Abstract: One or more computing devices, systems, and/or methods for synthetic data point generation are provided. In an example, error bounds for generating synthetic data points from a set of data points collected from mobile devices may be determined. Paths associated with groups of data points of the set of data points may be determined. Error bounds may be applied to limit the paths to generate limited path data. Synthetic data points may be generated based upon the limited path data.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: One or more computing devices, systems, and/or methods for constructing and implementing a network management plan are provided. Baseline classifications are assigned to a set of locations. Sub-locations at the set of locations are evaluated to determine whether to override any of the baseline classifications. A model is used to evaluate images depicting the set of locations to generate predicted classifications for the set of locations. Classifications are assigned to the set of locations by implementing conflict resolution rules to selectively retain or replace baseline classifications with predicted classifications. The classifications are used to construct and/or implement a network management plan.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: One or more computing devices, systems, and/or methods for synthetic data point generation are provided. In an example, error bounds for generating synthetic data points from a set of data points collected from mobile devices may be determined. Paths associated with groups of data points of the set of data points may be determined. Error bounds may be applied to limit the paths to generate limited path data. Synthetic data points may be generated based upon the limited path data.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.