Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Abstract: Embodiments are disclosed for performing content authentication. A method of content authentication may include dividing a query video into a plurality of chunks. A feature vector may be generated, using a fingerprinting model, for each chunk from the plurality of chunks. Similar video chunks are identified from a trusted chunk database based on the feature vectors using a multi-chunk search policy. One or more original videos corresponding to the query video are then returned.

Abstract: Various disclosed embodiments are directed to using one or more algorithms or models to select a suitable or optimal variation, among multiple variations, of a given content item based on feedback. Such feedback guides the algorithm or model to arrive at suitable variation result such that the variation result is produced as the output for consumption by users. Further, various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things, as described in more detail below.

Abstract: Embodiments of the present technology are directed to facilitating generation of experiment metric values, such as expected sample size and/or minimal detectable effect, for anytime valid confidence sequences (e.g., asymptotic confidence sequences). In one embodiment, a set of parameter values associated with an experiment using asymptotic confidence sequences are obtained. The set of parameter values include a minimal detectable effect and an uncertainty interval. Thereafter, an expected sample size for executing the experiment is determined based on the minimal detectable effect and the uncertainty interval. The expected sample size is provided for utilization in association with the experiment using asymptotic confidence sequences.

Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Abstract: Techniques for web bot detection using behavioral analysis and machine learning are disclosed. In an example method, a processing device receives an indication of a network interaction by a client agent, from which behaviors of the client agent can be determined. A heuristics module may classify the client agent as in an unknown class based on the behaviors of the client agent. A trained adversarial neural network may also classify the client agent as in the unknown class. The processing device then generates a graph representation of the network interaction. A trained graph convolutional neural network may classify the client agent as in a bot class using the graph representation. Based on the classification of the client agent as a bot, the processing device executes a command to cause a bot countermeasure and generates a notification including information about the behaviors of the client agent.

Abstract: A method, a system, and a computer program product for analyzing data collected during a randomized controlled experiment to determine an effect of variations of digital content. Determination of the effect includes execution of first and second testing sequences that prompt responses to first and second digital contents, respectively, from users. The testing sequences execute during a predetermined duration of time. Responses to the first and second testing sequences generate first and second test data, respectively. One or more confidence intervals for each first and second test data are generated at a randomly selected time during the predetermined duration of time. A testing metric indicating the effect of the second digital content over the first digital content is determined at the randomly selected time. The testing metric is determined at any time before expiration of the predetermined duration of time.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

Abstract: Systems and methods for diversity auditing are described. The systems and methods include identifying a plurality of images; detecting a face in each of the plurality of images using a face detection network; classifying the face in each of the plurality of images based on a sensitive attribute using an image classification network; generating a distribution of the sensitive attribute in the plurality of images based on the classification; and computing a diversity score for the plurality of images based on the distribution.

Abstract: Online testing data governance techniques and systems are described. These techniques support incorporation of data governance as part of online testing through use of a testing governance system implemented as part of a testing system. These techniques are configured to address technical challenges specific to online testing involving design of the online test, runtime during which the online test is executed, and reporting of test results.

Abstract: Systems and methods for media generation are provided. According to one aspect, a method for media generation includes obtaining a media object and context data describing a context of the media object, wherein the media object comprises one or more modification parameters; generating a modified media object by adjusting the one or more modification parameters using a reinforcement learning model based on the context data; and providing the modified media object within the context.

Abstract: Certain aspects and features of this disclosure relate to providing anytime-valid confidence sequences for multiple messaging treatments in an experiment. A process controls and/or corrects statistical error when multiple messaging treatments are being evaluated together. Messages can be stored, formatted, and transmitted from a communication server or other computing system. In one example, each test message from among multiple test messages is sent to an independent group of recipients over some period of time. An analytics application programmatically evaluates a metric related to message responses over time and determines a difference in the metric for each of several unique messages as compared to a baseline message. The analytics application also determines a confidence value and can display the changing confidence value in sequence over time along with the current difference, or lift, while maintaining the accuracy of the values.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.

Abstract: The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.

Abstract: In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.

Abstract: One aspect of systems and methods for segment size estimation includes identifying a segment of users for a first time period based on time series data, wherein the time series data includes a series of interactions between users and a content channel and wherein the segment includes a portion of the users interacting with the content channel during the first time period; computing a segment return value for a second time period based on the time series data by computing a first subset and a second subset of the segment, wherein the first subset includes users that interact with the content channel greater than a threshold number of times during a range of the time series data and the second subset comprises a complement of the first subset with respect to the segment; and providing customized content to a user in the segment based on the segment return value.

Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

Abstract: A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

Abstract: This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.

Abstract: Techniques for video manipulation detection are described to detect one or more manipulations present in digital content such as a digital video. A detection system, for instance, receives a frame of a digital video that depicts at least one entity. Coordinates of the frame that correspond to a gaze location of the entity are determined, and the detection system determines whether the coordinates correspond to a portion of an object depicted in the frame to calculate a gaze confidence score. A manipulation score is generated that indicates whether the digital video has been manipulated based on the gaze confidence score. In some examples, the manipulation score is based on at least one additional confidence score.