Abstract: One embodiment provides a method for recommending model characteristics to be used in developing a target geo-spatial physical model for a target geographic location utilizing historical lineage data corresponding to historical geo-spatial physical models, including: receiving information related to the target geographic location, wherein the information describes geographical and domain features of the target geographic location; identifying, using at least one similarity algorithm, at least one other geographic location that is similar to the target geographic location, wherein the at least one geographic location has at least one corresponding historical geo-spatial physical model; and recommending, using at least one machine-learning model and based upon the at least one other geographic location, initial model characteristics for developing and deploying the target geo-spatial physical model.

Abstract: Disclosed are various embodiments for a service for decentralized browser based wallets. Various embodiments of the present disclosure can receive a first passcode and a second passcode from a security provider device. Various embodiments can use a cryptowallet to decrypt a first encrypted private key using the first passcode to generate a decrypted private key. Various embodiments can use a cryptowallet to sign a transaction request with the decrypted private key. Various embodiments can use a cryptowallet to generate a second encrypted private key by encrypting the decrypted private key using the second passcode.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: Embodiments described herein relate to a method for probabilistically forecasting the state of hardware components. The method may include obtaining data items corresponding to a hardware component and performing an analysis of the hardware component. The analysis may include making a variety of probability predictions as to whether a label from among a set of possible labels is likely to be the correct label. The set of probabilities from the aforementioned analysis are then analyzed to determine which predicted label has the tightest range, and the prediction with the tightest range for a certain label is displayed to a user in a ranked fashion that includes a quantity of such probability prediction ranges. Such a display may allow an administrator to take action as to which hardware components should be replaced and in what order.

Abstract: Embodiments provide methods, and server systems connecting with a customer service representative/agent for a financial transaction. A method includes sending, by a server system, a message related to a financial transaction to a user device using a messaging protocol. The message includes at least one option to receive a response of the message from the user device for connecting with an agent. The method includes receiving the response of the message from the user device. The response includes an authentication data. The method includes verifying the authentication data. Upon successful verification, the method includes electronically extracting at least one context of the message from the response. The method includes sending the at least one context to a Customer Service Centre (CSC) server. The CSC server is configured to assign the agent to the user based on the at least one context for an upcoming conversation about the message.

Abstract: A hybrid identity service system is disclosed. The system may receive a transaction request. The system may generate a first passcode and a second passcode. The system may decrypt a first encrypted private key with the first passcode to recover a private key. The system may sign the transaction request with the private key and may encrypt the private key with the second passcode to generate a second encrypted private key.

Abstract: In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

Abstract: Systems and methods for peer-to-peer secure document exchange are disclosed. The system may allow a document provider to securely transmit a certified document to a document verifier using decentralized storage. The verifier system may generate a session key pair and transmit the session public key to a trusted API provider. The trusted API provider may generate a session nonce. The verifier system may transmit the session nonce to the provider system. The provider system may use the session nonce to retrieve the session public key. The provider system may encrypt a certified document using the session public key and store the encrypted certified document in the decentralized storage. The verifier system may retrieve the encrypted certified document by polling the trusted API provider based on the session nonce. The verifier system may decrypt the encrypted certified document using the session private key.

Abstract: A method and system generate news headlines from user input parameters. The user input parameters include a specified geographic region of interest and an industry of interest. Climate data and carbon emissions data for the specified geographic region of interest is retrieved. Supply chain dependencies are determined. A machine learning model is generated using the specified geographic region of interest, the industry, the climate data, the carbon emissions data, and the supply chain dependencies. The machine learning model performs an impact analysis on a supply chain based on the climate data and the carbon emissions data. The machine learning model predicts a supply chain performance for the industry based on the impact analysis. A news headline is automatically generated describing the predicted supply chain performance. The news headline includes an underlying basis for the predicted supply chain performance.

Abstract: Methods, systems, and computer program products for stochastic compression of raster data are provided herein. A computer-implemented method includes obtaining at least one compression ratio and at least one error value for a given set of raster data; compressing at least a portion of the given set of raster data based at least in part on the at least one compression ratio and the at least one error value; transmitting the compressed raster data, to at least one given destination, based at least in part on a given transmission speed variable; and performing one or more automated actions based at least in part on the transmitted compressed raster data.

Abstract: Techniques for using machine learning to model climatic data are disclosed. In one example, a computer implemented method comprises receiving climate data comprising a plurality of spatial components and a plurality of temporal components, and masking a portion of the climate data. A machine learning model is trained, wherein the training is based at least in part on the masked portion of the climate data. A vector representation of the climate data is generated via the machine learning model.

Abstract: One embodiment provides a method, including: obtaining information related to farming activities of a farmer; predicting an annotation category for the information, wherein the annotation category identifies a topic of the information; selecting an annotator for annotating the information based upon the annotation category, wherein the selecting comprises utilizing (i) a social proximity constraint identifying a social connection between the farmer and another farmer and (ii) a farm signature constraint identifying a similarity of the farmer to another farmer; assigning the annotator to annotate the obtained information; and receiving annotations for the information.

Abstract: A method, performed at a mobile device, for accessing a service, comprises: determining, from payment card information stored on or obtained at the mobile device, a list of services that are available in relation to the payment card; receiving a selection of one of the services; determining at least one corresponding communication channel by which the selected service is accessible; and opening the at least one corresponding communication channel to thereby access the service.

Abstract: One embodiment provides a computer implemented method of estimating replication completion time. The method includes creating a historical dataset of prior replication data; determining a set of replication parameters to consider; inputting the historical dataset and the set of replication parameters to a replication completion time estimator module; generating a replication completion time prediction based on the historical dataset and the set of replication parameters; and generating a confidence prediction corresponding to the replication completion time prediction.

Abstract: A computer-implemented method for effective agriculture and environment monitoring. The method may comprise measuring a desired variable over an area of interest using a remote inspection platform according to an inspection plan, predicting an occlusion of the remote inspection platform, and in response to the predicted occlusion, determining whether to invoke a local inspection platform to complete the inspection plan. The occlusion in some embodiments interrupts the inspection plan for the remote inspection platform.

Abstract: In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

Abstract: Embodiments are directed to a method and system of forecasting a disk drive survival period in a data storage network, by obtaining operating system data and manufacturer data for the disk drive to create a dataset, screening the dataset to identify a number of features to be selected for model creation, wherein the data set includes censored data and non-censored data, and performing, in an analytics engine, semi-parametric survival analysis on the data set using transfer learning on the model to provide a time-based failure prediction of the disk drive. A graphical user interface provides to a user the failure prediction in one of text form or graphical form.

Abstract: In an approach for forecasting environmental occlusion events, a processor receives a spatio-temporal zone of interest. A processor collects data associated with the spatio-temporal zone of interest. A processor builds a machine-learning model using the data. A processor generates an occlusion probability map for the spatio-temporal zone of interest based on the machine-learning model and enriched mathematical operators. A processor outputs the occlusion probability map.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: Methods, apparatus, and processor-readable storage media for estimating replication completion time using machine learning techniques are provided herein. An example computer-implemented method includes obtaining, from one or more data deduplication storage systems, data related to one or more historical replication operations; generating at least one curve fitting function by processing at least a portion of the obtained data using one or more machine learning techniques; generating an estimate for completion time of at least one unexecuted replication operation associated with at least one of the one or more data deduplication storage systems by processing input data from the at least one data deduplication storage system using the at least one curve fitting function; and performing one or more automated actions based at least in part on the generated estimate.