Abstract: Described implementations obtain credential information including an encrypted digital identity (ID). The encrypted digital ID may include a public component of a credential and identity data. Furthermore, the credential information may include cryptographically obfuscated data based on the identity data and a private component of the credential. A proof is obtained that includes proof data. The proof data may confirm that the credential information was correctly generated. Verification of the proof data, and confirmation that the cryptographically obfuscated data is not associated in a collection of cryptographically obfuscated data, cause a computer-implemented service to issue a pseudonym. The pseudonym is usable to generate a relationship associated with a computer-implemented service.

Abstract: This disclosure is directed to using cascading algorithms to automatically identify items placed in a tote or other receptacle utilized by users in material handling facilities as the users move around the facilities. A tote may store a database or “gallery” of item representations for all of the items that are stored in the facility that a user may place in their totes. The tote may use multiple algorithms in a cascading manner to analyze the gallery of item representations in order to iteratively narrow the search space of item representations in the gallery to determine which of the items was placed in the tote by a user. Upon identifying the item placed in the tote, the tote may add an item identifier for the item to a virtual listing of item identifiers representing items previously placed in the tote.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: This disclosure is directed to item-identifying carts that may generate and store image data of items being placed into, or removed from, the carts in the event that a cart is unable to recognize an item. For example, the cart may analyze frames of generated image data, representing item(s) being placed into or removed from the cart, to determine which one or more frames to store. If the cart is later unable to identify such an item, the cart may retrieve one or more of the stored frames, generate a user interface (UI) that includes these frame(s) and a request that a user operating the cart to help identify the item, and present the UI on a display of the cart.

Abstract: This disclosure is directed to using cascading algorithms to automatically identify items placed in a tote or other receptacle utilized by users in material handling facilities as the users move around the facilities. A tote may store a database or “gallery” of item representations for all of the items that are stored in the facility that a user may place in their totes. The tote may use multiple algorithms in a cascading manner to analyze the gallery of item representations in order to iteratively narrow the search space of item representations in the gallery to determine which of the items was placed in the tote by a user. Upon identifying the item placed in the tote, the tote may add an item identifier for the item to a virtual listing of item identifiers representing items previously placed in the tote.

Abstract: This disclosure is directed to using cascading algorithms to automatically identify items placed in a tote or other receptacle utilized by users in material handling facilities as the users move around the facilities. A tote may store a database or “gallery” of item representations for all of the items that are stored in the facility that a user may place in their totes. The tote may use multiple algorithms in a cascading manner to analyze the gallery of item representations in order to iteratively narrow the search space of item representations in the gallery to determine which of the items was placed in the tote by a user. Upon identifying the item placed in the tote, the tote may add an item identifier for the item to a virtual listing of item identifiers representing items previously placed in the tote.

Abstract: Described implementations obtain a proof of valid attestation data. The attestation data may include configuration data of a host computing system. A prover service may receive the attestation data. The prover service may generate a proof to prove that the attestation data includes valid configuration data of the host computer system, without revealing sensitive or private information of the host computing system. The proof may be a zero-knowledge proof.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: This disclosure describes using trained classifiers and additional data to identify items placed into a tote. The techniques may use a first classifier on image data representing an item to determine a region associated with a barcode of the item and may use one or more second classifiers to analyze the region for determining a candidate barcode. The techniques may then determine whether the candidate barcode corresponds to the actual barcode of the item. If so, the techniques may update a virtual cart associated with a user account to indicate the acquisition of the item. If not, then the techniques may use the additional data for identifying the item. For example, the techniques determine a current location of a tote for determining items that are within a threshold distance of the location and may use this information, along with the candidate barcode, to identify the item placed into the tote.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: There is provided a method of generating a machine learning (ML) model ensemble for computing likelihood of an entity failing to meet a target parameter, comprising: training ML-sub-models that each output sub-values for an input of raw data elements, training a principal ML model that outputs a value of an entity parameter corresponding to the target parameter for an input of the sub-values, using a training dataset including for sample entities, the ML-sub-values and corresponding entity parameters, inputting raw data elements associated with the entity into the ML-sub-models to obtain respective sub-values, in iterations: computing simulated adjustments to the sub-values to generate adjusted sub-values that are inputted into the principal ML model to obtain simulated values for the entity parameter, and generating a risk classifier that generates a likelihood of the entity failing to meet the target parameter according to an analysis of the simulated values for the entity parameter.

Abstract: This disclosure describes using trained classifiers and additional data to identify items placed into a tote. The techniques may use a first classifier on image data representing an item to determine a region associated with a barcode of the item and may use one or more second classifiers to analyze the region for determining a candidate barcode. The techniques may then determine whether the candidate barcode corresponds to the actual barcode of the item. If so, the techniques may update a virtual cart associated with a user account to indicate the acquisition of the item. If not, then the techniques may use the additional data for identifying the item. For example, the techniques determine a current location of a tote for determining items that are within a threshold distance of the location and may use this information, along with the candidate barcode, to identify the item placed into the tote.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: Outdoor apparel with a head cover and hand covers protect the wearer from the sun and other outdoor elements. The outdoor apparel comprises material that is resistant to ultraviolet light, traps heat, and is water wicking. The outdoor apparel further comprises a body and sleeves for additional protection from the elements. The hand covers are attached to the sleeves, which are attached to the body. The head cover may comprise an upper cover and a lower cover and be attached to the body. Together the body, head cover, sleeves, and hand covers protect the wearer from the sun and elements while participating in outdoor activities.

Abstract: An apparatus for a portable sail container may store a sail. In an embodiment, the apparatus has a portable sail container having a body and a cap. The body and cap both have a hollow interior. In addition, the body and cap further have energy attenuating means located on the longitudinal interior ends of the body and the cap individually.

Abstract: A resource may be abused if its users incur little or no cost. For example, e-mail abuse is rampant because sending an e-mail has negligible cost for the sender. Such abuse may be discouraged by introducing an artificial cost in the form of a moderately expensive computation. Thus, the sender of an e-mail might be required to pay by computing for a few seconds before the e-mail is accepted. Unfortunately, because of sharp disparities across computer systems, this approach may be ineffective against malicious users with high-end systems, prohibitively slow for legitimate users with low-end systems, or both. Starting from this observation, we identify moderately hard, memory bound functions that most recent computer systems will evaluate at about the same speed, and we explain how to use them for protecting against abuses.

Abstract: A resource may be abused if its users incur little or no cost. For example, e-mail abuse is rampant because sending an e-mail has negligible cost for the sender. Such abuse may be discouraged by introducing an artificial cost in the form of a moderately expensive computation. Thus, the sender of an e-mail might be required to pay by computing for a few seconds before the e-mail is accepted. Unfortunately, because of sharp disparities across computer systems, this approach may be ineffective against malicious users with high-end systems, prohibitively slow for legitimate users with low-end systems, or both. Starting from this observation, we identify moderately hard, memory bound functions that most recent computer systems will evaluate at about the same speed, and we explain how to use them for protecting against abuses.