Abstract: A computer-implemented method comprising: receiving, at a wireless network interface, telemetry data from a plurality of uniquely-identified end-devices in multiple communication networks, wherein the telemetry data is measured with respect to each of the end-devices over a one or more measuring periods of a predefined duration; processing the telemetry data to calculate features indicating usage patterns associated with each of the end-devices; and at a training stage, training a machine learning model on a training dataset comprising: (i) the features indicating usage patterns associated with each of the end-devices, and (ii) labels indicating one or more attributes associated with each of the end-devices, to obtain a trained machine learning classifier configured to predict the one or more attributes with respect to an unknown target end-device, by applying the trained machine learning model to telemetry data obtained from the unknown target end-device.

Abstract: A method comprising: receiving telemetry data representing data connections associated with a conferencing service instance provided to an end-device by a remote conferencing server; selecting a subset of the data connections, based on data traffic measurements; recursively, with respect to each current time window: (i) calculating data rate and packet roundtrip metrics from the telemetry data, (ii) determining a conferencing state of the conferencing service instance, based on the data rate metrics, (iii) determining a current conferencing service score for the conferencing service instance, based on applying respective scoring algorithms to the calculated metrics, wherein the scoring algorithms are selected based on the determined conferencing state, (iv) updating a quality of service (QoS) rating for the conferencing service instance based, at least in part, on the current conferencing service score, and (v) repeating steps (i)-(iv) with respect to a next time window.

Abstract: A computer-implemented method comprising: receiving, at a communications network interface, telemetry data representing a streaming service instance provided to an end-device by a remote streaming server over the Internet; and recursively, with respect to each current time window: (i) dividing, based on the telemetry data, the time window into segments, wherein each of the segments is classified based on its buffering status as a buffering segment or a non-buffering segment, (ii) calculating a current streaming service score, based on the buffering status of at least one of the segments within the current time window, and (iii) updating a quality of service (QoS) rating for the streaming media service instance, based, at least in part, on the current streaming service score.

Abstract: A computer-implemented method comprising: receiving, at a communications network interface, telemetry data representing data connections associated with a gaming service instance provided to an end-device by a remote gaming server over the Internet; selecting a subset of the data connections, based on data traffic measurements associated with each of the data connections; recursively, with respect to each current time window: (i) calculating a plurality of latency metrics over the subset of data connections, (ii) determining a current latency score of the gaming service instance, based on the plurality of latency metrics, (iii) updating a current Quality of Service (QoS) rating for the gaming service instance, based, at least in part, on the current latency score, and (iv) repeating steps (i)-(iii) with respect to a next time window.

Abstract: A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program commands, the program commands executable by the at least one hardware processor to: receive a sequence of computer program instructions, analyze the received sequence of program instructions to indicate execution points associated with non-deterministic program instructions, execute the sequence of computer program instructions while capturing program state data, wherein said program state data is associated with at least some of said indicated execution points, and, generate a replay of said execution, wherein said generating comprises inserting said captured program state data at said associated execution points.

Abstract: A computer-implemented method and a computer program product, the method comprising: receiving environmental sensory input from sensors embedded within a first device associated with the user, the environmental sensory input indicative of a user context; determining the context of the user from at least the environmental sensory input; receiving physiological sensory indicative of a user state input from physiological sensors embedded within a second device associated with the user; determining the state of the user from at least the physiological sensory input; determining a trigger based on at least the context and the state of the user; subject to the trigger, obtaining content materials based at least on the environmental sensory input, to be provided in association with the trigger; preparing message content to be sent in a message, using at least the content materials; and sending the message to at least one recipient over a digital communication channel.