Abstract: The present disclosure relates to a method and system for monitoring on route transportations. The method includes obtaining a driving route of a target vehicle; obtaining a reference position on the driving route away from a first current position of the target vehicle; determining a second current position of the target vehicle after a reference time; determining a distance between the second current position and the reference position is greater than a preset distance; and sending a signal to a target terminal indicating that the target vehicle is off-route.

Abstract: Multi-agent reinforcement learning may be used for rider order-dispatching via matching the distribution of orders and vehicles. Information may be obtained. The information may include a plurality of vehicle locations of a plurality of vehicles, a plurality of ride orders, and a current time. The obtained information may be input into a trained model. The trained model may be based on Kullback-Leibler divergence optimization and independent agents under a guidance of a joint policy. A plurality of order-dispatching tasks may be generated for the plurality of vehicles to fulfill.

Abstract: A method includes: obtaining a plurality of first signals corresponding to a vehicle and a plurality of second signals corresponding to a plurality of candidate carpool combinations each comprising one or more unassigned transportation orders, wherein: the plurality of first signals comprise a current time, a location of the vehicle at the current time, and one or more static features corresponding to the vehicle, the plurality of second signals comprise timestamps, origins, and destinations of the unassigned transportation orders, and the vehicle has an on-going transportation order at the current time; inputting the plurality of first and second signals to a trained machine learning model; and obtaining, from an output of the trained machine learning model, a utility score of each of the plurality of candidate carpool combinations.

Abstract: The present disclosure may provide an information interaction method, apparatus, electronic device, and readable storage medium relating to the field of computer technology. The method may include: in response to monitoring that an expected pick-up location is within a preset coordinate range, displaying at least one pick-up area within the preset coordinate range, wherein the expected pick-up location is a coordinate point of a map; receiving a pick-up area selecting instruction, and determining a target pick-up area; determining a target pick-up location corresponding to the target pick-up area; determining a recommended route between a current location and the target pick-up location in response to receiving a submitting instruction for the target pick-up location; based on the recommended route, displaying route guidance information.

Abstract: The present disclosure relates to systems and methods for transport capacity scheduling. The systems and methods may determine a target region, wherein a plurality of service requests that satisfy a preset condition initiate from the target region. The systems and methods may determine a non-busy region based on information of the target region. The non-busy region may include one or more available service providers that are free to accept a service request. The systems and methods may transmit, via a network, a scheduling instruction associated with the plurality of service requests to a user terminal associated with at least one of the one or more available service providers in the non-busy region. The scheduling instruction may include information inquiring whether the at least one of the one or more available service providers in the non-busy region agrees to go to the target region.

Abstract: In some cases, one or more heuristics can be automatically generated using a small dataset of segments previously labeled by one or more domain experts. The generated one or more heuristics along with one or more patterns can be used to assign training labels to a large unlabeled dataset of segments. A subset of segments representing an occurrence of verbal harassment can be selected using the assigned training labels. Randomly selected segments can be used as being indicative of a non-occurrence of verbal harassment. The selected subset of segments and randomly selected segments can be used to train one or more machine learning models for verbal harassment detection.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for acoustic echo cancellation and suppression are provided. An exemplary method comprises receiving a far-end acoustic signal and a corrupted near-end acoustic signal, wherein the corrupted near-end acoustic signal is generated based on (1) an echo of the far-end acoustic signal and (2) a near-end acoustic signal; feeding the far-end acoustic signal and the corrupted near-end acoustic signal into a neural network as an input to output a time-frequency (TF) mask that suppresses the echo and retains the near-end acoustic signal, and generating an enhanced version of the corrupted near-end acoustic signal by applying the obtained TF mask to the corrupted near-end acoustic signal.

Abstract: In accordance with one implementation of the present disclosure, a new approach for determining a movement orientation of a user is proposed in indoor navigation. Generally speaking, a device orientation of a terminal device is obtained based on at least one signal stream collected from the terminal device carried by a moving user. A deviation degree is determined based on the at least one signal stream, here the deviation degree represents a deviation between a movement orientation of the user and an actual device orientation of the terminal device. The movement orientation is determined based on the device orientation in accordance with a determination that the deviation degree is below a threshold degree. With the above implementation, the movement orientation of the user is determined in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

Abstract: The present disclosure provides a method, a first service requester, a system, and an electronic device for information display. The method may include obtaining a service request location where a first service requester is located when a carpooling request of the first service requester is received; determining dynamic carpooling information of at least one second service requester within a preset distance from the service request location during a carpooling process; and displaying the dynamic carpooling information of the at least one second service requester on a display interface of the first service request terminal.

Abstract: In accordance with one implementation of the present disclosure, a new approach for identifying a stepping event is proposed in indoor navigation. Generally speaking, a first signal fragment and a second signal fragment respectively within a first time window and a second time window in an acceleration signal stream are obtained, here the acceleration signal stream is collected from an acceleration sensor associated with a moving user, the first time window being shorter than the second time window. A first amplitude feature and a second amplitude feature are determined for the first and second time windows based on the first and second signal fragments, respectively. A stepping event of the user is identified based on a deviation between the first and second amplitude features. With the above implementation, the stepping event is identified in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

Abstract: Systems and methods for online to offline services are provided. The methods may include obtaining a first service request issued by a first service requester, the first service request including a request time point, a start location, and a destination. The methods may further include determining a target time point based on at least one of the request time point or an input by the first service requester, wherein the target time point is after the request time point. The methods may further include obtaining one or more candidate service requests, and determining a service request set and a target service provider that matches with the service request set by executing, starting at the target time point, a matching process based on the first service request and the one or more candidate service requests. The service request set may include the first service request.

Abstract: The present disclosure relates to methods and systems for determining an estimated time of arrival (ETA). The methods may include obtaining feature data related to an on-demand service order; obtain a parallel computing framework; determining a global ETA model based on the feature data and the parallel computing framework; and determining an ETA for a target route based on the global ETA model.

Abstract: The present disclosure relates to a system and method for recommending a service type to a user. The method comprises obtaining and storing in the device a plurality of previous service requests placed by the user, wherein each of the plurality of previous service requests comprises order information including the type of requested service and at least one of a service time or a service location. The method also comprises using the processor to generate a service type prediction model based on the order information of the plurality of previous service requests. The method further comprises receiving a service request including at least one of a currently service time or a currently service location from the user and using the service type prediction model to predict the user's preferred service type.

Abstract: A method and system for determining a heading of a vehicle. The method may include receiving, from a mobile computing device, a request for determining the heading of the vehicle. The method may also include retrieving, at least in response to the request, sensor data generated by a magnetometer of the mobile computing device within one or more first time slots, and obtaining a classifier trained to determine a predicted heading of the vehicle. The method may further include, for the sensor data of each of the one or more first time slots, obtaining a feature vector by extracting features from the sensor data, and determining, based on the obtained feature vector, a predicted heading by inputting the feature vector into the classifier. The method may also include determining the heading of the vehicle based on the obtained one or more predicted headings.

Abstract: The present disclosure relates to systems and methods for operation retry. The system may determine whether a retry is allowed for a target operation based on a retry strategy included in a retry proxy component. In response to determining that the retry is allowed for the target operation, the system may execute the target operation. The system may detect whether there is an exception associated with the execution of the target operation. In response to determining that there is the exception associated with the execution of the target operation, the system may determine whether a retry is needed for the target operation based on the retry strategy. In response to determining that the retry is unneeded for the target operation, the system may stop the target operation.

Abstract: A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.

Abstract: The present disclosure relates to a method and system for monitoring on route transportations. The method includes obtaining a driving route of a target vehicle; obtaining a reference position on the driving route away from a first current position of the target vehicle; determining a second current position of the target vehicle after a reference time; determining a distance between the second current position and the reference position is greater than a preset distance; and sending a signal to a target terminal indicating that the target vehicle is off-route.

Abstract: Embodiments of the disclosure provide systems and methods for verifying digital payments for a payment platform. The system includes a communication interface configured to receive service data associated with a transaction from a terminal device associated with a user of the payment platform. The system further includes at least one processor configured to automatically determine whether an authentication is waived for the transaction based on the received service data and a blacklist generated using a machine learning model. The blacklist includes a list of suspicious users and transaction behaviors. The at least one processor further configured to approve the transaction without requesting user authentication information from the terminal device when the authentication is waived. The at least one processor also configured to request the user authentication information from the terminal device and validate the user authentication information when the authentication is not waived.

Abstract: Methods, apparatuses, and storage media storing instructions for classifying text documents are provided. A plurality of text documents is obtained. The plurality of text documents is classified into one or more document categories based on a plurality of classification rules. Each of the one or more document categories include one or more first text documents of the plurality of text documents. A second text document of the plurality of text documents is classified based on the plurality of classification rules as belonging to none of the one or more document categories. One or more document fingerprints are generated for respective first text documents in the one or more document categories. The second text document is classified into one of the one or more document categories based on the one or more document fingerprints.