Abstract: An apparatus adapted to identify a user based on keystroke dynamics of an input by the user, the apparatus adapted to: execute a first training phase of training a keystroke sample generator to generate negative keystroke samples; execute a second training phase of training a user identification model based at least in part on a plurality of negative keystroke samples generated using the keystroke sample generator; and execute a deployment of the user identification model to authenticate an input sample associated with the user using the trained user identification model.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for partitioning a captured motion signal of a user into segments. One or more sets of features are extracted from the segmented motion signal by applying a plurality of feature extraction algorithms to the segments, where the feature extraction algorithms include Convolutional Vision Transformers (CVT) and convolutional gated recurrent units (convGRU) model algorithms. A subset of discriminative features are selected from the sets of extracted features. The subset of features is then analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: A method for providing an enhanced human-machine interface to control a robot, and a device supporting the method is provided. According to one embodiment of the present invention, a method includes: monitoring a manipulation input by a user for an operation of the robot on the enhanced HMI, which is enhanced from a proprietary HMI proprietary to the robot; generating a first program in a first program environment for the enhanced HMI in response to the monitored manipulation; retrieving action information relating to actions to be performed as part of the operation of the robot, wherein the actions are defined in a second program environment for the proprietary HMI; generating a second program in the second program environment corresponding to the first program by using the retrieved action information; and controlling the robot to perform the operation using the second program.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: A computer-implemented method for providing at least one service in a node or process of a cyber-physical system has at least two application modules, wherein a communication between the at least two application modules occurs via respective application interfaces of the at least two application modules, which application interfaces are offered specifically to the particular application module, wherein a particular application interface which is offered specifically to the particular application module represents a communication access to the particular application module. According to the invention, at least the application interfaces of the at least two application modules which are offered specifically to the application module are implemented by a coupling module which establishes the communication between the at least two application modules and which provides the at least one service in an activatable manner.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: A technical injection system for injecting a retrained machine learning model is provided, including a. a first computing unit including a first storage medium, wherein the first computing unit is configured for providing the retrained machine learning model; and preprocessing the retrained machine learning model; wherein the retrained machine learning model is stored in the first storage medium; b. a second computing unit comprising a second storage medium and an injection interface, wherein the injection interface is configured for injecting at least one relevant part of the retrained machine learning model after processing from the first storage medium of the first computing unit into the second storage medium of the second computing unit by the injection interface at runtime.

Abstract: A system and method for determining whether persons depicted in images are wearing personal protective equipment (PPE) is disclosed. The system includes a PPE detection platform that, during a testing phase, comprises a person detector configured to detect persons in images and a deep neural network PPE classifier. The PPE classifier comprises a spatial attention module and is trained to predict whether or not a PPE item is present in the image sub-region corresponding to the detected person. During a training phase, the PPE detection platform comprises a body pose estimator that, based on the output of the person detector, determines a pose of the detected person's body and provides a spatial attention supervision signal used to train the spatial attention module of the PPE classifier and tune the PPE classifier. The PPE platform is configured to bypass the body pose estimator during the inference phase.

Abstract: A system and method for determining whether persons depicted in images are wearing personal protective equipment (PPE) is disclosed. The system includes a PPE detection platform that, during a testing phase, comprises a person detector configured to detect persons in images and a deep neural network PPE classifier. The PPE classifier comprises a spatial attention module and is trained to predict whether or not a PPE item is present in the image sub-region corresponding to the detected person. During a training phase, the PPE detection platform comprises a body pose estimator that, based on the output of the person detector, determines a pose of the detected person's body and provides a spatial attention supervision signal used to train the spatial attention module of the PPE classifier and tune the PPE classifier. The PPE platform is configured to bypass the body pose estimator during the inference phase.

Abstract: Provided is a method for dynamic adaption of a workflow, WF, of an automation system, AS, wherein the workflow, WF, includes automated handling functions, AHFs, implementing associated production steps of a production process controlled by a workflow processor, WP, of a machine of the automation system and at least one automated handling function, AHF, shell configured to plug a pluggable automated handling function, pAHF, into the workflow, WF, of the automation system during runtime of the automation system for adaption of the workflow, WF.

Abstract: Methods and systems for disentangling discriminative features of a user of a device from motion signals and authenticating a user on a mobile device are provided. In at least one aspect of the methods and systems, each captured motion signal is divided into segments. The segments are then converted into translated segments using one or more trained translation algorithms. The segments and translated segments are then provided to a machine learning system. Discriminative features of the user are then extracted from the segments and translated segments with the processor using the machine learning system that applies one or more feature extraction algorithms.

Abstract: Provided is a method for dynamic adaption of a workflow, WF, of an automation system, AS, wherein the workflow, WF, includes automated handling functions, AHFs, implementing associated production steps of a production process controlled by a workflow processor, WP, of a machine of the automation system and at least one automated handling function, AHF, shell configured to plug a pluggable automated handling function, pAHF, into the workflow, WF, of the automation system during runtime of the automation system for adaption of the workflow, WF.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: An apparatus for estimating an ambient environment at which inorganic scale will form in a downhole fluid includes a stress chamber disposed in a borehole in a production zone at a location within a specified range of maximum pressure and configured to receive a sample of the fluid from the production zone and to apply an ambient condition to the sample that causes the formation of inorganic scale. An inorganic scale sensor is configured to sense formation of inorganic scale within the chamber and an ambient environment sensor is configured to sense an ambient environment within the chamber at which the formation of inorganic scale occurs. The apparatus further includes a processor configured to receive measurement data from the inorganic scale sensor and the ambient environment sensor and to identify the ambient environment at which the formation of inorganic scale occurs.

Abstract: An apparatus for estimating an ambient environment at which inorganic scale will form in a downhole fluid includes a stress chamber disposed in a borehole in a production zone at a location within a specified range of maximum pressure and configured to receive a sample of the fluid from the production zone and to apply an ambient condition to the sample that causes the formation of inorganic scale. An inorganic scale sensor is configured to sense formation of inorganic scale within the chamber and an ambient environment sensor is configured to sense an ambient environment within the chamber at which the formation of inorganic scale occurs. The apparatus further includes a processor configured to receive measurement data from the inorganic scale sensor and the ambient environment sensor and to identify the ambient environment at which the formation of inorganic scale occurs.

Abstract: An apparatus for estimating an ambient environment at which organic scale will form in a downhole fluid includes a stress chamber disposed in a borehole in a production zone at a location of maximum pressure and configured to receive a sample of the fluid from the production zone and to apply an ambient condition to the sample that causes the formation of organic scale. A sensor is configured to sense formation of organic scale within the chamber and an ambient environment sensor is configured to sense an ambient environment within the chamber at which the formation of organic scale occurs. The apparatus further includes a processor configured to receive measurement data from the organic scaling sensor and the ambient environment sensor and to identify the ambient environment at which the formation of organic scale occurs using the organic scaling sensor measurement data and ambient environment sensor measurement data.

Abstract: An apparatus for estimating an ambient environment at which organic scale will form in a downhole fluid includes a stress chamber disposed in a borehole in a production zone at a location of maximum pressure and configured to receive a sample of the fluid from the production zone and to apply an ambient condition to the sample that causes the formation of organic scale. A sensor is configured to sense formation of organic scale within the chamber and an ambient environment sensor is configured to sense an ambient environment within the chamber at which the formation of organic scale occurs. The apparatus further includes a processor configured to receive measurement data from the organic scaling sensor and the ambient environment sensor and to identify the ambient environment at which the formation of organic scale occurs using the organic scaling sensor measurement data and ambient environment sensor measurement data.

Abstract: An apparatus for estimating an ambient environment at which inorganic scale will form in a downhole fluid includes a stress chamber disposed in a borehole in a production zone at a location within a specified range of maximum pressure and configured to receive a sample of the fluid from the production zone and to apply an ambient condition to the sample that causes the formation of inorganic scale. An inorganic scale sensor is configured to sense formation of inorganic scale within the chamber and an ambient environment sensor is configured to sense an ambient environment within the chamber at which the formation of inorganic scale occurs. The apparatus further includes a processor configured to receive measurement data from the inorganic scale sensor and the ambient environment sensor and to identify the ambient environment at which the formation of inorganic scale occurs.