Abstract: A method for authenticating a counterparty to a digital transaction includes obtaining, at a mobile terminal from a unverified counterparty, characteristic content associated with the digital transaction to be displayed in a trusted user interface provided by the mobile terminal, sending, by the mobile terminal, data associated with the characteristic content to an authentication server; and obtaining, from the authentication server, a result of an authentication judgment by the authentication server, the authentication judgment based on the data associated with the characteristic content and an item of reference content.

Abstract: A loading/unloading system for a quay type full-automatic container terminal includes a plurality of shore cranes, an operation lane area between two rails of the shore cranes, an operation area from a rear side of a landside rail of the shore cranes to a yard, an automatic container yard area, an operation lane area of the yard, and facilities behind the yard. The shore cranes are disposed in parallel at a front edge of a container terminal to autonomously complete shipping and unshipping operations of containers, and autonomously complete loading/unloading processes of artificial intelligence transportation robots through information interaction with an artificial intelligence transportation robot system. The operation lane area between two rails of the shore cranes includes: a lambdoidal reverse operation area of inner container trucks, a ship lofting operation area, and a loading/unloading operation area of the inner container trucks, which are physically isolated by fences.

Abstract: The disclosure provides an intelligent horizontal transportation system for a completely automatic side-loading/unloading container terminal. The system includes a horizontal transportation device including an unmanned artificial intelligence robot of transportation (ART) and a horizontal transportation control system that intelligently manages and controls the ART to enable it to complete horizontal transportation. The horizontal transportation control system is in real-time connection and communication with a terminal operation system (TOS), an automatic field crane, an automatic shore crane and the ART to complete information interactive processing to realize information interconnection, so as to guarantee real-time utilization of information and intelligent control of the ART. The horizontal transportation control system realizes intelligent management and control of the horizontal transportation device by executing the following functions.

Abstract: This application discloses an image feature recognition method performed at a computing device. The computing device obtains a first neural network model by training parameters in a second neural network model using a first training set and a second training set consecutively, image features of training pictures in the first training set being marked and image features of training pictures in the second training set being not marked. After obtaining a first neural network model, the computing device obtains a recognition request for recognizing an image feature in a target picture. The computing device then recognizes the image feature in the target picture by applying the target picture to the first neural network model. Finally the computing device returns a first recognition result of the first neural network model, the first recognition result indicating an image feature (for example, a pathological feature) recognized in the target picture.

Abstract: A mobile communications device includes a near field communications (NFC) port and processor. The processor executes a trusted application within a trusted execution environment (TEE) for processing sensitive data. Additionally, the processor executes a NFC driver within the TEE for providing a secure channel between the trusted application and the NFC port such that sensitive data being exchanged across the secure channel are inaccessible to other processes being executed by the processor.

Abstract: The disclosure relates to the technical field of building construction, and provides a connecting member, an ALC batten mounting node structure, and a prefabricated mounting method. The connecting member includes a connecting part, a first pipe clamp, and a second pipe clamp. The connecting part includes a first connecting segment and a second connecting segment, the first pipe clamp and the second pipe clamp are respectively perpendicular to the first connecting segment, and symmetrically arranged on opposite sides of the first connecting segment, and the second connecting segment is bent towards one side of the first connecting segment.

Abstract: Embodiments of the present disclosure provide an image classification method for a computer device. The method includes obtaining an original image and a category of an object included in the original image; adjusting a display parameter of the original image to satisfy a value condition to obtain an adjusted original image; and transforming the display parameter of the original image according to a distribution condition that distribution of the display parameter needs to satisfy, to obtain a transformed image. The method also includes training a neural network model based on the category of the object and a training set constructed by the adjusted original image and the transformed image; and determining a category of an object included in a to-be-predicted image based on the trained neural network model.

Abstract: An image classification method includes: obtaining an original image and a category of an object included in the original image; adjusting a first display parameter of the original image to obtain an adjusted original image; and transforming a second display parameter of the original image to obtain a new image. The adjusted first display parameter satisfies a value condition; and the transformed second parameter of the new image satisfies a distribution condition. The method also includes training a neural network model based on the category of the included object and a training set constructed by combining the adjusted original image and the new image; and inputting a to-be-predicted image into the trained neural network model, and determining the category of the object included in the to-be-predicted.

Abstract: A method for generating a secure single-tap authentication user interface includes obtaining a screenshot of content from an application operating in an execution environment outside of a trusted execution environment, generating graphical elements of a single-tap authentication user interface, and generating a progress switching layer. Additionally, the method includes responsive to an authentication request, generating a composited display, the composited display comprising the screenshot of content from the application, the graphical elements of the single-tap authentication user interface, and the progress switching layer, passing the composited display from outside the trusted execution environment to the trusted execution environment and displaying, by the trusted execution environment, the composited display as part of a trusted user interface (TUI).

Abstract: An apparatus and method for providing a secure payment transaction on a mobile device are provided. The method includes determining, using a computer processor of the mobile device, whether a requested operation is a security sensitive operation, and when the requested operation is determined to be a security sensitive operation, executing the requested operation in a secure environment of the mobile device.

Abstract: In some examples, processing circuitry obtains a shallow hash neural network (SHNN) model that has been trained from a HNN model and based on a set of SHNN training images that aggregates image recognition results from at least two reference hash neural network (HNN) models. Further, the processing circuitry performs an image recognition on the image according to the SHNN model, to obtain an image class vector in an image class space. The image class vector includes probability values of respective image classes in the image class space. A probability value of an image class in the image class space is a combination of intermediate probability values of the image class that are resulted from the at least two reference HNN models. Further, the processing circuitry determines one of the image classes for the image according to the probability values of the respective image classes in the image class space.

Abstract: This application discloses an image feature recognition method performed at a computing device. The computing device obtains a first neural network model by training parameters in a second neural network model using a first training set and a second training set consecutively, image features of training pictures in the first training set being marked and image features of training pictures in the second training set being not marked. After obtaining a first neural network model, the computing device obtains a recognition request for recognizing an image feature in a target picture. The computing device then recognizes the image feature in the target picture by applying the target picture to the first neural network model. Finally the computing device returns a first recognition result of the first neural network model, the first recognition result indicating an image feature (for example, a pathological feature) recognized in the target picture.

Abstract: Embodiments of the present disclosure provide an image classification method for a computer device. The method includes obtaining an original image and a category of an object included in the original image; adjusting a display parameter of the original image to satisfy a value condition to obtain an adjusted original image; and transforming the display parameter of the original image according to a distribution condition that distribution of the display parameter needs to satisfy, to obtain a transformed image. The method also includes training a neural network model based on the category of the object and a training set constructed by the adjusted original image and the transformed image; and determining a category of an object included in a to-be-predicted image based on the trained neural network model.

Abstract: An image classification method includes: obtaining an original image and a category of an object included in the original image; adjusting a first display parameter of the original image to obtain an adjusted original image; and transforming a second display parameter of the original image to obtain a new image. The adjusted first display parameter satisfies a value condition; and the transformed second parameter of the new image satisfies a distribution condition. The method also includes training a neural network model based on the category of the included object and a training set constructed by combining the adjusted original image and the new image; and inputting a to-be-predicted image into the trained neural network model, and determining the category of the object included in the to-be-predicted.

Abstract: An apparatus and method for secure electronic payment are provided. The method includes authenticating a user of an electronic device executing a trusted payment application in a trusted execution environment of the electronic device, receiving credit card data from the user, generating credit card track data based on the received credit card data, and storing the credit card track data.

Abstract: A method for generating a secure single-tap authentication user interface includes obtaining a screenshot of content from an application operating in an execution environment outside of a trusted execution environment, generating graphical elements of a single-tap authentication user interface, and generating a progress switching layer. Additionally, the method includes responsive to an authentication request, generating a composited display, the composited display comprising the screenshot of content from the application, the graphical elements of the single-tap authentication user interface, and the progress switching layer, passing the composited display from outside the trusted execution environment to the trusted execution environment and displaying, by the trusted execution environment, the composited display as part of a trusted user interface (TUI).

Abstract: A mobile communications device includes a near field communications (NFC) port and processor. The processor executes a trusted application within a trusted execution environment (TEE) for processing sensitive data. Additionally, the processor executes a NFC driver within the TEE for providing a secure channel between the trusted application and the NFC port such that sensitive data being exchanged across the secure channel are inaccessible to other processes being executed by the processor.

Abstract: A method comprises: providing a weldment and a welded plate to which the weldment is to be welded, an end of the weldment to be welded to the welded plate being provided with a first welding face and a second welding face, the first welding face and the second welding face forming a pointed cone, the first welding face corresponding to a first welding area, and the second welding face corresponding to a second welding area; in the first welding area, welding being performed at the innermost side of the pointed cone to form a first welding pass; in the second welding area, welding being performed at the innermost side of the pointed cone to form a second welding pass, and then welding being performed at the outer side of the second welding pass to form a third welding pass adjacent to the second welding pass.

Abstract: In some examples, processing circuitry obtains a shallow hash neural network (SHNN) model that has been trained from a HNN model and based on a set of SHNN training images that aggregates image recognition results from at least two reference hash neural network (HNN) models. Further, the processing circuitry performs an image recognition on the image according to the SHNN model, to obtain an image class vector in an image class space. The image class vector includes probability values of respective image classes in the image class space. A probability value of an image class in the image class space is a combination of intermediate probability values of the image class that are resulted from the at least two reference HNN models. Further, the processing circuitry determines one of the image classes for the image according to the probability values of the respective image classes in the image class space.

Abstract: Methods, electronic devices, and systems for exchanging encrypted information. A method for exchanging encrypted information by an electronic device includes generating one or more device certificates and one or more device public private key pairs. The one or more device certificates are signed using a device unique private key that is pre-stored on the electronic device. The method also includes sending the one or more device certificates to a server of a token service provider (TSP). The method further includes receiving one or more TSP certificates from the TSP server. The method includes identifying one or more TSP public keys of the TSP server based on the one or more received TSP certificates. Additionally, the method includes transmitting a message including the information encrypted based on the one or more identified TSP public keys and a signature of the electronic device.