Abstract: A vision-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: An enterprise's data source relevant to their customers is obtained at predefined intervals of time. The data is processed through classification machine learning models (MLMs) and labeled with features. The labeled data is provided as input to an attrition predicting MLM and one or more lists are provided as output identifying customers likely to leave the enterprise and customers with a high likelihood of remaining with the enterprise when provided an incentive to do so. The one or more lists are provided to enterprise interfaces and/or promotion systems for mitigating customer attrition. In an embodiment, results for the attrition predicting MLM are compared against results predicted by a Recency, Frequency, Monetary (RFM) analyzer in view of subsequent actual observed results for the customers with the enterprise. A continuous feedback loop for retaining the attrition prediction MLM is processed based on the comparison to improve the prediction MLM's F1 accuracy metric.

Abstract: A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: A device attestation server and method for attesting to the integrity of a mobile device is provided. An attestation request is sent from a mobile device to a device attestation server. The device attestation server runs an attestation method that is supported by the mobile device. The device attestation server creates an attestation token that includes a validation result and a plurality of attributes. The device attestation server sends the attestation token to the mobile device, which performs a validation method using the attestation token.

Abstract: A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Abstract: A device attestation server and method for attesting to the integrity of a mobile device is provided. An attestation request is sent from a mobile device to a device attestation server. The device attestation server runs an attestation method that is supported by the mobile device. The device attestation server creates an attestation token that includes a validation result and a plurality of attributes. The device attestation server sends the attestation token to the mobile device, which performs a validation method using the attestation token.