificial intelligence models are trained using the set of control and coordination instructions.

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: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: Second machine learning models trained using respective second data sets can be received. The second machine learning models can be run using a first data set used in training a first machine learning model, where the second machine learning models produce respective outputs. Scores associated with the second machine learning models can be determined by comparing the respective outputs with ground truth associated with the first data set. Based on the scores associated with the second machine learning models, whether the first data set is to be discarded or kept can be determined for training the first machine learning model.

Abstract: In an approach to identifying occluded objects, a computer retrieves a first image that includes an object at least partially occluded by one or more occlusions. A computer removes the one or more occlusions from the first image to create a partial object in a second image. A computer runs a detection model with the second image to predict one or more identifications of a symbol represented by the partial object. A computer determines top predictions of the one or more identifications of the symbol by the detection model. A computer identifies at least one geometric property associated with the one or more identifications of the symbol included in the one or more top predictions. A computer applies the at least one geometric property to the partial object. A computer determines a probability of the one or more top predictions correctly identifying the symbol represented by the partial object.

Abstract: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Abstract: Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Abstract: A computer implemented method determines a placement of an application being added to a network. The method includes determining a logical-physical mapping for the application. Sub-graphs in the logical-physical mapping are identified. A stored application in a catalog of applications previously used in the network is located. The stored application includes one of the sub-graphs in the logical-physical mapping. An experiment is generated, performed by a computing device running a neural network model, using the sub-graphs. The experiment includes inducing a physical node mapping for the sub-graph. A cost function associated with a placement of the application being added to the network to one or more physical nodes in the induced physical node mapping is determined.

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: Embodiments relate to an input-encoding technique in conjunction with federation. Participating entities are arranged in a collaborative relationship. Each participating entity trains a machine learning model with an encoder on a training data set. The performance of each of the models is measured and at least one of the models is selectively identified based on the measured performance. An encoder of the selectively identified machine learning model is shared with each of the participating entities. The shared encoder is configured to be applied by the participating entities to train the first and second machine learning models, which are configured to be merged and shared in the federated learning environment.

Abstract: A computer-implemented method and a computer system establish network slices within a physical network having a plurality of network elements. The method includes receiving a request to instantiate a network slice at a network element. The method also includes determining a performance metric of the network element. The method further includes delaying instantiation of the requested network slice within the network element in response to determining that the performance metric of the network element is below a threshold. The method also includes instantiating the requested network slice within the network element in response to determining that the performance metric of the network element is at or above the threshold. Finally, the method includes deactivating the requested network slice in response to determining that the performance metric of the network element is below the threshold at a time subsequent to instantiating the requested network slice.

Abstract: An example operation may include one or more receiving an entry at a blockchain-as-a-service (Baas) provider, determining whether the entry satisfies a first set of policies, and controlling placement of the entry into a first queue when the first set of policies is satisfied and into a second queue when the first set of policies is not satisfied, wherein the first queue is to store confirmed entries to be submitted for consensus without validation and the second queue is to store pending entries that require validation before consensus.

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: 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: 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: 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.