Abstract: A solution for performing incremental mutation coverage testing is disclosed. The solution can identify, for a cycle of a test, a first signal not covered during preceding cycles of the test and select, for the current cycle, a first value for the first signal. The solution can, responsive to the first value, determine whether the first signal is covered by the test in the current cycle and identify whether a first assertion not covered during the one or more preceding cycles is covered in the current cycle. The solution can remove the first signal from a list of signals if it determines that it is not covered by the test and remove the first assertion from a list of assertions if the first assertion is determined to be covered. The system can identify, for a next cycle, a second signal from the updated one or more signals for the circuit.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for stochastic multi-period multi-objective optimization based recommendation system. An embodiment assigns a respective plurality of stochastic parameters to a plurality of recommendation objectives. The embodiment further associates each program of a plurality of programs with one or more recommendation objectives of the plurality of recommendation objectives, and selects, during a first recommendation time period, a first set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters. The embodiment then generates a first ordered list of recommended programs from the plurality of programs based on the first set of operative recommendation objectives.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Systems and methods are described for automated anomaly detection in computer networks using a dynamic network graph. Network data describing communications among computing entities are received, and a dynamic graph is constructed and maintained whose nodes represent the entities and whose edges represent observed communications. Behavior characteristics are computed for the nodes, and the nodes are clustered using a clustering algorithm to obtain cluster assignments. Anomalies are detected by identifying nodes whose behavior characteristics deviate from those of their assigned clusters, and alerts or security actions are generated in response. The system supports incremental updates to graph structure and cluster assignments as network conditions evolve, improving detection latency and accuracy.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic analysis and dynamic selection or creation of high quality supplemental content for a program (e.g., movies and TV shows) to maximize user engagement and the consumption of the media stream content by users. An example embodiment operates by using different machine learning (ML) models and large language models (LLMs) on existing supplemental content for the program to extract potential engaging features. The embodiment then conducts multivariate testing on the extracted potential engaging features to identify engaging features that improve user engagement for a user or a group of users.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining an optimal supplemental content for a media stream menu interface to maximize the consumption of the media stream content by users. An example embodiment operates by performing automated content recognition (ACR) on the media stream, thereby determining optimal supplemental content. The embodiment identifies a plurality of potential supplemental content items in the media stream based on the characteristics of the media stream. The embodiment then outputs the optimal supplemental content to a plurality of predetermined media devices.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a pseudo-user neural network model based a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy and an interest prompt based on an estimated state of the pseudo-user neural network model. The embodiment then generates, using the trained conversational recommendation system, a real-time recommendation having high play probability based on the minimal number of iterations of conversation between a user and the trained conversational recommendation system.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for pairwise comparison rating to reduce presentation bias in content recommendation. An embodiment operates by generating respective ranking values for a plurality of content items based on interactions between user devices and the content items. The respective ranking value for each content item is adjusted based on additional interactions between the user devices and the content items compared to predicted interactions between the user devices and content items. When a first user device of the plurality of user devices requests content, pairwise distances between the respective ranking values for the content items and respective weighted values for historical content items that have been previously interacted with by the first user device are determined.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.

Abstract: In some implementations, a device may receive, via a universal serial bus (USB) interface, configuration information and a supply of power from a network device. The device may receive, via an antenna that is external to the device, a first signal indicating timing information. The device may generate, based on the first signal, a second signal and a third signal, wherein the second signal comprises a one pulse per second signal and the third signal comprises a ten-megahertz signal. The device may provide, to the network device, the second signal and the third signal. The device may receive, via an input port, a clock signal to provide an extended holdover functionality to the network device.

Abstract: Aspects of the present disclosure provide an analyte sensor system. The analyte sensor system may include an analyte sensor configured to measure analyte levels of the user, a stamp antenna configured to transmit data indicative of the measured analyte levels, a printed circuit board (PCB) that operatively connects the analyte sensor to the stamp antenna, and a housing that encases at least the stamp antenna, the PCB, and a first portion of the analyte sensor. The housing may have a bottom portion through which a second portion of the analyte sensor protrudes to an exterior of the housing of the analyte sensor system. The bottom portion of the housing may be configured to be attached to a body of the user. The stamp antenna may be disposed on a bottom side of the PCB facing the bottom portion of the housing.

Abstract: A dynamic flip-flop circuit with a feedback loop includes a input tristate circuit configured to receive a data signal and a clock signal to output a tristate output data signal. The dynamic flip-flop circuit also includes a feedforward circuit configured to receive the tristate output data signal as input to output a feedforward output data signal. The dynamic flip-flop circuit also includes a feedback loop circuit configured to connect the output of the feedforward circuit and the output of the input tristate circuit. The feedback loop circuit includes a transmission gate circuit that is partially on.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for deep video understanding with large language models. An example embodiment operates by determining a relationship between respective first and second visual elements for each of a plurality of frames of a content item based on respective element types and respective locations for the respective first and second visual elements. For each of the plurality of frames, a respective visual prompt is generated describing the relationship between the respective first and second visual elements. Based on an audio-to-text conversion of audio content associated with the frame or classification of aural elements of the audio content, a respective audio prompt describing the audio content associated with each frame is generated.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Aspects of the present disclosure provide techniques for improving a communication range of an analyte sensor system. The analyte sensor system may include a analyte sensor configured to generate analyte data associated with analyte levels of a user of the analyte sensor system, an antenna system comprising a plurality of antennas, a transceiver circuit configured to transmit the analyte data to a communications device via one or more antennas of the plurality of antennas of the antenna system, a switching device configured to selectively couple the one or more antennas to the transceiver circuit, and a circuit board configured to operatively connect the transcutaneous analyte sensor with the transceiver circuit.