Abstract: Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log template. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and a particular semantic state of the original application.

Abstract: Described are techniques for oblique image rectification. The techniques include receiving an original image depicting an oblique view of a circular object and pre-processing the original image into an edge image. The techniques further include generating, by a machine learning model based on the edge image, a heatmap including an ellipse formed by the oblique view of the circular object. The techniques further include computing ellipse parameters describing the ellipse of the heatmap. The techniques further include performing, using the ellipse parameters, an affine transformation on the original image to generate a rectified image, where the rectified image converts the ellipse to a circle.

Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log templates. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and particular semantic state of the original application.

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: In various examples, a computer-implemented method includes: receiving, by one or more processing devices, acoustic content data; receiving, by the one or more processing devices, acoustic data for a target environment; training, by the one or more processing devices, a neural network model on the acoustic data for the target environment to extract features of the target environment; using, by the one or more processing devices, the neural network model to transfer the features of the target environment to the acoustic content data; constructing, by the one or more processing devices, the acoustic content data with the transferred features of the target environment; and outputting, by the one or more processing devices, via a user interface (UI), information on and configurable options for the training of the neural network model on the acoustic data for the target environment.

Abstract: Computer-implemented methods for deploying models at edge devices without retraining. Aspects include receiving, from a user an identification of a computing task to be performed by an edge device and obtaining, a data set corresponding to the computing task. Aspects also include determining a supernet model space based at least in part on the computing task and creating a plurality of trained models for the computing task by training a plurality of deep learning models within the supernet model space with the data set. Aspects further include deploying one of the plurality of trained models to the edge device, wherein the one of the plurality of trained models is determined based at least in part on one or more characteristics of the edge device.

Abstract: An example operation may include one or more of receiving, via a proxy located in a first geographic jurisdiction, a request for data from a software program, wherein the data is stored in a database located in a second geographic jurisdiction, identifying a regulatory constraint that must be enforced on the data stored in the database in the second geographic jurisdiction based on a policy associated with the second geographic jurisdiction, retrieving, via the proxy in the first geographic jurisdiction, the data requested from a proxy located in the second geographic jurisdiction, delivering the data received from the proxy located in the second geographic jurisdiction to the software program, and executing, via the proxy in the first geographic jurisdiction, an additional action on the data to comply with the regulatory constraint of the second geographic jurisdiction.

Abstract: Described are techniques for oblique image rectification. The techniques include receiving an original image depicting an oblique view of a circular object and pre-processing the original image into an edge image. The techniques further include generating, by a machine learning model based on the edge image, a heatmap including an ellipse formed by the oblique view of the circular object. The techniques further include computing ellipse parameters describing the ellipse of the heatmap. The techniques further include performing, using the ellipse parameters, an affine transformation on the original image to generate a rectified image, where the rectified image converts the ellipse to a circle.

Abstract: Described are techniques for oblique image rectification. The techniques include receiving an original image depicting an oblique view of a circular object and pre-processing the original image into an edge image. The techniques further include generating, by a machine learning model based on the edge image, a heatmap including an ellipse formed by the oblique view of the circular object. The techniques further include computing ellipse parameters describing the ellipse of the heatmap. The techniques further include performing, using the ellipse parameters, an affine transformation on the original image to generate a rectified image, where the rectified image converts the ellipse to a circle.

Abstract: Automated analog gauge reading is provided. The method comprises a computer system receiving input of an image and detecting at least one analog gauge in the image. The computer system corrects the orientation of the analog gauge in the image and detects scene text and tick labels on the analog gauge. The computer system determines a position of a pointer on the analog gauge relative to the scene text and outputs a gauge reading value based on an arithmetic progression of tick labels and angle of the pointer with respect to minimum and maximum values on the analog gauge.

Abstract: A computer-implemented method, a computer system and a computer program product route meeting sessions through a venue based on device availability. The method includes identifying a plurality of sessions in a venue network, where each session in the plurality of sessions comprises session data that is associated with a network slice. The network slice includes connection requirements. The method also includes receiving a connection request from a user, where the connection request includes a session in the plurality of sessions. The method further includes determining an available device to receive the session data, where the available device satisfies the connection requirements of the network slice. Lastly, the method includes associating the available device with the network slice, where the session data is transmitted to the available device.

Abstract: A computer-implemented method, a computer system and a computer program product provide differentiated services to spectrum zones based on user activity. The method includes obtaining a current spectrum zone configuration for a physical environment from a server. The method also includes capturing activity data from the physical environment. The method further includes identifying an event of interest in the activity data, wherein the event of interest comprises network requirements. The method also includes determining that the network requirements of the event of interest are not satisfied by the current spectrum zone configuration for the physical environment. Lastly, the method includes modifying the current spectrum zone configuration based on the network requirements.

Abstract: Personalization with on-device privacy preservation is provided by receiving, at a user device, a generalized recommendation from a remote provider backend, which include a plurality of candidate items responsive to a request from an application running on the user device; generating a plurality of candidate vectors corresponding to the plurality of candidate items representing a corresponding candidate item in a binary format across a plurality of metrics associated with the plurality of candidate items; generating a user vector based on personal data stored on the user device, the user vector representing a user in a binary format across one or more metrics associated with the personal data; determining interaction propensities based on the user vector and the plurality of candidate vectors according to a global model run on the user device; and displaying, on the user device, the plurality of candidate items according to the interaction propensities.

Abstract: Embodiments are disclosed for a method for a wearable secure data device. The method includes setting an operational mode for the wearable secure data device that stores a plurality of secure data items. The method also includes receiving a request for at least one of the secure data items from a client device that is communicating with a third-party device. Additionally, the method includes determining if the operational mode is associated with allowing access to the secure data items. Further, the method includes providing a response based on the determination.

Abstract: A method of determining a range between two devices, which can for instance be wireless devices, such as handheld devices. The method performs a double-sided two-way ranging protocol at the two devices. This protocol causes the two devices to transmit and receive signals, with a view to determining the range between the two devices, using a two-way ranging methods, where each of the signals is a composite tone generated as a composition of two waveforms that are timewise separated by a gap to form a bipolar waveform. The gap is a zero or low-amplitude signal, contrasting with the two waveforms. The particular pattern in the correlation footprint eases the determination of the time of flight. The method can be used to measure social distancing.

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: A method, computer program product, and system include a processor(s) that engages, based on a request for an inference, from a group of sensors of multiple modalities at a physical location, sensor(s) of a main modality to provide data to a pipeline to generate the inference. The pipeline includes one or more machine learning models which generate the inference for a downstream task. The processor(s) obtains raw data from the sensor(s) of the main modality and applies an outlier detector to the raw data. Based on determining that there is an outlier the processor(s) automatically engages sensor(s) of at least one different modality than the main modality from the group of sensors of multiple modalities and obtains new raw data from the sensor(s) of the at least one different modality. The processor(s) applies the one or more machine learning models to the new raw data to derive the inference.

Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (AI) includes receiving data at a control system having a computer from a robotic device. The control system communicating a policy to the robotic device for choosing navigation actions for the robotic device. The received data is analyzed using the control system for determining when the received data meets a threshold for determining quality of the data. The analysis can include generating a model based on the received data where the model includes vector representation of inputs detected by a sensor array at the location. In response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicating with the control system to collaborate in updating the policy to choose a next action.