Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located.

Abstract: A system and method for controlling access to a digital asset. A first host device is provided to a first host device owner and rights are determined to a digital asset. A digital charm is selected, wherein selecting includes granting to the digital charm certain rights to the digital asset. The digital charm is attached to the first host device to form a charm system, wherein attaching includes conferring, to the first host device owner, the rights to the digital asset that were granted to the digital charm.

Abstract: Systems, apparatuses and methods may leverage technology that generates textual descriptions of scenes based on visual content and audio content and generates haptic signals based on the textual descriptions if the textual descriptions satisfy a safety-related condition. Additionally, audio output signals may be generated based on the textual descriptions if the textual descriptions do not satisfy the safety-related conditions. In one example, a complex neural network (CNN) is trained and used to generate the textual descriptions in real-time.

Abstract: A convolutional neural network for classifying time series data uses a dynamic context selection. In one example a method includes receiving a plurality of inputs of different sizes at a convolutional neural network, applying convolution and pooling to each of the inputs to provide a plurality of outputs of different sizes, changing the size of each of the outputs to a selected uniform size, reshaping each of the outputs to a vector, and fully connecting the vectors.

Abstract: Methods, apparatus, systems and articles of manufacture to monitor tasks performed in an environment of interest are disclosed. One such apparatus is a task monitor that includes a signature comparator to compare a first signature to a second signature. The first signature is generated based on first audio collected in an environment of interest and the second signature is generated based on second audio collected during performance of a task in the environment of interest. The task monitor also includes an object identifier to identify an object corresponding to a location determined based on an angle of arrival of the first audio at a sensor. A task identifier is to identify the task as a cause of the first audio when the signature comparator determines the first signature matches the second signature and the object identifier identifies the object as a same object used to perform the task.

Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located.

Abstract: System and techniques for tracking caloric expenditure using sensor driven fingerprints are described herein. A set of outputs may be obtained from a plurality of sensors. A fingerprint may be generated using the set of outputs. The fingerprint may correspond to an activity observed by the plurality of sensors. The generated fingerprint may be compared to a set of fingerprints stored in a database. Each fingerprint of the set of fingerprints may correspond to a respective caloric expenditure. A caloric expenditure may be calculated for the activity based on the comparison.

Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located.

Abstract: Technologies for automated context-aware media curation include a computing device that captures context data associated with media objects. The context data may include location data, proximity data, behavior data of the user, and social activity data. The computing device generates inferred context data using one or more cognitive or machine learning algorithms. The inferred context data may include semantic time or location data, activity data, or sentiment data. The computing device updates a user context model and an expanded media object graph based on the context data and the inferred context data. The computing device selects one or more target media objects using the user context model and the expanded media object graph. The computing device may present context-aware media experiences to the user with the target media objects. Context-aware media experiences may include contextual semantic search and contextual media browsing. Other embodiments are described and claimed.

Abstract: Systems and methods may provide for determining a usage context of a headset and detecting one or more danger-related conditions based on the usage context. Additionally, one or more settings of the headset may be adjusted in response to at least one of the one or more danger-related conditions. In one example, adjusting the one or more settings includes one or more of deactivating noise cancellation, adjusting noise cancellation to increase an intensity of at least one of the one or more danger-related sounds, or adjusting one or more audio playback settings associated with the headset.

Abstract: Embodiments of apparatus and methods for voice based user enrollment with video assistance are described. In embodiments, an apparatus may include a face recognition module to identify a user from a first plurality of images and a lip motion detection module to detect the lip motion of the user from a second plurality of images. The apparatus may also include a recording module to activate a recording of the user. The apparatus may further include a user enrollment module, coupled with the recording module and the lip motion detection module, to establish a speaker model of the user based at least in part on the recording and the lip motion of the user. Other embodiments may be described and/or claimed.

Abstract: Systems and methods may provide for determining a usage context of a headset and detecting one or more danger-related conditions based on the usage context. Additionally, one or more settings of the headset may be adjusted in response to at least one of the one or more danger-related conditions. In one example, adjusting the one or more settings includes one or more of deactivating noise cancellation, adjusting noise cancellation to increase an intensity of at least one of the one or more danger-related sounds, or adjusting one or more audio playback settings associated with the headset.

Abstract: Embodiments of apparatus and methods for voice based user enrollment with video assistance are described. In embodiments, an apparatus may include a face recognition module to identify a user from a first plurality of images and a lip motion detection module to detect the lip motion of the user from a second plurality of images. The apparatus may also include a recording module to activate a recording of the user. The apparatus may further include a user enrollment module, coupled with the recording module and the lip motion detection module, to establish a speaker model of the user based at least in part on the recording and the lip motion of the user. Other embodiments may be described and/or claimed.

Abstract: Systems and methods for proximity detection between electronic devices are disclosed. One or more electronic devices transmit signals to a proximity server, which determines whether the first electronic device may be proximate the second electronic device. The proximity server transmits a signal to the first electronic device and the second device, and in response to the signal, the first and second electronic devices activate an environmental sensor, collect at least one sample of environmental data, extract at least one feature set of the environmental data, generate a first obscured feature from the feature set, transmit the first and second obscured feature sets to the proximity server. The proximity server uses the first obscured feature set and the second obscured feature set to determine whether the first electronic device and the second electronic device are proximate.

Abstract: Systems and methods for proximity detection between electronic devices are disclosed. One or more electronic devices transmit signals to a proximity server, which determines whether the first electronic device may be proximate the second electronic device. The proximity server transmits a signal to the first electronic device and the second device, and in response to the signal, the first and second electronic devices activate an environmental sensor, collect at least one sample of environmental data, extract at least one feature set of the environmental data, generate a first obscured feature from the feature set, transmit the first and second obscured feature sets to the proximity server. The proximity server uses the first obscured feature set and the second obscured feature set to determine whether the first electronic device and the second electronic device are proximate.

Abstract: A method is provided. The method includes receiving inputs typed by a user of a keyboard and analyzing the inputs to identify typing errors made by the user. The method also includes customizing a layout of the keyboard to reduce the identified typing errors.