Abstract: Method, apparatus, and computer program product are provided for estimating crop type and/or sowing date. In some embodiments, a historical crop growth time series and a plurality of simulated crop growth time series are determined, and the historical time series is matched against each simulated time series to determine an estimated crop type and/or sowing date. For example, one simulated time series may be determined for each crop type/sowing date combination within a set of one or more crop types and one or more sowing dates based on historical crop data. Each time series represents crop growth in an area of interest and comprises element(s) including crop-specific parameter(s), such as leaf area index (LAI). The historical time series may be determined based on remote sensor data. Each simulated time series may be determined using a crop growth simulation model and based on historical crop data, geospatial data, and weather data.

Abstract: A method for providing feedback between separate neural networks to predict crop yield of a polyculture by combining the crop yield predictions from the neural networks is described. The method includes configuring a set of neural networks, each neural network in the set of neural networks predicting crop yield for a respective crop from a set of crops based on a set of crop growth time series, one crop growth time series for each respective crop, the set of crops being grown in a polyculture. A crop simulator generates the set of crop growth time series based on application rates of farm inputs that are provided to the crop simulator. The method further includes determining a predicted crop yield of the polyculture at a timepoint after a predetermined duration by iteratively changing and applying application rates of farm inputs based on the crop growth time series.

Abstract: A smart lock system that includes a smart lock that electronically locks and unlocks a door and a server that outputs a command to the smart lock to lock or unlock the door via the internet in response to an instruction received via the internet from a user device. The user device may also (simultaneously) directly transmit an instruction to the smart lock (e.g., Bluetooth). The user device may also directly transmit an instruction to the smart lock to unlock the door in response to a determination that the user device is within a predefined geofenced region around the smart lock. The smart lock may also include a keypad. The smart lock may also include a camera that captures images and output those images to the server for transmittal to the user device. The smart may also include a motion sensor and capture images in response to detected motion.

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: 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: A smart lock system that includes a smart lock that electronically locks and unlocks a door and a server that outputs a command to the smart lock to lock or unlock the door via the internet in response to an instruction received via the internet from a user device. The user device may also (simultaneously) directly transmit an instruction to the smart lock (e.g., via Bluetooth). The user device may also directly transmit an instruction to the smart lock to unlock the door in response to a determination that the user device is within a predefined geofenced region around the smart lock. The smart lock may also include a keypad. The smart lock may also include a camera that captures images and output those images to the server for transmittal to the user device. The smart may also include a motion sensor and capture images in response to detected motion.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for generating an agriculture map of a region of land. The exemplary embodiments may include collecting agricultural data of one or more sub-regions of the region of land, wherein the agricultural data includes classified data and unclassified data, extracting one or more features from the collected classified agricultural data, training one or more models based on the extracted one or more features, and generating an agricultural map of the region of land based on applying the one or more models to the collected unclassified agricultural data.

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: A method for facilitating payment transactions includes receiving a transaction request for a payment transaction initiated at a payment terminal device using a transaction card of a user. A predefined category of the user is identified based on the transaction request and a service application is remotely activated in a first operation mode on a user device of the user such that the first operation mode is associated with the identified predefined category. Payment transaction messages associated with the payment transaction are communicated to the activated service application, which translates the payment transaction messages to a predefined message format. In response to the payment transaction messages, first authentication information is received from the activated service application such that the first authentication information is entered by the user in the predefined message format. The payment transaction is processed based on successful authentication of the user.

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: According to one embodiment, a method, computer system, and computer program product for detecting and managing anomalies in one or more sensors is provided. The present invention may include simulating, by one or more environmental models, an expected output of the one or more sensors; responsive to identifying that an actual output of the one or more sensors differs from the expected output by a threshold value, detecting one or more anomalous sensors; and performing one or more actions to manage the one or more anomalous sensors based on the presence of the one or more sensors within a plurality of anomaly reaction zones.

Abstract: Method, apparatus, and computer program product are provided for estimating crop pest risk and/or crop disease risk at sub-farm level. In some embodiments, a farm region is determined based on farm definition data, and input data associated with the farm region are retrieved from a plurality of data sources. The input data may include a plurality of pixel sets. In some embodiments, for each pixel set, crop risk data are determined based on the input data using one or more spatiotemporal regression models. The crop risk data may include an estimate of crop pest risk and/or crop disease risk for each pixel set. In some embodiments, the farm region is categorized into a plurality of sub-farms each defining a risk level category for that sub-farm based on the crop risk data. In some embodiments, the sub-farms are displayed as a visual heat-map, along with recommended antidote options.

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.

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 computer-implemented method can include obtaining irrigation data. The method can further include obtaining a first set of watering rates. The method can further include generating a first set and a second set of soil moisture estimates. The first and second sets of soil moisture estimates can be based at least in part on the irrigation data. The method can further include obtaining a custom constraint and making a first determination that the first set of soil moisture estimates satisfies the custom constraint. The method can further include obtaining a moisture reference value in response to making the first determination. The moisture reference value can be based at least in part on the second set of soil moisture estimates. The method can further include making a second determination that the moisture reference value exceeds a first threshold, and generating an irrigation plan in response to making the second determination.

Abstract: Devices, methods, and systems for distributed rule based automated fault detection are described herein. One system includes a data extractor engine configured to: extract configuration data relating to an environment based on a number of defined rules, and receive monitored data relating to the environment, an AFD engine configured to evaluate the monitored data in view of the configuration data to determine a state of the environment, and a fault generation engine to determine whether the state of the environment is outside a range defined by the number of defined rules.

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: Method, apparatus, and computer program product are provided for estimating crop type and/or sowing date. In some embodiments, a historical crop growth time series and a plurality of simulated crop growth time series are determined, and the historical time series is matched against each simulated time series to determine an estimated crop type and/or sowing date. For example, one simulated time series may be determined for each crop type/sowing date combination within a set of one or more crop types and one or more sowing dates based on historical crop data. Each time series represents crop growth in an area of interest and comprises element(s) including crop-specific parameter(s), such as leaf area index (LAI). The historical time series may be determined based on remote sensor data. Each simulated time series may be determined using a crop growth simulation model and based on historical crop data, geospatial data, and weather data.

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.