Abstract: A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user an

Abstract: The disclosure provides a method for monitoring a short-term voltage stability of a power system. The method includes: obtaining a topology and time series of a current power system; inputting the topology and the time series of the current power system into a trained spatial-temporal graph network model, the trained spatial-temporal graph network model being obtained by classification learning on a spatial-temporal graph network model based on a simulation sample dataset; outputting a status of the short-term voltage stability of the power system; and sending an alarm signal in response to outputting an unstable status of the short-term voltage stability of the power system.

Abstract: A system of performing boundary scan test on pin through test point and a method thereof are disclosed. When an under-test pin of a target connector is determined to be unable to perform a boundary scan test, a test point connected to and closest to the under-test pin is searched, a test signal is transmitted to a target connector, a result signal from the target connector in response to the test signal is received, an expected result and the result signal are compared to generate a test result, so that a boundary scan function can be applied to test a connector of a computer product, to achieve the technical effect of providing a better test range and a better test coverage to improve test efficiency and reduce test cost, compared to conventional boundary scan test.

Abstract: Embodiments provide a core network selection method, an apparatus, and a system. The method includes the following steps: obtaining first dedicated core network (DCN) information from an access network device, where the first DCN information includes information about at least one DCN that can be accessed by the access network device. The method also includes selecting a to-be-accessed DCN from the at least one DCN according to the first DCN information, and sending information about the to-be-accessed DCN to the access network device, so that the access network device determines a core network device according to the information about the to-be-accessed DCN.

Abstract: A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user an

Abstract: An apparatus for performing image searches including a camera, storage devices storing a set of instructions, and a processor coupled to the at least one storage device and the camera. The instructions configure the at least one processor to perform operations including identifying attributes of the captured image using a classification model; identifying first results based on the identified attributes; selecting a subset of first results based on corresponding probability scores, generating a first graphical user interface including interactive icons corresponding to first results in the subset, an input icon, and a first button. The operations may also include receiving a selection of the first button, performing a search to identify second results, and generating a second graphical user interface displaying the second results.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for determining color labeling for a vehicle search service. For instance, the method may include: obtaining vehicle images for a plurality of vehicles; filtering the vehicle images to obtain exterior and/or interior images of the plurality of vehicles, determining super-pixel values for the exterior images and/or the interior images; generating a standard color mapping by associating the super-pixel values to standard color names; and storing searchable vehicle information at least based on the standard color mapping.

Abstract: A device receives user interface interaction data that identifies an interaction that a user has with an interface that displays an image of a vehicle. The device generates, by using a data model to process at least a portion of the image, an array of vectors that includes one or more vectors relating to vehicle characteristics of the vehicle. The device assigns one or more weights to the one or more vectors based on the user interface action data. The device determines, based on a similarity analysis, similarity scores that indicate similarities between the array of vectors that include the one or more vectors that have been weighted and other arrays relating to the vehicles depicted in the images. The device selects a subset of the images based on the similarity scores and causes the subset of the images to be displayed.

Abstract: An apparatus for performing image searches including a camera, storage devices storing a set of instructions, and a processor coupled to the at least one storage device and the camera. The instructions configure the at least one processor to perform operations including identifying attributes of the captured image using a classification model; identifying first results based on the identified attributes; selecting a subset of first results based on corresponding probability scores, generating a first graphical user interface including interactive icons corresponding to first results in the subset, an input icon, and a first button. The operations may also include receiving a selection of the first button, performing a search to identify second results, and generating a second graphical user interface displaying the second results.

Abstract: A device receives user interface interaction data that identifies an interaction that a user has with an interface that displays an image of a vehicle. The device generates, by using a data model to process at least a portion of the image, an array of vectors that includes one or more vectors relating to vehicle characteristics of the vehicle. The device assigns one or more weights to the one or more vectors based on the user interface action data. The device determines, based on a similarity analysis, similarity scores that indicate similarities between the array of vectors that include the one or more vectors that have been weighted and other arrays relating to the vehicles depicted in the images. The device selects a subset of the images based on the similarity scores and causes the subset of the images to be displayed.

Abstract: A system for generating graphical user interfaces. The system may include processors and storage devices storing instructions. The instructions may configure the one or more processors to perform operations including identifying a plurality of attributes from an image captured with a client device, identifying a plurality of first results based on the attributes, generating a first graphical user interface for display in the client device. The first graphical user interface may include a plurality of result icons corresponding to a subset of the first results having confidence scores above a threshold, a plurality of filter icons displaying options, and a search button. The operations may also include receiving a selection of at least one of the result icons or at least one of the filter icons, performing a search, based on the selection, and generating a second graphical user interface.

Abstract: The present disclosure provides devices, systems and computer-readable media for identifying object characteristics using a machine learning model trained to de-emphasize brightness values. The machine learning model can be trained using modified training data. Modifying the training data can include converting the training data from an original color space into a color space having a brightness channel. The values of the brightness channel for the, training data can then be modified. After the values of the brightness channel are modified, the training data can be converted back into the original color space and used to train the machine learning model. A detection device can be configured with the machine learning model and used to identify object characteristics.

Abstract: Disclosed are systems and methods for displaying content on a user device. For example, a method of displaying content on a user device may include: receiving a first set of content data; arranging and displaying the first set of content in a first sequence of consecutive segments; detecting user input indicative of a scroll pattern of the user through the first sequence of consecutive segments; determining a preferred category based on the detected user input; receiving a second set of content data; arranging the second set of content data into a second sequence of consecutive segments based on the preferred category; and displaying the second set of content data in the arranged second sequence of consecutive segments on the display of the user device.

Abstract: Disclosed are systems and methods for displaying content on a user device. For example, a method of displaying content on a user device may include: receiving a first set of content data; arranging and displaying the first set of content in a first sequence of consecutive segments; detecting user input indicative of a scroll pattern of the user through the first sequence of consecutive segments; determining a preferred category based on the detected user input; receiving a second set of content data; arranging the second set of content data into a second sequence of consecutive segments based on the preferred category; and displaying the second set of content data in the arranged second sequence of consecutive segments on the display of the user device.

Abstract: A system for generating graphical user interfaces. The system may include processors and storage devices storing instructions. The instructions may configure the one or more processors to perform operations including identifying a plurality of attributes from an image captured with a client device, identifying a plurality of first results based on the attributes, generating a first graphical user interface for display in the client device. The first graphical user interface may include a plurality of result icons corresponding to a subset of the first results having confidence scores above a threshold, a plurality of filter icons displaying options, and a search button. The operations may also include receiving a selection of at least one of the result icons or at least one of the filter icons, performing a search, based on the selection, and generating a second graphical user interface.

Abstract: A test coverage rate improvement system for pins of tested circuit board and a method thereof are disclosed. In the system, partial pins of a circuit board connector in a tested circuit board are not electrically connected to the boundary scan chip, test pins of the test pin board are pressed with the partial pins by a fixture of a boundary scan interconnect testing workstation to electrically connect the test pins to the partial pins. A test access port controller receives a detection signal for detecting the partial pins, which are not electrically connected to the boundary scan chip, of the circuit board connector through the test pin board from the test adapter card, and determines whether conduction is formed based on the detection signal, thereby achieving the technical effect of improving a test coverage rate for the pins of the tested circuit board.

Abstract: A system for generating graphical user interfaces. The system may include processors and storage devices storing instructions. The instructions may configure the one or more processors to perform operations including identifying a plurality of attributes from an image captured with a client device, identifying a plurality of first results based on the attributes, generating a first graphical user interface for display in the client device. The first graphical user interface may include a plurality of result icons corresponding to a subset of the first results having confidence scores above a threshold, a plurality of filter icons displaying options, and a search button. The operations may also include receiving a selection of at least one of the result icons or at least one of the filter icons, performing a search, based on the selection, and generating a second graphical user interface.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for determining color labeling for a vehicle search service. For instance, the method may include: obtaining vehicle images for a plurality of vehicles; filtering the vehicle images to obtain exterior and/or interior images of the plurality of vehicles, determining super-pixel values for the exterior images and/or the interior images; generating a standard color mapping by associating the super-pixel values to standard color names; and storing searchable vehicle information at least based on the standard color mapping.

Abstract: The present disclosure provides devices, systems and computer-readable media for identifying object characteristics using a machine learning model trained to de-emphasize brightness values. The machine learning model can be trained using modified training data. Modifying the training data can include converting the training data from an original color space into a color space having a brightness channel. The values of the brightness channel for the, training data can then be modified. After the values of the brightness channel are modified, the training data can be converted back into the original color space and used to train the machine learning model. A detection device can be configured with the machine learning model and used to identify object characteristics.

Abstract: A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user an