Abstract: Computer-implemented systems and methods for Forward Error Correction (FEC) at the IP-Layer with adaptive bandwidth overhead minimization in a packet transmission network, the system including an FEC encoder to process IP packets and generate FEC encoded packets and repair packets, an FEC decoder to receive and process the FEC encoded packets and repair packets, and an FEC controller that includes a set of computer-implemented instructions to carry out functions including configuring an FEC algorithm to control FEC encoding and decoding, packet recovery, and retrieve packet transmission statistics, determining if network bandwidth overhead needs adjustment, controlling tuning parameters, and implementing predictive analysis based at least in part on historic data.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that control bias in machine learning models by utilizing a fairness deviation constraint to learn a decision matrix that modifies machine learning model predictions. In one or more embodiments, the disclosed systems generate, utilizing a machine learning model, predicted classification probabilities from a plurality of samples comprising a plurality of values for a data attribute. Moreover, the disclosed systems determine utilizing a decision matrix and the predicted classification probabilities, that the machine learning model fails to satisfy a fairness deviation constraint with respect to a value of the data attribute. In addition, the disclosed systems generate a modified decision matrix for the machine learning model to satisfy the fairness deviation constraint by selecting a modified decision threshold for the value of the data attribute.

Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

Abstract: Disclosed herein, among other things, are systems and methods for a hearing device antenna. One aspect of the present subject matter includes a hearing device configured to be worn in an ear of a wearer to perform wireless communication. The hearing device includes a housing, hearing electronics within the housing, and an inverted F antenna or loop antenna disposed at least partially in the housing and configured for performing 2.4 GHz wireless communication. In various embodiments, at least a portion of the antenna protrudes from an exterior of the housing.

Abstract: In some examples, a prototype model that includes a representative subset of data points (e.g., inputs and output classifications) of a machine learning model is analyzed to efficiently interpret the machine learning model's behavior. Performance metrics such as a critic fraction, local explanation scores, and global explanation scores are determined. A local explanation score capture an importance of a feature of a test point to the machine learning model determining a particular class for the test point and is computed by comparing a value of a feature of a test point to values for prototypes of the prototype model. Using a similar approach, global explanation scores may be computed for features by combining local explanation scores for data points. A critic fraction may be computed to quantify a misclassification rate of the prototype model, indicating the interpretability of the model.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for analyzing feature impact of a machine-learning model using prototypes across analytical spaces. For example, the disclosed system can identify features of data points used to generate outputs via a machine-learning model and then map the features to a feature space and the outputs to a label space. The disclosed system can then utilize an iterative process to determine prototypes from the data points based on distances between the data points in the feature space and the label space. Furthermore, the disclosed system can then use the prototypes to determine the impact of the features within the machine-learning model based on locally sensitive directions; region variability; or mean, range, and variance of features of the prototypes.

Abstract: Disclosed herein, among other things, are systems and methods for a hearing device antenna. One aspect of the present subject matter includes a hearing device configured to be worn in an ear of a wearer to perform wireless communication. The hearing device includes a housing, hearing electronics within the housing, and an inverted F antenna or loop antenna disposed at least partially in the housing and configured for performing 2.4 GHz wireless communication. In various embodiments, at least a portion of the antenna protrudes from an exterior of the housing.

Abstract: Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.

Abstract: In various embodiments of the present disclosure, output data generated by a deployed machine learning model may be received. An input data anomaly may be detected based at least in part on analyzing input data of the deployed machine learning model. An output data anomaly may further be detected based at least in part on analyzing the output data of the deployed machine learning model. A determination may be made that the input data anomaly contributed to the output data anomaly based at least in part on comparing the input data anomaly to the output data anomaly. A report may be generated that is indicative of the input data anomaly and the output data anomaly, and the report may be transmitted to a client device.

Abstract: The present disclosure relates to utilizing a graph convolutional neural network to generate similarity probabilities between pairs of digital identities associated with digital transactions based on time dependencies for use in identifying fraudulent transactions. For example, the disclosed systems can generate a transaction graph that includes nodes corresponding to digital identities. The disclosed systems can utilize a time-dependent graph convolutional neural network to generate node embeddings for the nodes based on the edge connections of the transaction graph. Further, the disclosed systems can utilize the node embeddings to determine whether a digital identity is associated with a fraudulent transaction.

Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

Abstract: A device such as an autonomous mobile device may include an extensible mast or other structure that changes length during operation. A cable between electronics at the ends of the structure includes a data line for signals and a power line for electrical power. A deployed length of the cable is determined and used to determine the phase of an antinoise signal that is radiated using the power line. The antinoise signal destructively interferes with at least a portion of the radiated noise from the data line, reducing the overall amplitude of the radiated noise. The deployed length may also be used to adjust other parameters, such as equalizer settings for one or more of a transmitter or receiver that is connected to the data line.

Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

Abstract: Technologies for dynamic noise cancelation using noise patterns are described. One device includes a moveable assembly having first and second degrees of freedom. A component within the base assembly radiates electromagnetic energy in a radiation pattern as a noise source when operating and an antenna, coupled to a receiver, is disposed on the support member. The processing device, using noise profile data about the radiation pattern of the noise source, determines that the component is operating and a current angle between the component and the antenna. The processing device determines a difference value between the current angle and the first angle and causes the moveable assembly to move at least one of the antenna or the base assembly according to the difference value.

Abstract: Embodiments of the present disclosure are directed towards generating images conditioned on a desired attribute. In particular, an attribute-based image generation system can use a directional-GAN architecture to generate images conditioned on a desired attribute. A latent vector and a desired attribute are received. A feature subspace is determined for the latent vector using a latent-attribute linear classifier trained to determine a relationship between the latent vector and the desired attribute. An image is generated using the latent vector such that the image contains the desired attribute. In embodiments, where the feature space differs from a desired feature subspace, a directional vector is applied to the latent vector that shifts the latent vector from the feature subspace to the desired feature subspace. This modified latent vector is then used during generation of the image.

Abstract: A streaming media player includes a printed circuit board, a video processor, a wireless communications processor, and a patch antenna. The patch antenna includes dielectric material, a conductive patch, and a first antenna feed in a first position with respect to the conductive patch. The patch antenna includes a second antenna feed in a second position with respect to the conductive patch. The first position is orthogonal to the second position. In an embodiment, the second position is rotationally offset from the first position by 90 degrees around an axis through a center of the conductive patch. The streaming media player includes a connector coupled to the video processor and configured to removably couple to a connection port on a display device.

Abstract: A device includes a substrate and a video processor on the substrate. A first patch antenna is mounted to the substrate and configured to transmit and receive radio frequency signals in a frequency range. The first patch antenna is closer to a first end of the substrate than a second end of the substrate. A second patch antenna is mounted to the substrate and is configured to transmit and receive radio frequency signals in the frequency range. The second patch antenna is closer to the first end of the substrate than the second end of the substrate. The substrate includes a ground isolation region between at least one of the first patch antenna and the second patch antenna and the video processor. A media connector is on the substrate and electrically connected to the video processor.

Abstract: Embodiments of the present disclosure are directed towards generating images conditioned on a desired attribute. In particular, an attribute-based image generation system can use a directional-GAN architecture to generate images conditioned on a desired attribute. A latent vector and a desired attribute are received. A feature subspace is determined for the latent vector using a latent-attribute linear classifier trained to determine a relationship between the latent vector and the desired attribute. An image is generated using the latent vector such that the image contains the desired attribute. In embodiments, where the feature space differs from a desired feature subspace, a directional vector is applied to the latent vector that shifts the latent vector from the feature subspace to the desired feature subspace. This modified latent vector is then used during generation of the image.