Abstract: Methods, systems, and devices for sounding reference signal (SRS) resource indicator (SRI) association for configured grant (CG) based transmission and reception point (TRP) physical uplink shared channel (PUSCH) transmission are described. In some examples, a user equipment (UE) may receive first control signaling indicating a first and second sounding reference signal (SRS) resource set associated with a first and second set of power control parameter, respectively. The UE may receive second control signaling indicating a CG configuration, the first and second power control parameters for transmissions in the CG configuration. In some examples, the UE may determine a configuration status for one or more fields the first control signaling, the second control signaling, or both. The UE may select an SRS resource set based on the configuration status and may transmit one or more CG uplink transmissions with the CG configuration using the selected SRS resource set.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a spatial division multiplexing configuration associated with a physical uplink shared channel (PUSCH) having a first set of transmission layers corresponding to a first sounding reference signal (SRS) resource set and a second set of transmission layers corresponding to a second SRS resource set. The UE may transmit a PUSCH communication that includes the first and second sets of transmission layers based at least in part on a port mapping that indicates a first association between a first subset of demodulation reference signal (DMRS) ports of a set of DMRS ports and the first set of transmission layers, and a second association between a second subset of DMRS ports and the second set of transmission layers. Numerous other aspects are described.

Abstract: Apparatus, methods, and computer-readable media for facilitating multi-cluster control resource sets for downlink control channel repetition are disclosed herein. An example method for wireless communication at a UE includes receiving a configuration for a CORESET having a first RB set associated with a first TCI state and a second RB set associated with a second TCI state. The example method also includes monitoring for a control signal in the first RB set and the second RB set. Another example method includes receiving a configuration for an SSS associated with a first CORESET having a first TCI state and a second CORESET having a second TCI state. The example method also includes monitoring for a control signal based on the configuration for the SSS.

Abstract: Methods, systems, and devices for wireless communication are described. A UE may receive control signaling scheduling transmission of a plurality of repetitions of an uplink message and indicating a time gap threshold between at least one pair of adjacent repetitions. The UE may transmit a first repetition of the uplink message based at least in part on the control signaling. The UE may transmit, based on the control signaling, a second repetition of the uplink message separated in time from transmission of the first repetition of the uplink message by a time gap satisfying the time gap threshold.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of a pair of transmission configuration indication (TCI) states associated with a physical downlink shared channel (PDSCH). The UE may select, based at least in part on the indication, a default beam for receiving information via the PDSCH. Numerous other aspects are described.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Embodiments described herein provide for evaluating call metadata and certificates of inbound calls for authentication. The computer identifies a service provider indicated by the SPID and/or the ANI (or other identifier) of the metadata and identifies a service provider indicated by the SPID and/or ANI (or other identifier) of the certificate, then compares identities of the service providers and/or compares the data values associated with the service providers (e.g., SPIDs, ANIs). Based on this comparison, the computer determines whether the service provider that signed the certificate is first-party signer (e.g., carrier) for the ANI or a third-party signer that is signing certificates as the first-party signer for the ANI.

Abstract: Embodiments described herein provide for systems and methods for verifying authentic JIPs associated with ANIs using CLLIs known to be associated with the ANIs, allowing a computer to authenticate calls using the verified JIPs, among various factors. The computer builds a trust model for JIPs by correlating unique CLLIs to JIPs. A malicious actor might spoof numerous ANIs mapped to a single CLLI, but the malicious actor is unlikely to spoof multiple CLLIs due to the complexity of spoofing the volumes of ANIs associated with multiple CLLIs, so the CLLIs can be trusted when determining whether a JIP is authentic. The computer identifies an authentic JIP when the trust model indicates that a number of CLLIs associated with the JIP satisfies one or more thresholds. A machine-learning architecture references the fact that the JIP is authentic as an authentication factor for downstream call authentication functions.

Abstract: A failsafe mechanism for installing and removing temporary instrumentation during a runtime of an application. Initially, an application is configured with a baseline set of instrumented components such as methods. Additional instrumentation is then deployed in the application, such as to diagnose a performance problem. The failsafe mechanism ensures that the additional instrumentation is automatically removed, even when there is an interruption in a communication link to the application, a computing device failure, a software failure, or some other type of failure, which renders it impossible to manually roll back the instrumentation from a remote user interface. The failsafe mechanism can be provided using callbacks between the computing devices which detect when a connection is unexpectedly lost or closed. Termination of one callback can cascade to one or more other callbacks. The instrumentation rollback can involve reloading un-instrumented byte code of the application.

Abstract: Techniques for analyzing software in which un-instrumented components can be discovered and conditionally instrumented during a runtime of the software. Initially, software such as an application can be configured with a baseline set of instrumented components such as methods. As the application runs, performance data gathered from the instrumentation may indicate that the performance of some methods is below expectations. To analyze this, any methods which are callable from a method at issue are discovered, such as by inspecting the byte code of loaded classes in a JAVA Virtual Machine (JVM). To limit and focus the diagnosis, the instrumentation which is added to the discovered components can be conditional, so that the instrumentation is executed only in a specified context. The context can involve, e.g., a specified sequence of components in which a discovered component is called, and/or transaction data in which a discovered component is called.

Abstract: A failsafe mechanism for installing and removing temporary instrumentation during a runtime of an application. Initially, an application is configured with a baseline set of instrumented components such as methods. Additional instrumentation is then deployed in the application, such as to diagnose a performance problem. The failsafe mechanism ensures that the additional instrumentation is automatically removed, even when there is an interruption in a communication link to the application, a computing device failure, a software failure, or some other type of failure, which renders it impossible to manually roll back the instrumentation from a remote user interface. The failsafe mechanism can be provided using callbacks between the computing devices which detect when a connection is unexpectedly lost or closed. Termination of one callback can cascade to one or more other callbacks. The instrumentation rollback can involve reloading un-instrumented byte code of the application.

Abstract: Techniques for analyzing software in which un-instrumented components can be discovered and conditionally instrumented during a runtime of the software. Initially, software such as an application can be configured with a baseline set of instrumented components such as methods. As the application runs, performance data gathered from the instrumentation may indicate that the performance of some methods is below expectations. To analyze this, any methods which are callable from a method at issue are discovered, such as by inspecting the byte code of loaded classes in a JAVA Virtual Machine (JVM). To limit and focus the diagnosis, the instrumentation which is added to the discovered components can be conditional, so that the instrumentation is executed only in a specified context. The context can involve, e.g., a specified sequence of components in which a discovered component is called, and/or transaction data in which a discovered component is called.

Abstract: An optical signal switch has improved port isolation and extinction ratio by utilizing cascaded or tandem Mach-Zehnder Interferometer (MZI) with thermo-optical or electro-optical refractive index-modulating electrodes on the MZI arms.