Abstract: Methods for detecting and mitigating abusive network activity based on versioned browser usage are performed by systems and devices. Usage values for network activity of legacy web browser versions are determined, where the usage values represent benign network activity associated with active instances of the legacy versions over prior time periods. The number of active instances of legacy browser versions is assumed to generally be monotonically decreasing over time, and thus a bound of benign network activity for each of the legacy versions can be estimated by associating an approximate percentage of benign traffic with a minimum past usage value. Current network activity is monitored to determine current usage values for the legacy versions, and network actions are performed based on current usage values deviating from past usage values according to the bound.

Abstract: A bot traffic detection system detects scripted network traffic. The bot traffic detection system may use a one-sided unsupervised machine learning technique to estimate distributions for human, non-scripted traffic (clean distributions). The clean distributions may be dynamically updated based on the latest traffic patterns. To estimate the clean distributions the bot traffic detection system may identify, for a certain subset of network traffic, feature values of the certain subset of network traffic that do not include bot traffic (clean buckets). Using clean traffic may provide more robust and stable behavior that can be tracked over time. Using the clean distributions, the bot traffic detection system may generate a rules table that indicates a likelihood that network traffic with a given combination of feature values is scripted network traffic. The bot traffic detection system may apply the rules table in real time to identify scripted network traffic.

Abstract: Examples described herein generally relate to a computer device including a memory, and at least one processor configured to evaluate a change to a user interface. The computer device monitor user interactions with the user interface prior to and after a change to the user interface. The monitoring includes collecting result metric data per user. The computer device divides users into a treated group and a control group based on whether each user engages in a particular interaction. The computer device generates a result metric time series for the treated group and a partitioned result metric time series for the control group. The computer device estimates a conditional distribution of the result metric and a counterfactual behavior using a Bayesian machine learning model based on the result metric time series. The computer device determines a treatment effect of the change to the user interface on the result metric data.

Abstract: A bot traffic detection system detects scripted network traffic. The bot traffic detection system may use a one-sided unsupervised machine learning technique to estimate distributions for human, non-scripted traffic (clean distributions). The clean distributions may be dynamically updated based on the latest traffic patterns. To estimate the clean distributions the bot traffic detection system may identify, for a certain subset of network traffic, feature values of the certain subset of network traffic that do not include bot traffic (clean buckets). Using clean traffic may provide more robust and stable behavior that can be tracked over time. Using the clean distributions, the bot traffic detection system may generate a rules table that indicates a likelihood that network traffic with a given combination of feature values is scripted network traffic. The bot traffic detection system may apply the rules table in real time to identify scripted network traffic.

Abstract: Examples described herein generally relate to a computer device including a memory, and at least one processor configured to evaluate a change to a user interface. The computer device monitor user interactions with the user interface prior to and after a change to the user interface. The monitoring includes collecting result metric data per user. The computer device divides users into a treated group and a control group based on whether each user engages in a particular interaction. The computer device generates a result metric time series for the treated group and a partitioned result metric time series for the control group. The computer device estimates a conditional distribution of the result metric and a counterfactual behavior using a Bayesian machine learning model based on the result metric time series. The computer device determines a treatment effect of the change to the user interface on the result metric data.

Abstract: An evaluation platform receives a data set and a description of an outcome, such as predicting results of trends, recognizing patterns, and evaluating options according to specified criteria. The description is evaluated to select candidate evaluators that may be capable of achieving the outcome, and to translate the outcome into a goal for each selected candidate evaluator. The evaluator candidate set is trained using a training data set, and an initial evaluator is selected that exhibits the highest performance to achieve the outcome over the data set. The initial evaluator is applied to achieve the requested outcome over the data set. Optionally, the performance of the initial evaluator may be monitored to detect performance drift. In this event, the evaluator candidate set is reevaluated to identify a substitute evaluator exhibiting higher performance than the initial evaluator, which replaces the initial evaluator in the continued evaluation of the data set.

Abstract: Embodiments are described herein for a compute resource-efficient technique for estimating a measurable effect attributable to participating in an activity. The foregoing is achieved by analyzing previously-collected features comprising different items of information associated with millions of users. The users comprise a first group that have participated in the activity and a second group that have not participated in the activity. Machine learning-based techniques are utilized to match users from each of the groups, with the goal being to match users having the same distribution of features. In particular, machine learning-based techniques are utilized to determine a propensity score for each user based on the features. Users from the first group are matched to users from the second group having a propensity score falling within a particular range. The measurable effect is determined based on an analysis of an average participation level of users matched in each range.

Abstract: A method of facilitating customization of a software-implemented business process includes storing, within a mobile computing device, a subscription list of entities. The subscription list is defined by subscription metadata. Customized data is received. The customized data corresponds to the entities identified in the subscription list. The received customized metadata is stored on the mobile computing device.

Abstract: An evaluation platform receives a data set and a description of an outcome, such as predicting results of trends, recognizing patterns, and evaluating options according to specified criteria. The description is evaluated to select candidate evaluators that may be capable of achieving the outcome, and to translate the outcome into a goal for each selected candidate evaluator. The evaluator candidate set is trained using a training data set, and an initial evaluator is selected that exhibits the highest performance to achieve the outcome over the data set. The initial evaluator is applied to achieve the requested outcome over the data set. Optionally, the performance of the initial evaluator may be monitored to detect performance drift. In this event, the evaluator candidate set is reevaluated to identify a substitute evaluator exhibiting higher performance than the initial evaluator, which replaces the initial evaluator in the continued evaluation of the data set.

Abstract: A method of facilitating customization of a software-implemented business process includes storing, within a mobile computing device, a subscription list of entities. The subscription list is defined by subscription metadata. Customized data is received. The customized data corresponds to the entities identified in the subscription list. The received customized metadata is stored on the mobile computing device.

Abstract: A method of facilitating customization of a software-implemented business process includes storing, within a mobile computing device, a subscription list of entities. The subscription list being defined by subscription metadata. Customized data is received. The customized data corresponds to the entities identified in the subscription list. The received customized metadata is stored on the mobile computing device.

Abstract: A method of facilitating customization of a software-implemented business process includes storing, within a mobile computing device, a subscription list of entities. The subscription list being defined by subscription metadata. Customized data is received. The customized data corresponds to the entities identified in the subscription list. The received customized metadata is stored on the mobile computing device.

Abstract: In a method of customizing a software-implemented business process on a mobile computing device, customized metadata defining customizations of the business process are provided. Next, the metadata is deployed to the mobile computing device and stored in a data store of the mobile computing device. The customizations defined by the metadata are then applied to the software-implemented business process.

Abstract: In a method of customizing a software-implemented business process on a mobile computing device, subscriptions are defined to business solutions entities that are defined by metadata. Next, the entities identified by the subscriptions are uploaded to the mobile computing device.

Abstract: In a method of customizing a software-implemented business process on a mobile computing device, customized metadata defining customizations of the business process are provided. Next, the metadata is deployed to the mobile computing device and stored in a data store of the mobile computing device. The customizations defined by the metadata are then applied to the software-implemented business process.