Abstract: Techniques for sanitizing personally identifiable information (PII) from audio and visual data are provided. For example, in a scenario where the data comprises an audio signal with speech uttered by a speaker S, these techniques can include removing, obfuscating, or transforming speech related and non-speech related audio cues in the audio signal that can be used to trace the identity of S, while allowing the content of S's speech to remain recognizable. As another example, in a scenario where the data comprises an image or video in which a person P appears, these techniques can include removing, obfuscating, or transforming P's visible biological features and visual indicators of P's location, belongings, or personal data in the image/video, while allowing the general nature of the footage to remain discernable. Through this PII sanitization process, the privacy of individuals portrayed in the audio or visual data can be preserved.

Abstract: Techniques for sanitizing personally identifiable information (PII) from audio and visual data are provided. For instance, in a scenario where the data comprises an audio signal with speech uttered by a person P, these techniques can include removing/obfuscating/transforming speech-related PII in the audio signal such as pitch and acoustic cues associated with P's vocal tract shape and/or vocal actuators (e.g., lips, nasal air bypass, teeth, tongue, etc.) while allowing the content of the speech to remain recognizable. Further, in a scenario where the data comprises a still image or video in which a person P appears, these techniques can include removing/obfuscating/transforming visual PII in the image or video such as P's biological features and indicators of P's location/belongings/data while allowing the general nature of the image or video to remain discernable. Through this PII sanitization process, the privacy of individuals portrayed in the audio or visual data can be preserved.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: Techniques for sanitizing personally identifiable information (PII) from audio and visual data are provided. For instance, in a scenario where the data comprises an audio signal with speech uttered by a person P, these techniques can include removing/obfuscating/transforming speech-related PII in the audio signal such as pitch and acoustic cues associated with P's vocal tract shape and/or vocal actuators (e.g., lips, nasal air bypass, teeth, tongue, etc.) while allowing the content of the speech to remain recognizable. Further, in a scenario where the data comprises a still image or video in which a person P appears, these techniques can include removing/obfuscating/transforming visual PII in the image or video such as P's biological features and indicators of P's location/belongings/data while allowing the general nature of the image or video to remain discernable. Through this PII sanitization process, the privacy of individuals portrayed in the audio or visual data can be preserved.

Abstract: Techniques for sanitizing personally identifiable information (PII) from audio and visual data are provided. For example, in a scenario where the data comprises an audio signal with speech uttered by a speaker S, these techniques can include removing, obfuscating, or transforming speech related and non-speech related audio cues in the audio signal that can be used to trace the identity of S, while allowing the content of S's speech to remain recognizable. As another example, in a scenario where the data comprises an image or video in which a person P appears, these techniques can include removing, obfuscating, or transforming P's visible biological features and visual indicators of P's location, belongings, or personal data in the image/video, while allowing the general nature of the footage to remain discernable. Through this PII sanitization process, the privacy of individuals portrayed in the audio or visual data can be preserved.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: Quantum circuits and circuit designs are based on factorizations of diagonal unitaries using a phase context. The cost/complexity of phase sparse/phase dense approximations is compared, and a suitable implementation is selected. For phase sparse implementations in the Clifford+T basis, required entangling circuits are defined based on a number of occurrences of a phase in the phase context in a factor of the diagonal unitary.

Abstract: Quantum circuits and circuit designs are based on factorizations of diagonal unitaries using a phase context. The cost/complexity of phase sparse/phase dense approximations is compared, and a suitable implementation is selected. For phase sparse implementations in the Clifford+T basis, required entangling circuits are defined based on a number of occurrences of a phase in the phase context in a factor of the diagonal unitary.