Abstract: The present disclosure provides techniques for identifying regions of a three-dimensional (3D) printable object that are accessible by a tool. An example method includes receiving, from a remote computing device, an encrypted object file that includes a specification of a 3D printable object and receiving a request to process the encrypted object file to identify regions of the 3D printable object that are accessible by a tool. The method also includes obtaining a tool specification, computing a complement of the tool specification, and encrypting the complement of the tool specification to generate an encrypted comparison file. The method also includes computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that describes the regions of the 3D printable object that are accessible by the tool. The encrypted Minkowski sum is performed without decrypting the encrypted object file.

Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file or object model in a privacy-preserving manner. An example method includes receiving, from a remote computing device, an encrypted object file comprising a specification of a 3D printable object and receiving a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes obtaining an encrypted comparison file and computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that comprises information about the characteristic. Computing the encrypted Minkowski sum is performed without decrypting the encrypted object file. The method also includes sending the encrypted result file to the remote computing device.

Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file in a privacy-preserving manner. An example method includes obtaining an object file that comprises a specification of a 3D printable object, encrypting the object file using a public key to generate an encrypted object file, and sending, to a remote computing system, the encrypted object file and a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes receiving an encrypted result file from the remote computing system, wherein the encrypted result file comprises an encrypted Minkowski sum of the encrypted object file and an encrypted comparison file. The method also includes decrypting the encrypted result file using a private key corresponding with the public key to generate an unencrypted result file and processing the unencrypted result file to determine the characteristic of the 3D printable object.

Abstract: The present disclosure provides techniques for finding non-printable features in a three-dimensional (3D) printable object. An example method includes receiving a first encrypted file and a request to process the first encrypted file to identify non-printable features of a 3D printable object, wherein the first encrypted file is an encrypted complement of an object file that comprises a specification of the 3D printable object. The method also includes computing a first encrypted Minkowski sum of the first encrypted file and an encrypted minimum feature file to generate an encrypted intermediate file and sending the encrypted intermediate file. The method also includes receiving a second encrypted file that comprises an encrypted complement of the encrypted intermediate file and computing a second encrypted Minkowski sum of the second encrypted file and the encrypted minimum feature file to generate an encrypted result file that describes non-printable features of the 3D printable object.

Abstract: A system and method for captures of physical object data for environment modeling is provided. Mesh data is displayed over an image of a physical space for which a floorplan is to be generated. Instructions to mark an object are received and the instructions include a voice command and eye gaze or gesture. The mesh data is annotated with a marker based on the instructions by placing the marker at a location at which an object is to be identified. A floorplan of the space is generated based on the mesh data. The marker is placed in the floorplan.

Abstract: A system and method for rapid sensor commissioning is provided. A three dimensional model of a space is generated. The space is scanned for identifiers associated with wireless sensors placed within the space. At least one of the identifiers associated with the wireless sensors is identified. The model is annotated with a representation of the sensor identified by the identifier, and data is collected from the wireless sensor.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of machine learning models. During operation, the system determines a training set of data objects, wherein each data object is associated with one of a plurality of classes. The system divides the training set of data objects into a number of partitions. The system generates a respective machine learning model for each respective partition using a universal kernel function, which processes the data objects divided into a respective partition to obtain the ensemble of machine learning models. The system trains the machine learning models based on the data objects of the training set. The system predicts an outcome for a testing data object based on the ensemble of machine learning models and an ensemble decision rule.

Abstract: A system determines, in a graph which represents a system of components: vulnerability nodes representing known vulnerabilities to the system, including exposed and non-exposed vulnerability nodes associated with an exploitation likelihood; and dependency nodes representing components in the system, including direct and indirect dependency nodes associated with an exposure factor indicating an amount of degradation based on exploitation of an associated vulnerability. The system calculates, across all non-exposed vulnerability nodes and all direct dependency nodes, a score which indicates an attack volume based on at least: a respective second likelihood associated with a non-exposed vulnerability node; an exposure factor associated with a dependency node which represents a component directly degraded based on exploitation of a vulnerability; and a loss of utility of the component.

Abstract: A building environmental sensor includes a sensing element for collecting measurements of environmental parameters such as temperature, humidity, light, sound or the absence or presence of gas. The sensor will: (a) detect that a data collection device is within a communication range of the sensor; (b) generate a data stream that includes the data that the sensor collected; (c) transmit the data stream to the first data collection device; (d) determine that a communication link between the sensor and the first data collection device was lost before the first data stream was fully transmitted; (e) detect that a second data collection device is within the communication range of the sensor; (f) generate a second data stream that includes the remaining data; and (g) transmit the second data stream to the second data collection device.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of neural network-based classifiers that optimize a diversity metric. During operation, the system defines a diversity metric based on pairwise angles between decision boundaries of three or more affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing a pair of mutually orthogonal affine classifiers of the three or more affine classifiers. The system trains one or more neural networks such that parameters of the one or more neural networks are consistent with parameters of the affine classifiers to obtain an ensemble of neural network-based classifiers which optimize the diversity metric. The system predicts an outcome for a testing data object based on the obtained ensemble of neural-network based classifiers which optimize the diversity metric.

Abstract: One embodiment provides a method and system which facilitates optimizing a pair of affine classifiers based on a diversity metric. During operation, the system defines a diversity metric based on an angle between decision boundaries of a pair of affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing the pair of affine classifiers, wherein the designed pair of affine classifiers are mutually orthogonal. The system predicts an outcome for a testing data object based on the designed pair of mutually orthogonal affine classifiers.

Abstract: A system determines, in a graph which represents a system of components: vulnerability nodes representing known vulnerabilities to the system, including exposed and non-exposed vulnerability nodes associated with an exploitation likelihood; and dependency nodes representing components in the system, including direct and indirect dependency nodes associated with an exposure factor indicating an amount of degradation based on exploitation of an associated vulnerability. The system calculates, across all non-exposed vulnerability nodes and all direct dependency nodes, a score which indicates an attack volume based on at least: a respective second likelihood associated with a non-exposed vulnerability node; an exposure factor associated with a dependency node which represents a component directly degraded based on exploitation of a vulnerability; and a loss of utility of the component.

Abstract: A system for collecting building environmental sensor data includes a first data collection device having a first transceiver that is configured to receive data from proximate building environmental sensors and a second transceiver that is configured to communicate with an external service. The device will, upon detecting that the first transceiver is within a receiving range of a first building environmental sensor, communicatively connect with the first building environmental sensor to receive a data stream that comprises sensor data captured by the first building environmental sensor. The device will continue to receive the data stream until the first transceiver either receives all of the sensor data or moves out of the receiving range. The device will repeat this process for additional building environmental sensors. The device will use its second transceiver to transfer the sensor data to the external service.

Abstract: A system for collecting building environmental sensor data includes a first data collection device having a first transceiver that is configured to receive data from proximate building environmental sensors and a second transceiver that is configured to communicate with an external service. The device will, upon detecting that the first transceiver is within a receiving range of a first building environmental sensor, communicatively connect with the first building environmental sensor to receive a data stream that comprises sensor data captured by the first building environmental sensor. The device will continue to receive the data stream until the first transceiver either receives all of the sensor data or moves out of the receiving range. The device will repeat this process for additional building environmental sensors. The device will use its second transceiver to transfer the sensor data to the external service.

Abstract: A building environmental sensor includes a sensing element for collecting measurements of environmental parameters such as temperature, humidity, light, sound or the absence or presence of gas. The sensor will: (a) detect that a data collection device is within a communication range of the sensor; (b) generate a data stream that includes the data that the sensor collected; (c) transmit the data stream to the first data collection device; (d) determine that a communication link between the sensor and the first data collection device was lost before the first data stream was fully transmitted; (e) detect that a second data collection device is within the communication range of the sensor; (f) generate a second data stream that includes the remaining data; and (g) transmit the second data stream to the second data collection device.

Abstract: A certified application is installed onto a content creation device and a mobile certified application is installed onto a mobile device, the applications establish first and second trust relationships with the cloud service. The certified application and mobile certified application establish the third trust relationship via a proximity network. The mobile certified application generates a first ephemeral key pair having a private part. The certified application generates a second ephemeral key pair having a private part. The mobile certified application requests a service from the content creation device involving the transfer of data between the content creation device and the cloud service. The data is protected by at least one of the first and second ephemeral key pairs in response to invocation of the service. The service results in the data being stored at the cloud service and/or rendered at the content creation device.

Abstract: A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of machine learning models. During operation, the system determines a training set of data objects, wherein each data object is associated with one of a plurality of classes. The system divides the training set of data objects into a number of partitions. The system generates a respective machine learning model for each respective partition using a universal kernel function, which processes the data objects divided into a respective partition to obtain the ensemble of machine learning models. The system trains the machine learning models based on the data objects of the training set. The system predicts an outcome for a testing data object based on the ensemble of machine learning models and an ensemble decision rule.

Abstract: The system determines a version space associated with a set of data comprising a pool of unlabeled samples and a first plurality of labeled samples, wherein the version space includes a first set of classifiers corresponding to the first plurality of labeled samples. The system selects, from the pool of unlabeled samples, a second plurality of unlabeled samples comprising informative samples and non-informative samples. A respective informative sample corresponds to a first hyperplane which intersects the version space, and a respective non-informative sample corresponds to a second hyperplane which does not intersect the version space. The system acquires labels corresponding to the second plurality of unlabeled samples to obtain a third plurality of labeled samples. The system updates the first set of classifiers based on the third plurality of labeled samples, thereby improving accuracy of the first set of classifiers.

Abstract: One embodiment provides a system which facilitates reasoning about classifiers. During operation, the system determines a plurality of neural networks. The system derives, from a respective neural network, a linear model, wherein the linear model is constructed based on an output of a penultimate layer of the respective neural network. The system trains the linear model based on activations of the penultimate layer. The system maps parameters of the trained linear model into a version space.