Abstract: The disclosed technology is generally directed to communications in an IoT environment. In one example of the technology, a virtual IoT device is maintained and controlled to act as a representation of a connected device. Communications are received from an IoT support service. The received communications from the IoT support service are acted in response to, including sending communications to the IoT support service in response to the received communications, and changing the virtual IoT device as if the virtual IoT device were the connected device. The connected device is communicated with based on changes in the virtual IoT device.

Abstract: Technologies are described herein for event delivery and stream processing utilizing virtual processing agents. Upon receiving an event publication in a queue, a runtime system identifies one or more virtual processing agents that might be interested in, but have not explicitly subscribed to, the published event. Event information of the published event is then delivered to the identified virtual processing agents. Prior to the actual delivery, the runtime system further determines if the virtual processing agents have been activated and activates those processing agents that have not been activated. Based on the received event information, some of the virtual processing agents might decide to explicitly submit subscriptions to receive more events from the queue. The explicit subscriptions will trigger the runtime system to deliver the subscribed events to the processing agents, which might include past events that have been published in the queue before the explicit subscription is received.

Abstract: A computer-implemented technique is described herein for using a machine-trained model to identify individual objects within images. The technique then creates a relational index for the identified objects. That is, each index entry in the relational index is associated with a given object, and includes a set of attributes pertaining to the given object. One such attribute identifies at least one latent semantic vector associated with the given object. Each attribute provides a way of linking the given object to one or more other objects in the relational index. In one application of this technique, a user may submit a query that specifies a query object. The technique consults the relational index to find one or more objects that are related to the query object. In some cases, the query object and each of the other objects have a complementary relationship.

Abstract: Described herein are systems and methods for supporting multicast for virtual networks. In some embodiments, a native multicast approach can utilized in which packet replication is performed on a host node of a virtual machine (VM) with a multicast data packet encapsulated in uniquely address unicast packets. In some embodiments, a network virtual appliance can be utilized. A multicast packet sent from the VM can be unicasted to the network virtual appliance. The multicast appliance can then replicate the packet into multiple copies and send the packets to the receivers in the virtual network as unicast data packets encapsulating the multicast packet.

Abstract: Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Abstract: Embodiments may include collection of a first batch of acoustic feature frames of an audio signal, the number of acoustic feature frames of the first batch equal to a first batch size, input of the first batch to a speech recognition network, collection, in response to detection of a word hypothesis output by the speech recognition network, of a second batch of acoustic feature frames of the audio signal, the number of acoustic feature frames of the second batch equal to a second batch size greater than the first batch size, and input of the second batch to the speech recognition network.

Abstract: Techniques performed by a data processing system for reconstructing a three-dimensional (3D) model of the face of a human subject herein include obtaining source data comprising a two-dimensional (2D) image, three-dimensional (3D) image, or depth information representing a face of a human subject. Reconstructing the 3D model of the face also includes generating a 3D model of the face of the human subject based on the source data by analyzing the source data to produce a coarse 3D model of the face of the human subject, and refining the coarse 3D model through free form deformation to produce a fitted 3D model. The coarse 3D model may be a 3D Morphable Model (3DMM), and the coarse 3D model may be refined through free-form deformation in which the deformation of the mesh is limited by applying an as-rigid-as-possible (ARAP) deformation constraint.

Abstract: Innovations in encoder-side options for intra block copy (“BC”) prediction mode facilitate intra BC prediction that is more effective in terms of rate-distortion performance and/or computational efficiency of encoding. For example, some of the innovations relate to concurrently performing block vector (“BV”) estimation and making block splitting decisions for a block. Other innovations relate to selectively merging blocks into a larger block during BV estimation.

Abstract: A method of training a machine learning system. The method comprises collecting a first simulation dataset derived from a computer simulating a hypothetical scenario with a first simulation configuration having a first degree of fidelity. The method further comprises collecting a second simulation dataset derived from a computer simulating the hypothetical scenario with a second simulation configuration having a second degree of fidelity different than the first degree of fidelity. The method further comprises building a multi-fidelity training dataset including training data from both the first simulation dataset and the second simulation dataset according to an interleaving protocol.

Abstract: Example imaging systems are disclosed. One example includes a signal source and a signal receiver configured to receive a reflected electromagnetic signal from an imaged object. The imaging system further includes a processor configured to, for each of N wavelengths, determine a phase value of a reflected component of the reflected electromagnetic signal having that wavelength. The processor may compute an estimated distance to the imaged object at least in part by mapping the plurality of phase values to a 2N-dimensional vector, and computing a plurality of zeroes of a trigonometric polynomial. For each of the plurality of zeroes, computing the estimated distance may further include computing a respective geodesic distance between the 2N-dimensional vector and a point along the curve evaluated at that zero, and selecting and outputting a shortest geodesic distance multiplied by a least common multiple of the wavelengths.

Abstract: According to examples, an apparatus may include a memory on which is stored machine-readable instructions that may cause a processor to receive a user credential from a terminal, in which the user credential is stored in a machine-readable code on a user device and the terminal obtained the machine-readable code from the user device. The processor may also identify at least one authentication factor associated with the user based on the user credential, in which the authentication factor(s) includes a physical location associated with the user and/or a time-based factor. The processor may further determine whether the authentication factor(s) indicates that the user is to be granted access to the terminal and based on a determination that the authentication factor(s) indicates that the user is to be granted access to the terminal, may grant the user access to the terminal.

Abstract: This document relates to training of machine learning models. One example method involves providing a machine learning model having a first classification layer, a second classification layer, and an encoder that feeds into the first classification layer and the second classification layer. The example method also involves obtaining first training examples having explicit labels and second training examples having inferred labels. The inferred labels are based at least on actions associated with the second training examples. The example method also involves training the machine learning model using the first training examples and the second training examples using a training objective that considers first training loss of the first classification layer for the explicit labels and second training loss of the second classification layer for the inferred labels. The method also involves outputting a trained machine learning model having the encoder and the first classification layer.

Abstract: The description relates to hinged devices, such as hinged computing devices. One example can include first and second portions that rotate around a hinge shaft that is fixedly secured to the first portion and rotationally secured to the second portion. The second portion defining a first contact surface spaced apart from a second contact surface. Multiple friction clips friction fit around the hinge shaft and rotating with the hinge shaft between the first contact surface and the second contact surface.

Abstract: The present concepts relate to placing gameplay locations in the real world, where gameplay elements can be generated at the gameplay locations. One example categorizes types of physical elements described in geolocation data, and determines scores for the physical elements based on the categorizations. Gameplay locations can then be utilized according to the scores, and the scores can be continuously refined through user or moderator interaction with gameplay elements that may be generated at the gameplay locations.

Abstract: Examples are disclosed that relate to processing files between a local network and a cloud computing service. One example provides a computing device configured to be located between a local network and a cloud computing service, comprising a logic machine and a storage machine comprising instructions executable to receive, from a device within the local network, a file at a local share of the computing device, and in response to receiving the file, generate a file event indicating receipt of the file at the local share and provide the file event to a virtual machine executing on the computing device. The instructions are further executable to, based upon a property of the file, provide the file to a program operating within a container in the virtual machine to process the file, and send a result of executing the program on the file to the cloud computing service.

Abstract: Examples are disclosed herein relating to time-of-flight camera systems. One example provides a time-of-flight camera, comprising a global shutter image sensor comprising a plurality of pixels, each pixel of the plurality of pixels comprising a drain gate, and two or more taps, each tap comprising a storage diode configured to receive charge during an integration period, a floating diffusion capacitor configured to receive charge overflow from the storage diode during the integration period, and a dual conversion gate capacitor configured to receive charge overflow from the floating diffusion capacitor during the integration period.

Abstract: This application relates to video encoding and decoding, and specifically to tools and techniques for using and providing supplemental enhancement information in bitstreams. Among other things, the detailed description presents innovations for bitstreams having supplemental enhancement information (SEI). In particular embodiments, the SEI message includes picture source data (e.g., data indicating whether the associated picture is a progressive scan picture or an interlaced scan picture and/or data indicating whether the associated picture is a duplicate picture). The SEI message can also express a confidence level of the encoder's relative confidence in the accuracy of this picture source data. A decoder can use the confidence level indication to determine whether the decoder should separately identify the picture as progressive or interlaced and/or a duplicate picture or honor the picture source scanning information in the SEI as it is.

Abstract: A method of and system for translating a software resource of an application in real time is disclosed. The method may include receiving an indication to load the software resource, the software resource being in a first language, determining if the first language is a preferred language for a user, if the first language is not the preferred language for the user, sending a request to a machine translation model to translate the software resource from the first language to the preferred language, receiving a translated software resource in the preferred language, and loading the translated software resource.

Abstract: An example inline frame monitor is disclosed. The inline frame monitor injects monitoring logic into a document object model to monitor an activity within a dynamically loaded inline frame of a web page. Data regarding the activity within the dynamically loaded inline frame is received. A policy is applied to validate or invalidate the activity within the dynamically loaded inline frame.

Abstract: Examples of the present disclosure describe systems and methods for detecting monotone speech. In aspects, audio data provided by a user may be received a device. Pitch values may be calculated and/or extracted from the audio data. The non-zero pitch values may be divided into clusters. For each cluster, a Pitch Variation Quotient (PVQ) value may be calculated. The weighted average of PVQ values across the clusters may be calculated and compared to a threshold for determining monotone speech. Based on the comparison, the audio data may be classified as monotone or non-monotone and an indication of the classification may be provided to the user in real-time via a user interface. Upon the completion of the audio session in which the audio data is received, feedback for the audio data may be provided to the user via the user interface.