Abstract: Systems and methods are described herein for generating adaptive feedback in response to a user request. Input indicative of a user request may be received and utilized to identify an item in an electronic catalog. A title for the item may be retrieved and provided, as input, to a machine-learning model. The machine-learning model may be trained to identify one or more segments of an input title. A shortened title may be generated from these identified segments and provided as output at the user device (e.g., via audible output provided at a speaker of the user device). In some embodiments, the length and content of the shortened title may vary based at least in part on the contextual intent of the user's request.

Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between items in the usage matrix. The usage matrix relates source items that a user already has to target items that a user might acquire. A cell in the usage matrix may store a value that describes the likelihood (e.g., probability) that an acquisition of item x will lead to an acquisition of item y. The value stored in cell (x,y) is not transitive with the value stored in cell (y,x). Values that are missing in the usage matrix may be computed using vectors in the latent space. Once the usage matrix is updated, a directed recommendation may be produced from data in the usage matrix. Initial values in the usage matrix may be produced from data associated with actual acquisitions.