Abstract: The present disclosure belongs to an identity authentication technology in network security field, and relates to a lightweight identity authentication method. The method utilizes lightweight operations of the physical unclonable function, Hash operation, XOR operation, etc.

Abstract: Disclosed are performance metrics for evaluating the accuracy of a dynamic model in predicting the trajectory of ADV when simulating the behavior of the ADV under the control commands. The performance metrics may indicate the degree of similarity between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle when applied with identical control commands. The performance metrics measure deviations of the predicted trajectory of the dynamic model from the actual trajectory based on the ground truths. The performance metrics may include cumulative or mean absolute trajectory error, end-pose difference (ED), two-sigma defect rate (?2?), the Hausdirff Distance (HAU), the longest common sub-sequence error (LCSS), or dynamic time warping (DTW). The two-sigma defect rate represents the ratio of the number of points with true location error falling out of the 2? range of the predicted location error over the total number of points in the trajectory.

Abstract: Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Abstract: The present disclosure belongs to the technical field of robots, in particularly relates to a robot control method, a device, a computer-readable storage medium and a robot. The method includes selecting each candidate navigation point from a map of the robot; determining a target navigation point from each candidate navigation point according to the relative positional relationship between each candidate navigation point and the robot; and controlling the robot to move to the target navigation point. Through the above method, the best target navigation point can be determined according to the relative positional relationship between each candidate navigation point and the robot, which realizes the regular selection of the target navigation point, improves the efficiency of robot exploration and has strong practicability and ease of use.

Abstract: Disclosed is a method for updating a node model that resists discrimination propagation in federated learning. The method includes: obtaining a node model corresponding to a data node; calculating a mean value of the distribution of class features and a quantity ratio corresponding to training data of the data node, calculating a distribution weighted aggregation model based on the node model, the mean value of the distribution of class features and the quantity ratio; calculating a regularization term corresponding to the data node based on the node model and the distribution weighted aggregation model; calculating a variance of the distribution of the class features corresponding to the data node, calculating a class balanced complementary term by using a cross-domain feature generator; and updating the node model based on the distribution weighted aggregation model, the regularization term, and the class balanced complementary term.

Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.

Abstract: A data processing system includes an array of reconfigurable units and a compiler configured to generate one or more configuration files for an application for execution on one or more reconfigurable processors. The data processing system further includes an execution flow logic which is configured to cause execution of the configuration files on the reconfigurable processors to be dependent upon one or more breakpoint conditions. The data processing further includes a runtime logic configured to execute the configuration files depending upon the breakpoint conditions. A corresponding method is also disclosed herein.

Abstract: Disclosed is a method for updating a node model that resists discrimination propagation in federated learning. The method includes: obtaining a node model corresponding to a data node; calculating a mean value of the distribution of class features and a quantity ratio corresponding to training data of the data node, calculating a distribution weighted aggregation model based on the node model, the mean value of the distribution of class features and the quantity ratio; calculating a regularization term corresponding to the data node based on the node model and the distribution weighted aggregation model; calculating a variance of the distribution of the class features corresponding to the data node, calculating a class balanced complementary term by using a cross-domain feature generator; and updating the node model based on the distribution weighted aggregation model, the regularization term, and the class balanced complementary term.

Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.

Abstract: According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

Abstract: A data processing method based on simultaneous interpretation, applied to a server in a simultaneous interpretation system, including: obtaining audio transmitted by a simultaneous interpretation device; processing the audio by using a simultaneous interpretation model to obtain an initial text; transmitting the initial text to a user terminal; receiving a modified text fed back by the user terminal, the modified text being obtained after the user terminal modifies the initial text; and updating the simultaneous interpretation model according to the initial text and the modified text.

Abstract: A bus bar subassembly adapted to connect a first circuit board and a second circuit board includes a first bus bar, a second bus bar and a first electrical connector. The first bus bar is electrically connected to the first circuit board. The second bus bar is electrically connected to the second circuit board. The first connector is electrically connected to the first bus bar and the second bus bar.

Abstract: A method, an apparatus and a medium for optimizing allocation of switching resources in the polymorphic network. The method selects the ASIC switching chip, FPGA and PPK software switching on the polymorphic network element based on machine learning, and specifically comprises the following steps: manually pre-configuring, formulating basic rules for polymorphic software and hardware co-processing; offline learning, designing training configuration in the offline learning stage to capture different switching resource usage variables, running experiments to generate the original data of a training classifier, and using the generated performance indices to train the model offline; and online reasoning, obtaining switching resource allocation advises, and updating modality codes according to the switching resource allocation advises.

Abstract: Sound signals are received by one or more microphones disposed at an ADV. The sound signals are analyzed to extract a feature of a road on which the ADV is driving. A road condition of the road is determined based on the extracted feature. A path planning and speed planning is performed to generate a trajectory based on the road condition. The ADV is controlled to drive autonomously according to the generated trajectory.

Abstract: A data flow classification method includes obtaining, by a packet forwarding device, a plurality of data flows, extracting, by the packet forwarding device, address information and time information of each of the data flows, selecting, by the packet forwarding device, a data flow set that is generated when a first client device accesses a plurality of services, determining, by the packet forwarding device, a service set that includes a first service and a second service and that is accessed by the first client device based on the address information of the data flows, determining, by the packet forwarding device, a correlation between services in the service set based on time information of each data flow in the data flow set, and determining, by the packet forwarding device, that the first service and the second service implement a first application.

Abstract: Sound signals that are not audible are generated by one or more speakers disposed in the vehicle. Reflected sound signals from a driver or a passenger of the vehicle that are not audible are received by one or more microphones disposed in the vehicle. A behavior-induced acoustic pattern is detected based on the reflected ultrasound signals. The behavior-induced acoustic pattern is analyzed to recognize a behavior of the driver or the passenger of the vehicle. A response or an alert is generated according to the recognized behavior of the driver or the passenger in the vehicle.

Abstract: This application discloses a method, apparatus and storage medium for application identification. A network device performs flow behavior feature analysis according to a flow table to obtain a plurality of services. Each service includes one IP address and one port identifier, and one application may usually include a group of services. Therefore, the network device clusters the plurality of services according to the flow table and a domain name table, to obtain a plurality of application types. Each application type includes a plurality of services. Each application type corresponds to one application. Further, the network device may determine a label corresponding to each of the plurality of application types, the label identifying an application to which a data flow belongs.

Abstract: A sensor package, a sensor system, and a method for fabricating the sensor package are described that include a sensing chip having dispense chemistry disposed over an array of conductive elements. In an implementation, the sensor package may include a sensing chip that may include at least one conductive element, wherein the at least one conductive element may be part of an array of conductive elements defining a M by N matrix, where M is a number of rows of the at least one conductive element and N is a number of columns of the at least one conductive element. The sensing chip may further include dispense chemistry that may be disposed on the at least one conductive element and at least one contact pad. The sensor package may further include a microfluidic cap that may be positioned over at least a portion of the sensing chip, wherein the microfluidic cap and the sensing chip may define a cavity that may be configured to receive a fluid sample.

Abstract: In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.