Abstract: Systems and methods are provided for URI (Uniform Resource Identifier) consolidation. An exemplary method for URI consolidation may comprise receiving a URI comprising one or more URI segments in a string from left to right, parsing the URI to determine if any of the URI segments matches with any of a plurality of URI segment records in a URI database, and in response to determining one or more URI segments matching respectively with one or more of the URI segment records, using a portion of the received URI up to and including a rightmost URI segment matching with a URI segment record in the URI database to represent the received URI.

Abstract: Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Abstract: Systems and methods are provided for ride order dispatching. Such method may comprise obtaining information on a location of a vehicle and a time to input into a trained neural network algorithm; and based on a policy generated from the trained neural network algorithm, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order.

Abstract: A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Abstract: A method for point-to-point traffic prediction comprises: obtaining, from a plurality of computing devices, time-series locations of a plurality of vehicles respectively associated with the computing devices, wherein: the time-series locations form first trajectory data comprising corresponding trajectories at least passing from a first point O to a second point D within a first time interval; obtaining a traffic volume between O and D for a second time interval that is temporally after the first time interval; training one or more weights of a neural network model by inputting the first trajectory data and the traffic volume to the neural network model and using the obtained traffic volume as ground truth to obtain a trained neural network model; and inputting second trajectory data between O and D to the trained neural network model to predict a future traffic volume between O and D for the a future time interval.

Abstract: A ride order dispatching system comprises a processor, and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to perform a method. The method comprises: obtaining, from a computing device, a current location of a vehicle; inputting the current location of the vehicle and a time to a trained neural network model to obtain action information for the vehicle, the action information comprising: staying at the current location of the vehicle, re-positioning the vehicle, or accepting a ride order; and transmitting the action information to the computing device to cause the vehicle to stay at the current location, re-position to another location, or accept the ride order by proceeding to a pick-up location of the ride order.

Abstract: A computer-implemented method for domain analysis comprises: obtaining, by a computing device, a domain; and inputting, by the computing device, the obtained domain to a trained detection model to determine if the obtained domain was generated by one or more domain generation algorithms. The detection model comprises a neural network model, a n-gram-based machine learning model, and an ensemble layer. Inputting the obtained domain to the detection model comprises inputting the obtained domain to each of the neural network model and the n-gram-based machine learning model. The neural network model and the n-gram-based machine learning model both output to the ensemble layer. The ensemble layer outputs a probability that the obtained domain was generated by the domain generation algorithms.

Abstract: A computer-implemented method for domain analysis comprises: obtaining, by a computing device, a domain; and inputting, by the computing device, the obtained domain to a trained detection model to determine if the obtained domain was generated by one or more domain generation algorithms. The detection model comprises a neural network model, a n-gram-based machine learning model, and an ensemble layer. Inputting the obtained domain to the detection model comprises inputting the obtained domain to each of the neural network model and the n-gram-based machine learning model. The neural network model and the n-gram-based machine learning model both output to the ensemble layer. The ensemble layer outputs a probability that the obtained domain was generated by the domain generation algorithms.

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: Hierarchical multi-agent reinforcement learning may be used for joint order dispatching and fleet management for ride-sharing platforms. Information may be obtained. The information may include a status of a ride-sharing platform and a set of messages. The obtained information may be input into a trained hierarchical reinforcement learning (HRL) model. The trained HRL model may include at least one manager module corresponding to a region, and the at least one manager module may include a set of worker modules each corresponding to a division the region. At least one goal of the division in the region may be obtained based on the status of the ride-sharing platform and the set of messages. A vehicle action may be generated for each vehicle in the division in the region based on the status of the ride-sharing platform, the set of messages, and the at least one goal.

Abstract: A method for ride order dispatching comprises: obtaining a current location of a current vehicle from a computing device associated with the current vehicle; obtaining a current list of available orders nearby based on the current location; feeding the current location, the current list of available orders nearby, and a current time to a trained Markov Decision Process (MDP) model to obtain action information, the action information being repositioning the current vehicle to another current location or completing a current ride order by the current vehicle; and transmitting the generated action information to the computing device to cause the current vehicle to reposition to the another current location, stay at the current location, or accept the current ride order by proceeding to a pick-up location of the current ride order.

Abstract: A method of determining an inventory mirroring plan for a set of distinct items in a heterogeneous fulfillment network is presented. The fulfillment network can include a plurality of distribution centers, each distribution center having differing capabilities. The method can include determining a solution value of the number of clusters for each distinct item that minimizes a sum of a total shipping cost of each distinct item, subject to a total distinct item capacity of the plurality of distribution centers in the fulfillment network. The method can further include using a probability of the item being placed in a specific distribution center based on either the capacity of the distribution center or historical data. The probability can be used to stock items. Overages and deficits can be used to further refine the distribution. Other embodiments are also disclosed.

Abstract: The invention discloses an automatic powder cleaning system for mixed-line hub bolt holes and a combined powder cleaning gun. The powder cleaning system comprises a control system, a recognition system, a powder cleaning robot, a gun storage and an accessory device. A plurality of combined powder spraying guns are arranged in the gun storage, and a quick disassembly fixture is arranged at the end of the powder cleaning robot.

Abstract: A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

Abstract: Systems and methods for providing recommendations based on seeded supervised learning are disclosed. The method may include acquiring, through a communication network, similarity data associated with a first entity, a second entity, and a third entity, and acquiring, through the communication network, external data associated with the first entity and the second entity. The method may further include training a classification model based on the external data and the similarity data. The method may also include determining an expectation score of the third entity based on classification model, and providing, through the communication network, a recommendation based on the expectation score to the third entity.

Abstract: Systems and methods are provided for ride order dispatching and vehicle repositioning. A method for ride order dispatching and vehicle repositioning, comprises: obtaining information comprising a location of a vehicle, current orders, and a current time; inputting the obtained information to a trained model; and determining action information for the vehicle based on an output of the trained model, the action information comprising: re-positioning the vehicle or accepting a ride order. The model is configured with: receiving information of drivers and information of orders as inputs; obtaining a global state based on the information of drivers, the information of orders, and a global time; and querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine the action information as the output.

Abstract: Systems and methods are provided for ride order dispatching and vehicle repositioning. A method for ride order dispatching and vehicle repositioning, comprises: obtaining information comprising a location of a vehicle, current orders, and a current time; inputting the obtained information to a trained model; and determining action information for the vehicle based on an output of the trained model, the action information comprising: re-positioning the vehicle or accepting a ride order. The model is configured with: receiving information of drivers and information of orders as inputs; obtaining a global state based on the information of drivers, the information of orders, and a global time; and querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine the action information as the output.

Abstract: A computer-implemented method for domain analysis comprises: obtaining, by a computing device, a domain; and inputting, by the computing device, the obtained domain to a trained detection model to determine if the obtained domain was generated by one or more domain generation algorithms. The detection model comprises a neural network model, a n-gram-based machine learning model, and an ensemble layer. Inputting the obtained domain to the detection model comprises inputting the obtained domain to each of the neural network model and the n-gram-based machine learning model. The neural network model and the n-gram-based machine learning model both output to the ensemble layer. The ensemble layer outputs a probability that the obtained domain was generated by the domain generation algorithms.

Abstract: A method for point-to-point traffic prediction comprises: obtaining, from a plurality of computing devices, time-series locations of a plurality of vehicles respectively associated with the computing devices, wherein: the time-series locations form first trajectory data comprising corresponding trajectories at least passing from a first point O to a second point D within a first time interval; obtaining a traffic volume between O and D for a second time interval that is temporally after the first time interval; training one or more weights of a neural network model by inputting the first trajectory data and the traffic volume to the neural network model and using the obtained traffic volume as ground truth to obtain a trained neural network model; and inputting second trajectory data between O and D to the trained neural network model to predict a future traffic volume between O and D for the a future time interval.

Abstract: A method of determining an inventory mirroring plan for a set of distinct items in a fulfillment network. The fulfillment network can include a plurality of distribution centers. The method can include determining, for each distinct item of the set of distinct items and for each demand zone of a set of demand zones, a location-specific demand. The method also can include determining, for each of a number of clusters ranging from 1 to a predetermined maximum number of clusters, a k-cluster profile that partitions the plurality of distribution centers in the fulfillment network into k distribution center clusters. The method further can include determining, for each of the number of clusters and for each demand zone of the set of demand zones, a closest distribution center cluster of the k distribution center clusters that is nearest to the demand zone.